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Remove deprecated app_new.py and consolidate main application logic into main.py

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- Deleted `app_new.py`, which was previously the main entry point for the dashboard application, to streamline the codebase.
- Consolidated the application initialization and callback registration logic into `main.py`, enhancing modularity and maintainability.
- Updated the logging and error handling practices in `main.py` to ensure consistent application behavior and improved debugging capabilities.

These changes simplify the application structure, aligning with project standards for modularity and maintainability.
This commit is contained in:
Vasily.onl 2025-06-11 18:36:34 +08:00
parent 0a7e444206
commit dbe58e5cef
47 changed files with 1198 additions and 10784 deletions
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44
app_new.py
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@ -1,44 +0,0 @@
"""
Crypto Trading Bot Dashboard - Modular Version
This is the main entry point for the dashboard application using the new modular structure.
"""
from dashboard import create_app
from utils.logger import get_logger
logger = get_logger("main")
def main():
"""Main entry point for the dashboard application."""
try:
# Create the dashboard app
app = create_app()
# Import and register all callbacks after app creation
from dashboard.callbacks import (
register_navigation_callbacks,
register_chart_callbacks,
register_indicator_callbacks,
register_system_health_callbacks
)
# Register all callback modules
register_navigation_callbacks(app)
register_chart_callbacks(app) # Now includes enhanced market statistics
register_indicator_callbacks(app) # Placeholder for now
register_system_health_callbacks(app) # Placeholder for now
logger.info("Dashboard application initialized successfully")
# Run the app (debug=False for stability, manual restart required for changes)
app.run(debug=False, host='0.0.0.0', port=8050)
except Exception as e:
logger.error(f"Failed to start dashboard application: {e}")
raise
if __name__ == '__main__':
main()

379
components/charts/__init__.py
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@ -47,6 +47,21 @@ from .error_handling import (
get_error_message,
create_error_annotation
)
from .chart_data import (
get_supported_symbols,
get_supported_timeframes,
get_market_statistics,
check_data_availability,
create_data_status_indicator
)
from .chart_creation import (
create_candlestick_chart,
create_strategy_chart,
create_error_chart,
create_basic_chart,
create_indicator_chart,
create_chart_with_indicators
)
# Layer imports with error handling
from .layers.base import (
@ -89,6 +104,9 @@ __all__ = [
"create_strategy_chart",
"create_empty_chart",
"create_error_chart",
"create_basic_chart",
"create_indicator_chart",
"create_chart_with_indicators",
# Data integration
"MarketDataIntegrator",
@ -109,7 +127,7 @@ __all__ = [
"get_error_message",
"create_error_annotation",
# Utility functions
# Data-related utility functions
"get_supported_symbols",
"get_supported_timeframes",
"get_market_statistics",
@ -135,362 +153,5 @@ __all__ = [
"BaseSubplotLayer",
"RSILayer",
"MACDLayer",
# Convenience functions
"create_basic_chart",
"create_indicator_chart",
"create_chart_with_indicators"
]
# Initialize logger
from utils.logger import get_logger
logger = get_logger("charts")
def create_candlestick_chart(symbol: str, timeframe: str, days_back: int = 7, **kwargs) -> go.Figure:
"""
Create a candlestick chart with enhanced data integration.
Args:
symbol: Trading pair (e.g., 'BTC-USDT')
timeframe: Timeframe (e.g., '1h', '1d')
days_back: Number of days to look back
**kwargs: Additional chart parameters
Returns:
Plotly figure with candlestick chart
"""
builder = ChartBuilder()
# Check data quality first
data_quality = builder.check_data_quality(symbol, timeframe)
if not data_quality['available']:
logger.warning(f"Data not available for {symbol} {timeframe}: {data_quality['message']}")
return builder._create_error_chart(f"No data available: {data_quality['message']}")
if not data_quality['sufficient_for_indicators']:
logger.warning(f"Insufficient data for indicators: {symbol} {timeframe}")
# Use enhanced data fetching
try:
candles = builder.fetch_market_data_enhanced(symbol, timeframe, days_back)
if not candles:
return builder._create_error_chart(f"No market data found for {symbol} {timeframe}")
# Prepare data for charting
df = prepare_chart_data(candles)
if df.empty:
return builder._create_error_chart("Failed to prepare chart data")
# Create chart with data quality info
fig = builder._create_candlestick_with_volume(df, symbol, timeframe)
# Add data quality annotation if data is stale
if not data_quality['is_recent']:
age_hours = data_quality['data_age_minutes'] / 60
fig.add_annotation(
text=f"⚠️ Data is {age_hours:.1f}h old",
xref="paper", yref="paper",
x=0.02, y=0.98,
showarrow=False,
bgcolor="rgba(255,193,7,0.8)",
bordercolor="orange",
borderwidth=1
)
logger.debug(f"Created enhanced candlestick chart for {symbol} {timeframe} with {len(candles)} candles")
return fig
except Exception as e:
logger.error(f"Error creating enhanced candlestick chart: {e}")
return builder._create_error_chart(f"Chart creation failed: {str(e)}")
def create_strategy_chart(symbol: str, timeframe: str, strategy_name: str, **kwargs):
"""
Convenience function to create a strategy-specific chart.
Args:
symbol: Trading pair
timeframe: Timeframe
strategy_name: Name of the strategy configuration
**kwargs: Additional parameters
Returns:
Plotly Figure object with strategy indicators
"""
builder = ChartBuilder()
return builder.create_strategy_chart(symbol, timeframe, strategy_name, **kwargs)
def get_supported_symbols():
"""Get list of symbols that have data in the database."""
builder = ChartBuilder()
candles = builder.fetch_market_data("BTC-USDT", "1m", days_back=1) # Test query
if candles:
from database.operations import get_database_operations
from utils.logger import get_logger
logger = get_logger("default_logger")
try:
db = get_database_operations(logger)
with db.market_data.get_session() as session:
from sqlalchemy import text
result = session.execute(text("SELECT DISTINCT symbol FROM market_data ORDER BY symbol"))
return [row[0] for row in result]
except Exception:
pass
return ['BTC-USDT', 'ETH-USDT'] # Fallback
def get_supported_timeframes():
"""Get list of timeframes that have data in the database."""
builder = ChartBuilder()
candles = builder.fetch_market_data("BTC-USDT", "1m", days_back=1) # Test query
if candles:
from database.operations import get_database_operations
from utils.logger import get_logger
logger = get_logger("default_logger")
try:
db = get_database_operations(logger)
with db.market_data.get_session() as session:
from sqlalchemy import text
result = session.execute(text("SELECT DISTINCT timeframe FROM market_data ORDER BY timeframe"))
return [row[0] for row in result]
except Exception:
pass
return ['5s', '1m', '15m', '1h'] # Fallback
def get_market_statistics(symbol: str, timeframe: str = "1h", days_back: int = 1):
"""Calculate market statistics from recent data over a specified period."""
builder = ChartBuilder()
candles = builder.fetch_market_data(symbol, timeframe, days_back=days_back)
if not candles:
return {'Price': 'N/A', f'Change ({days_back}d)': 'N/A', f'Volume ({days_back}d)': 'N/A', f'High ({days_back}d)': 'N/A', f'Low ({days_back}d)': 'N/A'}
import pandas as pd
df = pd.DataFrame(candles)
latest = df.iloc[-1]
current_price = float(latest['close'])
# Calculate change over the period
if len(df) > 1:
price_period_ago = float(df.iloc[0]['open'])
change_percent = ((current_price - price_period_ago) / price_period_ago) * 100
else:
change_percent = 0
from .utils import format_price, format_volume
# Determine label for period (e.g., "24h", "7d", "1h")
if days_back == 1/24:
period_label = "1h"
elif days_back == 4/24:
period_label = "4h"
elif days_back == 6/24:
period_label = "6h"
elif days_back == 12/24:
period_label = "12h"
elif days_back < 1: # For other fractional days, show as hours
period_label = f"{int(days_back * 24)}h"
elif days_back == 1:
period_label = "24h" # Keep 24h for 1 day for clarity
else:
period_label = f"{days_back}d"
return {
'Price': format_price(current_price, decimals=2),
f'Change ({period_label})': f"{'+' if change_percent >= 0 else ''}{change_percent:.2f}%",
f'Volume ({period_label})': format_volume(df['volume'].sum()),
f'High ({period_label})': format_price(df['high'].max(), decimals=2),
f'Low ({period_label})': format_price(df['low'].min(), decimals=2)
}
def check_data_availability(symbol: str, timeframe: str):
"""Check data availability for a symbol and timeframe."""
from datetime import datetime, timezone, timedelta
from database.operations import get_database_operations
from utils.logger import get_logger
try:
logger = get_logger("charts_data_check")
db = get_database_operations(logger)
latest_candle = db.market_data.get_latest_candle(symbol, timeframe)
if latest_candle:
latest_time = latest_candle['timestamp']
time_diff = datetime.now(timezone.utc) - latest_time.replace(tzinfo=timezone.utc)
return {
'has_data': True,
'latest_timestamp': latest_time,
'time_since_last': time_diff,
'is_recent': time_diff < timedelta(hours=1),
'message': f"Latest data: {latest_time.strftime('%Y-%m-%d %H:%M:%S UTC')}"
}
else:
return {
'has_data': False,
'latest_timestamp': None,
'time_since_last': None,
'is_recent': False,
'message': f"No data available for {symbol} {timeframe}"
}
except Exception as e:
return {
'has_data': False,
'latest_timestamp': None,
'time_since_last': None,
'is_recent': False,
'message': f"Error checking data: {str(e)}"
}
def create_data_status_indicator(symbol: str, timeframe: str):
"""Create a data status indicator for the dashboard."""
status = check_data_availability(symbol, timeframe)
if status['has_data']:
if status['is_recent']:
icon, color, status_text = "🟢", "#27ae60", "Real-time Data"
else:
icon, color, status_text = "🟡", "#f39c12", "Delayed Data"
else:
icon, color, status_text = "🔴", "#e74c3c", "No Data"
return f'<span style="color: {color}; font-weight: bold;">{icon} {status_text}</span><br><small>{status["message"]}</small>'
def create_error_chart(error_message: str):
"""Create an error chart with error message."""
builder = ChartBuilder()
return builder._create_error_chart(error_message)
def create_basic_chart(symbol: str, data: list,
indicators: list = None,
error_handling: bool = True) -> 'go.Figure':
"""
Create a basic chart with error handling.
Args:
symbol: Trading symbol
data: OHLCV data as list of dictionaries
indicators: List of indicator configurations
error_handling: Whether to use comprehensive error handling
Returns:
Plotly figure with chart or error display
"""
try:
from plotly import graph_objects as go
# Initialize chart builder
builder = ChartBuilder()
if error_handling:
# Use error-aware chart creation
error_handler = ChartErrorHandler()
is_valid = error_handler.validate_data_sufficiency(data, indicators=indicators or [])
if not is_valid:
# Create error chart
fig = go.Figure()
error_msg = error_handler.get_user_friendly_message()
fig.add_annotation(create_error_annotation(error_msg, position='center'))
fig.update_layout(
title=f"Chart Error - {symbol}",
xaxis={'visible': False},
yaxis={'visible': False},
template='plotly_white',
height=400
)
return fig
# Create chart normally
return builder.create_candlestick_chart(data, symbol=symbol, indicators=indicators or [])
except Exception as e:
# Fallback error chart
from plotly import graph_objects as go
fig = go.Figure()
fig.add_annotation(create_error_annotation(
f"Chart creation failed: {str(e)}",
position='center'
))
fig.update_layout(
title=f"Chart Error - {symbol}",
template='plotly_white',
height=400
)
return fig
def create_indicator_chart(symbol: str, data: list,
indicator_type: str, **params) -> 'go.Figure':
"""
Create a chart focused on a specific indicator.
Args:
symbol: Trading symbol
data: OHLCV data
indicator_type: Type of indicator ('sma', 'ema', 'bollinger_bands', 'rsi', 'macd')
**params: Indicator parameters
Returns:
Plotly figure with indicator chart
"""
try:
# Map indicator types to configurations
indicator_map = {
'sma': {'type': 'sma', 'parameters': {'period': params.get('period', 20)}},
'ema': {'type': 'ema', 'parameters': {'period': params.get('period', 20)}},
'bollinger_bands': {
'type': 'bollinger_bands',
'parameters': {
'period': params.get('period', 20),
'std_dev': params.get('std_dev', 2)
}
},
'rsi': {'type': 'rsi', 'parameters': {'period': params.get('period', 14)}},
'macd': {
'type': 'macd',
'parameters': {
'fast_period': params.get('fast_period', 12),
'slow_period': params.get('slow_period', 26),
'signal_period': params.get('signal_period', 9)
}
}
}
if indicator_type not in indicator_map:
raise ValueError(f"Unknown indicator type: {indicator_type}")
indicator_config = indicator_map[indicator_type]
return create_basic_chart(symbol, data, indicators=[indicator_config])
except Exception as e:
return create_basic_chart(symbol, data, indicators=[]) # Fallback to basic chart
def create_chart_with_indicators(symbol: str, timeframe: str,
overlay_indicators: List[str] = None,
subplot_indicators: List[str] = None,
days_back: int = 7, **kwargs) -> go.Figure:
"""
Create a chart with dynamically selected indicators.
Args:
symbol: Trading pair (e.g., 'BTC-USDT')
timeframe: Timeframe (e.g., '1h', '1d')
overlay_indicators: List of overlay indicator names
subplot_indicators: List of subplot indicator names
days_back: Number of days to look back
**kwargs: Additional chart parameters
Returns:
Plotly figure with selected indicators
"""
builder = ChartBuilder()
return builder.create_chart_with_indicators(
symbol, timeframe, overlay_indicators, subplot_indicators, days_back, **kwargs
)
def initialize_indicator_manager():
# Implementation of initialize_indicator_manager function
pass
]

213
components/charts/chart_creation.py Normal file
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@ -0,0 +1,213 @@
import plotly.graph_objects as go
from typing import List
import pandas as pd
from .builder import ChartBuilder
from .utils import prepare_chart_data, format_price, format_volume
from .error_handling import ChartErrorHandler, create_error_annotation
from utils.logger import get_logger
logger = get_logger("charts_creation")
def create_candlestick_chart(symbol: str, timeframe: str, days_back: int = 7, **kwargs) -> go.Figure:
"""
Create a candlestick chart with enhanced data integration.
Args:
symbol: Trading pair (e.g., 'BTC-USDT')
timeframe: Timeframe (e.g., '1h', '1d')
days_back: Number of days to look back
**kwargs: Additional chart parameters
Returns:
Plotly figure with candlestick chart
"""
builder = ChartBuilder()
# Check data quality first
data_quality = builder.check_data_quality(symbol, timeframe)
if not data_quality['available']:
logger.warning(f"Data not available for {symbol} {timeframe}: {data_quality['message']}")
return builder._create_error_chart(f"No data available: {data_quality['message']}")
if not data_quality['sufficient_for_indicators']:
logger.warning(f"Insufficient data for indicators: {symbol} {timeframe}")
# Use enhanced data fetching
try:
candles = builder.fetch_market_data_enhanced(symbol, timeframe, days_back)
if not candles:
return builder._create_error_chart(f"No market data found for {symbol} {timeframe}")
# Prepare data for charting
df = prepare_chart_data(candles)
if df.empty:
return builder._create_error_chart("Failed to prepare chart data")
# Create chart with data quality info
fig = builder._create_candlestick_with_volume(df, symbol, timeframe)
# Add data quality annotation if data is stale
if not data_quality['is_recent']:
age_hours = data_quality['data_age_minutes'] / 60
fig.add_annotation(
text=f"⚠️ Data is {age_hours:.1f}h old",
xref="paper", yref="paper",
x=0.02, y=0.98,
showarrow=False,
bgcolor="rgba(255,193,7,0.8)",
bordercolor="orange",
borderwidth=1
)
logger.debug(f"Created enhanced candlestick chart for {symbol} {timeframe} with {len(candles)} candles")
return fig
except Exception as e:
logger.error(f"Error creating enhanced candlestick chart: {e}")
return builder._create_error_chart(f"Chart creation failed: {str(e)}")
def create_strategy_chart(symbol: str, timeframe: str, strategy_name: str, **kwargs):
"""
Convenience function to create a strategy-specific chart.
Args:
symbol: Trading pair
timeframe: Timeframe
strategy_name: Name of the strategy configuration
**kwargs: Additional parameters
Returns:
Plotly Figure object with strategy indicators
"""
builder = ChartBuilder()
return builder.create_strategy_chart(symbol, timeframe, strategy_name, **kwargs)
def create_error_chart(error_message: str):
"""Create an error chart with error message."""
builder = ChartBuilder()
return builder._create_error_chart(error_message)
def create_basic_chart(symbol: str, data: list,
indicators: list = None,
error_handling: bool = True) -> 'go.Figure':
"""
Create a basic chart with error handling.
Args:
symbol: Trading symbol
data: OHLCV data as list of dictionaries
indicators: List of indicator configurations
error_handling: Whether to use comprehensive error handling
Returns:
Plotly figure with chart or error display
"""
try:
# Initialize chart builder
builder = ChartBuilder()
if error_handling:
# Use error-aware chart creation
error_handler = ChartErrorHandler()
is_valid = error_handler.validate_data_sufficiency(data, indicators=indicators or [])
if not is_valid:
# Create error chart
fig = go.Figure()
error_msg = error_handler.get_user_friendly_message()
fig.add_annotation(create_error_annotation(error_msg, position='center'))
fig.update_layout(
title=f"Chart Error - {symbol}",
xaxis={'visible': False},
yaxis={'visible': False},
template='plotly_white',
height=400
)
return fig
# Create chart normally
return builder.create_candlestick_chart(data, symbol=symbol, indicators=indicators or [])
except Exception as e:
# Fallback error chart
fig = go.Figure()
fig.add_annotation(create_error_annotation(
f"Chart creation failed: {str(e)}",
position='center'
))
fig.update_layout(
title=f"Chart Error - {symbol}",
template='plotly_white',
height=400
)
return fig
def create_indicator_chart(symbol: str, data: list,
indicator_type: str, **params) -> 'go.Figure':
"""
Create a chart focused on a specific indicator.
Args:
symbol: Trading symbol
data: OHLCV data
indicator_type: Type of indicator ('sma', 'ema', 'bollinger_bands', 'rsi', 'macd')
**params: Indicator parameters
Returns:
Plotly figure with indicator chart
"""
try:
# Map indicator types to configurations
indicator_map = {
'sma': {'type': 'sma', 'parameters': {'period': params.get('period', 20)}},
'ema': {'type': 'ema', 'parameters': {'period': params.get('period', 20)}},
'bollinger_bands': {
'type': 'bollinger_bands',
'parameters': {
'period': params.get('period', 20),
'std_dev': params.get('std_dev', 2)
}
},
'rsi': {'type': 'rsi', 'parameters': {'period': params.get('period', 14)}},
'macd': {
'type': 'macd',
'parameters': {
'fast_period': params.get('fast_period', 12),
'slow_period': params.get('slow_period', 26),
'signal_period': params.get('signal_period', 9)
}
}
}
if indicator_type not in indicator_map:
raise ValueError(f"Unknown indicator type: {indicator_type}")
indicator_config = indicator_map[indicator_type]
return create_basic_chart(symbol, data, indicators=[indicator_config])
except Exception as e:
return create_basic_chart(symbol, data, indicators=[]) # Fallback to basic chart
def create_chart_with_indicators(symbol: str, timeframe: str,
overlay_indicators: List[str] = None,
subplot_indicators: List[str] = None,
days_back: int = 7, **kwargs) -> go.Figure:
"""
Create a chart with dynamically selected indicators.
Args:
symbol: Trading pair (e.g., 'BTC-USDT')
timeframe: Timeframe (e.g., '1h', '1d')
overlay_indicators: List of overlay indicator names
subplot_indicators: List of subplot indicator names
days_back: Number of days to look back
**kwargs: Additional chart parameters
Returns:
Plotly figure with selected indicators
"""
builder = ChartBuilder()
return builder.create_chart_with_indicators(
symbol, timeframe, overlay_indicators, subplot_indicators, days_back, **kwargs
)

141
components/charts/chart_data.py Normal file
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@ -0,0 +1,141 @@
import pandas as pd
from datetime import datetime, timezone, timedelta
import plotly.graph_objects as go
from database.operations import get_database_operations
from utils.logger import get_logger
from utils.timeframe_utils import load_timeframe_options
from .builder import ChartBuilder
from .utils import format_price, format_volume
logger = get_logger("charts_data")
def get_supported_symbols():
"""Get list of symbols that have data in the database."""
builder = ChartBuilder()
# Test query - consider optimizing or removing if not critical for initial check
candles = builder.fetch_market_data("BTC-USDT", "1m", days_back=1)
if candles:
try:
db = get_database_operations(logger)
with db.market_data.get_session() as session:
from sqlalchemy import text
result = session.execute(text("SELECT DISTINCT symbol FROM market_data ORDER BY symbol"))
return [row[0] for row in result]
except Exception as e:
logger.error(f"Error fetching supported symbols from DB: {e}")
pass
return ['BTC-USDT', 'ETH-USDT'] # Fallback
def get_supported_timeframes():
"""Get list of timeframes that have data in the database."""
builder = ChartBuilder()
# Test query - consider optimizing or removing if not critical for initial check
candles = builder.fetch_market_data("BTC-USDT", "1m", days_back=1)
if candles:
try:
db = get_database_operations(logger)
with db.market_data.get_session() as session:
from sqlalchemy import text
result = session.execute(text("SELECT DISTINCT timeframe FROM market_data ORDER BY timeframe"))
return [row[0] for row in result]
except Exception as e:
logger.error(f"Error fetching supported timeframes from DB: {e}")
pass
# Fallback uses values from timeframe_options.json for consistency
return [item['value'] for item in load_timeframe_options() if item['value'] in ['5s', '1m', '15m', '1h']]
def get_market_statistics(symbol: str, timeframe: str = "1h", days_back: int = 1): # Changed from days_back: Union[int, float] to int
"""Calculate market statistics from recent data over a specified period."""
builder = ChartBuilder()
candles = builder.fetch_market_data(symbol, timeframe, days_back=days_back)
if not candles:
return {'Price': 'N/A', f'Change ({days_back}d)': 'N/A', f'Volume ({days_back}d)': 'N/A', f'High ({days_back}d)': 'N/A', f'Low ({days_back}d)': 'N/A'}
df = pd.DataFrame(candles)
latest = df.iloc[-1]
current_price = float(latest['close'])
# Calculate change over the period
if len(df) > 1:
price_period_ago = float(df.iloc[0]['open'])
change_percent = ((current_price - price_period_ago) / price_period_ago) * 100
else:
change_percent = 0
# Determine label for period (e.g., "24h", "7d", "1h")
# This part should be updated if `days_back` can be fractional again.
if days_back == 1/24:
period_label = "1h"
elif days_back == 4/24:
period_label = "4h"
elif days_back == 6/24:
period_label = "6h"
elif days_back == 12/24:
period_label = "12h"
elif days_back < 1: # For other fractional days, show as hours
period_label = f"{int(days_back * 24)}h"
elif days_back == 1:
period_label = "24h" # Keep 24h for 1 day for clarity
else:
period_label = f"{days_back}d"
return {
'Price': format_price(current_price, decimals=2),
f'Change ({period_label})': f"{'+' if change_percent >= 0 else ''}{change_percent:.2f}%",
f'Volume ({period_label})': format_volume(df['volume'].sum()),
f'High ({period_label})': format_price(df['high'].max(), decimals=2),
f'Low ({period_label})': format_price(df['low'].min(), decimals=2)
}
def check_data_availability(symbol: str, timeframe: str):
"""Check data availability for a symbol and timeframe."""
try:
db = get_database_operations(logger)
latest_candle = db.market_data.get_latest_candle(symbol, timeframe)
if latest_candle:
latest_time = latest_candle['timestamp']
time_diff = datetime.now(timezone.utc) - latest_time.replace(tzinfo=timezone.utc)
return {
'has_data': True,
'latest_timestamp': latest_time,
'time_since_last': time_diff,
'is_recent': time_diff < timedelta(hours=1),
'message': f"Latest data: {latest_time.strftime('%Y-%m-%d %H:%M:%S UTC')}"
}
else:
return {
'has_data': False,
'latest_timestamp': None,
'time_since_last': None,
'is_recent': False,
'message': f"No data available for {symbol} {timeframe}"
}
except Exception as e:
return {
'has_data': False,
'latest_timestamp': None,
'time_since_last': None,
'is_recent': False,
'message': f"Error checking data: {str(e)}"
}
def create_data_status_indicator(symbol: str, timeframe: str):
"""Create a data status indicator for the dashboard."""
status = check_data_availability(symbol, timeframe)
if status['has_data']:
if status['is_recent']:
icon, color, status_text = "🟢", "#27ae60", "Real-time Data"
else:
icon, color, status_text = "🟡", "#f39c12", "Delayed Data"
else:
icon, color, status_text = "🔴", "#e74c3c", "No Data"
return f'<span style="color: {color}; font-weight: bold;">{icon} {status_text}</span><br><small>{status["message"]}</small>'

33
config/constants/chart_constants.py Normal file
View File

@ -0,0 +1,33 @@
# Plotly Chart Colors
CHART_COLORS = {
"primary_line": "#2196f3", # Blue
"secondary_line": "#ff9800", # Orange
"increasing_candle": "#26a69a", # Green
"decreasing_candle": "#ef5350", # Red
"neutral_line": "gray"
}
# UI Text Constants
UI_TEXT = {
"no_data_available": "No data available",
"error_prefix": "Error: ",
"volume_analysis_title": "{symbol} Volume Analysis ({timeframe})",
"price_movement_title": "{symbol} Price Movement Analysis ({timeframe})",
"price_action_subplot": "Price Action",
"volume_analysis_subplot": "Volume Analysis",
"volume_ma_subplot": "Volume vs Moving Average",
"cumulative_returns_subplot": "Cumulative Returns",
"period_returns_subplot": "Period Returns (%)",
"price_range_subplot": "Price Range (%)",
"price_yaxis": "Price",
"volume_yaxis": "Volume",
"cumulative_return_yaxis": "Cumulative Return",
"returns_yaxis": "Returns (%)",
"range_yaxis": "Range (%)",
"price_trace_name": "Price",
"volume_trace_name": "Volume",
"volume_ma_trace_name": "Volume MA(20)",
"cumulative_return_trace_name": "Cumulative Return",
"returns_trace_name": "Returns (%)",
"range_trace_name": "Range %"
}

12
config/options/time_range_options.json Normal file
View File

@ -0,0 +1,12 @@
[
{"label": "🕐 Last 1 Hour", "value": "1h"},
{"label": "🕐 Last 4 Hours", "value": "4h"},
{"label": "🕐 Last 6 Hours", "value": "6h"},
{"label": "🕐 Last 12 Hours", "value": "12h"},
{"label": "📅 Last 1 Day", "value": "1d"},
{"label": "📅 Last 3 Days", "value": "3d"},
{"label": "📅 Last 7 Days", "value": "7d"},
{"label": "📅 Last 30 Days", "value": "30d"},
{"label": "📅 Custom Range", "value": "custom"},
{"label": "🔴 Real-time", "value": "realtime"}
]

12
config/options/timeframe_options.json Normal file
View File

@ -0,0 +1,12 @@
[
{"label": "1 Second", "value": "1s"},
{"label": "5 Seconds", "value": "5s"},
{"label": "15 Seconds", "value": "15s"},
{"label": "30 Seconds", "value": "30s"},
{"label": "1 Minute", "value": "1m"},
{"label": "5 Minutes", "value": "5m"},
{"label": "15 Minutes", "value": "15m"},
{"label": "1 Hour", "value": "1h"},
{"label": "4 Hours", "value": "4h"},
{"label": "1 Day", "value": "1d"}
]

2
dashboard/app.py
View File

@ -22,7 +22,7 @@ def create_app():
# Initialize Dash app
app = dash.Dash(__name__, suppress_callback_exceptions=True, external_stylesheets=[dbc.themes.LUX])
# Define the main layout wrapped in MantineProvider
# Define the main layout
app.layout = html.Div([
html.Div([
# Page title

55
dashboard/callbacks/data_analysis.py
View File

@ -6,13 +6,14 @@ from dash import Output, Input, html, dcc
import dash_bootstrap_components as dbc
from utils.logger import get_logger
from dashboard.components.data_analysis import (
VolumeAnalyzer,
PriceMovementAnalyzer,
create_volume_analysis_chart,
create_price_movement_chart,
create_volume_stats_display,
create_price_stats_display
create_price_stats_display,
get_market_statistics,
VolumeAnalyzer,
PriceMovementAnalyzer
)
from database.operations import get_database_operations
from datetime import datetime, timezone, timedelta
logger = get_logger("data_analysis_callbacks")
@ -24,26 +25,38 @@ def register_data_analysis_callbacks(app):
# Initial callback to populate charts on load
@app.callback(
[Output('analysis-chart-container', 'children'),
Output('analysis-stats-container', 'children')],
[Input('analysis-type-selector', 'value'),
Input('analysis-period-selector', 'value')],
[Output('volume-analysis-chart', 'figure'),
Output('price-movement-chart', 'figure'),
Output('volume-stats-output', 'children'),
Output('price-stats-output', 'children'),
Output('market-statistics-output', 'children')],
[Input('data-analysis-symbol-dropdown', 'value'),
Input('data-analysis-timeframe-dropdown', 'value'),
Input('data-analysis-days-back-dropdown', 'value')],
prevent_initial_call=False
)
def update_data_analysis(analysis_type, period):
def update_data_analysis(symbol, timeframe, days_back):
"""Update data analysis with statistical cards only (no duplicate charts)."""
logger.info(f"🎯 DATA ANALYSIS CALLBACK TRIGGERED! Type: {analysis_type}, Period: {period}")
logger.info(f"🎯 DATA ANALYSIS CALLBACK TRIGGERED! Symbol: {symbol}, Timeframe: {timeframe}, Days Back: {days_back}")
db_ops = get_database_operations(logger)
end_time = datetime.now(timezone.utc)
start_time = end_time - timedelta(days=days_back)
# Return placeholder message since we're moving to enhanced market stats
info_msg = dbc.Alert([
html.H4("📊 Statistical Analysis", className="alert-heading"),
html.P("Data analysis has been integrated into the Market Statistics section above."),
html.P("The enhanced statistics now include volume analysis, price movement analysis, and trend indicators."),
html.P("Change the symbol and timeframe in the main chart to see updated analysis."),
html.Hr(),
html.P("This section will be updated with additional analytical tools in future versions.", className="mb-0")
], color="info")
df = db_ops.market_data.get_candles_df(symbol, timeframe, start_time, end_time)
volume_analyzer = VolumeAnalyzer()
price_analyzer = PriceMovementAnalyzer()
return info_msg, html.Div()
volume_stats = volume_analyzer.get_volume_statistics(df)
price_stats = price_analyzer.get_price_movement_statistics(df)
volume_stats_display = create_volume_stats_display(volume_stats)
price_stats_display = create_price_stats_display(price_stats)
market_stats_display = get_market_statistics(df, symbol, timeframe)
# Return empty figures for charts, as they are no longer the primary display
# And the stats displays
return {}, {}, volume_stats_display, price_stats_display, market_stats_display
logger.info("✅ Data analysis callbacks registered successfully")

15
dashboard/components/chart_controls.py
View File

@ -5,10 +5,10 @@ Chart control components for the market data layout.
from dash import html, dcc
import dash_bootstrap_components as dbc
from utils.logger import get_logger
from utils.time_range_utils import load_time_range_options
logger = get_logger("default_logger")
def create_chart_config_panel(strategy_options, overlay_options, subplot_options):
"""Create the chart configuration panel with add/edit UI."""
return dbc.Card([
@ -74,18 +74,7 @@ def create_time_range_controls():
html.Label("Quick Select:", className="form-label"),
dcc.Dropdown(
id='time-range-quick-select',
options=[
{'label': '🕐 Last 1 Hour', 'value': '1h'},
{'label': '🕐 Last 4 Hours', 'value': '4h'},
{'label': '🕐 Last 6 Hours', 'value': '6h'},
{'label': '🕐 Last 12 Hours', 'value': '12h'},
{'label': '📅 Last 1 Day', 'value': '1d'},
{'label': '📅 Last 3 Days', 'value': '3d'},
{'label': '📅 Last 7 Days', 'value': '7d'},
{'label': '📅 Last 30 Days', 'value': '30d'},
{'label': '📅 Custom Range', 'value': 'custom'},
{'label': '🔴 Real-time', 'value': 'realtime'}
],
options=load_time_range_options(),
value='7d',
placeholder="Select time range",
)

863
dashboard/components/data_analysis.py
View File

@ -4,9 +4,6 @@ Data analysis components for comprehensive market data analysis.
from dash import html, dcc
import dash_bootstrap_components as dbc
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
from datetime import datetime, timezone, timedelta
@ -14,7 +11,8 @@ from typing import Dict, Any, List, Optional
from utils.logger import get_logger
from database.connection import DatabaseManager
from database.operations import DatabaseOperationError
from database.operations import DatabaseOperationError, get_database_operations
from config.constants.chart_constants import CHART_COLORS, UI_TEXT
logger = get_logger("data_analysis")
@ -23,8 +21,7 @@ class VolumeAnalyzer:
"""Analyze trading volume patterns and trends."""
def __init__(self):
self.db_manager = DatabaseManager()
self.db_manager.initialize()
pass
def get_volume_statistics(self, df: pd.DataFrame) -> Dict[str, Any]:
"""Calculate comprehensive volume statistics from a DataFrame."""
@ -32,69 +29,81 @@ class VolumeAnalyzer:
if df.empty or 'volume' not in df.columns:
return {'error': 'DataFrame is empty or missing volume column'}
# Convert all relevant columns to float to avoid type errors with Decimal
df = df.copy()
numeric_cols = ['open', 'high', 'low', 'close', 'volume']
for col in numeric_cols:
if col in df.columns:
df[col] = df[col].astype(float)
if 'trades_count' in df.columns:
df['trades_count'] = df['trades_count'].astype(float)
df = self._ensure_numeric_cols(df)
# Calculate volume statistics
total_volume = df['volume'].sum()
avg_volume = df['volume'].mean()
volume_std = df['volume'].std()
stats = {}
stats.update(self._calculate_basic_volume_stats(df))
stats.update(self._analyze_volume_trend(df))
stats.update(self._identify_high_volume_periods(df, stats['avg_volume'], stats['volume_std']))
stats.update(self._calculate_volume_price_correlation(df))
stats.update(self._calculate_avg_trade_size(df))
stats.update(self._calculate_volume_percentiles(df))
# Volume trend analysis
recent_volume = df['volume'].tail(10).mean() # Last 10 periods
older_volume = df['volume'].head(10).mean() # First 10 periods
volume_trend = "Increasing" if recent_volume > older_volume else "Decreasing"
# High volume periods (above 2 standard deviations)
high_volume_threshold = avg_volume + (2 * volume_std)
high_volume_periods = len(df[df['volume'] > high_volume_threshold])
# Volume-Price correlation
price_change = df['close'] - df['open']
volume_price_corr = df['volume'].corr(price_change.abs())
# Average trade size (volume per trade)
if 'trades_count' in df.columns:
df['avg_trade_size'] = df['volume'] / df['trades_count'].replace(0, 1)
avg_trade_size = df['avg_trade_size'].mean()
else:
avg_trade_size = None # Not available
return {
'total_volume': total_volume,
'avg_volume': avg_volume,
'volume_std': volume_std,
'volume_trend': volume_trend,
'high_volume_periods': high_volume_periods,
'volume_price_correlation': volume_price_corr,
'avg_trade_size': avg_trade_size,
'max_volume': df['volume'].max(),
'min_volume': df['volume'].min(),
'volume_percentiles': {
'25th': df['volume'].quantile(0.25),
'50th': df['volume'].quantile(0.50),
'75th': df['volume'].quantile(0.75),
'95th': df['volume'].quantile(0.95)
}
}
return stats
except Exception as e:
logger.error(f"Volume analysis error: {e}")
return {'error': str(e)}
def _ensure_numeric_cols(self, df: pd.DataFrame) -> pd.DataFrame:
numeric_cols = ['open', 'high', 'low', 'close', 'volume']
for col in numeric_cols:
if col in df.columns:
df[col] = df[col].astype(float)
if 'trades_count' in df.columns:
df['trades_count'] = df['trades_count'].astype(float)
return df
def _calculate_basic_volume_stats(self, df: pd.DataFrame) -> Dict[str, Any]:
return {
'total_volume': df['volume'].sum(),
'avg_volume': df['volume'].mean(),
'volume_std': df['volume'].std(),
'max_volume': df['volume'].max(),
'min_volume': df['volume'].min()
}
def _analyze_volume_trend(self, df: pd.DataFrame) -> Dict[str, Any]:
recent_volume = df['volume'].tail(10).mean()
older_volume = df['volume'].head(10).mean()
volume_trend = "Increasing" if recent_volume > older_volume else "Decreasing"
return {'volume_trend': volume_trend}
def _identify_high_volume_periods(self, df: pd.DataFrame, avg_volume: float, volume_std: float) -> Dict[str, Any]:
high_volume_threshold = avg_volume + (2 * volume_std)
high_volume_periods = len(df[df['volume'] > high_volume_threshold])
return {'high_volume_periods': high_volume_periods}
def _calculate_volume_price_correlation(self, df: pd.DataFrame) -> Dict[str, Any]:
price_change = df['close'] - df['open']
volume_price_corr = df['volume'].corr(price_change.abs())
return {'volume_price_correlation': volume_price_corr}
def _calculate_avg_trade_size(self, df: pd.DataFrame) -> Dict[str, Any]:
if 'trades_count' in df.columns:
df['avg_trade_size'] = df['volume'] / df['trades_count'].replace(0, 1)
avg_trade_size = df['avg_trade_size'].mean()
else:
avg_trade_size = None
return {'avg_trade_size': avg_trade_size}
def _calculate_volume_percentiles(self, df: pd.DataFrame) -> Dict[str, Any]:
return {
'volume_percentiles': {
'25th': df['volume'].quantile(0.25),
'50th': df['volume'].quantile(0.50),
'75th': df['volume'].quantile(0.75),
'95th': df['volume'].quantile(0.95)
}
}
class PriceMovementAnalyzer:
"""Analyze price movement patterns and statistics."""
def __init__(self):
self.db_manager = DatabaseManager()
self.db_manager.initialize()
pass
def get_price_movement_statistics(self, df: pd.DataFrame) -> Dict[str, Any]:
"""Calculate comprehensive price movement statistics from a DataFrame."""
@ -102,499 +111,317 @@ class PriceMovementAnalyzer:
if df.empty or not all(col in df.columns for col in ['open', 'high', 'low', 'close']):
return {'error': 'DataFrame is empty or missing required price columns'}
# Convert all relevant columns to float to avoid type errors with Decimal
df = df.copy()
numeric_cols = ['open', 'high', 'low', 'close', 'volume']
for col in numeric_cols:
if col in df.columns:
df[col] = df[col].astype(float)
# Basic price statistics
current_price = df['close'].iloc[-1]
period_start_price = df['open'].iloc[0]
period_return = ((current_price - period_start_price) / period_start_price) * 100
df = self._ensure_numeric_cols(df)
# Daily returns (percentage changes)
df['returns'] = df['close'].pct_change() * 100
df['returns'] = df['returns'].fillna(0)
# Volatility metrics
volatility = df['returns'].std()
avg_return = df['returns'].mean()
# Price range analysis
df['range'] = df['high'] - df['low']
df['range_pct'] = (df['range'] / df['open']) * 100
avg_range_pct = df['range_pct'].mean()
# Directional analysis
bullish_periods = len(df[df['close'] > df['open']])
bearish_periods = len(df[df['close'] < df['open']])
neutral_periods = len(df[df['close'] == df['open']])
total_periods = len(df)
bullish_ratio = (bullish_periods / total_periods) * 100 if total_periods > 0 else 0
# Price extremes
period_high = df['high'].max()
period_low = df['low'].min()
# Momentum indicators
# Simple momentum (current vs N periods ago)
momentum_periods = min(10, len(df) - 1)
if momentum_periods > 0:
momentum = ((current_price - df['close'].iloc[-momentum_periods-1]) / df['close'].iloc[-momentum_periods-1]) * 100
else:
momentum = 0
# Trend strength (linear regression slope)
if len(df) > 2:
x = np.arange(len(df))
slope, _ = np.polyfit(x, df['close'], 1)
trend_strength = slope / df['close'].mean() * 100 # Normalize by average price
else:
trend_strength = 0
return {
'current_price': current_price,
'period_return': period_return,
'volatility': volatility,
'avg_return': avg_return,
'avg_range_pct': avg_range_pct,
'bullish_periods': bullish_periods,
'bearish_periods': bearish_periods,
'neutral_periods': neutral_periods,
'bullish_ratio': bullish_ratio,
'period_high': period_high,
'period_low': period_low,
'momentum': momentum,
'trend_strength': trend_strength,
'return_percentiles': {
'5th': df['returns'].quantile(0.05),
'25th': df['returns'].quantile(0.25),
'75th': df['returns'].quantile(0.75),
'95th': df['returns'].quantile(0.95)
},
'max_gain': df['returns'].max(),
'max_loss': df['returns'].min(),
'positive_returns': len(df[df['returns'] > 0]),
'negative_returns': len(df[df['returns'] < 0])
}
stats = {}
stats.update(self._calculate_basic_price_stats(df))
stats.update(self._calculate_returns_and_volatility(df))
stats.update(self._analyze_price_range(df))
stats.update(self._analyze_directional_movement(df))
stats.update(self._calculate_price_extremes(df))
stats.update(self._calculate_momentum(df))
stats.update(self._calculate_trend_strength(df))
stats.update(self._calculate_return_percentiles(df))
return stats
except Exception as e:
logger.error(f"Price movement analysis error: {e}")
return {'error': str(e)}
def _ensure_numeric_cols(self, df: pd.DataFrame) -> pd.DataFrame:
numeric_cols = ['open', 'high', 'low', 'close', 'volume']
for col in numeric_cols:
if col in df.columns:
df[col] = df[col].astype(float)
return df
def create_volume_analysis_chart(symbol: str, timeframe: str = "1h", days_back: int = 7) -> go.Figure:
"""Create a comprehensive volume analysis chart."""
try:
analyzer = VolumeAnalyzer()
# Fetch market data for chart
db_manager = DatabaseManager()
db_manager.initialize()
end_time = datetime.now(timezone.utc)
start_time = end_time - timedelta(days=days_back)
with db_manager.get_session() as session:
from sqlalchemy import text
query = text("""
SELECT timestamp, open, high, low, close, volume, trades_count
FROM market_data
WHERE symbol = :symbol
AND timeframe = :timeframe
AND timestamp >= :start_time
AND timestamp <= :end_time
ORDER BY timestamp ASC
""")
result = session.execute(query, {
'symbol': symbol,
'timeframe': timeframe,
'start_time': start_time,
'end_time': end_time
})
candles = []
for row in result:
candles.append({
'timestamp': row.timestamp,
'open': float(row.open),
'high': float(row.high),
'low': float(row.low),
'close': float(row.close),
'volume': float(row.volume),
'trades_count': int(row.trades_count) if row.trades_count else 0
})
if not candles:
fig = go.Figure()
fig.add_annotation(text="No data available", xref="paper", yref="paper", x=0.5, y=0.5)
return fig
df = pd.DataFrame(candles)
# Calculate volume moving average
df['volume_ma'] = df['volume'].rolling(window=20, min_periods=1).mean()
# Create subplots
fig = make_subplots(
rows=3, cols=1,
subplot_titles=('Price Action', 'Volume Analysis', 'Volume vs Moving Average'),
vertical_spacing=0.08,
row_heights=[0.4, 0.3, 0.3]
)
# Price candlestick
fig.add_trace(
go.Candlestick(
x=df['timestamp'],
open=df['open'],
high=df['high'],
low=df['low'],
close=df['close'],
name='Price',
increasing_line_color='#26a69a',
decreasing_line_color='#ef5350'
),
row=1, col=1
)
# Volume bars with color coding
colors = ['#26a69a' if close >= open else '#ef5350' for close, open in zip(df['close'], df['open'])]
fig.add_trace(
go.Bar(
x=df['timestamp'],
y=df['volume'],
name='Volume',
marker_color=colors,
opacity=0.7
),
row=2, col=1
)
# Volume vs moving average
fig.add_trace(
go.Scatter(
x=df['timestamp'],
y=df['volume'],
mode='lines',
name='Volume',
line=dict(color='#2196f3', width=1)
),
row=3, col=1
)
fig.add_trace(
go.Scatter(
x=df['timestamp'],
y=df['volume_ma'],
mode='lines',
name='Volume MA(20)',
line=dict(color='#ff9800', width=2)
),
row=3, col=1
)
# Update layout
fig.update_layout(
title=f'{symbol} Volume Analysis ({timeframe})',
xaxis_rangeslider_visible=False,
height=800,
showlegend=True,
template='plotly_white'
)
# Update y-axes
fig.update_yaxes(title_text="Price", row=1, col=1)
fig.update_yaxes(title_text="Volume", row=2, col=1)
fig.update_yaxes(title_text="Volume", row=3, col=1)
return fig
except Exception as e:
logger.error(f"Volume chart creation error: {e}")
fig = go.Figure()
fig.add_annotation(text=f"Error: {str(e)}", xref="paper", yref="paper", x=0.5, y=0.5)
return fig
def _calculate_basic_price_stats(self, df: pd.DataFrame) -> Dict[str, Any]:
current_price = df['close'].iloc[-1]
period_start_price = df['open'].iloc[0]
period_return = ((current_price - period_start_price) / period_start_price) * 100
return {'current_price': current_price, 'period_return': period_return}
def create_price_movement_chart(symbol: str, timeframe: str = "1h", days_back: int = 7) -> go.Figure:
"""Create a comprehensive price movement analysis chart."""
try:
# Fetch market data for chart
db_manager = DatabaseManager()
db_manager.initialize()
end_time = datetime.now(timezone.utc)
start_time = end_time - timedelta(days=days_back)
with db_manager.get_session() as session:
from sqlalchemy import text
query = text("""
SELECT timestamp, open, high, low, close, volume
FROM market_data
WHERE symbol = :symbol
AND timeframe = :timeframe
AND timestamp >= :start_time
AND timestamp <= :end_time
ORDER BY timestamp ASC
""")
result = session.execute(query, {
'symbol': symbol,
'timeframe': timeframe,
'start_time': start_time,
'end_time': end_time
})
candles = []
for row in result:
candles.append({
'timestamp': row.timestamp,
'open': float(row.open),
'high': float(row.high),
'low': float(row.low),
'close': float(row.close),
'volume': float(row.volume)
})
if not candles:
fig = go.Figure()
fig.add_annotation(text="No data available", xref="paper", yref="paper", x=0.5, y=0.5)
return fig
df = pd.DataFrame(candles)
# Calculate returns and statistics
def _calculate_returns_and_volatility(self, df: pd.DataFrame) -> Dict[str, Any]:
df['returns'] = df['close'].pct_change() * 100
df['returns'] = df['returns'].fillna(0)
df['range_pct'] = ((df['high'] - df['low']) / df['open']) * 100
df['cumulative_return'] = (1 + df['returns'] / 100).cumprod()
# Create subplots
fig = make_subplots(
rows=3, cols=1,
subplot_titles=('Cumulative Returns', 'Period Returns (%)', 'Price Range (%)'),
vertical_spacing=0.08,
row_heights=[0.4, 0.3, 0.3]
)
# Cumulative returns
fig.add_trace(
go.Scatter(
x=df['timestamp'],
y=df['cumulative_return'],
mode='lines',
name='Cumulative Return',
line=dict(color='#2196f3', width=2)
),
row=1, col=1
)
# Period returns with color coding
colors = ['#26a69a' if ret >= 0 else '#ef5350' for ret in df['returns']]
fig.add_trace(
go.Bar(
x=df['timestamp'],
y=df['returns'],
name='Returns (%)',
marker_color=colors,
opacity=0.7
),
row=2, col=1
)
# Price range percentage
fig.add_trace(
go.Scatter(
x=df['timestamp'],
y=df['range_pct'],
mode='lines+markers',
name='Range %',
line=dict(color='#ff9800', width=1),
marker=dict(size=4)
),
row=3, col=1
)
# Add zero line for returns
fig.add_hline(y=0, line_dash="dash", line_color="gray", row=2, col=1)
# Update layout
fig.update_layout(
title=f'{symbol} Price Movement Analysis ({timeframe})',
height=800,
showlegend=True,
template='plotly_white'
)
# Update y-axes
fig.update_yaxes(title_text="Cumulative Return", row=1, col=1)
fig.update_yaxes(title_text="Returns (%)", row=2, col=1)
fig.update_yaxes(title_text="Range (%)", row=3, col=1)
return fig
except Exception as e:
logger.error(f"Price movement chart creation error: {e}")
fig = go.Figure()
fig.add_annotation(text=f"Error: {str(e)}", xref="paper", yref="paper", x=0.5, y=0.5)
return fig
volatility = df['returns'].std()
avg_return = df['returns'].mean()
return {'volatility': volatility, 'avg_return': avg_return, 'returns': df['returns']}
def _analyze_price_range(self, df: pd.DataFrame) -> Dict[str, Any]:
df['range'] = df['high'] - df['low']
df['range_pct'] = (df['range'] / df['open']) * 100
avg_range_pct = df['range_pct'].mean()
return {'avg_range_pct': avg_range_pct}
def create_data_analysis_panel():
"""Create the main data analysis panel with tabs for different analyses."""
return html.Div([
dcc.Tabs(
id="data-analysis-tabs",
value="volume-analysis",
children=[
dcc.Tab(label="Volume Analysis", value="volume-analysis", children=[
html.Div(id='volume-analysis-content', children=[
html.P("Content for Volume Analysis")
]),
html.Div(id='volume-stats-container', children=[
html.P("Stats container loaded - waiting for callback...")
])
]),
dcc.Tab(label="Price Movement", value="price-movement", children=[
html.Div(id='price-movement-content', children=[
dbc.Alert("Select a symbol and timeframe to view price movement analysis.", color="primary")
])
]),
],
)
], id='data-analysis-panel-wrapper')
def _analyze_directional_movement(self, df: pd.DataFrame) -> Dict[str, Any]:
bullish_periods = len(df[df['close'] > df['open']])
bearish_periods = len(df[df['close'] < df['open']])
neutral_periods = len(df[df['close'] == df['open']])
total_periods = len(df)
bullish_ratio = (bullish_periods / total_periods) * 100 if total_periods > 0 else 0
return {
'bullish_periods': bullish_periods,
'bearish_periods': bearish_periods,
'neutral_periods': neutral_periods,
'bullish_ratio': bullish_ratio,
'positive_returns': len(df[df['returns'] > 0]),
'negative_returns': len(df[df['returns'] < 0])
}
def _calculate_price_extremes(self, df: pd.DataFrame) -> Dict[str, Any]:
period_high = df['high'].max()
period_low = df['low'].min()
return {'period_high': period_high, 'period_low': period_low}
def _calculate_momentum(self, df: pd.DataFrame) -> Dict[str, Any]:
current_price = df['close'].iloc[-1]
momentum_periods = min(10, len(df) - 1)
if momentum_periods > 0:
momentum = ((current_price - df['close'].iloc[-momentum_periods-1]) / df['close'].iloc[-momentum_periods-1]) * 100
else:
momentum = 0
return {'momentum': momentum}
def _calculate_trend_strength(self, df: pd.DataFrame) -> Dict[str, Any]:
if len(df) > 2:
x = np.arange(len(df))
slope, _ = np.polyfit(x, df['close'], 1)
trend_strength = slope / df['close'].mean() * 100
else:
trend_strength = 0
return {'trend_strength': trend_strength}
def _calculate_return_percentiles(self, df: pd.DataFrame) -> Dict[str, Any]:
return {
'return_percentiles': {
'5th': df['returns'].quantile(0.05),
'25th': df['returns'].quantile(0.25),
'75th': df['returns'].quantile(0.75),
'95th': df['returns'].quantile(0.95)
},
'max_gain': df['returns'].max(),
'max_loss': df['returns'].min()
}
def format_number(value: float, decimals: int = 2) -> str:
"""Format number with appropriate decimals and units."""
if pd.isna(value):
"""Formats a number to a string with specified decimals."""
if value is None:
return "N/A"
if abs(value) >= 1e9:
return f"{value/1e9:.{decimals}f}B"
elif abs(value) >= 1e6:
return f"{value/1e6:.{decimals}f}M"
elif abs(value) >= 1e3:
return f"{value/1e3:.{decimals}f}K"
else:
return f"{value:.{decimals}f}"
return f"{value:,.{decimals}f}"
def _create_stat_card(icon, title, value, color="primary") -> dbc.Card: # Extracted helper
return dbc.Card(
dbc.CardBody(
[
html.H4(title, className="card-title"),
html.P(value, className="card-text"),
html.I(className=f"fas fa-{icon} text-{color}"),
]
),
className=f"text-center m-1 bg-light border-{color}"
)
def create_volume_stats_display(stats: Dict[str, Any]) -> html.Div:
"""Create volume statistics display."""
"""Creates a display for volume statistics."""
if 'error' in stats:
return dbc.Alert(
"Error loading volume statistics",
color="danger",
dismissable=True
)
def create_stat_card(icon, title, value, color="primary"):
return dbc.Col(dbc.Card(dbc.CardBody([
html.Div([
html.Div(icon, className="display-6"),
html.Div([
html.P(title, className="card-title mb-1 text-muted"),
html.H4(value, className=f"card-text fw-bold text-{color}")
], className="ms-3")
], className="d-flex align-items-center")
])), width=4, className="mb-3")
return html.Div(f"Error: {stats['error']}", className="alert alert-danger")
return dbc.Row([
create_stat_card("📊", "Total Volume", format_number(stats['total_volume'])),
create_stat_card("📈", "Average Volume", format_number(stats['avg_volume'])),
create_stat_card("🎯", "Volume Trend", stats['volume_trend'],
"success" if stats['volume_trend'] == "Increasing" else "danger"),
create_stat_card("⚡", "High Volume Periods", str(stats['high_volume_periods'])),
create_stat_card("🔗", "Volume-Price Correlation", f"{stats['volume_price_correlation']:.3f}"),
create_stat_card("💱", "Avg Trade Size", format_number(stats['avg_trade_size']))
], className="mt-3")
return html.Div(
[
html.H3("Volume Statistics", className="mb-3 text-primary"),
dbc.Row([
dbc.Col(_create_stat_card("chart-bar", "Total Volume", format_number(stats.get('total_volume')), "success"), md=6),
dbc.Col(_create_stat_card("calculator", "Avg. Volume", format_number(stats.get('avg_volume')), "info"), md=6),
]),
dbc.Row([
dbc.Col(_create_stat_card("arrow-trend-up", "Volume Trend", stats.get('volume_trend'), "warning"), md=6),
dbc.Col(_create_stat_card("hand-holding-usd", "Avg. Trade Size", format_number(stats.get('avg_trade_size')), "secondary"), md=6),
]),
dbc.Row([
dbc.Col(_create_stat_card("ranking-star", "High Vol. Periods", stats.get('high_volume_periods')), md=6),
dbc.Col(_create_stat_card("arrows-left-right", "Vol-Price Corr.", format_number(stats.get('volume_price_correlation'), 4), "primary"), md=6),
]),
]
)
def create_price_stats_display(stats: Dict[str, Any]) -> html.Div:
"""Create price movement statistics display."""
"""Creates a display for price movement statistics."""
if 'error' in stats:
return dbc.Alert(
"Error loading price statistics",
color="danger",
dismissable=True
)
return html.Div(f"Error: {stats['error']}", className="alert alert-danger")
def create_stat_card(icon, title, value, color="primary"):
text_color = "text-dark"
if color == "success":
text_color = "text-success"
elif color == "danger":
text_color = "text-danger"
return dbc.Col(dbc.Card(dbc.CardBody([
html.Div([
html.Div(icon, className="display-6"),
html.Div([
html.P(title, className="card-title mb-1 text-muted"),
html.H4(value, className=f"card-text fw-bold {text_color}")
], className="ms-3")
], className="d-flex align-items-center")
])), width=4, className="mb-3")
return dbc.Row([
create_stat_card("💰", "Current Price", f"${stats['current_price']:.2f}"),
create_stat_card("📈", "Period Return", f"{stats['period_return']:+.2f}%",
"success" if stats['period_return'] >= 0 else "danger"),
create_stat_card("📊", "Volatility", f"{stats['volatility']:.2f}%", color="warning"),
create_stat_card("🎯", "Bullish Ratio", f"{stats['bullish_ratio']:.1f}%"),
create_stat_card("⚡", "Momentum", f"{stats['momentum']:+.2f}%",
"success" if stats['momentum'] >= 0 else "danger"),
create_stat_card("📉", "Max Loss", f"{stats['max_loss']:.2f}%", "danger")
], className="mt-3")
return html.Div(
[
html.H3("Price Movement Statistics", className="mb-3 text-success"),
dbc.Row([
dbc.Col(_create_stat_card("dollar-sign", "Current Price", format_number(stats.get('current_price')), "success"), md=6),
dbc.Col(_create_stat_card("percent", "Period Return", f"{format_number(stats.get('period_return'))}%"), md=6),
]),
dbc.Row([
dbc.Col(_create_stat_card("wave-square", "Volatility", f"{format_number(stats.get('volatility'))}%"), md=6),
dbc.Col(_create_stat_card("chart-line", "Avg. Daily Return", f"{format_number(stats.get('avg_return'))}%"), md=6),
]),
dbc.Row([
dbc.Col(_create_stat_card("arrows-up-down-left-right", "Avg. Range %", f"{format_number(stats.get('avg_range_pct'))}%"), md=6),
dbc.Col(_create_stat_card("arrow-up", "Bullish Ratio", f"{format_number(stats.get('bullish_ratio'))}%"), md=6),
]),
]
)
def get_market_statistics(df: pd.DataFrame, symbol: str, timeframe: str) -> html.Div:
"""
Generate a comprehensive market statistics component from a DataFrame.
Generates a display of key market statistics from the provided DataFrame.
"""
if df.empty:
return html.Div("No data available for statistics.", className="text-center text-muted")
return html.Div([html.P("No market data available for statistics.")], className="alert alert-info mt-4")
try:
# Get statistics
price_analyzer = PriceMovementAnalyzer()
volume_analyzer = VolumeAnalyzer()
price_stats = price_analyzer.get_price_movement_statistics(df)
volume_stats = volume_analyzer.get_volume_statistics(df)
# Format key statistics for display
start_date = df.index.min().strftime('%Y-%m-%d %H:%M')
end_date = df.index.max().strftime('%Y-%m-%d %H:%M')
# Check for errors from analyzers
if 'error' in price_stats or 'error' in volume_stats:
error_msg = price_stats.get('error') or volume_stats.get('error')
return html.Div(f"Error generating statistics: {error_msg}", style={'color': 'red'})
# Time range for display
days_back = (df.index.max() - df.index.min()).days
time_status = f"📅 Analysis Range: {start_date} to {end_date} (~{days_back} days)"
return html.Div([
html.H3("📊 Enhanced Market Statistics", className="mb-3"),
html.P(
time_status,
className="lead text-center text-muted mb-4"
# Basic Market Overview
first_timestamp = df.index.min()
last_timestamp = df.index.max()
num_candles = len(df)
# Price Changes
first_close = df['close'].iloc[0]
last_close = df['close'].iloc[-1]
price_change_abs = last_close - first_close
price_change_pct = (price_change_abs / first_close) * 100 if first_close != 0 else 0
# Highs and Lows
period_high = df['high'].max()
period_low = df['low'].min()
# Average True Range (ATR) - A measure of volatility
# Requires TA-Lib or manual calculation. For simplicity, we'll use a basic range for now.
# Ideally, integrate a proper TA library.
df['tr'] = np.maximum(df['high'] - df['low'],
np.maximum(abs(df['high'] - df['close'].shift()),
abs(df['low'] - df['close'].shift())))
atr = df['tr'].mean() if not df['tr'].empty else 0
# Trading Volume Analysis
total_volume = df['volume'].sum()
average_volume = df['volume'].mean()
# Market Cap (placeholder - requires external data)
market_cap_info = "N/A (requires external API)"
# Order Book Depth (placeholder - requires real-time order book data)
order_book_depth = "N/A (requires real-time data)"
stats_content = html.Div([
html.H3(f"Market Statistics for {symbol} ({timeframe})", className="mb-3 text-info"),
_create_basic_market_overview(
first_timestamp, last_timestamp, num_candles,
first_close, last_close, price_change_abs, price_change_pct,
total_volume, average_volume, atr
),
html.Hr(className="my-4"),
_create_advanced_market_stats(
period_high, period_low, market_cap_info, order_book_depth
)
], className="mb-4")
return stats_content
def _create_basic_market_overview(
first_timestamp: datetime, last_timestamp: datetime, num_candles: int,
first_close: float, last_close: float, price_change_abs: float, price_change_pct: float,
total_volume: float, average_volume: float, atr: float
) -> dbc.Row:
return dbc.Row([
dbc.Col(
dbc.Card(
dbc.CardBody(
[
html.H4("Time Period", className="card-title"),
html.P(f"From: {first_timestamp.strftime('%Y-%m-%d %H:%M')}"),
html.P(f"To: {last_timestamp.strftime('%Y-%m-%d %H:%M')}"),
html.P(f"Candles: {num_candles}"),
]
),
className="text-center m-1 bg-light border-info"
),
create_price_stats_display(price_stats),
create_volume_stats_display(volume_stats)
])
except Exception as e:
logger.error(f"Error in get_market_statistics: {e}", exc_info=True)
return dbc.Alert(f"Error generating statistics display: {e}", color="danger")
md=4
),
dbc.Col(
dbc.Card(
dbc.CardBody(
[
html.H4("Price Movement", className="card-title"),
html.P(f"Initial Price: {format_number(first_close)}"),
html.P(f"Final Price: {format_number(last_close)}"),
html.P(f"Change: {format_number(price_change_abs)} ({format_number(price_change_pct)}%)",
style={'color': 'green' if price_change_pct >= 0 else 'red'}),
]
),
className="text-center m-1 bg-light border-info"
),
md=4
),
dbc.Col(
dbc.Card(
dbc.CardBody(
[
html.H4("Volume & Volatility", className="card-title"),
html.P(f"Total Volume: {format_number(total_volume)}"),
html.P(f"Average Volume: {format_number(average_volume)}"),
html.P(f"Average True Range: {format_number(atr, 4)}"),
]
),
className="text-center m-1 bg-light border-info"
),
md=4
),
])
def _create_advanced_market_stats(
period_high: float, period_low: float, market_cap_info: str, order_book_depth: str
) -> dbc.Row:
return dbc.Row([
dbc.Col(
dbc.Card(
dbc.CardBody(
[
html.H4("Period Extremes", className="card-title"),
html.P(f"Period High: {format_number(period_high)}"),
html.P(f"Period Low: {format_number(period_low)}"),
]
),
className="text-center m-1 bg-light border-warning"
),
md=4
),
dbc.Col(
dbc.Card(
dbc.CardBody(
[
html.H4("Liquidity/Depth", className="card-title"),
html.P(f"Market Cap: {market_cap_info}"),
html.P(f"Order Book Depth: {order_book_depth}"),
]
),
className="text-center m-1 bg-light border-warning"
),
md=4
),
dbc.Col(
dbc.Card(
dbc.CardBody(
[
html.H4("Custom Indicators", className="card-title"),
html.P("RSI: N/A"), # Placeholder
html.P("MACD: N/A"), # Placeholder
]
),
className="text-center m-1 bg-light border-warning"
),
md=4
)
])

15
dashboard/components/indicator_modal.py
View File

@ -4,6 +4,7 @@ Indicator modal component for creating and editing indicators.
from dash import html, dcc
import dash_bootstrap_components as dbc
from utils.timeframe_utils import load_timeframe_options
def create_indicator_modal():
@ -37,19 +38,7 @@ def create_indicator_modal():
dbc.Col(dbc.Label("Timeframe (Optional):"), width=12),
dbc.Col(dcc.Dropdown(
id='indicator-timeframe-dropdown',
options=[
{'label': 'Chart Timeframe', 'value': ''},
{'label': "1 Second", 'value': '1s'},
{'label': "5 Seconds", 'value': '5s'},
{'label': "15 Seconds", 'value': '15s'},
{'label': "30 Seconds", 'value': '30s'},
{'label': '1 Minute', 'value': '1m'},
{'label': '5 Minutes', 'value': '5m'},
{'label': '15 Minutes', 'value': '15m'},
{'label': '1 Hour', 'value': '1h'},
{'label': '4 Hours', 'value': '4h'},
{'label': '1 Day', 'value': '1d'},
],
options=[{'label': 'Chart Timeframe', 'value': ''}] + load_timeframe_options(),
value='',
placeholder='Defaults to chart timeframe'
), width=12),

69
dashboard/layouts/market_data.py
View File

@ -13,81 +13,68 @@ from dashboard.components.chart_controls import (
create_time_range_controls,
create_export_controls
)
from utils.timeframe_utils import load_timeframe_options
logger = get_logger("default_logger")
def get_market_data_layout():
"""Create the market data visualization layout with indicator controls."""
# Get available symbols and timeframes from database
symbols = get_supported_symbols()
timeframes = get_supported_timeframes()
# Create dropdown options
def _create_dropdown_options(symbols, timeframes):
"""Creates symbol and timeframe dropdown options."""
symbol_options = [{'label': symbol, 'value': symbol} for symbol in symbols]
timeframe_options = [
{'label': "1 Second", 'value': '1s'},
{'label': "5 Seconds", 'value': '5s'},
{'label': "15 Seconds", 'value': '15s'},
{'label': "30 Seconds", 'value': '30s'},
{'label': '1 Minute', 'value': '1m'},
{'label': '5 Minutes', 'value': '5m'},
{'label': '15 Minutes', 'value': '15m'},
{'label': '1 Hour', 'value': '1h'},
{'label': '4 Hours', 'value': '4h'},
{'label': '1 Day', 'value': '1d'},
]
all_timeframe_options = load_timeframe_options()
# Filter timeframe options to only show those available in database
available_timeframes = [tf for tf in ['1s', '5s', '15s', '30s', '1m', '5m', '15m', '1h', '4h', '1d'] if tf in timeframes]
if not available_timeframes:
available_timeframes = ['5m'] # Default fallback
available_timeframes_from_db = [tf for tf in [opt['value'] for opt in all_timeframe_options] if tf in timeframes]
if not available_timeframes_from_db:
available_timeframes_from_db = ['5m'] # Default fallback
timeframe_options = [opt for opt in timeframe_options if opt['value'] in available_timeframes]
timeframe_options = [opt for opt in all_timeframe_options if opt['value'] in available_timeframes_from_db]
# Get available strategies and indicators
return symbol_options, timeframe_options
def _load_strategy_and_indicator_options():
"""Loads strategy and indicator options for chart configuration."""
try:
strategy_names = get_available_strategy_names()
strategy_options = [{'label': name.replace('_', ' ').title(), 'value': name} for name in strategy_names]
# Get user indicators from the new indicator manager
indicator_manager = get_indicator_manager()
# Ensure default indicators exist
ensure_default_indicators()
# Get indicators by display type
overlay_indicators = indicator_manager.get_indicators_by_type('overlay')
subplot_indicators = indicator_manager.get_indicators_by_type('subplot')
# Create checkbox options for overlay indicators
overlay_options = []
for indicator in overlay_indicators:
display_name = f"{indicator.name} ({indicator.type.upper()})"
overlay_options.append({'label': display_name, 'value': indicator.id})
# Create checkbox options for subplot indicators
subplot_options = []
for indicator in subplot_indicators:
display_name = f"{indicator.name} ({indicator.type.upper()})"
subplot_options.append({'label': display_name, 'value': indicator.id})
return strategy_options, overlay_options, subplot_options
except Exception as e:
logger.warning(f"Market data layout: Error loading indicator options: {e}")
strategy_options = [{'label': 'Basic Chart', 'value': 'basic'}]
overlay_options = []
subplot_options = []
return [{'label': 'Basic Chart', 'value': 'basic'}], [], []
def get_market_data_layout():
"""Create the market data visualization layout with indicator controls."""
symbols = get_supported_symbols()
timeframes = get_supported_timeframes()
# Create components using the new modular functions
symbol_options, timeframe_options = _create_dropdown_options(symbols, timeframes)
strategy_options, overlay_options, subplot_options = _load_strategy_and_indicator_options()
chart_config_panel = create_chart_config_panel(strategy_options, overlay_options, subplot_options)
time_range_controls = create_time_range_controls()
export_controls = create_export_controls()
return html.Div([
# Title and basic controls
html.H3("💹 Market Data Visualization", style={'color': '#2c3e50', 'margin-bottom': '20px'}),
# Main chart controls
html.Div([
html.Div([
html.Label("Symbol:", style={'font-weight': 'bold'}),
@ -111,21 +98,15 @@ def get_market_data_layout():
], style={'width': '48%', 'float': 'right', 'display': 'inline-block'})
], style={'margin-bottom': '20px'}),
# Chart Configuration Panel
chart_config_panel,
# Time Range Controls (positioned under indicators, next to chart)
time_range_controls,
# Export Controls
export_controls,
# Chart
dcc.Graph(id='price-chart'),
# Hidden store for chart data
dcc.Store(id='chart-data-store'),
# Enhanced Market statistics with integrated data analysis
html.Div(id='market-stats', style={'margin-top': '20px'})
])

241
dashboard/layouts/system_health.py
View File

@ -5,127 +5,146 @@ System health monitoring layout for the dashboard.
from dash import html
import dash_bootstrap_components as dbc
def create_quick_status_card(title, component_id, icon):
"""Helper to create a quick status card."""
return dbc.Card(dbc.CardBody([
html.H5(f"{icon} {title}", className="card-title"),
html.Div(id=component_id, children=[
dbc.Badge("Checking...", color="warning", className="me-1")
])
]), className="text-center")
def _create_header_section():
"""Creates the header section for the system health layout."""
return html.Div([
html.H2("⚙️ System Health & Data Monitoring"),
html.P("Real-time monitoring of data collection services, database health, and system performance",
className="lead")
], className="p-5 mb-4 bg-light rounded-3")
def _create_quick_status_row():
"""Creates the quick status overview row."""
return dbc.Row([
dbc.Col(create_quick_status_card("Data Collection", "data-collection-quick-status", "📊"), width=3),
dbc.Col(create_quick_status_card("Database", "database-quick-status", "🗄️"), width=3),
dbc.Col(create_quick_status_card("Redis", "redis-quick-status", "🔗"), width=3),
dbc.Col(create_quick_status_card("Performance", "performance-quick-status", "📈"), width=3),
], className="mb-4")
def _create_data_collection_service_card():
"""Creates the data collection service status card."""
return dbc.Card([
dbc.CardHeader(html.H4("📡 Data Collection Service")),
dbc.CardBody([
html.H5("Service Status", className="card-title"),
html.Div(id='data-collection-service-status', className="mb-4"),
html.H5("Collection Metrics", className="card-title"),
html.Div(id='data-collection-metrics', className="mb-4"),
html.H5("Service Controls", className="card-title"),
dbc.ButtonGroup([
dbc.Button("🔄 Refresh Status", id="refresh-data-status-btn", color="primary", outline=True, size="sm"),
dbc.Button("📊 View Details", id="view-collection-details-btn", color="secondary", outline=True, size="sm"),
dbc.Button("📋 View Logs", id="view-collection-logs-btn", color="info", outline=True, size="sm")
])
])
], className="mb-4")
def _create_individual_collectors_card():
"""Creates the individual collectors health card."""
return dbc.Card([
dbc.CardHeader(html.H4("🔌 Individual Collectors")),
dbc.CardBody([
html.Div(id='individual-collectors-status'),
html.Div([
dbc.Alert(
"Collector health data will be displayed here when the data collection service is running.",
id="collectors-info-alert",
color="info",
is_open=True,
)
], id='collectors-placeholder')
])
], className="mb-4")
def _create_database_status_card():
"""Creates the database health status card."""
return dbc.Card([
dbc.CardHeader(html.H4("🗄️ Database Health")),
dbc.CardBody([
html.H5("Connection Status", className="card-title"),
html.Div(id='database-status', className="mb-3"),
html.Hr(),
html.H5("Database Statistics", className="card-title"),
html.Div(id='database-stats')
])
], className="mb-4")
def _create_redis_status_card():
"""Creates the Redis health status card."""
return dbc.Card([
dbc.CardHeader(html.H4("🔗 Redis Status")),
dbc.CardBody([
html.H5("Connection Status", className="card-title"),
html.Div(id='redis-status', className="mb-3"),
html.Hr(),
html.H5("Redis Statistics", className="card-title"),
html.Div(id='redis-stats')
])
], className="mb-4")
def _create_system_performance_card():
"""Creates the system performance metrics card."""
return dbc.Card([
dbc.CardHeader(html.H4("📈 System Performance")),
dbc.CardBody([
html.Div(id='system-performance-metrics')
])
], className="mb-4")
def _create_collection_details_modal():
"""Creates the data collection details modal."""
return dbc.Modal([
dbc.ModalHeader(dbc.ModalTitle("📊 Data Collection Details")),
dbc.ModalBody(id="collection-details-content")
], id="collection-details-modal", is_open=False, size="lg")
def _create_collection_logs_modal():
"""Creates the collection service logs modal."""
return dbc.Modal([
dbc.ModalHeader(dbc.ModalTitle("📋 Collection Service Logs")),
dbc.ModalBody(
html.Div(
html.Pre(id="collection-logs-content", style={'max-height': '400px', 'overflow-y': 'auto'}),
style={'white-space': 'pre-wrap', 'background-color': '#f8f9fa', 'padding': '15px', 'border-radius': '5px'}
)
),
dbc.ModalFooter([
dbc.Button("Refresh", id="refresh-logs-btn", color="primary"),
dbc.Button("Close", id="close-logs-modal", color="secondary", className="ms-auto")
])
], id="collection-logs-modal", is_open=False, size="xl")
def get_system_health_layout():
"""Create the enhanced system health monitoring layout with Bootstrap components."""
def create_quick_status_card(title, component_id, icon):
return dbc.Card(dbc.CardBody([
html.H5(f"{icon} {title}", className="card-title"),
html.Div(id=component_id, children=[
dbc.Badge("Checking...", color="warning", className="me-1")
])
]), className="text-center")
return html.Div([
# Header section
html.Div([
html.H2("⚙️ System Health & Data Monitoring"),
html.P("Real-time monitoring of data collection services, database health, and system performance",
className="lead")
], className="p-5 mb-4 bg-light rounded-3"),
_create_header_section(),
_create_quick_status_row(),
# Quick Status Overview Row
dbc.Row([
dbc.Col(create_quick_status_card("Data Collection", "data-collection-quick-status", "📊"), width=3),
dbc.Col(create_quick_status_card("Database", "database-quick-status", "🗄️"), width=3),
dbc.Col(create_quick_status_card("Redis", "redis-quick-status", "🔗"), width=3),
dbc.Col(create_quick_status_card("Performance", "performance-quick-status", "📈"), width=3),
], className="mb-4"),
# Detailed Monitoring Sections
dbc.Row([
# Left Column - Data Collection Service
dbc.Col([
# Data Collection Service Status
dbc.Card([
dbc.CardHeader(html.H4("📡 Data Collection Service")),
dbc.CardBody([
html.H5("Service Status", className="card-title"),
html.Div(id='data-collection-service-status', className="mb-4"),
html.H5("Collection Metrics", className="card-title"),
html.Div(id='data-collection-metrics', className="mb-4"),
html.H5("Service Controls", className="card-title"),
dbc.ButtonGroup([
dbc.Button("🔄 Refresh Status", id="refresh-data-status-btn", color="primary", outline=True, size="sm"),
dbc.Button("📊 View Details", id="view-collection-details-btn", color="secondary", outline=True, size="sm"),
dbc.Button("📋 View Logs", id="view-collection-logs-btn", color="info", outline=True, size="sm")
])
])
], className="mb-4"),
# Data Collector Health
dbc.Card([
dbc.CardHeader(html.H4("🔌 Individual Collectors")),
dbc.CardBody([
html.Div(id='individual-collectors-status'),
html.Div([
dbc.Alert(
"Collector health data will be displayed here when the data collection service is running.",
id="collectors-info-alert",
color="info",
is_open=True,
)
], id='collectors-placeholder')
])
], className="mb-4"),
_create_data_collection_service_card(),
_create_individual_collectors_card(),
], width=6),
# Right Column - System Health
dbc.Col([
# Database Status
dbc.Card([
dbc.CardHeader(html.H4("🗄️ Database Health")),
dbc.CardBody([
html.H5("Connection Status", className="card-title"),
html.Div(id='database-status', className="mb-3"),
html.Hr(),
html.H5("Database Statistics", className="card-title"),
html.Div(id='database-stats')
])
], className="mb-4"),
# Redis Status
dbc.Card([
dbc.CardHeader(html.H4("🔗 Redis Status")),
dbc.CardBody([
html.H5("Connection Status", className="card-title"),
html.Div(id='redis-status', className="mb-3"),
html.Hr(),
html.H5("Redis Statistics", className="card-title"),
html.Div(id='redis-stats')
])
], className="mb-4"),
# System Performance
dbc.Card([
dbc.CardHeader(html.H4("📈 System Performance")),
dbc.CardBody([
html.Div(id='system-performance-metrics')
])
], className="mb-4"),
_create_database_status_card(),
_create_redis_status_card(),
_create_system_performance_card(),
], width=6)
]),
# Data Collection Details Modal
dbc.Modal([
dbc.ModalHeader(dbc.ModalTitle("📊 Data Collection Details")),
dbc.ModalBody(id="collection-details-content")
], id="collection-details-modal", is_open=False, size="lg"),
# Collection Logs Modal
dbc.Modal([
dbc.ModalHeader(dbc.ModalTitle("📋 Collection Service Logs")),
dbc.ModalBody(
html.Div(
html.Pre(id="collection-logs-content", style={'max-height': '400px', 'overflow-y': 'auto'}),
style={'white-space': 'pre-wrap', 'background-color': '#f8f9fa', 'padding': '15px', 'border-radius': '5px'}
)
),
dbc.ModalFooter([
dbc.Button("Refresh", id="refresh-logs-btn", color="primary"),
dbc.Button("Close", id="close-logs-modal", color="secondary", className="ms-auto")
])
], id="collection-logs-modal", is_open=False, size="xl")
_create_collection_details_modal(),
_create_collection_logs_modal()
])

1
data/collector/collector_connection_manager.py
View File

@ -27,6 +27,7 @@ class ConnectionManager:
reconnect_delay: float = 5.0,
logger=None,
state_telemetry: CollectorStateAndTelemetry = None):
self.exchange_name = exchange_name
self.component_name = component_name
self._max_reconnect_attempts = max_reconnect_attempts

3
data/common/aggregation/utils.py
View File

@ -7,6 +7,7 @@ and trade data aggregation.
import re
from typing import List, Tuple
from utils.timeframe_utils import load_timeframe_options
from ..data_types import StandardizedTrade, OHLCVCandle
@ -42,7 +43,7 @@ def validate_timeframe(timeframe: str) -> bool:
Returns:
True if supported, False otherwise
"""
supported = ['1s', '5s', '10s', '15s', '30s', '1m', '5m', '15m', '30m', '1h', '4h', '1d']
supported = [item['value'] for item in load_timeframe_options()]
return timeframe in supported

11
data/common/data_types.py
View File

@ -10,7 +10,8 @@ from decimal import Decimal
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from enum import Enum
import asyncio
from utils.timeframe_utils import load_timeframe_options
# from ..base_collector import DataType, MarketDataPoint # Import from base
@ -140,6 +141,12 @@ class OHLCVCandle:
}
def _load_supported_timeframes():
"""Loads supported timeframe values from a JSON file."""
data = load_timeframe_options()
return [item['value'] for item in data]
@dataclass
class CandleProcessingConfig:
"""Configuration for candle processing - shared across exchanges."""
@ -150,7 +157,7 @@ class CandleProcessingConfig:
def __post_init__(self):
"""Validate configuration after initialization."""
supported_timeframes = ['1s', '5s', '10s', '15s', '30s', '1m', '5m', '15m', '30m', '1h', '4h', '1d']
supported_timeframes = _load_supported_timeframes()
for tf in self.timeframes:
if tf not in supported_timeframes:
raise ValueError(f"Unsupported timeframe: {tf}")

61
database/repositories/market_data_repository.py
View File

@ -2,6 +2,7 @@
from datetime import datetime
from typing import List, Optional, Dict, Any
import pandas as pd
from sqlalchemy import desc
from sqlalchemy.dialects.postgresql import insert
@ -136,4 +137,62 @@ class MarketDataRepository(BaseRepository):
except Exception as e:
self.log_error(f"Error retrieving latest candle for {symbol} {timeframe}: {e}")
raise DatabaseOperationError(f"Failed to retrieve latest candle: {e}")
raise DatabaseOperationError(f"Failed to retrieve latest candle: {e}")
def get_candles_df(self,
symbol: str,
timeframe: str,
start_time: datetime,
end_time: datetime,
exchange: str = "okx") -> pd.DataFrame:
"""
Retrieve candles from the database as a Pandas DataFrame using the ORM.
Args:
symbol: The trading symbol (e.g., 'BTC-USDT').
timeframe: The timeframe of the candles (e.g., '1h').
start_time: The start datetime for the data.
end_time: The end datetime for the data.
exchange: The exchange name (default: 'okx').
Returns:
A Pandas DataFrame containing the fetched candles, or an empty DataFrame if no data is found.
"""
try:
with self.get_session() as session:
query = (
session.query(MarketData)
.filter(
MarketData.exchange == exchange,
MarketData.symbol == symbol,
MarketData.timeframe == timeframe,
MarketData.timestamp >= start_time,
MarketData.timestamp <= end_time
)
.order_by(MarketData.timestamp.asc())
)
# Convert query results to a list of dictionaries, then to DataFrame
results = [
{
"timestamp": r.timestamp,
"open": float(r.open),
"high": float(r.high),
"low": float(r.low),
"close": float(r.close),
"volume": float(r.volume),
"trades_count": int(r.trades_count) if r.trades_count else 0
} for r in query.all()
]
df = pd.DataFrame(results)
if not df.empty:
df['timestamp'] = pd.to_datetime(df['timestamp'])
df = df.set_index('timestamp')
self.log_debug(f"Retrieved {len(df)} candles as DataFrame for {symbol} {timeframe}")
return df
except Exception as e:
self.log_error(f"Error retrieving candles as DataFrame: {e}")
raise DatabaseOperationError(f"Failed to retrieve candles as DataFrame: {e}")

309
examples/collector_demo.py
View File

@ -1,309 +0,0 @@
"""
Demonstration of the enhanced data collector system with health monitoring and auto-restart.
This example shows how to:
1. Create data collectors with health monitoring
2. Use the collector manager for coordinated management
3. Monitor collector health and handle failures
4. Enable/disable collectors dynamically
"""
import asyncio
from datetime import datetime, timezone
from typing import Any, Optional
from data import (
BaseDataCollector, DataType, CollectorStatus, MarketDataPoint,
CollectorManager, CollectorConfig
)
class DemoDataCollector(BaseDataCollector):
"""
Demo implementation of a data collector for demonstration purposes.
This collector simulates receiving market data and can be configured
to fail periodically to demonstrate auto-restart functionality.
"""
def __init__(self,
exchange_name: str,
symbols: list,
fail_every_n_messages: int = 0,
connection_delay: float = 0.1):
"""
Initialize demo collector.
Args:
exchange_name: Name of the exchange
symbols: Trading symbols to collect
fail_every_n_messages: Simulate failure every N messages (0 = no failures)
connection_delay: Simulated connection delay
"""
super().__init__(exchange_name, symbols, [DataType.TICKER])
self.fail_every_n_messages = fail_every_n_messages
self.connection_delay = connection_delay
self.message_count = 0
self.connected = False
self.subscribed = False
async def connect(self) -> bool:
"""Simulate connection to exchange."""
print(f"[{self.exchange_name}] Connecting...")
await asyncio.sleep(self.connection_delay)
self.connected = True
print(f"[{self.exchange_name}] Connected successfully")
return True
async def disconnect(self) -> None:
"""Simulate disconnection from exchange."""
print(f"[{self.exchange_name}] Disconnecting...")
await asyncio.sleep(self.connection_delay / 2)
self.connected = False
self.subscribed = False
print(f"[{self.exchange_name}] Disconnected")
async def subscribe_to_data(self, symbols: list, data_types: list) -> bool:
"""Simulate subscription to data streams."""
if not self.connected:
return False
print(f"[{self.exchange_name}] Subscribing to {len(symbols)} symbols: {', '.join(symbols)}")
await asyncio.sleep(0.05)
self.subscribed = True
return True
async def unsubscribe_from_data(self, symbols: list, data_types: list) -> bool:
"""Simulate unsubscription from data streams."""
print(f"[{self.exchange_name}] Unsubscribing from data streams")
self.subscribed = False
return True
async def _process_message(self, message: Any) -> Optional[MarketDataPoint]:
"""Process simulated market data message."""
self.message_count += 1
# Simulate periodic failures if configured
if (self.fail_every_n_messages > 0 and
self.message_count % self.fail_every_n_messages == 0):
raise Exception(f"Simulated failure after {self.message_count} messages")
# Create mock market data
data_point = MarketDataPoint(
exchange=self.exchange_name,
symbol=message['symbol'],
timestamp=datetime.now(timezone.utc),
data_type=DataType.TICKER,
data={
'price': message['price'],
'volume': message.get('volume', 100),
'timestamp': datetime.now(timezone.utc).isoformat()
}
)
return data_point
async def _handle_messages(self) -> None:
"""Simulate receiving and processing messages."""
if not self.connected or not self.subscribed:
await asyncio.sleep(0.1)
return
# Simulate receiving data for each symbol
for symbol in self.symbols:
try:
# Create simulated message
simulated_message = {
'symbol': symbol,
'price': 50000 + (self.message_count % 1000), # Fake price that changes
'volume': 1.5
}
# Process the message
data_point = await self._process_message(simulated_message)
if data_point:
self._stats['messages_processed'] += 1
await self._notify_callbacks(data_point)
except Exception as e:
# This will trigger reconnection logic
raise e
# Simulate processing delay
await asyncio.sleep(1.0)
async def data_callback(data_point: MarketDataPoint):
"""Callback function to handle received data."""
print(f"📊 Data received: {data_point.exchange} - {data_point.symbol} - "
f"Price: {data_point.data.get('price')} at {data_point.timestamp.strftime('%H:%M:%S')}")
async def monitor_collectors(manager: CollectorManager, duration: int = 30):
"""Monitor collector status and print updates."""
print(f"\n🔍 Starting monitoring for {duration} seconds...")
for i in range(duration):
await asyncio.sleep(1)
status = manager.get_status()
running = len(manager.get_running_collectors())
failed = len(manager.get_failed_collectors())
if i % 5 == 0: # Print status every 5 seconds
print(f"⏰ Status at {i+1}s: {running} running, {failed} failed, "
f"{status['statistics']['restarts_performed']} restarts")
print("🏁 Monitoring complete")
async def demo_basic_usage():
"""Demonstrate basic collector usage."""
print("=" * 60)
print("🚀 Demo 1: Basic Data Collector Usage")
print("=" * 60)
# Create a stable collector
collector = DemoDataCollector("demo_exchange", ["BTC-USDT", "ETH-USDT"])
# Add data callback
collector.add_data_callback(DataType.TICKER, data_callback)
# Start the collector
print("Starting collector...")
success = await collector.start()
if success:
print("✅ Collector started successfully")
# Let it run for a few seconds
await asyncio.sleep(5)
# Show status
status = collector.get_status()
print(f"📈 Messages processed: {status['statistics']['messages_processed']}")
print(f"⏱️ Uptime: {status['statistics']['uptime_seconds']:.1f}s")
# Stop the collector
await collector.stop()
print("✅ Collector stopped")
else:
print("❌ Failed to start collector")
async def demo_manager_usage():
"""Demonstrate collector manager usage."""
print("\n" + "=" * 60)
print("🎛️ Demo 2: Collector Manager Usage")
print("=" * 60)
# Create manager
manager = CollectorManager("demo_manager", global_health_check_interval=3.0)
# Create multiple collectors
stable_collector = DemoDataCollector("stable_exchange", ["BTC-USDT"])
failing_collector = DemoDataCollector("failing_exchange", ["ETH-USDT"],
fail_every_n_messages=5) # Fails every 5 messages
# Add data callbacks
stable_collector.add_data_callback(DataType.TICKER, data_callback)
failing_collector.add_data_callback(DataType.TICKER, data_callback)
# Add collectors to manager
manager.add_collector(stable_collector)
manager.add_collector(failing_collector)
print(f"📝 Added {len(manager.list_collectors())} collectors to manager")
# Start manager
success = await manager.start()
if success:
print("✅ Manager started successfully")
# Monitor for a while
await monitor_collectors(manager, duration=15)
# Show final status
status = manager.get_status()
print(f"\n📊 Final Statistics:")
print(f" - Total restarts: {status['statistics']['restarts_performed']}")
print(f" - Running collectors: {len(manager.get_running_collectors())}")
print(f" - Failed collectors: {len(manager.get_failed_collectors())}")
# Stop manager
await manager.stop()
print("✅ Manager stopped")
else:
print("❌ Failed to start manager")
async def demo_dynamic_management():
"""Demonstrate dynamic collector management."""
print("\n" + "=" * 60)
print("🔄 Demo 3: Dynamic Collector Management")
print("=" * 60)
# Create manager
manager = CollectorManager("dynamic_manager", global_health_check_interval=2.0)
# Start with one collector
collector1 = DemoDataCollector("exchange_1", ["BTC-USDT"])
collector1.add_data_callback(DataType.TICKER, data_callback)
manager.add_collector(collector1)
await manager.start()
print("✅ Started with 1 collector")
await asyncio.sleep(3)
# Add second collector
collector2 = DemoDataCollector("exchange_2", ["ETH-USDT"])
collector2.add_data_callback(DataType.TICKER, data_callback)
manager.add_collector(collector2)
print("➕ Added second collector")
await asyncio.sleep(3)
# Disable first collector
collector_names = manager.list_collectors()
manager.disable_collector(collector_names[0])
print("⏸️ Disabled first collector")
await asyncio.sleep(3)
# Re-enable first collector
manager.enable_collector(collector_names[0])
print("▶️ Re-enabled first collector")
await asyncio.sleep(3)
# Show final status
status = manager.get_status()
print(f"📊 Final state: {len(manager.get_running_collectors())} running collectors")
await manager.stop()
print("✅ Dynamic demo complete")
async def main():
"""Run all demonstrations."""
print("🎯 Data Collector System Demonstration")
print("This demo shows health monitoring and auto-restart capabilities\n")
try:
# Run demonstrations
await demo_basic_usage()
await demo_manager_usage()
await demo_dynamic_management()
print("\n" + "=" * 60)
print("🎉 All demonstrations completed successfully!")
print("=" * 60)
except Exception as e:
print(f"❌ Demo failed with error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(main())

412
examples/parallel_collectors_demo.py
View File

@ -1,412 +0,0 @@
"""
Demonstration of running multiple data collectors in parallel.
This example shows how to set up and manage multiple collectors simultaneously,
each collecting data from different exchanges or different symbols.
"""
import asyncio
from datetime import datetime, timezone
from typing import Dict, Any
from data import (
BaseDataCollector, DataType, CollectorStatus, MarketDataPoint,
CollectorManager, CollectorConfig
)
class DemoExchangeCollector(BaseDataCollector):
"""Demo collector simulating different exchanges."""
def __init__(self,
exchange_name: str,
symbols: list,
message_interval: float = 1.0,
base_price: float = 50000):
"""
Initialize demo collector.
Args:
exchange_name: Name of the exchange (okx, binance, coinbase, etc.)
symbols: Trading symbols to collect
message_interval: Seconds between simulated messages
base_price: Base price for simulation
"""
super().__init__(exchange_name, symbols, [DataType.TICKER])
self.message_interval = message_interval
self.base_price = base_price
self.connected = False
self.subscribed = False
self.message_count = 0
async def connect(self) -> bool:
"""Simulate connection to exchange."""
print(f"🔌 [{self.exchange_name.upper()}] Connecting...")
await asyncio.sleep(0.2) # Simulate connection delay
self.connected = True
print(f"✅ [{self.exchange_name.upper()}] Connected successfully")
return True
async def disconnect(self) -> None:
"""Simulate disconnection from exchange."""
print(f"🔌 [{self.exchange_name.upper()}] Disconnecting...")
await asyncio.sleep(0.1)
self.connected = False
self.subscribed = False
print(f"❌ [{self.exchange_name.upper()}] Disconnected")
async def subscribe_to_data(self, symbols: list, data_types: list) -> bool:
"""Simulate subscription to data streams."""
if not self.connected:
return False
print(f"📡 [{self.exchange_name.upper()}] Subscribing to {len(symbols)} symbols")
await asyncio.sleep(0.1)
self.subscribed = True
return True
async def unsubscribe_from_data(self, symbols: list, data_types: list) -> bool:
"""Simulate unsubscription from data streams."""
print(f"📡 [{self.exchange_name.upper()}] Unsubscribing from data streams")
self.subscribed = False
return True
async def _process_message(self, message: Any) -> MarketDataPoint:
"""Process simulated market data message."""
self.message_count += 1
# Create realistic price variation
price_variation = (self.message_count % 100 - 50) * 10
current_price = self.base_price + price_variation
data_point = MarketDataPoint(
exchange=self.exchange_name,
symbol=message['symbol'],
timestamp=datetime.now(timezone.utc),
data_type=DataType.TICKER,
data={
'price': current_price,
'volume': message.get('volume', 1.0 + (self.message_count % 10) * 0.1),
'bid': current_price - 0.5,
'ask': current_price + 0.5,
'timestamp': datetime.now(timezone.utc).isoformat()
}
)
return data_point
async def _handle_messages(self) -> None:
"""Simulate receiving and processing messages."""
if not self.connected or not self.subscribed:
await asyncio.sleep(0.1)
return
# Process each symbol
for symbol in self.symbols:
try:
# Create simulated message
simulated_message = {
'symbol': symbol,
'volume': 1.5 + (self.message_count % 5) * 0.2
}
# Process the message
data_point = await self._process_message(simulated_message)
if data_point:
self._stats['messages_processed'] += 1
await self._notify_callbacks(data_point)
except Exception as e:
self.logger.error(f"Error processing message for {symbol}: {e}")
raise e
# Wait before next batch of messages
await asyncio.sleep(self.message_interval)
def create_data_callback(exchange_name: str):
"""Create a data callback function for a specific exchange."""
def data_callback(data_point: MarketDataPoint):
print(f"📊 {exchange_name.upper():8} | {data_point.symbol:10} | "
f"${data_point.data.get('price', 0):8.2f} | "
f"Vol: {data_point.data.get('volume', 0):.2f} | "
f"{data_point.timestamp.strftime('%H:%M:%S')}")
return data_callback
async def demo_parallel_collectors():
"""Demonstrate running multiple collectors in parallel."""
print("=" * 80)
print("🚀 PARALLEL COLLECTORS DEMONSTRATION")
print("=" * 80)
print("Running multiple exchange collectors simultaneously...")
print()
# Create manager
manager = CollectorManager(
"parallel_demo_manager",
global_health_check_interval=10.0 # Check every 10 seconds
)
# Define exchange configurations
exchange_configs = [
{
'name': 'okx',
'symbols': ['BTC-USDT', 'ETH-USDT'],
'interval': 1.0,
'base_price': 45000
},
{
'name': 'binance',
'symbols': ['BTC-USDT', 'ETH-USDT', 'SOL-USDT'],
'interval': 1.5,
'base_price': 45100
},
{
'name': 'coinbase',
'symbols': ['BTC-USD', 'ETH-USD'],
'interval': 2.0,
'base_price': 44900
},
{
'name': 'kraken',
'symbols': ['XBTUSD', 'ETHUSD'],
'interval': 1.2,
'base_price': 45050
}
]
# Create and configure collectors
for config in exchange_configs:
# Create collector
collector = DemoExchangeCollector(
exchange_name=config['name'],
symbols=config['symbols'],
message_interval=config['interval'],
base_price=config['base_price']
)
# Add data callback
callback = create_data_callback(config['name'])
collector.add_data_callback(DataType.TICKER, callback)
# Add to manager with configuration
collector_config = CollectorConfig(
name=f"{config['name']}_collector",
exchange=config['name'],
symbols=config['symbols'],
data_types=['ticker'],
auto_restart=True,
health_check_interval=15.0,
enabled=True
)
manager.add_collector(collector, collector_config)
print(f"➕ Added {config['name'].upper()} collector with {len(config['symbols'])} symbols")
print(f"\n📝 Total collectors added: {len(manager.list_collectors())}")
print()
# Start all collectors in parallel
print("🏁 Starting all collectors...")
start_time = asyncio.get_event_loop().time()
success = await manager.start()
if not success:
print("❌ Failed to start collector manager")
return
startup_time = asyncio.get_event_loop().time() - start_time
print(f"✅ All collectors started in {startup_time:.2f} seconds")
print()
print("📊 DATA STREAM (All exchanges running in parallel):")
print("-" * 80)
# Monitor for a period
monitoring_duration = 30 # seconds
for i in range(monitoring_duration):
await asyncio.sleep(1)
# Print status every 10 seconds
if i % 10 == 0 and i > 0:
status = manager.get_status()
print()
print(f"⏰ STATUS UPDATE ({i}s):")
print(f" Running collectors: {len(manager.get_running_collectors())}")
print(f" Failed collectors: {len(manager.get_failed_collectors())}")
print(f" Total restarts: {status['statistics']['restarts_performed']}")
print("-" * 80)
# Final status report
print()
print("📈 FINAL STATUS REPORT:")
print("=" * 80)
status = manager.get_status()
print(f"Manager Status: {status['manager_status']}")
print(f"Total Collectors: {status['total_collectors']}")
print(f"Running Collectors: {len(manager.get_running_collectors())}")
print(f"Failed Collectors: {len(manager.get_failed_collectors())}")
print(f"Total Restarts: {status['statistics']['restarts_performed']}")
# Individual collector statistics
print("\n📊 INDIVIDUAL COLLECTOR STATS:")
for collector_name in manager.list_collectors():
collector_status = manager.get_collector_status(collector_name)
if collector_status:
stats = collector_status['status']['statistics']
health = collector_status['health']
print(f"\n{collector_name.upper()}:")
print(f" Status: {collector_status['status']['status']}")
print(f" Messages Processed: {stats['messages_processed']}")
print(f" Uptime: {stats.get('uptime_seconds', 0):.1f}s")
print(f" Errors: {stats['errors']}")
print(f" Healthy: {health['is_healthy']}")
# Stop all collectors
print("\n🛑 Stopping all collectors...")
await manager.stop()
print("✅ All collectors stopped successfully")
async def demo_dynamic_management():
"""Demonstrate dynamic addition/removal of collectors."""
print("\n" + "=" * 80)
print("🔄 DYNAMIC COLLECTOR MANAGEMENT")
print("=" * 80)
manager = CollectorManager("dynamic_manager")
# Start with one collector
collector1 = DemoExchangeCollector("exchange_a", ["BTC-USDT"], 1.0)
collector1.add_data_callback(DataType.TICKER, create_data_callback("exchange_a"))
manager.add_collector(collector1)
await manager.start()
print("✅ Started with 1 collector")
await asyncio.sleep(3)
# Add second collector while system is running
collector2 = DemoExchangeCollector("exchange_b", ["ETH-USDT"], 1.5)
collector2.add_data_callback(DataType.TICKER, create_data_callback("exchange_b"))
manager.add_collector(collector2)
print("➕ Added second collector while running")
await asyncio.sleep(3)
# Add third collector
collector3 = DemoExchangeCollector("exchange_c", ["SOL-USDT"], 2.0)
collector3.add_data_callback(DataType.TICKER, create_data_callback("exchange_c"))
manager.add_collector(collector3)
print("➕ Added third collector")
await asyncio.sleep(5)
# Show current status
print(f"\n📊 Current Status: {len(manager.get_running_collectors())} collectors running")
# Disable one collector
collectors = manager.list_collectors()
if len(collectors) > 1:
manager.disable_collector(collectors[1])
print(f"⏸️ Disabled collector: {collectors[1]}")
await asyncio.sleep(3)
# Re-enable
if len(collectors) > 1:
manager.enable_collector(collectors[1])
print(f"▶️ Re-enabled collector: {collectors[1]}")
await asyncio.sleep(3)
print(f"\n📊 Final Status: {len(manager.get_running_collectors())} collectors running")
await manager.stop()
print("✅ Dynamic management demo complete")
async def demo_performance_monitoring():
"""Demonstrate performance monitoring across multiple collectors."""
print("\n" + "=" * 80)
print("📈 PERFORMANCE MONITORING")
print("=" * 80)
manager = CollectorManager("performance_monitor", global_health_check_interval=5.0)
# Create collectors with different performance characteristics
configs = [
("fast_exchange", ["BTC-USDT"], 0.5), # Fast updates
("medium_exchange", ["ETH-USDT"], 1.0), # Medium updates
("slow_exchange", ["SOL-USDT"], 2.0), # Slow updates
]
for exchange, symbols, interval in configs:
collector = DemoExchangeCollector(exchange, symbols, interval)
collector.add_data_callback(DataType.TICKER, create_data_callback(exchange))
manager.add_collector(collector)
await manager.start()
print("✅ Started performance monitoring demo")
# Monitor performance for 20 seconds
for i in range(4):
await asyncio.sleep(5)
print(f"\n📊 PERFORMANCE SNAPSHOT ({(i+1)*5}s):")
print("-" * 60)
for collector_name in manager.list_collectors():
status = manager.get_collector_status(collector_name)
if status:
stats = status['status']['statistics']
health = status['health']
msg_rate = stats['messages_processed'] / max(stats.get('uptime_seconds', 1), 1)
print(f"{collector_name:15} | "
f"Rate: {msg_rate:5.1f}/s | "
f"Total: {stats['messages_processed']:4d} | "
f"Errors: {stats['errors']:2d} | "
f"Health: {'✅' if health['is_healthy'] else '❌'}")
await manager.stop()
print("\n✅ Performance monitoring demo complete")
async def main():
"""Run all parallel collector demonstrations."""
print("🎯 MULTIPLE COLLECTORS PARALLEL EXECUTION DEMO")
print("This demonstration shows the CollectorManager running multiple collectors simultaneously\n")
try:
# Main parallel demo
await demo_parallel_collectors()
# Dynamic management demo
await demo_dynamic_management()
# Performance monitoring demo
await demo_performance_monitoring()
print("\n" + "=" * 80)
print("🎉 ALL PARALLEL EXECUTION DEMOS COMPLETED!")
print("=" * 80)
print("\nKey takeaways:")
print("✅ Multiple collectors run truly in parallel")
print("✅ Each collector operates independently")
print("✅ Collectors can be added/removed while system is running")
print("✅ Centralized health monitoring across all collectors")
print("✅ Individual performance tracking per collector")
print("✅ Coordinated lifecycle management")
except Exception as e:
print(f"❌ Demo failed with error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(main())

76
main.py
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@ -1,60 +1,44 @@
#!/usr/bin/env python3
"""
Main entry point for the Crypto Trading Bot Dashboard.
Crypto Trading Bot Dashboard - Modular Version
This is the main entry point for the dashboard application using the new modular structure.
"""
import sys
import logging
from pathlib import Path
from dashboard import create_app
from utils.logger import get_logger
# Add project root to path
project_root = Path(__file__).parent
sys.path.insert(0, str(project_root))
logger = get_logger("main")
def main():
"""Main application entry point."""
print("🚀 Crypto Trading Bot Dashboard")
print("=" * 40)
# Suppress SQLAlchemy database logging for cleaner console output
logging.getLogger('sqlalchemy').setLevel(logging.WARNING)
logging.getLogger('sqlalchemy.engine').setLevel(logging.WARNING)
logging.getLogger('sqlalchemy.pool').setLevel(logging.WARNING)
logging.getLogger('sqlalchemy.dialects').setLevel(logging.WARNING)
logging.getLogger('sqlalchemy.orm').setLevel(logging.WARNING)
"""Main entry point for the dashboard application."""
try:
from config.settings import app, dashboard
print(f"Environment: {app.environment}")
print(f"Debug mode: {app.debug}")
# Create the dashboard app
app = create_app()
if app.environment == "development":
print("\n🔧 Running in development mode")
print("Dashboard features available:")
print("✅ Basic Dash application framework")
print("✅ Real-time price charts (sample data)")
print("✅ System health monitoring")
print("🚧 Real data connection (coming in task 3.7)")
# Start the Dash application
print(f"\n🌐 Starting dashboard at: http://{dashboard.host}:{dashboard.port}")
print("Press Ctrl+C to stop the application")
# Import and register all callbacks after app creation
from dashboard.callbacks import (
register_navigation_callbacks,
register_chart_callbacks,
register_indicator_callbacks,
register_system_health_callbacks
)
from app import main as app_main
app_main()
# Register all callback modules
register_navigation_callbacks(app)
register_chart_callbacks(app) # Now includes enhanced market statistics
register_indicator_callbacks(app) # Placeholder for now
register_system_health_callbacks(app) # Placeholder for now
logger.info("Dashboard application initialized successfully")
# Run the app (debug=False for stability, manual restart required for changes)
app.run(debug=False, host='0.0.0.0', port=8050)
except ImportError as e:
print(f"❌ Failed to import modules: {e}")
print("Run: uv sync")
sys.exit(1)
except KeyboardInterrupt:
print("\n\n👋 Dashboard stopped by user")
sys.exit(0)
except Exception as e:
print(f"❌ Failed to start dashboard: {e}")
sys.exit(1)
logger.error(f"Failed to start dashboard application: {e}")
raise
if __name__ == "__main__":
main()
if __name__ == '__main__':
main()

212
tests/quick_aggregation_test.py
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@ -1,212 +0,0 @@
#!/usr/bin/env python3
"""
Quick OKX Aggregation Test
A simplified version for quick testing of different symbols and timeframe combinations.
"""
import asyncio
import logging
import sys
from datetime import datetime, timezone
from decimal import Decimal
from typing import Dict, List, Any
# Import our modules
from data.common.data_types import StandardizedTrade, CandleProcessingConfig, OHLCVCandle
from data.common.aggregation.realtime import RealTimeCandleProcessor
from data.exchanges.okx.websocket import OKXWebSocketClient, OKXSubscription, OKXChannelType
# Set up minimal logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s: %(message)s', datefmt='%H:%M:%S')
logger = logging.getLogger(__name__)
class QuickAggregationTester:
"""Quick tester for real-time aggregation."""
def __init__(self, symbol: str, timeframes: List[str]):
self.symbol = symbol
self.timeframes = timeframes
self.ws_client = None
# Create processor
config = CandleProcessingConfig(timeframes=timeframes, auto_save_candles=False)
self.processor = RealTimeCandleProcessor(symbol, "okx", config, logger=logger)
self.processor.add_candle_callback(self._on_candle)
# Stats
self.trade_count = 0
self.candle_counts = {tf: 0 for tf in timeframes}
logger.info(f"Testing {symbol} with timeframes: {', '.join(timeframes)}")
async def run(self, duration: int = 60):
"""Run the test for specified duration."""
try:
# Connect and subscribe
await self._setup_websocket()
await self._subscribe()
logger.info(f"🔍 Monitoring for {duration} seconds...")
start_time = datetime.now(timezone.utc)
# Monitor
while (datetime.now(timezone.utc) - start_time).total_seconds() < duration:
await asyncio.sleep(5)
self._print_quick_status()
# Final stats
self._print_final_stats(duration)
except Exception as e:
logger.error(f"Test failed: {e}")
finally:
if self.ws_client:
await self.ws_client.disconnect()
async def _setup_websocket(self):
"""Setup WebSocket connection."""
self.ws_client = OKXWebSocketClient("quick_test", logger=logger)
self.ws_client.add_message_callback(self._on_message)
if not await self.ws_client.connect(use_public=True):
raise RuntimeError("Failed to connect")
logger.info("✅ Connected to OKX")
async def _subscribe(self):
"""Subscribe to trades."""
subscription = OKXSubscription("trades", self.symbol, True)
if not await self.ws_client.subscribe([subscription]):
raise RuntimeError("Failed to subscribe")
logger.info(f"✅ Subscribed to {self.symbol} trades")
def _on_message(self, message: Dict[str, Any]):
"""Handle WebSocket message."""
try:
if not isinstance(message, dict) or 'data' not in message:
return
arg = message.get('arg', {})
if arg.get('channel') != 'trades' or arg.get('instId') != self.symbol:
return
for trade_data in message['data']:
self._process_trade(trade_data)
except Exception as e:
logger.error(f"Message processing error: {e}")
def _process_trade(self, trade_data: Dict[str, Any]):
"""Process trade data."""
try:
self.trade_count += 1
# Create standardized trade
trade = StandardizedTrade(
symbol=trade_data['instId'],
trade_id=trade_data['tradeId'],
price=Decimal(trade_data['px']),
size=Decimal(trade_data['sz']),
side=trade_data['side'],
timestamp=datetime.fromtimestamp(int(trade_data['ts']) / 1000, tz=timezone.utc),
exchange="okx",
raw_data=trade_data
)
# Process through aggregation
self.processor.process_trade(trade)
# Log every 20th trade
if self.trade_count % 20 == 1:
logger.info(f"Trade #{self.trade_count}: {trade.side} {trade.size} @ ${trade.price}")
except Exception as e:
logger.error(f"Trade processing error: {e}")
def _on_candle(self, candle: OHLCVCandle):
"""Handle completed candle."""
self.candle_counts[candle.timeframe] += 1
# Calculate metrics
change = candle.close - candle.open
change_pct = (change / candle.open * 100) if candle.open > 0 else 0
logger.info(
f"🕯️ {candle.timeframe.upper()} at {candle.end_time.strftime('%H:%M:%S')}: "
f"${candle.close} ({change_pct:+.2f}%) V={candle.volume} T={candle.trade_count}"
)
def _print_quick_status(self):
"""Print quick status update."""
total_candles = sum(self.candle_counts.values())
candle_summary = ", ".join([f"{tf}:{count}" for tf, count in self.candle_counts.items()])
logger.info(f"📊 Trades: {self.trade_count} | Candles: {total_candles} ({candle_summary})")
def _print_final_stats(self, duration: int):
"""Print final statistics."""
logger.info("=" * 50)
logger.info("📊 FINAL RESULTS")
logger.info(f"Duration: {duration}s")
logger.info(f"Trades processed: {self.trade_count}")
logger.info(f"Trade rate: {self.trade_count/duration:.1f}/sec")
total_candles = sum(self.candle_counts.values())
logger.info(f"Total candles: {total_candles}")
for tf in self.timeframes:
count = self.candle_counts[tf]
expected = self._expected_candles(tf, duration)
logger.info(f" {tf}: {count} candles (expected ~{expected})")
logger.info("=" * 50)
def _expected_candles(self, timeframe: str, duration: int) -> int:
"""Calculate expected number of candles."""
if timeframe == '1s':
return duration
elif timeframe == '5s':
return duration // 5
elif timeframe == '10s':
return duration // 10
elif timeframe == '15s':
return duration // 15
elif timeframe == '30s':
return duration // 30
elif timeframe == '1m':
return duration // 60
else:
return 0
async def main():
"""Main function with argument parsing."""
# Parse command line arguments
symbol = sys.argv[1] if len(sys.argv) > 1 else "BTC-USDT"
duration = int(sys.argv[2]) if len(sys.argv) > 2 else 60
# Default to testing all second timeframes
timeframes = sys.argv[3].split(',') if len(sys.argv) > 3 else ['1s', '5s', '10s', '15s', '30s']
print(f"🚀 Quick Aggregation Test")
print(f"Symbol: {symbol}")
print(f"Duration: {duration} seconds")
print(f"Timeframes: {', '.join(timeframes)}")
print("Press Ctrl+C to stop early\n")
# Run test
tester = QuickAggregationTester(symbol, timeframes)
await tester.run(duration)
if __name__ == "__main__":
try:
asyncio.run(main())
except KeyboardInterrupt:
print("\n⏹️ Test stopped")
except Exception as e:
print(f"\n❌ Error: {e}")
import traceback
traceback.print_exc()

306
tests/test_chart_builder.py
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@ -1,306 +0,0 @@
#!/usr/bin/env python3
"""
Unit Tests for ChartBuilder Class
Tests for the core ChartBuilder functionality including:
- Chart creation
- Data fetching
- Error handling
- Market data integration
"""
import pytest
import pandas as pd
from datetime import datetime, timezone, timedelta
from unittest.mock import Mock, patch, MagicMock
from typing import List, Dict, Any
import sys
from pathlib import Path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from components.charts.builder import ChartBuilder
from components.charts.utils import validate_market_data, prepare_chart_data
class TestChartBuilder:
"""Test suite for ChartBuilder class"""
@pytest.fixture
def mock_logger(self):
"""Mock logger for testing"""
return Mock()
@pytest.fixture
def chart_builder(self, mock_logger):
"""Create ChartBuilder instance for testing"""
return ChartBuilder(mock_logger)
@pytest.fixture
def sample_candles(self):
"""Sample candle data for testing"""
base_time = datetime.now(timezone.utc) - timedelta(hours=24)
return [
{
'timestamp': base_time + timedelta(minutes=i),
'open': 50000 + i * 10,
'high': 50100 + i * 10,
'low': 49900 + i * 10,
'close': 50050 + i * 10,
'volume': 1000 + i * 5,
'exchange': 'okx',
'symbol': 'BTC-USDT',
'timeframe': '1m'
}
for i in range(100)
]
def test_chart_builder_initialization(self, mock_logger):
"""Test ChartBuilder initialization"""
builder = ChartBuilder(mock_logger)
assert builder.logger == mock_logger
assert builder.db_ops is not None
assert builder.default_colors is not None
assert builder.default_height == 600
assert builder.default_template == "plotly_white"
def test_chart_builder_default_logger(self):
"""Test ChartBuilder initialization with default logger"""
builder = ChartBuilder()
assert builder.logger is not None
@patch('components.charts.builder.get_database_operations')
def test_fetch_market_data_success(self, mock_db_ops, chart_builder, sample_candles):
"""Test successful market data fetching"""
# Mock database operations
mock_db = Mock()
mock_db.market_data.get_candles.return_value = sample_candles
mock_db_ops.return_value = mock_db
# Replace the db_ops attribute with our mock
chart_builder.db_ops = mock_db
# Test fetch
result = chart_builder.fetch_market_data('BTC-USDT', '1m', days_back=1)
assert result == sample_candles
mock_db.market_data.get_candles.assert_called_once()
@patch('components.charts.builder.get_database_operations')
def test_fetch_market_data_empty(self, mock_db_ops, chart_builder):
"""Test market data fetching with empty result"""
# Mock empty database result
mock_db = Mock()
mock_db.market_data.get_candles.return_value = []
mock_db_ops.return_value = mock_db
# Replace the db_ops attribute with our mock
chart_builder.db_ops = mock_db
result = chart_builder.fetch_market_data('BTC-USDT', '1m')
assert result == []
@patch('components.charts.builder.get_database_operations')
def test_fetch_market_data_exception(self, mock_db_ops, chart_builder):
"""Test market data fetching with database exception"""
# Mock database exception
mock_db = Mock()
mock_db.market_data.get_candles.side_effect = Exception("Database error")
mock_db_ops.return_value = mock_db
# Replace the db_ops attribute with our mock
chart_builder.db_ops = mock_db
result = chart_builder.fetch_market_data('BTC-USDT', '1m')
assert result == []
chart_builder.logger.error.assert_called()
def test_create_candlestick_chart_with_data(self, chart_builder, sample_candles):
"""Test candlestick chart creation with valid data"""
# Mock fetch_market_data to return sample data
chart_builder.fetch_market_data = Mock(return_value=sample_candles)
fig = chart_builder.create_candlestick_chart('BTC-USDT', '1m')
assert fig is not None
assert len(fig.data) >= 1 # Should have at least candlestick trace
assert 'BTC-USDT' in fig.layout.title.text
def test_create_candlestick_chart_with_volume(self, chart_builder, sample_candles):
"""Test candlestick chart creation with volume subplot"""
chart_builder.fetch_market_data = Mock(return_value=sample_candles)
fig = chart_builder.create_candlestick_chart('BTC-USDT', '1m', include_volume=True)
assert fig is not None
assert len(fig.data) >= 2 # Should have candlestick + volume traces
def test_create_candlestick_chart_no_data(self, chart_builder):
"""Test candlestick chart creation with no data"""
chart_builder.fetch_market_data = Mock(return_value=[])
fig = chart_builder.create_candlestick_chart('BTC-USDT', '1m')
assert fig is not None
# Check for annotation with message instead of title
assert len(fig.layout.annotations) > 0
assert "No data available" in fig.layout.annotations[0].text
def test_create_candlestick_chart_invalid_data(self, chart_builder):
"""Test candlestick chart creation with invalid data"""
invalid_data = [{'invalid': 'data'}]
chart_builder.fetch_market_data = Mock(return_value=invalid_data)
fig = chart_builder.create_candlestick_chart('BTC-USDT', '1m')
assert fig is not None
# Should show error chart
assert len(fig.layout.annotations) > 0
assert "Invalid market data" in fig.layout.annotations[0].text
def test_create_strategy_chart_basic_implementation(self, chart_builder, sample_candles):
"""Test strategy chart creation (currently returns basic chart)"""
chart_builder.fetch_market_data = Mock(return_value=sample_candles)
result = chart_builder.create_strategy_chart('BTC-USDT', '1m', 'test_strategy')
assert result is not None
# Should currently return a basic candlestick chart
assert 'BTC-USDT' in result.layout.title.text
def test_create_empty_chart(self, chart_builder):
"""Test empty chart creation"""
fig = chart_builder._create_empty_chart("Test message")
assert fig is not None
assert len(fig.layout.annotations) > 0
assert "Test message" in fig.layout.annotations[0].text
assert len(fig.data) == 0
def test_create_error_chart(self, chart_builder):
"""Test error chart creation"""
fig = chart_builder._create_error_chart("Test error")
assert fig is not None
assert len(fig.layout.annotations) > 0
assert "Test error" in fig.layout.annotations[0].text
class TestChartBuilderIntegration:
"""Integration tests for ChartBuilder with real components"""
@pytest.fixture
def chart_builder(self):
"""Create ChartBuilder for integration testing"""
return ChartBuilder()
def test_market_data_validation_integration(self, chart_builder):
"""Test integration with market data validation"""
# Test with valid data structure
valid_data = [
{
'timestamp': datetime.now(timezone.utc),
'open': 50000,
'high': 50100,
'low': 49900,
'close': 50050,
'volume': 1000
}
]
assert validate_market_data(valid_data) is True
def test_chart_data_preparation_integration(self, chart_builder):
"""Test integration with chart data preparation"""
raw_data = [
{
'timestamp': datetime.now(timezone.utc) - timedelta(hours=1),
'open': '50000', # String values to test conversion
'high': '50100',
'low': '49900',
'close': '50050',
'volume': '1000'
},
{
'timestamp': datetime.now(timezone.utc),
'open': '50050',
'high': '50150',
'low': '49950',
'close': '50100',
'volume': '1200'
}
]
df = prepare_chart_data(raw_data)
assert isinstance(df, pd.DataFrame)
assert len(df) == 2
assert all(col in df.columns for col in ['timestamp', 'open', 'high', 'low', 'close', 'volume'])
assert df['open'].dtype.kind in 'fi' # Float or integer
class TestChartBuilderEdgeCases:
"""Test edge cases and error conditions"""
@pytest.fixture
def chart_builder(self):
return ChartBuilder()
def test_chart_creation_with_single_candle(self, chart_builder):
"""Test chart creation with only one candle"""
single_candle = [{
'timestamp': datetime.now(timezone.utc),
'open': 50000,
'high': 50100,
'low': 49900,
'close': 50050,
'volume': 1000
}]
chart_builder.fetch_market_data = Mock(return_value=single_candle)
fig = chart_builder.create_candlestick_chart('BTC-USDT', '1m')
assert fig is not None
assert len(fig.data) >= 1
def test_chart_creation_with_missing_volume(self, chart_builder):
"""Test chart creation with missing volume data"""
no_volume_data = [{
'timestamp': datetime.now(timezone.utc),
'open': 50000,
'high': 50100,
'low': 49900,
'close': 50050
# No volume field
}]
chart_builder.fetch_market_data = Mock(return_value=no_volume_data)
fig = chart_builder.create_candlestick_chart('BTC-USDT', '1m', include_volume=True)
assert fig is not None
# Should handle missing volume gracefully
def test_chart_creation_with_none_values(self, chart_builder):
"""Test chart creation with None values in data"""
data_with_nulls = [{
'timestamp': datetime.now(timezone.utc),
'open': 50000,
'high': None, # Null value
'low': 49900,
'close': 50050,
'volume': 1000
}]
chart_builder.fetch_market_data = Mock(return_value=data_with_nulls)
fig = chart_builder.create_candlestick_chart('BTC-USDT', '1m')
assert fig is not None
# Should handle null values gracefully
if __name__ == '__main__':
# Run tests if executed directly
pytest.main([__file__, '-v'])

711
tests/test_chart_layers.py
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@ -1,711 +0,0 @@
#!/usr/bin/env python3
"""
Comprehensive Unit Tests for Chart Layer Components
Tests for all chart layer functionality including:
- Error handling system
- Base layer components (CandlestickLayer, VolumeLayer, LayerManager)
- Indicator layers (SMA, EMA, Bollinger Bands)
- Subplot layers (RSI, MACD)
- Integration and error recovery
"""
import pytest
import pandas as pd
import plotly.graph_objects as go
from datetime import datetime, timezone, timedelta
from unittest.mock import Mock, patch, MagicMock
from typing import List, Dict, Any
from decimal import Decimal
import sys
from pathlib import Path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
# Import components to test
from components.charts.error_handling import (
ChartErrorHandler, ChartError, ErrorSeverity, DataRequirements,
ErrorRecoveryStrategies, check_data_sufficiency, get_error_message
)
from components.charts.layers.base import (
LayerConfig, BaseLayer, CandlestickLayer, VolumeLayer, LayerManager
)
from components.charts.layers.indicators import (
IndicatorLayerConfig, BaseIndicatorLayer, SMALayer, EMALayer, BollingerBandsLayer
)
from components.charts.layers.subplots import (
SubplotLayerConfig, BaseSubplotLayer, RSILayer, MACDLayer
)
class TestErrorHandlingSystem:
"""Test suite for chart error handling system"""
@pytest.fixture
def sample_data(self):
"""Sample market data for testing"""
base_time = datetime.now(timezone.utc) - timedelta(hours=24)
return [
{
'timestamp': base_time + timedelta(minutes=i),
'open': 50000 + i * 10,
'high': 50100 + i * 10,
'low': 49900 + i * 10,
'close': 50050 + i * 10,
'volume': 1000 + i * 5
}
for i in range(50) # 50 data points
]
@pytest.fixture
def insufficient_data(self):
"""Insufficient market data for testing"""
base_time = datetime.now(timezone.utc)
return [
{
'timestamp': base_time + timedelta(minutes=i),
'open': 50000,
'high': 50100,
'low': 49900,
'close': 50050,
'volume': 1000
}
for i in range(5) # Only 5 data points
]
def test_chart_error_creation(self):
"""Test ChartError dataclass creation"""
error = ChartError(
code='TEST_ERROR',
message='Test error message',
severity=ErrorSeverity.ERROR,
context={'test': 'value'},
recovery_suggestion='Fix the test'
)
assert error.code == 'TEST_ERROR'
assert error.message == 'Test error message'
assert error.severity == ErrorSeverity.ERROR
assert error.context == {'test': 'value'}
assert error.recovery_suggestion == 'Fix the test'
# Test dict conversion
error_dict = error.to_dict()
assert error_dict['code'] == 'TEST_ERROR'
assert error_dict['severity'] == 'error'
def test_data_requirements_candlestick(self):
"""Test data requirements checking for candlestick charts"""
# Test sufficient data
error = DataRequirements.check_candlestick_requirements(50)
assert error.severity == ErrorSeverity.INFO
assert error.code == 'SUFFICIENT_DATA'
# Test insufficient data
error = DataRequirements.check_candlestick_requirements(5)
assert error.severity == ErrorSeverity.WARNING
assert error.code == 'INSUFFICIENT_CANDLESTICK_DATA'
# Test no data
error = DataRequirements.check_candlestick_requirements(0)
assert error.severity == ErrorSeverity.CRITICAL
assert error.code == 'NO_DATA'
def test_data_requirements_indicators(self):
"""Test data requirements checking for indicators"""
# Test SMA with sufficient data
error = DataRequirements.check_indicator_requirements('sma', 50, {'period': 20})
assert error.severity == ErrorSeverity.INFO
# Test SMA with insufficient data
error = DataRequirements.check_indicator_requirements('sma', 15, {'period': 20})
assert error.severity == ErrorSeverity.WARNING
assert error.code == 'INSUFFICIENT_INDICATOR_DATA'
# Test unknown indicator
error = DataRequirements.check_indicator_requirements('unknown', 50, {})
assert error.severity == ErrorSeverity.ERROR
assert error.code == 'UNKNOWN_INDICATOR'
def test_chart_error_handler(self, sample_data, insufficient_data):
"""Test ChartErrorHandler functionality"""
handler = ChartErrorHandler()
# Test with sufficient data
is_valid = handler.validate_data_sufficiency(sample_data)
assert is_valid == True
assert len(handler.errors) == 0
# Test with insufficient data and indicators
indicators = [{'type': 'sma', 'parameters': {'period': 30}}]
is_valid = handler.validate_data_sufficiency(insufficient_data, indicators=indicators)
assert is_valid == False
assert len(handler.errors) > 0 or len(handler.warnings) > 0
# Test error summary
summary = handler.get_error_summary()
assert 'has_errors' in summary
assert 'can_proceed' in summary
def test_convenience_functions(self, sample_data, insufficient_data):
"""Test convenience functions for error handling"""
# Test check_data_sufficiency
is_sufficient, summary = check_data_sufficiency(sample_data)
assert is_sufficient == True
assert summary['can_proceed'] == True
# Test with insufficient data
indicators = [{'type': 'sma', 'parameters': {'period': 100}}]
is_sufficient, summary = check_data_sufficiency(insufficient_data, indicators)
assert is_sufficient == False
# Test get_error_message
error_msg = get_error_message(insufficient_data, indicators)
assert isinstance(error_msg, str)
assert len(error_msg) > 0
class TestBaseLayerSystem:
"""Test suite for base layer components"""
@pytest.fixture
def sample_df(self):
"""Sample DataFrame for testing"""
base_time = datetime.now(timezone.utc) - timedelta(hours=24)
data = []
for i in range(100):
data.append({
'timestamp': base_time + timedelta(minutes=i),
'open': 50000 + i * 10,
'high': 50100 + i * 10,
'low': 49900 + i * 10,
'close': 50050 + i * 10,
'volume': 1000 + i * 5
})
return pd.DataFrame(data)
@pytest.fixture
def invalid_df(self):
"""Invalid DataFrame for testing error handling"""
return pd.DataFrame([
{'timestamp': datetime.now(), 'open': -100, 'high': 50, 'low': 60, 'close': 40, 'volume': -50},
{'timestamp': datetime.now(), 'open': None, 'high': None, 'low': None, 'close': None, 'volume': None}
])
def test_layer_config(self):
"""Test LayerConfig creation"""
config = LayerConfig(name="test", enabled=True, color="#FF0000")
assert config.name == "test"
assert config.enabled == True
assert config.color == "#FF0000"
assert config.style == {}
assert config.subplot_row is None
def test_base_layer(self):
"""Test BaseLayer functionality"""
config = LayerConfig(name="test_layer")
layer = BaseLayer(config)
assert layer.config.name == "test_layer"
assert hasattr(layer, 'error_handler')
assert hasattr(layer, 'logger')
def test_candlestick_layer_validation(self, sample_df, invalid_df):
"""Test CandlestickLayer data validation"""
layer = CandlestickLayer()
# Test valid data
is_valid = layer.validate_data(sample_df)
assert is_valid == True
# Test invalid data
is_valid = layer.validate_data(invalid_df)
assert is_valid == False
assert len(layer.error_handler.errors) > 0
def test_candlestick_layer_render(self, sample_df):
"""Test CandlestickLayer rendering"""
layer = CandlestickLayer()
fig = go.Figure()
result_fig = layer.render(fig, sample_df)
assert result_fig is not None
assert len(result_fig.data) >= 1 # Should have candlestick trace
def test_volume_layer_validation(self, sample_df, invalid_df):
"""Test VolumeLayer data validation"""
layer = VolumeLayer()
# Test valid data
is_valid = layer.validate_data(sample_df)
assert is_valid == True
# Test invalid data (some volume issues)
is_valid = layer.validate_data(invalid_df)
# Volume layer should handle invalid data gracefully
assert len(layer.error_handler.warnings) >= 0 # May have warnings
def test_volume_layer_render(self, sample_df):
"""Test VolumeLayer rendering"""
layer = VolumeLayer()
fig = go.Figure()
result_fig = layer.render(fig, sample_df)
assert result_fig is not None
def test_layer_manager(self, sample_df):
"""Test LayerManager functionality"""
manager = LayerManager()
# Add layers
candlestick_layer = CandlestickLayer()
volume_layer = VolumeLayer()
manager.add_layer(candlestick_layer)
manager.add_layer(volume_layer)
assert len(manager.layers) == 2
# Test enabled layers
enabled = manager.get_enabled_layers()
assert len(enabled) == 2
# Test overlay vs subplot layers
overlays = manager.get_overlay_layers()
subplots = manager.get_subplot_layers()
assert len(overlays) == 1 # Candlestick is overlay
assert len(subplots) >= 1 # Volume is subplot
# Test layout calculation
layout_config = manager.calculate_subplot_layout()
assert 'rows' in layout_config
assert 'cols' in layout_config
assert layout_config['rows'] >= 2 # Main chart + volume subplot
# Test rendering all layers
fig = manager.render_all_layers(sample_df)
assert fig is not None
assert len(fig.data) >= 2 # Candlestick + volume
class TestIndicatorLayers:
"""Test suite for indicator layer components"""
@pytest.fixture
def sample_df(self):
"""Sample DataFrame with trend for indicator testing"""
base_time = datetime.now(timezone.utc) - timedelta(hours=24)
data = []
for i in range(100):
# Create trending data for better indicator calculation
trend = i * 0.1
base_price = 50000 + trend
data.append({
'timestamp': base_time + timedelta(minutes=i),
'open': base_price + (i % 3) * 10,
'high': base_price + 50 + (i % 3) * 10,
'low': base_price - 50 + (i % 3) * 10,
'close': base_price + (i % 2) * 10,
'volume': 1000 + i * 5
})
return pd.DataFrame(data)
@pytest.fixture
def insufficient_df(self):
"""Insufficient data for indicator testing"""
base_time = datetime.now(timezone.utc)
data = []
for i in range(10): # Only 10 data points
data.append({
'timestamp': base_time + timedelta(minutes=i),
'open': 50000,
'high': 50100,
'low': 49900,
'close': 50050,
'volume': 1000
})
return pd.DataFrame(data)
def test_indicator_layer_config(self):
"""Test IndicatorLayerConfig creation"""
config = IndicatorLayerConfig(
name="test_indicator",
indicator_type="sma",
parameters={'period': 20}
)
assert config.name == "test_indicator"
assert config.indicator_type == "sma"
assert config.parameters == {'period': 20}
assert config.line_width == 2
assert config.opacity == 1.0
def test_sma_layer(self, sample_df, insufficient_df):
"""Test SMALayer functionality"""
config = IndicatorLayerConfig(
name="SMA(20)",
indicator_type='sma',
parameters={'period': 20}
)
layer = SMALayer(config)
# Test with sufficient data
is_valid = layer.validate_indicator_data(sample_df, required_columns=['close', 'timestamp'])
assert is_valid == True
# Test calculation
sma_data = layer._calculate_sma(sample_df, 20)
assert sma_data is not None
assert 'sma' in sma_data.columns
assert len(sma_data) > 0
# Test with insufficient data
is_valid = layer.validate_indicator_data(insufficient_df, required_columns=['close', 'timestamp'])
# Should have warnings but may still be valid for short periods
assert len(layer.error_handler.warnings) >= 0
def test_ema_layer(self, sample_df):
"""Test EMALayer functionality"""
config = IndicatorLayerConfig(
name="EMA(12)",
indicator_type='ema',
parameters={'period': 12}
)
layer = EMALayer(config)
# Test validation
is_valid = layer.validate_indicator_data(sample_df, required_columns=['close', 'timestamp'])
assert is_valid == True
# Test calculation
ema_data = layer._calculate_ema(sample_df, 12)
assert ema_data is not None
assert 'ema' in ema_data.columns
assert len(ema_data) > 0
def test_bollinger_bands_layer(self, sample_df):
"""Test BollingerBandsLayer functionality"""
config = IndicatorLayerConfig(
name="BB(20,2)",
indicator_type='bollinger_bands',
parameters={'period': 20, 'std_dev': 2}
)
layer = BollingerBandsLayer(config)
# Test validation
is_valid = layer.validate_indicator_data(sample_df, required_columns=['close', 'timestamp'])
assert is_valid == True
# Test calculation
bb_data = layer._calculate_bollinger_bands(sample_df, 20, 2)
assert bb_data is not None
assert 'upper_band' in bb_data.columns
assert 'middle_band' in bb_data.columns
assert 'lower_band' in bb_data.columns
assert len(bb_data) > 0
def test_safe_calculate_indicator(self, sample_df, insufficient_df):
"""Test safe indicator calculation with error handling"""
config = IndicatorLayerConfig(
name="SMA(20)",
indicator_type='sma',
parameters={'period': 20}
)
layer = SMALayer(config)
# Test successful calculation
result = layer.safe_calculate_indicator(
sample_df,
layer._calculate_sma,
period=20
)
assert result is not None
# Test with insufficient data - should attempt recovery
result = layer.safe_calculate_indicator(
insufficient_df,
layer._calculate_sma,
period=50 # Too large for data
)
# Should either return adjusted result or None
assert result is None or len(result) > 0
class TestSubplotLayers:
"""Test suite for subplot layer components"""
@pytest.fixture
def sample_df(self):
"""Sample DataFrame for RSI/MACD testing"""
base_time = datetime.now(timezone.utc) - timedelta(hours=24)
data = []
# Create more realistic price data for RSI/MACD
prices = [50000]
for i in range(100):
# Random walk with trend
change = (i % 7 - 3) * 50 # Some volatility
new_price = prices[-1] + change
prices.append(new_price)
data.append({
'timestamp': base_time + timedelta(minutes=i),
'open': prices[i],
'high': prices[i] + abs(change) + 20,
'low': prices[i] - abs(change) - 20,
'close': prices[i+1],
'volume': 1000 + i * 5
})
return pd.DataFrame(data)
def test_subplot_layer_config(self):
"""Test SubplotLayerConfig creation"""
config = SubplotLayerConfig(
name="RSI(14)",
indicator_type="rsi",
parameters={'period': 14},
subplot_height_ratio=0.25,
y_axis_range=(0, 100),
reference_lines=[30, 70]
)
assert config.name == "RSI(14)"
assert config.indicator_type == "rsi"
assert config.subplot_height_ratio == 0.25
assert config.y_axis_range == (0, 100)
assert config.reference_lines == [30, 70]
def test_rsi_layer(self, sample_df):
"""Test RSILayer functionality"""
layer = RSILayer(period=14)
# Test validation
is_valid = layer.validate_indicator_data(sample_df, required_columns=['close', 'timestamp'])
assert is_valid == True
# Test RSI calculation
rsi_data = layer._calculate_rsi(sample_df, 14)
assert rsi_data is not None
assert 'rsi' in rsi_data.columns
assert len(rsi_data) > 0
# Validate RSI values are in correct range
assert (rsi_data['rsi'] >= 0).all()
assert (rsi_data['rsi'] <= 100).all()
# Test subplot properties
assert layer.has_fixed_range() == True
assert layer.get_y_axis_range() == (0, 100)
assert 30 in layer.get_reference_lines()
assert 70 in layer.get_reference_lines()
def test_macd_layer(self, sample_df):
"""Test MACDLayer functionality"""
layer = MACDLayer(fast_period=12, slow_period=26, signal_period=9)
# Test validation
is_valid = layer.validate_indicator_data(sample_df, required_columns=['close', 'timestamp'])
assert is_valid == True
# Test MACD calculation
macd_data = layer._calculate_macd(sample_df, 12, 26, 9)
assert macd_data is not None
assert 'macd' in macd_data.columns
assert 'signal' in macd_data.columns
assert 'histogram' in macd_data.columns
assert len(macd_data) > 0
# Test subplot properties
assert layer.should_show_zero_line() == True
assert layer.get_subplot_height_ratio() == 0.3
def test_rsi_calculation_edge_cases(self, sample_df):
"""Test RSI calculation with edge cases"""
layer = RSILayer(period=14)
# Test with very short period
short_data = sample_df.head(20)
rsi_data = layer._calculate_rsi(short_data, 5) # Short period
assert rsi_data is not None
assert len(rsi_data) > 0
# Test with period too large for data
try:
layer._calculate_rsi(sample_df.head(10), 20) # Period larger than data
assert False, "Should have raised an error"
except Exception:
pass # Expected to fail
def test_macd_calculation_edge_cases(self, sample_df):
"""Test MACD calculation with edge cases"""
layer = MACDLayer(fast_period=12, slow_period=26, signal_period=9)
# Test with invalid periods (fast >= slow)
try:
layer._calculate_macd(sample_df, 26, 12, 9) # fast >= slow
assert False, "Should have raised an error"
except Exception:
pass # Expected to fail
class TestLayerIntegration:
"""Test suite for layer integration and complex scenarios"""
@pytest.fixture
def sample_df(self):
"""Sample DataFrame for integration testing"""
base_time = datetime.now(timezone.utc) - timedelta(hours=24)
data = []
for i in range(150): # Enough data for all indicators
trend = i * 0.1
base_price = 50000 + trend
volatility = (i % 10) * 20
data.append({
'timestamp': base_time + timedelta(minutes=i),
'open': base_price + volatility,
'high': base_price + volatility + 50,
'low': base_price + volatility - 50,
'close': base_price + volatility + (i % 3 - 1) * 10,
'volume': 1000 + i * 5
})
return pd.DataFrame(data)
def test_full_chart_creation(self, sample_df):
"""Test creating a full chart with multiple layers"""
manager = LayerManager()
# Add base layers
manager.add_layer(CandlestickLayer())
manager.add_layer(VolumeLayer())
# Add indicator layers
manager.add_layer(SMALayer(IndicatorLayerConfig(
name="SMA(20)",
indicator_type='sma',
parameters={'period': 20}
)))
manager.add_layer(EMALayer(IndicatorLayerConfig(
name="EMA(12)",
indicator_type='ema',
parameters={'period': 12}
)))
# Add subplot layers
manager.add_layer(RSILayer(period=14))
manager.add_layer(MACDLayer(fast_period=12, slow_period=26, signal_period=9))
# Calculate layout
layout_config = manager.calculate_subplot_layout()
assert layout_config['rows'] >= 4 # Main + volume + RSI + MACD
# Render all layers
fig = manager.render_all_layers(sample_df)
assert fig is not None
assert len(fig.data) >= 6 # Candlestick + volume + SMA + EMA + RSI + MACD components
def test_error_recovery_integration(self):
"""Test error recovery with insufficient data"""
manager = LayerManager()
# Create insufficient data
base_time = datetime.now(timezone.utc)
insufficient_data = pd.DataFrame([
{
'timestamp': base_time + timedelta(minutes=i),
'open': 50000,
'high': 50100,
'low': 49900,
'close': 50050,
'volume': 1000
}
for i in range(15) # Only 15 data points
])
# Add layers that require more data
manager.add_layer(CandlestickLayer())
manager.add_layer(SMALayer(IndicatorLayerConfig(
name="SMA(50)", # Requires too much data
indicator_type='sma',
parameters={'period': 50}
)))
# Should still create a chart (graceful degradation)
fig = manager.render_all_layers(insufficient_data)
assert fig is not None
# Should have at least candlestick layer
assert len(fig.data) >= 1
def test_mixed_valid_invalid_data(self):
"""Test handling mixed valid and invalid data"""
# Create data with some invalid entries
base_time = datetime.now(timezone.utc)
mixed_data = []
for i in range(50):
if i % 10 == 0: # Every 10th entry is invalid
data_point = {
'timestamp': base_time + timedelta(minutes=i),
'open': -100, # Invalid negative price
'high': None, # Missing data
'low': None,
'close': None,
'volume': -50 # Invalid negative volume
}
else:
data_point = {
'timestamp': base_time + timedelta(minutes=i),
'open': 50000 + i * 10,
'high': 50100 + i * 10,
'low': 49900 + i * 10,
'close': 50050 + i * 10,
'volume': 1000 + i * 5
}
mixed_data.append(data_point)
df = pd.DataFrame(mixed_data)
# Test candlestick layer with mixed data
candlestick_layer = CandlestickLayer()
is_valid = candlestick_layer.validate_data(df)
# Should handle mixed data gracefully
if not is_valid:
# Should have warnings but possibly still proceed
assert len(candlestick_layer.error_handler.warnings) > 0
def test_layer_manager_dynamic_layout(self):
"""Test LayerManager dynamic layout calculation"""
manager = LayerManager()
# Test with no subplots
manager.add_layer(CandlestickLayer())
layout = manager.calculate_subplot_layout()
assert layout['rows'] == 1
# Add one subplot
manager.add_layer(VolumeLayer())
layout = manager.calculate_subplot_layout()
assert layout['rows'] == 2
# Add more subplots
manager.add_layer(RSILayer(period=14))
manager.add_layer(MACDLayer(fast_period=12, slow_period=26, signal_period=9))
layout = manager.calculate_subplot_layout()
assert layout['rows'] == 4 # Main + volume + RSI + MACD
assert layout['cols'] == 1
assert len(layout['subplot_titles']) == 4
assert len(layout['row_heights']) == 4
# Test row height calculation
total_height = sum(layout['row_heights'])
assert abs(total_height - 1.0) < 0.01 # Should sum to approximately 1.0
if __name__ == "__main__":
pytest.main([__file__, "-v"])

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tests/test_configuration_integration.py
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@ -1,519 +0,0 @@
"""
Comprehensive Integration Tests for Configuration System
Tests the entire configuration system end-to-end, ensuring all components
work together seamlessly including validation, error handling, and strategy creation.
"""
import pytest
import json
from typing import Dict, List, Any
from components.charts.config import (
# Core configuration classes
StrategyChartConfig,
SubplotConfig,
SubplotType,
ChartStyle,
ChartLayout,
TradingStrategy,
IndicatorCategory,
# Configuration functions
create_custom_strategy_config,
validate_configuration,
validate_configuration_strict,
check_configuration_health,
# Example strategies
create_ema_crossover_strategy,
create_momentum_breakout_strategy,
create_mean_reversion_strategy,
create_scalping_strategy,
create_swing_trading_strategy,
get_all_example_strategies,
# Indicator management
get_all_default_indicators,
get_indicators_by_category,
create_indicator_config,
# Error handling
ErrorSeverity,
ConfigurationError,
validate_strategy_name,
get_indicator_suggestions,
# Validation
ValidationLevel,
ConfigurationValidator
)
class TestConfigurationSystemIntegration:
"""Test the entire configuration system working together."""
def test_complete_strategy_creation_workflow(self):
"""Test complete workflow from strategy creation to validation."""
# 1. Create a custom strategy configuration
config, errors = create_custom_strategy_config(
strategy_name="Integration Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="A comprehensive test strategy",
timeframes=["15m", "1h", "4h"],
overlay_indicators=["ema_12", "ema_26", "sma_50"],
subplot_configs=[
{
"subplot_type": "rsi",
"height_ratio": 0.25,
"indicators": ["rsi_14"],
"title": "RSI Momentum"
},
{
"subplot_type": "macd",
"height_ratio": 0.25,
"indicators": ["macd_12_26_9"],
"title": "MACD Convergence"
}
]
)
# 2. Validate configuration was created successfully
# Note: Config might be None if indicators don't exist in test environment
if config is not None:
assert config.strategy_name == "Integration Test Strategy"
assert len(config.overlay_indicators) == 3
assert len(config.subplot_configs) == 2
# 3. Validate the configuration using basic validation
is_valid, validation_errors = config.validate()
# 4. Perform strict validation
error_report = validate_configuration_strict(config)
# 5. Check configuration health
health_check = check_configuration_health(config)
assert "is_healthy" in health_check
assert "total_indicators" in health_check
else:
# Configuration failed to create - check that we got errors
assert len(errors) > 0
def test_example_strategies_integration(self):
"""Test all example strategies work with the validation system."""
strategies = get_all_example_strategies()
assert len(strategies) >= 5 # We created 5 example strategies
for strategy_name, strategy_example in strategies.items():
config = strategy_example.config
# Test configuration is valid
assert isinstance(config, StrategyChartConfig)
assert config.strategy_name is not None
assert config.strategy_type is not None
assert len(config.overlay_indicators) > 0 or len(config.subplot_configs) > 0
# Test validation passes (using the main validation function)
validation_report = validate_configuration(config)
# Note: May have warnings in test environment due to missing indicators
assert isinstance(validation_report.is_valid, bool)
# Test health check
health = check_configuration_health(config)
assert "is_healthy" in health
assert "total_indicators" in health
def test_indicator_system_integration(self):
"""Test indicator system integration with configurations."""
# Get all available indicators
indicators = get_all_default_indicators()
assert len(indicators) > 20 # Should have many indicators
# Test indicators by category
for category in IndicatorCategory:
category_indicators = get_indicators_by_category(category)
assert isinstance(category_indicators, dict)
# Test creating configurations for each indicator
for indicator_name, indicator_preset in list(category_indicators.items())[:3]: # Test first 3
# Test that indicator preset has required properties
assert hasattr(indicator_preset, 'config')
assert hasattr(indicator_preset, 'name')
assert hasattr(indicator_preset, 'category')
def test_error_handling_integration(self):
"""Test error handling integration across the system."""
# Test with invalid strategy name
error = validate_strategy_name("nonexistent_strategy")
assert error is not None
assert error.severity == ErrorSeverity.CRITICAL
assert len(error.suggestions) > 0
# Test with invalid configuration
invalid_config = StrategyChartConfig(
strategy_name="Invalid Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Strategy with missing indicators",
timeframes=["1h"],
overlay_indicators=["nonexistent_indicator_999"]
)
# Validate with strict validation
error_report = validate_configuration_strict(invalid_config)
assert not error_report.is_usable
assert len(error_report.missing_indicators) > 0
# Check that error handling provides suggestions
suggestions = get_indicator_suggestions("nonexistent")
assert isinstance(suggestions, list)
def test_validation_system_integration(self):
"""Test validation system with different validation approaches."""
# Create a configuration with potential issues
config = StrategyChartConfig(
strategy_name="Test Validation",
strategy_type=TradingStrategy.SCALPING,
description="Test strategy",
timeframes=["1d"], # Wrong timeframe for scalping
overlay_indicators=["ema_12", "sma_20"]
)
# Test main validation function
validation_report = validate_configuration(config)
assert isinstance(validation_report.is_valid, bool)
# Test strict validation
strict_report = validate_configuration_strict(config)
assert hasattr(strict_report, 'is_usable')
# Test basic validation
is_valid, errors = config.validate()
assert isinstance(is_valid, bool)
assert isinstance(errors, list)
def test_json_serialization_integration(self):
"""Test JSON serialization/deserialization of configurations."""
# Create a strategy
strategy = create_ema_crossover_strategy()
config = strategy.config
# Convert to dict (simulating JSON serialization)
config_dict = {
"strategy_name": config.strategy_name,
"strategy_type": config.strategy_type.value,
"description": config.description,
"timeframes": config.timeframes,
"overlay_indicators": config.overlay_indicators,
"subplot_configs": [
{
"subplot_type": subplot.subplot_type.value,
"height_ratio": subplot.height_ratio,
"indicators": subplot.indicators,
"title": subplot.title
}
for subplot in config.subplot_configs
]
}
# Verify serialization works
json_str = json.dumps(config_dict)
assert len(json_str) > 0
# Verify deserialization works
restored_dict = json.loads(json_str)
assert restored_dict["strategy_name"] == config.strategy_name
assert restored_dict["strategy_type"] == config.strategy_type.value
def test_configuration_modification_workflow(self):
"""Test modifying and re-validating configurations."""
# Start with a valid configuration
config = create_swing_trading_strategy().config
# Verify it's initially valid (may have issues due to missing indicators in test env)
initial_health = check_configuration_health(config)
assert "is_healthy" in initial_health
# Modify the configuration (add an invalid indicator)
config.overlay_indicators.append("invalid_indicator_999")
# Verify it's now invalid
modified_health = check_configuration_health(config)
assert not modified_health["is_healthy"]
assert modified_health["missing_indicators"] > 0
# Remove the invalid indicator
config.overlay_indicators.remove("invalid_indicator_999")
# Verify it's valid again (or at least better)
final_health = check_configuration_health(config)
# Note: May still have issues due to test environment
assert final_health["missing_indicators"] < modified_health["missing_indicators"]
def test_multi_timeframe_strategy_integration(self):
"""Test strategies with multiple timeframes."""
config, errors = create_custom_strategy_config(
strategy_name="Multi-Timeframe Strategy",
strategy_type=TradingStrategy.SWING_TRADING,
description="Strategy using multiple timeframes",
timeframes=["1h", "4h", "1d"],
overlay_indicators=["ema_21", "sma_50", "sma_200"],
subplot_configs=[
{
"subplot_type": "rsi",
"height_ratio": 0.2,
"indicators": ["rsi_14"],
"title": "RSI (14)"
}
]
)
if config is not None:
assert len(config.timeframes) == 3
# Validate the multi-timeframe strategy
validation_report = validate_configuration(config)
health_check = check_configuration_health(config)
# Should be valid and healthy (or at least structured correctly)
assert isinstance(validation_report.is_valid, bool)
assert "total_indicators" in health_check
else:
# Configuration failed - check we got errors
assert len(errors) > 0
def test_strategy_type_consistency_integration(self):
"""Test strategy type consistency validation across the system."""
test_cases = [
{
"strategy_type": TradingStrategy.SCALPING,
"timeframes": ["1m", "5m"],
"expected_consistent": True
},
{
"strategy_type": TradingStrategy.SCALPING,
"timeframes": ["1d", "1w"],
"expected_consistent": False
},
{
"strategy_type": TradingStrategy.SWING_TRADING,
"timeframes": ["4h", "1d"],
"expected_consistent": True
},
{
"strategy_type": TradingStrategy.SWING_TRADING,
"timeframes": ["1m", "5m"],
"expected_consistent": False
}
]
for case in test_cases:
config = StrategyChartConfig(
strategy_name=f"Test {case['strategy_type'].value}",
strategy_type=case["strategy_type"],
description="Test strategy for consistency",
timeframes=case["timeframes"],
overlay_indicators=["ema_12", "sma_20"]
)
# Check validation report
validation_report = validate_configuration(config)
error_report = validate_configuration_strict(config)
# Just verify the system processes the configurations
assert isinstance(validation_report.is_valid, bool)
assert hasattr(error_report, 'is_usable')
class TestConfigurationSystemPerformance:
"""Test performance and scalability of the configuration system."""
def test_large_configuration_performance(self):
"""Test system performance with large configurations."""
# Create a configuration with many indicators
large_config, errors = create_custom_strategy_config(
strategy_name="Large Configuration Test",
strategy_type=TradingStrategy.DAY_TRADING,
description="Strategy with many indicators",
timeframes=["5m", "15m", "1h", "4h"],
overlay_indicators=[
"ema_12", "ema_26", "ema_50", "sma_20", "sma_50", "sma_200"
],
subplot_configs=[
{
"subplot_type": "rsi",
"height_ratio": 0.15,
"indicators": ["rsi_7", "rsi_14", "rsi_21"],
"title": "RSI Multi-Period"
},
{
"subplot_type": "macd",
"height_ratio": 0.15,
"indicators": ["macd_12_26_9"],
"title": "MACD"
}
]
)
if large_config is not None:
assert len(large_config.overlay_indicators) == 6
assert len(large_config.subplot_configs) == 2
# Validate performance is acceptable
import time
start_time = time.time()
# Perform multiple operations
for _ in range(10):
validate_configuration_strict(large_config)
check_configuration_health(large_config)
end_time = time.time()
execution_time = end_time - start_time
# Should complete in reasonable time (less than 5 seconds for 10 iterations)
assert execution_time < 5.0
else:
# Large configuration failed - verify we got errors
assert len(errors) > 0
def test_multiple_strategies_performance(self):
"""Test performance when working with multiple strategies."""
# Get all example strategies
strategies = get_all_example_strategies()
# Time the validation of all strategies
import time
start_time = time.time()
for strategy_name, strategy_example in strategies.items():
config = strategy_example.config
validate_configuration_strict(config)
check_configuration_health(config)
end_time = time.time()
execution_time = end_time - start_time
# Should complete in reasonable time
assert execution_time < 3.0
class TestConfigurationSystemRobustness:
"""Test system robustness and edge cases."""
def test_empty_configuration_handling(self):
"""Test handling of empty configurations."""
empty_config = StrategyChartConfig(
strategy_name="Empty Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Empty strategy",
timeframes=["1h"],
overlay_indicators=[],
subplot_configs=[]
)
# System should handle empty config gracefully
error_report = validate_configuration_strict(empty_config)
assert not error_report.is_usable # Should be unusable
assert len(error_report.errors) > 0 # Should have errors
health_check = check_configuration_health(empty_config)
assert not health_check["is_healthy"]
assert health_check["total_indicators"] == 0
def test_invalid_data_handling(self):
"""Test handling of invalid data types and values."""
# Test with None values - basic validation
try:
config = StrategyChartConfig(
strategy_name="Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Test with edge cases",
timeframes=["1h"],
overlay_indicators=["ema_12"]
)
# Should handle gracefully
error_report = validate_configuration_strict(config)
assert isinstance(error_report.is_usable, bool)
except (TypeError, ValueError):
# Also acceptable to raise an error
pass
def test_configuration_boundary_cases(self):
"""Test boundary cases in configuration."""
# Test with single indicator
minimal_config = StrategyChartConfig(
strategy_name="Minimal Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Minimal viable strategy",
timeframes=["1h"],
overlay_indicators=["ema_12"]
)
error_report = validate_configuration_strict(minimal_config)
health_check = check_configuration_health(minimal_config)
# Should be processed without crashing
assert isinstance(error_report.is_usable, bool)
assert health_check["total_indicators"] >= 0
assert len(health_check["recommendations"]) >= 0
def test_configuration_versioning_compatibility(self):
"""Test that configurations are forward/backward compatible."""
# Create a basic configuration
config = create_ema_crossover_strategy().config
# Verify all required fields are present
required_fields = [
'strategy_name', 'strategy_type', 'description',
'timeframes', 'overlay_indicators', 'subplot_configs'
]
for field in required_fields:
assert hasattr(config, field)
assert getattr(config, field) is not None
class TestConfigurationSystemDocumentation:
"""Test that configuration system is well-documented and discoverable."""
def test_available_indicators_discovery(self):
"""Test that available indicators can be discovered."""
indicators = get_all_default_indicators()
assert len(indicators) > 0
# Test that indicators are categorized
for category in IndicatorCategory:
category_indicators = get_indicators_by_category(category)
assert isinstance(category_indicators, dict)
def test_available_strategies_discovery(self):
"""Test that available strategies can be discovered."""
strategies = get_all_example_strategies()
assert len(strategies) >= 5
# Each strategy should have required metadata
for strategy_name, strategy_example in strategies.items():
# Check for core attributes (these are the actual attributes)
assert hasattr(strategy_example, 'config')
assert hasattr(strategy_example, 'description')
assert hasattr(strategy_example, 'difficulty')
assert hasattr(strategy_example, 'risk_level')
assert hasattr(strategy_example, 'author')
def test_error_message_quality(self):
"""Test that error messages are helpful and informative."""
# Test missing strategy error
error = validate_strategy_name("nonexistent_strategy")
assert error is not None
assert len(error.message) > 10 # Should be descriptive
assert len(error.suggestions) > 0 # Should have suggestions
assert len(error.recovery_steps) > 0 # Should have recovery steps
# Test missing indicator suggestions
suggestions = get_indicator_suggestions("nonexistent_indicator")
assert isinstance(suggestions, list)
if __name__ == "__main__":
pytest.main([__file__, "-v"])

795
tests/test_data_collection_aggregation.py
View File

@ -1,795 +0,0 @@
#!/usr/bin/env python3
"""
Comprehensive Unit Tests for Data Collection and Aggregation Logic
This module provides comprehensive unit tests for the data collection and aggregation
functionality, covering:
- OKX data collection and processing
- Real-time candle aggregation
- Data validation and transformation
- Error handling and edge cases
- Performance and reliability testing
This completes task 2.9 of phase 2.
"""
import pytest
import asyncio
import json
from datetime import datetime, timezone, timedelta
from decimal import Decimal
from typing import Dict, List, Any, Optional
from unittest.mock import Mock, AsyncMock, patch
from collections import defaultdict
# Import modules under test
from data.base_collector import BaseDataCollector, DataType, MarketDataPoint, CollectorStatus
from data.collector_manager import CollectorManager
from data.collector_types import CollectorConfig
from data.collection_service import DataCollectionService
from data.exchanges.okx.collector import OKXCollector
from data.exchanges.okx.data_processor import OKXDataProcessor, OKXDataValidator, OKXDataTransformer
from data.exchanges.okx.websocket import OKXWebSocketClient, OKXSubscription, OKXChannelType
from data.common.data_types import (
StandardizedTrade, OHLCVCandle, CandleProcessingConfig,
DataValidationResult
)
from data.common.aggregation.realtime import RealTimeCandleProcessor
from data.common.validation import BaseDataValidator, ValidationResult
from data.common.transformation import BaseDataTransformer
from utils.logger import get_logger
@pytest.fixture
def logger():
"""Create test logger."""
return get_logger("test_data_collection", log_level="DEBUG")
@pytest.fixture
def sample_trade_data():
"""Sample OKX trade data for testing."""
return {
"instId": "BTC-USDT",
"tradeId": "123456789",
"px": "50000.50",
"sz": "0.1",
"side": "buy",
"ts": "1640995200000" # 2022-01-01 00:00:00 UTC
}
@pytest.fixture
def sample_orderbook_data():
"""Sample OKX orderbook data for testing."""
return {
"instId": "BTC-USDT",
"asks": [["50001.00", "0.5", "0", "2"]],
"bids": [["49999.00", "0.3", "0", "1"]],
"ts": "1640995200000",
"seqId": "12345"
}
@pytest.fixture
def sample_ticker_data():
"""Sample OKX ticker data for testing."""
return {
"instId": "BTC-USDT",
"last": "50000.50",
"lastSz": "0.1",
"askPx": "50001.00",
"askSz": "0.5",
"bidPx": "49999.00",
"bidSz": "0.3",
"open24h": "49500.00",
"high24h": "50500.00",
"low24h": "49000.00",
"vol24h": "1000.5",
"volCcy24h": "50000000.00",
"ts": "1640995200000"
}
@pytest.fixture
def candle_config():
"""Sample candle processing configuration."""
return CandleProcessingConfig(
timeframes=['1s', '5s', '1m', '5m'],
auto_save_candles=False,
emit_incomplete_candles=False
)
class TestDataCollectionAndAggregation:
"""Comprehensive test suite for data collection and aggregation logic."""
def test_basic_imports(self):
"""Test that all required modules can be imported."""
# This test ensures all imports are working
assert StandardizedTrade is not None
assert OHLCVCandle is not None
assert CandleProcessingConfig is not None
assert DataValidationResult is not None
assert RealTimeCandleProcessor is not None
assert BaseDataValidator is not None
assert ValidationResult is not None
class TestOKXDataValidation:
"""Test OKX-specific data validation."""
@pytest.fixture
def validator(self, logger):
"""Create OKX data validator."""
return OKXDataValidator("test_validator", logger)
def test_symbol_format_validation(self, validator):
"""Test OKX symbol format validation."""
# Valid symbols
valid_symbols = ["BTC-USDT", "ETH-USDC", "SOL-USD", "DOGE-USDT"]
for symbol in valid_symbols:
result = validator.validate_symbol_format(symbol)
assert result.is_valid, f"Symbol {symbol} should be valid"
assert len(result.errors) == 0
# Invalid symbols
invalid_symbols = ["BTCUSDT", "BTC/USDT", "btc-usdt", "BTC-", "-USDT", ""]
for symbol in invalid_symbols:
result = validator.validate_symbol_format(symbol)
assert not result.is_valid, f"Symbol {symbol} should be invalid"
assert len(result.errors) > 0
def test_trade_data_validation(self, validator, sample_trade_data):
"""Test trade data validation."""
# Valid trade data
result = validator.validate_trade_data(sample_trade_data)
assert result.is_valid
assert len(result.errors) == 0
assert result.sanitized_data is not None
# Missing required field
incomplete_data = sample_trade_data.copy()
del incomplete_data['px']
result = validator.validate_trade_data(incomplete_data)
assert not result.is_valid
assert any("Missing required trade field: px" in error for error in result.errors)
# Invalid price
invalid_price_data = sample_trade_data.copy()
invalid_price_data['px'] = "invalid_price"
result = validator.validate_trade_data(invalid_price_data)
assert not result.is_valid
assert any("price" in error.lower() for error in result.errors)
def test_orderbook_data_validation(self, validator, sample_orderbook_data):
"""Test orderbook data validation."""
# Valid orderbook data
result = validator.validate_orderbook_data(sample_orderbook_data)
assert result.is_valid
assert len(result.errors) == 0
# Missing asks/bids
incomplete_data = sample_orderbook_data.copy()
del incomplete_data['asks']
result = validator.validate_orderbook_data(incomplete_data)
assert not result.is_valid
assert any("asks" in error.lower() for error in result.errors)
def test_ticker_data_validation(self, validator, sample_ticker_data):
"""Test ticker data validation."""
# Valid ticker data
result = validator.validate_ticker_data(sample_ticker_data)
assert result.is_valid
assert len(result.errors) == 0
# Missing required field
incomplete_data = sample_ticker_data.copy()
del incomplete_data['last']
result = validator.validate_ticker_data(incomplete_data)
assert not result.is_valid
assert any("last" in error.lower() for error in result.errors)
class TestOKXDataTransformation:
"""Test OKX-specific data transformation."""
@pytest.fixture
def transformer(self, logger):
"""Create OKX data transformer."""
return OKXDataTransformer("test_transformer", logger)
def test_trade_data_transformation(self, transformer, sample_trade_data):
"""Test trade data transformation to StandardizedTrade."""
result = transformer.transform_trade_data(sample_trade_data, "BTC-USDT")
assert result is not None
assert isinstance(result, StandardizedTrade)
assert result.symbol == "BTC-USDT"
assert result.trade_id == "123456789"
assert result.price == Decimal("50000.50")
assert result.size == Decimal("0.1")
assert result.side == "buy"
assert result.exchange == "okx"
assert result.timestamp.year == 2022
def test_orderbook_data_transformation(self, transformer, sample_orderbook_data):
"""Test orderbook data transformation."""
result = transformer.transform_orderbook_data(sample_orderbook_data, "BTC-USDT")
assert result is not None
assert result['symbol'] == "BTC-USDT"
assert result['exchange'] == "okx"
assert 'asks' in result
assert 'bids' in result
assert len(result['asks']) > 0
assert len(result['bids']) > 0
def test_ticker_data_transformation(self, transformer, sample_ticker_data):
"""Test ticker data transformation."""
result = transformer.transform_ticker_data(sample_ticker_data, "BTC-USDT")
assert result is not None
assert result['symbol'] == "BTC-USDT"
assert result['exchange'] == "okx"
assert result['last'] == Decimal("50000.50")
assert result['bid'] == Decimal("49999.00")
assert result['ask'] == Decimal("50001.00")
class TestRealTimeCandleAggregation:
"""Test real-time candle aggregation logic."""
@pytest.fixture
def processor(self, candle_config, logger):
"""Create real-time candle processor."""
return RealTimeCandleProcessor(
symbol="BTC-USDT",
exchange="okx",
config=candle_config,
component_name="test_processor",
logger=logger
)
def test_single_trade_processing(self, processor):
"""Test processing a single trade."""
trade = StandardizedTrade(
symbol="BTC-USDT",
trade_id="123",
price=Decimal("50000"),
size=Decimal("0.1"),
side="buy",
timestamp=datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc),
exchange="okx"
)
completed_candles = processor.process_trade(trade)
# First trade shouldn't complete any candles
assert len(completed_candles) == 0
# Check that candles are being built
stats = processor.get_stats()
assert stats['trades_processed'] == 1
assert 'active_timeframes' in stats
assert len(stats['active_timeframes']) > 0 # Should have active timeframes
def test_candle_completion_timing(self, processor):
"""Test that candles complete at the correct time boundaries."""
base_time = datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
completed_candles = []
def candle_callback(candle):
completed_candles.append(candle)
processor.add_candle_callback(candle_callback)
# Add trades at different seconds to trigger candle completions
for i in range(6): # 6 seconds of trades
trade = StandardizedTrade(
symbol="BTC-USDT",
trade_id=str(i),
price=Decimal("50000") + Decimal(str(i)),
size=Decimal("0.1"),
side="buy",
timestamp=base_time + timedelta(seconds=i),
exchange="okx"
)
processor.process_trade(trade)
# Should have completed some 1s and 5s candles
assert len(completed_candles) > 0
# Check candle properties
for candle in completed_candles:
assert candle.symbol == "BTC-USDT"
assert candle.exchange == "okx"
assert candle.timeframe in ['1s', '5s']
assert candle.trade_count > 0
assert candle.volume > 0
def test_ohlcv_calculation_accuracy(self, processor):
"""Test OHLCV calculation accuracy."""
base_time = datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
completed_candles = []
def candle_callback(candle):
completed_candles.append(candle)
processor.add_candle_callback(candle_callback)
# Add trades with known prices to test OHLCV calculation
prices = [Decimal("50000"), Decimal("50100"), Decimal("49900"), Decimal("50050")]
sizes = [Decimal("0.1"), Decimal("0.2"), Decimal("0.15"), Decimal("0.05")]
for i, (price, size) in enumerate(zip(prices, sizes)):
trade = StandardizedTrade(
symbol="BTC-USDT",
trade_id=str(i),
price=price,
size=size,
side="buy",
timestamp=base_time + timedelta(milliseconds=i * 100),
exchange="okx"
)
processor.process_trade(trade)
# Force completion by adding trade in next second
trade = StandardizedTrade(
symbol="BTC-USDT",
trade_id="final",
price=Decimal("50000"),
size=Decimal("0.1"),
side="buy",
timestamp=base_time + timedelta(seconds=1),
exchange="okx"
)
processor.process_trade(trade)
# Find 1s candle
candle_1s = next((c for c in completed_candles if c.timeframe == '1s'), None)
assert candle_1s is not None
# Verify OHLCV values
assert candle_1s.open == Decimal("50000") # First trade price
assert candle_1s.high == Decimal("50100") # Highest price
assert candle_1s.low == Decimal("49900") # Lowest price
assert candle_1s.close == Decimal("50050") # Last trade price
assert candle_1s.volume == sum(sizes) # Total volume
assert candle_1s.trade_count == 4 # Number of trades
def test_multiple_timeframe_aggregation(self, processor):
"""Test aggregation across multiple timeframes."""
base_time = datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
completed_candles = []
def candle_callback(candle):
completed_candles.append(candle)
processor.add_candle_callback(candle_callback)
# Add trades over 6 seconds to trigger multiple timeframe completions
for second in range(6):
for ms in range(0, 1000, 100): # 10 trades per second
trade = StandardizedTrade(
symbol="BTC-USDT",
trade_id=f"{second}_{ms}",
price=Decimal("50000") + Decimal(str(second)),
size=Decimal("0.01"),
side="buy",
timestamp=base_time + timedelta(seconds=second, milliseconds=ms),
exchange="okx"
)
processor.process_trade(trade)
# Check that we have candles for different timeframes
timeframes_found = set(c.timeframe for c in completed_candles)
assert '1s' in timeframes_found
assert '5s' in timeframes_found
# Verify candle relationships (5s candle should aggregate 5 1s candles)
candles_1s = [c for c in completed_candles if c.timeframe == '1s']
candles_5s = [c for c in completed_candles if c.timeframe == '5s']
if candles_5s:
# Check that 5s candle volume is sum of constituent 1s candles
candle_5s = candles_5s[0]
related_1s_candles = [
c for c in candles_1s
if c.start_time >= candle_5s.start_time and c.end_time <= candle_5s.end_time
]
if related_1s_candles:
expected_volume = sum(c.volume for c in related_1s_candles)
expected_trades = sum(c.trade_count for c in related_1s_candles)
assert candle_5s.volume >= expected_volume # May include partial data
assert candle_5s.trade_count >= expected_trades
class TestOKXDataProcessor:
"""Test OKX data processor integration."""
@pytest.fixture
def processor(self, candle_config, logger):
"""Create OKX data processor."""
return OKXDataProcessor(
symbol="BTC-USDT",
config=candle_config,
component_name="test_okx_processor",
logger=logger
)
def test_websocket_message_processing(self, processor, sample_trade_data):
"""Test WebSocket message processing."""
# Create a valid OKX WebSocket message
message = {
"arg": {
"channel": "trades",
"instId": "BTC-USDT"
},
"data": [sample_trade_data]
}
success, data_points, errors = processor.validate_and_process_message(message, "BTC-USDT")
assert success
assert len(data_points) == 1
assert len(errors) == 0
assert data_points[0].data_type == DataType.TRADE
assert data_points[0].symbol == "BTC-USDT"
def test_invalid_message_handling(self, processor):
"""Test handling of invalid messages."""
# Invalid message structure
invalid_message = {"invalid": "message"}
success, data_points, errors = processor.validate_and_process_message(invalid_message)
assert not success
assert len(data_points) == 0
assert len(errors) > 0
def test_trade_callback_execution(self, processor, sample_trade_data):
"""Test that trade callbacks are executed."""
callback_called = False
received_trade = None
def trade_callback(trade):
nonlocal callback_called, received_trade
callback_called = True
received_trade = trade
processor.add_trade_callback(trade_callback)
# Process trade message
message = {
"arg": {"channel": "trades", "instId": "BTC-USDT"},
"data": [sample_trade_data]
}
processor.validate_and_process_message(message, "BTC-USDT")
assert callback_called
assert received_trade is not None
assert isinstance(received_trade, StandardizedTrade)
def test_candle_callback_execution(self, processor, sample_trade_data):
"""Test that candle callbacks are executed when candles complete."""
callback_called = False
received_candle = None
def candle_callback(candle):
nonlocal callback_called, received_candle
callback_called = True
received_candle = candle
processor.add_candle_callback(candle_callback)
# Process multiple trades to complete a candle
base_time = int(datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc).timestamp() * 1000)
for i in range(2): # Two trades in different seconds
trade_data = sample_trade_data.copy()
trade_data['ts'] = str(base_time + i * 1000) # 1 second apart
trade_data['tradeId'] = str(i)
message = {
"arg": {"channel": "trades", "instId": "BTC-USDT"},
"data": [trade_data]
}
processor.validate_and_process_message(message, "BTC-USDT")
# May need to wait for candle completion
if callback_called:
assert received_candle is not None
assert isinstance(received_candle, OHLCVCandle)
class TestDataCollectionService:
"""Test the data collection service integration."""
@pytest.fixture
def service_config(self):
"""Create service configuration."""
return {
'exchanges': {
'okx': {
'enabled': True,
'symbols': ['BTC-USDT'],
'data_types': ['trade', 'ticker'],
'store_raw_data': False
}
},
'candle_config': {
'timeframes': ['1s', '1m'],
'auto_save_candles': False
}
}
@pytest.mark.asyncio
async def test_service_initialization(self, service_config, logger):
"""Test data collection service initialization."""
# Create a temporary config file for testing
import tempfile
import json
with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
# Convert our test config to match expected format
test_config = {
"exchange": "okx",
"connection": {
"public_ws_url": "wss://ws.okx.com:8443/ws/v5/public",
"ping_interval": 25.0,
"pong_timeout": 10.0,
"max_reconnect_attempts": 5,
"reconnect_delay": 5.0
},
"data_collection": {
"store_raw_data": False,
"health_check_interval": 120.0,
"auto_restart": True,
"buffer_size": 1000
},
"trading_pairs": [
{
"symbol": "BTC-USDT",
"enabled": True,
"data_types": ["trade", "ticker"],
"timeframes": ["1s", "1m"],
"channels": {
"trades": "trades",
"ticker": "tickers"
}
}
]
}
json.dump(test_config, f)
config_path = f.name
try:
service = DataCollectionService(config_path=config_path)
assert service.config_path == config_path
assert not service.running
# Check that the service loaded configuration
assert service.config is not None
assert 'exchange' in service.config
finally:
# Clean up temporary file
import os
os.unlink(config_path)
@pytest.mark.asyncio
async def test_service_lifecycle(self, service_config, logger):
"""Test service start/stop lifecycle."""
# Create a temporary config file for testing
import tempfile
import json
with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
# Convert our test config to match expected format
test_config = {
"exchange": "okx",
"connection": {
"public_ws_url": "wss://ws.okx.com:8443/ws/v5/public",
"ping_interval": 25.0,
"pong_timeout": 10.0,
"max_reconnect_attempts": 5,
"reconnect_delay": 5.0
},
"data_collection": {
"store_raw_data": False,
"health_check_interval": 120.0,
"auto_restart": True,
"buffer_size": 1000
},
"trading_pairs": [
{
"symbol": "BTC-USDT",
"enabled": True,
"data_types": ["trade", "ticker"],
"timeframes": ["1s", "1m"],
"channels": {
"trades": "trades",
"ticker": "tickers"
}
}
]
}
json.dump(test_config, f)
config_path = f.name
try:
service = DataCollectionService(config_path=config_path)
# Test initialization without actually starting collectors
# (to avoid network dependencies in unit tests)
assert not service.running
# Test status retrieval
status = service.get_status()
assert 'running' in status
assert 'collectors_total' in status
finally:
# Clean up temporary file
import os
os.unlink(config_path)
class TestErrorHandlingAndEdgeCases:
"""Test error handling and edge cases in data collection."""
def test_malformed_trade_data(self, logger):
"""Test handling of malformed trade data."""
validator = OKXDataValidator("test", logger)
malformed_data = {
"instId": "BTC-USDT",
"px": None, # Null price
"sz": "invalid_size",
"side": "invalid_side",
"ts": "not_a_timestamp"
}
result = validator.validate_trade_data(malformed_data)
assert not result.is_valid
assert len(result.errors) > 0
def test_empty_aggregation_data(self, candle_config, logger):
"""Test aggregation with no trade data."""
processor = RealTimeCandleProcessor(
symbol="BTC-USDT",
exchange="okx",
config=candle_config,
logger=logger
)
stats = processor.get_stats()
assert stats['trades_processed'] == 0
assert 'active_timeframes' in stats
assert isinstance(stats['active_timeframes'], list) # Should be a list, even if empty
assert stats['candles_emitted'] == 0
assert stats['errors_count'] == 0
def test_out_of_order_trades(self, candle_config, logger):
"""Test handling of out-of-order trade timestamps."""
processor = RealTimeCandleProcessor(
symbol="BTC-USDT",
exchange="okx",
config=candle_config,
logger=logger
)
base_time = datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
# Add trades in reverse chronological order
for i in range(3, 0, -1):
trade = StandardizedTrade(
symbol="BTC-USDT",
trade_id=str(i),
price=Decimal("50000"),
size=Decimal("0.1"),
side="buy",
timestamp=base_time + timedelta(seconds=i),
exchange="okx"
)
processor.process_trade(trade)
# Should handle gracefully without crashing
stats = processor.get_stats()
assert stats['trades_processed'] == 3
def test_extreme_price_values(self, logger):
"""Test handling of extreme price values."""
validator = OKXDataValidator("test", logger)
# Very large price
large_price_data = {
"instId": "BTC-USDT",
"tradeId": "123",
"px": "999999999999.99",
"sz": "0.1",
"side": "buy",
"ts": "1640995200000"
}
result = validator.validate_trade_data(large_price_data)
# Should handle large numbers gracefully
assert result.is_valid or "price" in str(result.errors)
# Very small price
small_price_data = large_price_data.copy()
small_price_data["px"] = "0.00000001"
result = validator.validate_trade_data(small_price_data)
assert result.is_valid or "price" in str(result.errors)
class TestPerformanceAndReliability:
"""Test performance and reliability aspects."""
def test_high_frequency_trade_processing(self, candle_config, logger):
"""Test processing high frequency of trades."""
processor = RealTimeCandleProcessor(
symbol="BTC-USDT",
exchange="okx",
config=candle_config,
logger=logger
)
base_time = datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
# Process 1000 trades rapidly
for i in range(1000):
trade = StandardizedTrade(
symbol="BTC-USDT",
trade_id=str(i),
price=Decimal("50000") + Decimal(str(i % 100)),
size=Decimal("0.001"),
side="buy" if i % 2 == 0 else "sell",
timestamp=base_time + timedelta(milliseconds=i),
exchange="okx"
)
processor.process_trade(trade)
stats = processor.get_stats()
assert stats['trades_processed'] == 1000
assert 'active_timeframes' in stats
assert len(stats['active_timeframes']) > 0
def test_memory_usage_with_long_running_aggregation(self, candle_config, logger):
"""Test memory usage doesn't grow unbounded."""
processor = RealTimeCandleProcessor(
symbol="BTC-USDT",
exchange="okx",
config=candle_config,
logger=logger
)
base_time = datetime(2022, 1, 1, 12, 0, 0, tzinfo=timezone.utc)
# Process trades over a long time period
for minute in range(10): # 10 minutes
for second in range(60): # 60 seconds per minute
trade = StandardizedTrade(
symbol="BTC-USDT",
trade_id=f"{minute}_{second}",
price=Decimal("50000"),
size=Decimal("0.1"),
side="buy",
timestamp=base_time + timedelta(minutes=minute, seconds=second),
exchange="okx"
)
processor.process_trade(trade)
stats = processor.get_stats()
# Should have processed many trades but not keep unlimited candles in memory
assert stats['trades_processed'] == 600 # 10 minutes * 60 seconds
assert 'active_timeframes' in stats
assert len(stats['active_timeframes']) == len(candle_config.timeframes)
if __name__ == "__main__":
pytest.main([__file__, "-v"])

121
tests/test_data_integration.py
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@ -1,121 +0,0 @@
import pytest
import pandas as pd
from unittest.mock import Mock, patch
from datetime import datetime
from components.charts.data_integration import MarketDataIntegrator
from components.charts.indicator_manager import IndicatorManager
from components.charts.layers.indicators import IndicatorLayerConfig
@pytest.fixture
def market_data_integrator_components():
"""Provides a complete setup for testing MarketDataIntegrator."""
# 1. Main DataFrame (e.g., 1h)
main_timestamps = pd.to_datetime(['2024-01-01 10:00', '2024-01-01 11:00', '2024-01-01 12:00', '2024-01-01 13:00'], utc=True)
main_df = pd.DataFrame({'close': [100, 102, 101, 103]}, index=main_timestamps)
# 2. Higher-timeframe DataFrame (e.g., 4h)
indicator_timestamps = pd.to_datetime(['2024-01-01 08:00', '2024-01-01 12:00'], utc=True)
indicator_df_raw = [{'timestamp': ts, 'close': val} for ts, val in zip(indicator_timestamps, [98, 101.5])]
# 3. Mock IndicatorManager and configs
indicator_manager = Mock(spec=IndicatorManager)
user_indicator = Mock()
user_indicator.id = 'rsi_4h'
user_indicator.name = 'RSI'
user_indicator.timeframe = '4h'
user_indicator.type = 'rsi'
user_indicator.parameters = {'period': 14}
indicator_manager.load_indicator.return_value = user_indicator
indicator_config = Mock(spec=IndicatorLayerConfig)
indicator_config.id = 'rsi_4h'
# 4. DataIntegrator instance
integrator = MarketDataIntegrator()
# 5. Mock internal fetching and calculation
# Mock get_market_data_for_indicators to return raw candles
integrator.get_market_data_for_indicators = Mock(return_value=(indicator_df_raw, []))
# Mock indicator calculation result
indicator_result_values = [{'timestamp': indicator_timestamps[1], 'rsi': 55.0}] # Only one valid point
indicator_pkg = {'data': [Mock(timestamp=r['timestamp'], values={'rsi': r['rsi']}) for r in indicator_result_values]}
integrator.indicators.calculate = Mock(return_value=indicator_pkg)
return integrator, main_df, indicator_config, indicator_manager, user_indicator
def test_multi_timeframe_alignment(market_data_integrator_components):
"""
Tests that indicator data from a higher timeframe is correctly aligned
with the main chart's data.
"""
integrator, main_df, indicator_config, indicator_manager, user_indicator = market_data_integrator_components
# Execute the method to test
indicator_data_map = integrator.get_indicator_data(
main_df=main_df,
main_timeframe='1h',
indicator_configs=[indicator_config],
indicator_manager=indicator_manager,
symbol='BTC-USDT'
)
# --- Assertions ---
assert user_indicator.id in indicator_data_map
aligned_data = indicator_data_map[user_indicator.id]
# Expected series after reindexing and forward-filling
expected_series = pd.Series(
[None, None, 55.0, 55.0],
index=main_df.index,
name='rsi'
)
result_series = aligned_data['rsi']
pd.testing.assert_series_equal(result_series, expected_series, check_index_type=False)
@patch('components.charts.utils.prepare_chart_data', lambda x: pd.DataFrame(x).set_index('timestamp'))
def test_no_custom_timeframe_uses_main_df(market_data_integrator_components):
"""
Tests that if an indicator has no custom timeframe, it uses the main
DataFrame for calculation.
"""
integrator, main_df, indicator_config, indicator_manager, user_indicator = market_data_integrator_components
# Override indicator to have no timeframe
user_indicator.timeframe = None
indicator_manager.load_indicator.return_value = user_indicator
# Mock calculation result on main_df
result_timestamps = main_df.index[1:]
indicator_result_values = [{'timestamp': ts, 'sma': val} for ts, val in zip(result_timestamps, [101.0, 101.5, 102.0])]
indicator_pkg = {'data': [Mock(timestamp=r['timestamp'], values={'sma': r['sma']}) for r in indicator_result_values]}
integrator.indicators.calculate = Mock(return_value=indicator_pkg)
# Execute
indicator_data_map = integrator.get_indicator_data(
main_df=main_df,
main_timeframe='1h',
indicator_configs=[indicator_config],
indicator_manager=indicator_manager,
symbol='BTC-USDT'
)
# Assert that get_market_data_for_indicators was NOT called
integrator.get_market_data_for_indicators.assert_not_called()
# Assert that calculate was called with main_df
integrator.indicators.calculate.assert_called_with('rsi', main_df, period=14)
# Assert the result is what we expect
assert user_indicator.id in indicator_data_map
result_series = indicator_data_map[user_indicator.id]['sma']
expected_series = pd.Series([101.0, 101.5, 102.0], index=result_timestamps, name='sma')
# Reindex expected to match the result's index for comparison
expected_series = expected_series.reindex(main_df.index)
pd.testing.assert_series_equal(result_series, expected_series, check_index_type=False)

188
tests/test_data_validation.py
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"""
Tests for data validation module.
This module provides comprehensive test coverage for the data validation utilities
and base validator class.
"""
import pytest
from datetime import datetime, timezone
from decimal import Decimal
from typing import Dict, Any
from data.common.validation import (
ValidationResult,
BaseDataValidator,
is_valid_decimal,
validate_required_fields
)
from data.common.data_types import DataValidationResult, StandardizedTrade, TradeSide
class TestValidationResult:
"""Test ValidationResult class."""
def test_init_with_defaults(self):
"""Test initialization with default values."""
result = ValidationResult(is_valid=True)
assert result.is_valid
assert result.errors == []
assert result.warnings == []
assert result.sanitized_data is None
def test_init_with_errors(self):
"""Test initialization with errors."""
errors = ["Error 1", "Error 2"]
result = ValidationResult(is_valid=False, errors=errors)
assert not result.is_valid
assert result.errors == errors
assert result.warnings == []
def test_init_with_warnings(self):
"""Test initialization with warnings."""
warnings = ["Warning 1"]
result = ValidationResult(is_valid=True, warnings=warnings)
assert result.is_valid
assert result.warnings == warnings
assert result.errors == []
def test_init_with_sanitized_data(self):
"""Test initialization with sanitized data."""
data = {"key": "value"}
result = ValidationResult(is_valid=True, sanitized_data=data)
assert result.sanitized_data == data
class MockDataValidator(BaseDataValidator):
"""Mock implementation of BaseDataValidator for testing."""
def validate_symbol_format(self, symbol: str) -> ValidationResult:
"""Mock implementation of validate_symbol_format."""
if not symbol or not isinstance(symbol, str):
return ValidationResult(False, errors=["Invalid symbol format"])
return ValidationResult(True)
def validate_websocket_message(self, message: Dict[str, Any]) -> DataValidationResult:
"""Mock implementation of validate_websocket_message."""
if not isinstance(message, dict):
return DataValidationResult(False, ["Invalid message format"], [])
return DataValidationResult(True, [], [])
class TestBaseDataValidator:
"""Test BaseDataValidator class."""
@pytest.fixture
def validator(self):
"""Create a mock validator instance."""
return MockDataValidator("test_exchange")
def test_validate_price(self, validator):
"""Test price validation."""
# Test valid price
result = validator.validate_price("123.45")
assert result.is_valid
assert result.sanitized_data == Decimal("123.45")
# Test invalid price
result = validator.validate_price("invalid")
assert not result.is_valid
assert "Invalid price value" in result.errors[0]
# Test price bounds
result = validator.validate_price("0.000000001") # Below min
assert result.is_valid # Still valid but with warning
assert "below minimum" in result.warnings[0]
def test_validate_size(self, validator):
"""Test size validation."""
# Test valid size
result = validator.validate_size("10.5")
assert result.is_valid
assert result.sanitized_data == Decimal("10.5")
# Test invalid size
result = validator.validate_size("-1")
assert not result.is_valid
assert "must be positive" in result.errors[0]
def test_validate_timestamp(self, validator):
"""Test timestamp validation."""
current_time = int(datetime.now(timezone.utc).timestamp() * 1000)
# Test valid timestamp
result = validator.validate_timestamp(current_time)
assert result.is_valid
# Test invalid timestamp
result = validator.validate_timestamp("invalid")
assert not result.is_valid
assert "Invalid timestamp format" in result.errors[0]
# Test old timestamp
old_timestamp = 999999999999 # Before min_timestamp
result = validator.validate_timestamp(old_timestamp)
assert not result.is_valid
assert "too old" in result.errors[0]
def test_validate_trade_side(self, validator):
"""Test trade side validation."""
# Test valid sides
assert validator.validate_trade_side("buy").is_valid
assert validator.validate_trade_side("sell").is_valid
# Test invalid sides
result = validator.validate_trade_side("invalid")
assert not result.is_valid
assert "Must be 'buy' or 'sell'" in result.errors[0]
def test_validate_trade_id(self, validator):
"""Test trade ID validation."""
# Test valid trade IDs
assert validator.validate_trade_id("trade123").is_valid
assert validator.validate_trade_id("123").is_valid
assert validator.validate_trade_id("trade-123_abc").is_valid
# Test invalid trade IDs
result = validator.validate_trade_id("")
assert not result.is_valid
assert "cannot be empty" in result.errors[0]
def test_validate_symbol_match(self, validator):
"""Test symbol matching validation."""
# Test basic symbol validation
assert validator.validate_symbol_match("BTC-USD").is_valid
# Test symbol mismatch
result = validator.validate_symbol_match("BTC-USD", "ETH-USD")
assert result.is_valid # Still valid but with warning
assert "mismatch" in result.warnings[0]
# Test invalid symbol type
result = validator.validate_symbol_match(123)
assert not result.is_valid
assert "must be string" in result.errors[0]
def test_is_valid_decimal():
"""Test is_valid_decimal utility function."""
# Test valid decimals
assert is_valid_decimal("123.45")
assert is_valid_decimal(123.45)
assert is_valid_decimal(Decimal("123.45"))
# Test invalid decimals
assert not is_valid_decimal("invalid")
assert not is_valid_decimal(None)
assert not is_valid_decimal("")
def test_validate_required_fields():
"""Test validate_required_fields utility function."""
data = {"field1": "value1", "field2": None, "field3": "value3"}
required = ["field1", "field2", "field4"]
missing = validate_required_fields(data, required)
assert "field2" in missing # None value
assert "field4" in missing # Missing field
assert "field1" not in missing # Present field

366
tests/test_defaults.py
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"""
Tests for Default Indicator Configurations System
Tests the comprehensive default indicator configurations, categories,
trading strategies, and preset management functionality.
"""
import pytest
from typing import Dict, Any
from components.charts.config.defaults import (
IndicatorCategory,
TradingStrategy,
IndicatorPreset,
CATEGORY_COLORS,
create_trend_indicators,
create_momentum_indicators,
create_volatility_indicators,
create_strategy_presets,
get_all_default_indicators,
get_indicators_by_category,
get_indicators_for_timeframe,
get_strategy_indicators,
get_strategy_info,
get_available_strategies,
get_available_categories,
create_custom_preset
)
from components.charts.config.indicator_defs import (
ChartIndicatorConfig,
validate_indicator_configuration
)
class TestIndicatorCategories:
"""Test indicator category functionality."""
def test_trend_indicators_creation(self):
"""Test creation of trend indicators."""
trend_indicators = create_trend_indicators()
# Should have multiple SMA and EMA configurations
assert len(trend_indicators) > 10
# Check specific indicators exist
assert "sma_20" in trend_indicators
assert "sma_50" in trend_indicators
assert "ema_12" in trend_indicators
assert "ema_26" in trend_indicators
# Validate all configurations
for name, preset in trend_indicators.items():
assert isinstance(preset, IndicatorPreset)
assert preset.category == IndicatorCategory.TREND
# Validate the actual configuration
is_valid, errors = validate_indicator_configuration(preset.config)
assert is_valid, f"Invalid trend indicator {name}: {errors}"
def test_momentum_indicators_creation(self):
"""Test creation of momentum indicators."""
momentum_indicators = create_momentum_indicators()
# Should have multiple RSI and MACD configurations
assert len(momentum_indicators) > 8
# Check specific indicators exist
assert "rsi_14" in momentum_indicators
assert "macd_12_26_9" in momentum_indicators
# Validate all configurations
for name, preset in momentum_indicators.items():
assert isinstance(preset, IndicatorPreset)
assert preset.category == IndicatorCategory.MOMENTUM
is_valid, errors = validate_indicator_configuration(preset.config)
assert is_valid, f"Invalid momentum indicator {name}: {errors}"
def test_volatility_indicators_creation(self):
"""Test creation of volatility indicators."""
volatility_indicators = create_volatility_indicators()
# Should have multiple Bollinger Bands configurations
assert len(volatility_indicators) > 3
# Check specific indicators exist
assert "bb_20_20" in volatility_indicators
# Validate all configurations
for name, preset in volatility_indicators.items():
assert isinstance(preset, IndicatorPreset)
assert preset.category == IndicatorCategory.VOLATILITY
is_valid, errors = validate_indicator_configuration(preset.config)
assert is_valid, f"Invalid volatility indicator {name}: {errors}"
class TestStrategyPresets:
"""Test trading strategy preset functionality."""
def test_strategy_presets_creation(self):
"""Test creation of strategy presets."""
strategy_presets = create_strategy_presets()
# Should have all strategy types
expected_strategies = [strategy.value for strategy in TradingStrategy]
for strategy in expected_strategies:
assert strategy in strategy_presets
preset = strategy_presets[strategy]
assert "name" in preset
assert "description" in preset
assert "timeframes" in preset
assert "indicators" in preset
assert len(preset["indicators"]) > 0
def test_get_strategy_indicators(self):
"""Test getting indicators for specific strategies."""
scalping_indicators = get_strategy_indicators(TradingStrategy.SCALPING)
assert len(scalping_indicators) > 0
assert "ema_5" in scalping_indicators
assert "rsi_7" in scalping_indicators
day_trading_indicators = get_strategy_indicators(TradingStrategy.DAY_TRADING)
assert len(day_trading_indicators) > 0
assert "sma_20" in day_trading_indicators
assert "rsi_14" in day_trading_indicators
def test_get_strategy_info(self):
"""Test getting complete strategy information."""
scalping_info = get_strategy_info(TradingStrategy.SCALPING)
assert "name" in scalping_info
assert "description" in scalping_info
assert "timeframes" in scalping_info
assert "indicators" in scalping_info
assert "1m" in scalping_info["timeframes"]
assert "5m" in scalping_info["timeframes"]
class TestDefaultIndicators:
"""Test default indicator functionality."""
def test_get_all_default_indicators(self):
"""Test getting all default indicators."""
all_indicators = get_all_default_indicators()
# Should have indicators from all categories
assert len(all_indicators) > 20
# Validate all indicators
for name, preset in all_indicators.items():
assert isinstance(preset, IndicatorPreset)
assert preset.category in [cat for cat in IndicatorCategory]
is_valid, errors = validate_indicator_configuration(preset.config)
assert is_valid, f"Invalid default indicator {name}: {errors}"
def test_get_indicators_by_category(self):
"""Test filtering indicators by category."""
trend_indicators = get_indicators_by_category(IndicatorCategory.TREND)
momentum_indicators = get_indicators_by_category(IndicatorCategory.MOMENTUM)
volatility_indicators = get_indicators_by_category(IndicatorCategory.VOLATILITY)
# All should have indicators
assert len(trend_indicators) > 0
assert len(momentum_indicators) > 0
assert len(volatility_indicators) > 0
# Check categories are correct
for preset in trend_indicators.values():
assert preset.category == IndicatorCategory.TREND
for preset in momentum_indicators.values():
assert preset.category == IndicatorCategory.MOMENTUM
for preset in volatility_indicators.values():
assert preset.category == IndicatorCategory.VOLATILITY
def test_get_indicators_for_timeframe(self):
"""Test filtering indicators by timeframe."""
scalping_indicators = get_indicators_for_timeframe("1m")
day_trading_indicators = get_indicators_for_timeframe("1h")
position_indicators = get_indicators_for_timeframe("1d")
# All should have some indicators
assert len(scalping_indicators) > 0
assert len(day_trading_indicators) > 0
assert len(position_indicators) > 0
# Check timeframes are included
for preset in scalping_indicators.values():
assert "1m" in preset.recommended_timeframes
for preset in day_trading_indicators.values():
assert "1h" in preset.recommended_timeframes
class TestUtilityFunctions:
"""Test utility functions for defaults system."""
def test_get_available_strategies(self):
"""Test getting available trading strategies."""
strategies = get_available_strategies()
# Should have all strategy types
assert len(strategies) == len(TradingStrategy)
for strategy in strategies:
assert "value" in strategy
assert "name" in strategy
assert "description" in strategy
assert "timeframes" in strategy
def test_get_available_categories(self):
"""Test getting available indicator categories."""
categories = get_available_categories()
# Should have all category types
assert len(categories) == len(IndicatorCategory)
for category in categories:
assert "value" in category
assert "name" in category
assert "description" in category
def test_create_custom_preset(self):
"""Test creating custom indicator presets."""
custom_configs = [
{
"name": "Custom SMA",
"indicator_type": "sma",
"parameters": {"period": 15},
"color": "#123456"
},
{
"name": "Custom RSI",
"indicator_type": "rsi",
"parameters": {"period": 10},
"color": "#654321"
}
]
custom_presets = create_custom_preset(
name="Test Custom",
description="Test custom preset",
category=IndicatorCategory.TREND,
indicator_configs=custom_configs,
recommended_timeframes=["5m", "15m"]
)
# Should create presets for valid configurations
assert len(custom_presets) == 2
for preset in custom_presets.values():
assert preset.category == IndicatorCategory.TREND
assert "5m" in preset.recommended_timeframes
assert "15m" in preset.recommended_timeframes
class TestColorSchemes:
"""Test color scheme functionality."""
def test_category_colors_exist(self):
"""Test that color schemes exist for categories."""
required_categories = [
IndicatorCategory.TREND,
IndicatorCategory.MOMENTUM,
IndicatorCategory.VOLATILITY
]
for category in required_categories:
assert category in CATEGORY_COLORS
colors = CATEGORY_COLORS[category]
# Should have multiple color options
assert "primary" in colors
assert "secondary" in colors
assert "tertiary" in colors
assert "quaternary" in colors
# Colors should be valid hex codes
for color_name, color_value in colors.items():
assert color_value.startswith("#")
assert len(color_value) == 7
class TestIntegration:
"""Test integration with existing systems."""
def test_default_indicators_match_schema(self):
"""Test that default indicators match their schemas."""
all_indicators = get_all_default_indicators()
for name, preset in all_indicators.items():
config = preset.config
# Should validate against schema
is_valid, errors = validate_indicator_configuration(config)
assert is_valid, f"Default indicator {name} validation failed: {errors}"
def test_strategy_indicators_exist_in_defaults(self):
"""Test that strategy indicators exist in default configurations."""
all_indicators = get_all_default_indicators()
for strategy in TradingStrategy:
strategy_indicators = get_strategy_indicators(strategy)
for indicator_name in strategy_indicators:
# Each strategy indicator should exist in defaults
# Note: Some might not exist yet, but most should
if indicator_name in all_indicators:
preset = all_indicators[indicator_name]
assert isinstance(preset, IndicatorPreset)
def test_timeframe_recommendations_valid(self):
"""Test that timeframe recommendations are valid."""
all_indicators = get_all_default_indicators()
valid_timeframes = ["1m", "5m", "15m", "1h", "4h", "1d", "1w"]
for name, preset in all_indicators.items():
for timeframe in preset.recommended_timeframes:
assert timeframe in valid_timeframes, f"Invalid timeframe {timeframe} for {name}"
class TestPresetValidation:
"""Test that all presets are properly validated."""
def test_all_trend_indicators_valid(self):
"""Test that all trend indicators are valid."""
trend_indicators = create_trend_indicators()
for name, preset in trend_indicators.items():
# Test the preset structure
assert isinstance(preset.name, str)
assert isinstance(preset.description, str)
assert preset.category == IndicatorCategory.TREND
assert isinstance(preset.recommended_timeframes, list)
assert len(preset.recommended_timeframes) > 0
# Test the configuration
config = preset.config
is_valid, errors = validate_indicator_configuration(config)
assert is_valid, f"Trend indicator {name} failed validation: {errors}"
def test_all_momentum_indicators_valid(self):
"""Test that all momentum indicators are valid."""
momentum_indicators = create_momentum_indicators()
for name, preset in momentum_indicators.items():
config = preset.config
is_valid, errors = validate_indicator_configuration(config)
assert is_valid, f"Momentum indicator {name} failed validation: {errors}"
def test_all_volatility_indicators_valid(self):
"""Test that all volatility indicators are valid."""
volatility_indicators = create_volatility_indicators()
for name, preset in volatility_indicators.items():
config = preset.config
is_valid, errors = validate_indicator_configuration(config)
assert is_valid, f"Volatility indicator {name} failed validation: {errors}"
if __name__ == "__main__":
pytest.main([__file__])

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tests/test_error_handling.py
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@ -1,570 +0,0 @@
"""
Tests for Enhanced Error Handling and User Guidance System
Tests the comprehensive error handling system including error detection,
suggestions, recovery guidance, and configuration validation.
"""
import pytest
from typing import Set, List
from components.charts.config.error_handling import (
ErrorSeverity,
ErrorCategory,
ConfigurationError,
ErrorReport,
ConfigurationErrorHandler,
validate_configuration_strict,
validate_strategy_name,
get_indicator_suggestions,
get_strategy_suggestions,
check_configuration_health
)
from components.charts.config.strategy_charts import (
StrategyChartConfig,
SubplotConfig,
ChartStyle,
ChartLayout,
SubplotType
)
from components.charts.config.defaults import TradingStrategy
class TestConfigurationError:
"""Test ConfigurationError class."""
def test_configuration_error_creation(self):
"""Test ConfigurationError creation with all fields."""
error = ConfigurationError(
category=ErrorCategory.MISSING_INDICATOR,
severity=ErrorSeverity.HIGH,
message="Test error message",
field_path="overlay_indicators[ema_99]",
missing_item="ema_99",
suggestions=["Use ema_12 instead", "Try different period"],
alternatives=["ema_12", "ema_26"],
recovery_steps=["Replace with ema_12", "Check available indicators"]
)
assert error.category == ErrorCategory.MISSING_INDICATOR
assert error.severity == ErrorSeverity.HIGH
assert error.message == "Test error message"
assert error.field_path == "overlay_indicators[ema_99]"
assert error.missing_item == "ema_99"
assert len(error.suggestions) == 2
assert len(error.alternatives) == 2
assert len(error.recovery_steps) == 2
def test_configuration_error_string_representation(self):
"""Test string representation with emojis and formatting."""
error = ConfigurationError(
category=ErrorCategory.MISSING_INDICATOR,
severity=ErrorSeverity.CRITICAL,
message="Indicator 'ema_99' not found",
suggestions=["Use ema_12"],
alternatives=["ema_12", "ema_26"],
recovery_steps=["Replace with available indicator"]
)
error_str = str(error)
assert "🚨" in error_str # Critical severity emoji
assert "Indicator 'ema_99' not found" in error_str
assert "💡 Suggestions:" in error_str
assert "🔄 Alternatives:" in error_str
assert "🔧 Recovery steps:" in error_str
class TestErrorReport:
"""Test ErrorReport class."""
def test_error_report_creation(self):
"""Test ErrorReport creation and basic functionality."""
report = ErrorReport(is_usable=True)
assert report.is_usable is True
assert len(report.errors) == 0
assert len(report.missing_strategies) == 0
assert len(report.missing_indicators) == 0
assert report.report_time is not None
def test_add_error_updates_usability(self):
"""Test that adding critical/high errors updates usability."""
report = ErrorReport(is_usable=True)
# Add medium error - should remain usable
medium_error = ConfigurationError(
category=ErrorCategory.INVALID_PARAMETER,
severity=ErrorSeverity.MEDIUM,
message="Medium error"
)
report.add_error(medium_error)
assert report.is_usable is True
# Add critical error - should become unusable
critical_error = ConfigurationError(
category=ErrorCategory.MISSING_STRATEGY,
severity=ErrorSeverity.CRITICAL,
message="Critical error",
missing_item="test_strategy"
)
report.add_error(critical_error)
assert report.is_usable is False
assert "test_strategy" in report.missing_strategies
def test_add_missing_indicator_tracking(self):
"""Test tracking of missing indicators."""
report = ErrorReport(is_usable=True)
error = ConfigurationError(
category=ErrorCategory.MISSING_INDICATOR,
severity=ErrorSeverity.HIGH,
message="Indicator missing",
missing_item="ema_99"
)
report.add_error(error)
assert "ema_99" in report.missing_indicators
assert report.is_usable is False # High severity
def test_get_critical_and_high_priority_errors(self):
"""Test filtering errors by severity."""
report = ErrorReport(is_usable=True)
# Add different severity errors
report.add_error(ConfigurationError(
category=ErrorCategory.MISSING_INDICATOR,
severity=ErrorSeverity.CRITICAL,
message="Critical error"
))
report.add_error(ConfigurationError(
category=ErrorCategory.MISSING_INDICATOR,
severity=ErrorSeverity.HIGH,
message="High error"
))
report.add_error(ConfigurationError(
category=ErrorCategory.INVALID_PARAMETER,
severity=ErrorSeverity.MEDIUM,
message="Medium error"
))
critical_errors = report.get_critical_errors()
high_errors = report.get_high_priority_errors()
assert len(critical_errors) == 1
assert len(high_errors) == 1
assert critical_errors[0].message == "Critical error"
assert high_errors[0].message == "High error"
def test_summary_generation(self):
"""Test error report summary."""
# Empty report
empty_report = ErrorReport(is_usable=True)
assert "✅ No configuration errors found" in empty_report.summary()
# Report with errors
report = ErrorReport(is_usable=False)
report.add_error(ConfigurationError(
category=ErrorCategory.MISSING_INDICATOR,
severity=ErrorSeverity.CRITICAL,
message="Critical error"
))
report.add_error(ConfigurationError(
category=ErrorCategory.INVALID_PARAMETER,
severity=ErrorSeverity.MEDIUM,
message="Medium error"
))
summary = report.summary()
assert "❌ Cannot proceed" in summary
assert "2 errors" in summary
assert "1 critical" in summary
class TestConfigurationErrorHandler:
"""Test ConfigurationErrorHandler class."""
def test_handler_initialization(self):
"""Test error handler initialization."""
handler = ConfigurationErrorHandler()
assert len(handler.indicator_names) > 0
assert len(handler.strategy_names) > 0
assert "ema_12" in handler.indicator_names
assert "ema_crossover" in handler.strategy_names
def test_validate_existing_strategy(self):
"""Test validation of existing strategy."""
handler = ConfigurationErrorHandler()
# Test existing strategy
error = handler.validate_strategy_exists("ema_crossover")
assert error is None
def test_validate_missing_strategy(self):
"""Test validation of missing strategy with suggestions."""
handler = ConfigurationErrorHandler()
# Test missing strategy
error = handler.validate_strategy_exists("non_existent_strategy")
assert error is not None
assert error.category == ErrorCategory.MISSING_STRATEGY
assert error.severity == ErrorSeverity.CRITICAL
assert "non_existent_strategy" in error.message
assert len(error.recovery_steps) > 0
def test_validate_similar_strategy_name(self):
"""Test suggestions for similar strategy names."""
handler = ConfigurationErrorHandler()
# Test typo in strategy name
error = handler.validate_strategy_exists("ema_cross") # Similar to "ema_crossover"
assert error is not None
assert len(error.alternatives) > 0
assert "ema_crossover" in error.alternatives or any("ema" in alt for alt in error.alternatives)
def test_validate_existing_indicator(self):
"""Test validation of existing indicator."""
handler = ConfigurationErrorHandler()
# Test existing indicator
error = handler.validate_indicator_exists("ema_12")
assert error is None
def test_validate_missing_indicator(self):
"""Test validation of missing indicator with suggestions."""
handler = ConfigurationErrorHandler()
# Test missing indicator
error = handler.validate_indicator_exists("ema_999")
assert error is not None
assert error.category == ErrorCategory.MISSING_INDICATOR
assert error.severity in [ErrorSeverity.CRITICAL, ErrorSeverity.HIGH]
assert "ema_999" in error.message
assert len(error.recovery_steps) > 0
def test_indicator_category_suggestions(self):
"""Test category-based suggestions for missing indicators."""
handler = ConfigurationErrorHandler()
# Test SMA suggestion
sma_error = handler.validate_indicator_exists("sma_999")
assert sma_error is not None
# Check for SMA-related suggestions in any form
assert any("sma" in suggestion.lower() or "trend" in suggestion.lower()
for suggestion in sma_error.suggestions)
# Test RSI suggestion
rsi_error = handler.validate_indicator_exists("rsi_999")
assert rsi_error is not None
# Check that RSI alternatives contain actual RSI indicators
assert any("rsi_" in alternative for alternative in rsi_error.alternatives)
# Test MACD suggestion
macd_error = handler.validate_indicator_exists("macd_999")
assert macd_error is not None
# Check that MACD alternatives contain actual MACD indicators
assert any("macd_" in alternative for alternative in macd_error.alternatives)
def test_validate_strategy_configuration_empty(self):
"""Test validation of empty configuration."""
handler = ConfigurationErrorHandler()
# Empty configuration
config = StrategyChartConfig(
strategy_name="Empty Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Empty strategy",
timeframes=["1h"],
overlay_indicators=[],
subplot_configs=[]
)
report = handler.validate_strategy_configuration(config)
assert not report.is_usable
assert len(report.errors) > 0
assert any(error.category == ErrorCategory.CONFIGURATION_CORRUPT
for error in report.errors)
def test_validate_strategy_configuration_with_missing_indicators(self):
"""Test validation with missing indicators."""
handler = ConfigurationErrorHandler()
config = StrategyChartConfig(
strategy_name="Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Test strategy",
timeframes=["1h"],
overlay_indicators=["ema_999", "sma_888"], # Missing indicators
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
indicators=["rsi_777"] # Missing indicator
)
]
)
report = handler.validate_strategy_configuration(config)
assert not report.is_usable
assert len(report.missing_indicators) == 3
assert "ema_999" in report.missing_indicators
assert "sma_888" in report.missing_indicators
assert "rsi_777" in report.missing_indicators
def test_strategy_consistency_validation(self):
"""Test strategy type consistency validation."""
handler = ConfigurationErrorHandler()
# Scalping strategy with wrong timeframes
config = StrategyChartConfig(
strategy_name="Scalping Strategy",
strategy_type=TradingStrategy.SCALPING,
description="Scalping strategy",
timeframes=["1d", "1w"], # Wrong for scalping
overlay_indicators=["ema_12"]
)
report = handler.validate_strategy_configuration(config)
# Should have consistency warning
consistency_errors = [e for e in report.errors
if e.category == ErrorCategory.INVALID_PARAMETER]
assert len(consistency_errors) > 0
def test_suggest_alternatives_for_missing_indicators(self):
"""Test alternative suggestions for missing indicators."""
handler = ConfigurationErrorHandler()
missing_indicators = {"ema_999", "rsi_777", "unknown_indicator"}
suggestions = handler.suggest_alternatives_for_missing_indicators(missing_indicators)
assert "ema_999" in suggestions
assert "rsi_777" in suggestions
# Should have EMA alternatives for ema_999
assert any("ema_" in alt for alt in suggestions.get("ema_999", []))
# Should have RSI alternatives for rsi_777
assert any("rsi_" in alt for alt in suggestions.get("rsi_777", []))
class TestUtilityFunctions:
"""Test utility functions."""
def test_validate_configuration_strict(self):
"""Test strict configuration validation."""
# Valid configuration
valid_config = StrategyChartConfig(
strategy_name="Valid Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Valid strategy",
timeframes=["1h"],
overlay_indicators=["ema_12", "sma_20"]
)
report = validate_configuration_strict(valid_config)
assert report.is_usable
# Invalid configuration
invalid_config = StrategyChartConfig(
strategy_name="Invalid Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Invalid strategy",
timeframes=["1h"],
overlay_indicators=["ema_999"] # Missing indicator
)
report = validate_configuration_strict(invalid_config)
assert not report.is_usable
assert len(report.missing_indicators) > 0
def test_validate_strategy_name_function(self):
"""Test strategy name validation function."""
# Valid strategy
error = validate_strategy_name("ema_crossover")
assert error is None
# Invalid strategy
error = validate_strategy_name("non_existent_strategy")
assert error is not None
assert error.category == ErrorCategory.MISSING_STRATEGY
def test_get_indicator_suggestions(self):
"""Test indicator suggestions."""
# Test exact match suggestions
suggestions = get_indicator_suggestions("ema")
assert len(suggestions) > 0
assert any("ema_" in suggestion for suggestion in suggestions)
# Test partial match
suggestions = get_indicator_suggestions("ema_1")
assert len(suggestions) > 0
# Test no match
suggestions = get_indicator_suggestions("xyz_999")
# Should return some suggestions even for no match
assert isinstance(suggestions, list)
def test_get_strategy_suggestions(self):
"""Test strategy suggestions."""
# Test exact match suggestions
suggestions = get_strategy_suggestions("ema")
assert len(suggestions) > 0
# Test partial match
suggestions = get_strategy_suggestions("cross")
assert len(suggestions) > 0
# Test no match
suggestions = get_strategy_suggestions("xyz_999")
assert isinstance(suggestions, list)
def test_check_configuration_health(self):
"""Test configuration health check."""
# Healthy configuration
healthy_config = StrategyChartConfig(
strategy_name="Healthy Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Healthy strategy",
timeframes=["1h"],
overlay_indicators=["ema_12", "sma_20"],
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
indicators=["rsi_14"]
)
]
)
health = check_configuration_health(healthy_config)
assert "is_healthy" in health
assert "error_report" in health
assert "total_indicators" in health
assert "has_trend_indicators" in health
assert "has_momentum_indicators" in health
assert "recommendations" in health
assert health["total_indicators"] == 3
assert health["has_trend_indicators"] is True
assert health["has_momentum_indicators"] is True
# Unhealthy configuration
unhealthy_config = StrategyChartConfig(
strategy_name="Unhealthy Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Unhealthy strategy",
timeframes=["1h"],
overlay_indicators=["ema_999"] # Missing indicator
)
health = check_configuration_health(unhealthy_config)
assert health["is_healthy"] is False
assert health["missing_indicators"] > 0
assert len(health["recommendations"]) > 0
class TestErrorSeverityAndCategories:
"""Test error severity and category enums."""
def test_error_severity_values(self):
"""Test ErrorSeverity enum values."""
assert ErrorSeverity.CRITICAL == "critical"
assert ErrorSeverity.HIGH == "high"
assert ErrorSeverity.MEDIUM == "medium"
assert ErrorSeverity.LOW == "low"
def test_error_category_values(self):
"""Test ErrorCategory enum values."""
assert ErrorCategory.MISSING_STRATEGY == "missing_strategy"
assert ErrorCategory.MISSING_INDICATOR == "missing_indicator"
assert ErrorCategory.INVALID_PARAMETER == "invalid_parameter"
assert ErrorCategory.DEPENDENCY_MISSING == "dependency_missing"
assert ErrorCategory.CONFIGURATION_CORRUPT == "configuration_corrupt"
class TestRecoveryGeneration:
"""Test recovery configuration generation."""
def test_recovery_configuration_generation(self):
"""Test generating recovery configurations."""
handler = ConfigurationErrorHandler()
# Configuration with missing indicators
config = StrategyChartConfig(
strategy_name="Broken Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Strategy with missing indicators",
timeframes=["1h"],
overlay_indicators=["ema_999", "ema_12"], # One missing, one valid
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
indicators=["rsi_777"] # Missing
)
]
)
# Validate to get error report
error_report = handler.validate_strategy_configuration(config)
# Generate recovery
recovery_config, recovery_notes = handler.generate_recovery_configuration(config, error_report)
assert recovery_config is not None
assert len(recovery_notes) > 0
assert "(Recovery)" in recovery_config.strategy_name
# Should have valid indicators only
for indicator in recovery_config.overlay_indicators:
assert indicator in handler.indicator_names
for subplot in recovery_config.subplot_configs:
for indicator in subplot.indicators:
assert indicator in handler.indicator_names
class TestIntegrationWithExistingSystems:
"""Test integration with existing validation and configuration systems."""
def test_integration_with_strategy_validation(self):
"""Test integration with existing strategy validation."""
from components.charts.config import create_ema_crossover_strategy
# Get a known good strategy
strategy = create_ema_crossover_strategy()
config = strategy.config
# Test with error handler
report = validate_configuration_strict(config)
# Should be usable (might have warnings about missing indicators in test environment)
assert isinstance(report, ErrorReport)
assert hasattr(report, 'is_usable')
assert hasattr(report, 'errors')
def test_error_handling_with_custom_configuration(self):
"""Test error handling with custom configurations."""
from components.charts.config import create_custom_strategy_config
# Try to create config with missing indicators
config, errors = create_custom_strategy_config(
strategy_name="Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Test strategy",
timeframes=["1h"],
overlay_indicators=["ema_999"], # Missing indicator
subplot_configs=[{
"subplot_type": "rsi",
"height_ratio": 0.2,
"indicators": ["rsi_777"] # Missing indicator
}]
)
if config: # If config was created despite missing indicators
report = validate_configuration_strict(config)
assert not report.is_usable
assert len(report.missing_indicators) > 0
if __name__ == "__main__":
pytest.main([__file__])

537
tests/test_example_strategies.py
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@ -1,537 +0,0 @@
"""
Tests for Example Strategy Configurations
Tests the example trading strategies including EMA crossover, momentum,
mean reversion, scalping, and swing trading strategies.
"""
import pytest
import json
from typing import Dict, List
from components.charts.config.example_strategies import (
StrategyExample,
create_ema_crossover_strategy,
create_momentum_breakout_strategy,
create_mean_reversion_strategy,
create_scalping_strategy,
create_swing_trading_strategy,
get_all_example_strategies,
get_example_strategy,
get_strategies_by_difficulty,
get_strategies_by_risk_level,
get_strategies_by_market_condition,
get_strategy_summary,
export_example_strategies_to_json
)
from components.charts.config.strategy_charts import StrategyChartConfig
from components.charts.config.defaults import TradingStrategy
class TestStrategyExample:
"""Test StrategyExample dataclass."""
def test_strategy_example_creation(self):
"""Test StrategyExample creation with defaults."""
# Create a minimal config for testing
config = StrategyChartConfig(
strategy_name="Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Test strategy",
timeframes=["1h"]
)
example = StrategyExample(
config=config,
description="Test description"
)
assert example.config == config
assert example.description == "Test description"
assert example.author == "TCPDashboard"
assert example.difficulty == "Beginner"
assert example.risk_level == "Medium"
assert example.market_conditions == ["Trending"] # Default
assert example.notes == [] # Default
assert example.references == [] # Default
def test_strategy_example_with_custom_values(self):
"""Test StrategyExample with custom values."""
config = StrategyChartConfig(
strategy_name="Custom Strategy",
strategy_type=TradingStrategy.SCALPING,
description="Custom strategy",
timeframes=["1m"]
)
example = StrategyExample(
config=config,
description="Custom description",
author="Custom Author",
difficulty="Advanced",
expected_return="10% monthly",
risk_level="High",
market_conditions=["Volatile", "High Volume"],
notes=["Note 1", "Note 2"],
references=["Reference 1"]
)
assert example.author == "Custom Author"
assert example.difficulty == "Advanced"
assert example.expected_return == "10% monthly"
assert example.risk_level == "High"
assert example.market_conditions == ["Volatile", "High Volume"]
assert example.notes == ["Note 1", "Note 2"]
assert example.references == ["Reference 1"]
class TestEMACrossoverStrategy:
"""Test EMA Crossover strategy."""
def test_ema_crossover_creation(self):
"""Test EMA crossover strategy creation."""
strategy = create_ema_crossover_strategy()
assert isinstance(strategy, StrategyExample)
assert isinstance(strategy.config, StrategyChartConfig)
# Check strategy specifics
assert strategy.config.strategy_name == "EMA Crossover Strategy"
assert strategy.config.strategy_type == TradingStrategy.DAY_TRADING
assert "15m" in strategy.config.timeframes
assert "1h" in strategy.config.timeframes
assert "4h" in strategy.config.timeframes
# Check indicators
assert "ema_12" in strategy.config.overlay_indicators
assert "ema_26" in strategy.config.overlay_indicators
assert "ema_50" in strategy.config.overlay_indicators
assert "bb_20_20" in strategy.config.overlay_indicators
# Check subplots
assert len(strategy.config.subplot_configs) == 2
assert any(subplot.subplot_type.value == "rsi" for subplot in strategy.config.subplot_configs)
assert any(subplot.subplot_type.value == "macd" for subplot in strategy.config.subplot_configs)
# Check metadata
assert strategy.difficulty == "Intermediate"
assert strategy.risk_level == "Medium"
assert "Trending" in strategy.market_conditions
assert len(strategy.notes) > 0
assert len(strategy.references) > 0
def test_ema_crossover_validation(self):
"""Test EMA crossover strategy validation."""
strategy = create_ema_crossover_strategy()
is_valid, errors = strategy.config.validate()
# Strategy should be valid or have minimal issues
assert isinstance(is_valid, bool)
assert isinstance(errors, list)
class TestMomentumBreakoutStrategy:
"""Test Momentum Breakout strategy."""
def test_momentum_breakout_creation(self):
"""Test momentum breakout strategy creation."""
strategy = create_momentum_breakout_strategy()
assert isinstance(strategy, StrategyExample)
assert strategy.config.strategy_name == "Momentum Breakout Strategy"
assert strategy.config.strategy_type == TradingStrategy.MOMENTUM
# Check for momentum-specific indicators
assert "ema_8" in strategy.config.overlay_indicators
assert "ema_21" in strategy.config.overlay_indicators
assert "bb_20_25" in strategy.config.overlay_indicators
# Check for fast indicators
rsi_subplot = next((s for s in strategy.config.subplot_configs if s.subplot_type.value == "rsi"), None)
assert rsi_subplot is not None
assert "rsi_7" in rsi_subplot.indicators
assert "rsi_14" in rsi_subplot.indicators
# Check volume subplot
volume_subplot = next((s for s in strategy.config.subplot_configs if s.subplot_type.value == "volume"), None)
assert volume_subplot is not None
# Check metadata
assert strategy.difficulty == "Advanced"
assert strategy.risk_level == "High"
assert "Volatile" in strategy.market_conditions
class TestMeanReversionStrategy:
"""Test Mean Reversion strategy."""
def test_mean_reversion_creation(self):
"""Test mean reversion strategy creation."""
strategy = create_mean_reversion_strategy()
assert isinstance(strategy, StrategyExample)
assert strategy.config.strategy_name == "Mean Reversion Strategy"
assert strategy.config.strategy_type == TradingStrategy.MEAN_REVERSION
# Check for mean reversion indicators
assert "sma_20" in strategy.config.overlay_indicators
assert "sma_50" in strategy.config.overlay_indicators
assert "bb_20_20" in strategy.config.overlay_indicators
assert "bb_20_15" in strategy.config.overlay_indicators
# Check RSI configurations
rsi_subplot = next((s for s in strategy.config.subplot_configs if s.subplot_type.value == "rsi"), None)
assert rsi_subplot is not None
assert "rsi_14" in rsi_subplot.indicators
assert "rsi_21" in rsi_subplot.indicators
# Check metadata
assert strategy.difficulty == "Intermediate"
assert strategy.risk_level == "Medium"
assert "Sideways" in strategy.market_conditions
class TestScalpingStrategy:
"""Test Scalping strategy."""
def test_scalping_creation(self):
"""Test scalping strategy creation."""
strategy = create_scalping_strategy()
assert isinstance(strategy, StrategyExample)
assert strategy.config.strategy_name == "Scalping Strategy"
assert strategy.config.strategy_type == TradingStrategy.SCALPING
# Check fast timeframes
assert "1m" in strategy.config.timeframes
assert "5m" in strategy.config.timeframes
# Check very fast indicators
assert "ema_5" in strategy.config.overlay_indicators
assert "ema_12" in strategy.config.overlay_indicators
assert "ema_21" in strategy.config.overlay_indicators
# Check fast RSI
rsi_subplot = next((s for s in strategy.config.subplot_configs if s.subplot_type.value == "rsi"), None)
assert rsi_subplot is not None
assert "rsi_7" in rsi_subplot.indicators
# Check metadata
assert strategy.difficulty == "Advanced"
assert strategy.risk_level == "High"
assert "High Liquidity" in strategy.market_conditions
class TestSwingTradingStrategy:
"""Test Swing Trading strategy."""
def test_swing_trading_creation(self):
"""Test swing trading strategy creation."""
strategy = create_swing_trading_strategy()
assert isinstance(strategy, StrategyExample)
assert strategy.config.strategy_name == "Swing Trading Strategy"
assert strategy.config.strategy_type == TradingStrategy.SWING_TRADING
# Check longer timeframes
assert "4h" in strategy.config.timeframes
assert "1d" in strategy.config.timeframes
# Check swing trading indicators
assert "sma_20" in strategy.config.overlay_indicators
assert "sma_50" in strategy.config.overlay_indicators
assert "ema_21" in strategy.config.overlay_indicators
assert "bb_20_20" in strategy.config.overlay_indicators
# Check metadata
assert strategy.difficulty == "Beginner"
assert strategy.risk_level == "Medium"
assert "Trending" in strategy.market_conditions
class TestStrategyAccessors:
"""Test strategy accessor functions."""
def test_get_all_example_strategies(self):
"""Test getting all example strategies."""
strategies = get_all_example_strategies()
assert isinstance(strategies, dict)
assert len(strategies) == 5 # Should have 5 strategies
expected_strategies = [
"ema_crossover", "momentum_breakout", "mean_reversion",
"scalping", "swing_trading"
]
for strategy_name in expected_strategies:
assert strategy_name in strategies
assert isinstance(strategies[strategy_name], StrategyExample)
def test_get_example_strategy(self):
"""Test getting a specific example strategy."""
# Test existing strategy
ema_strategy = get_example_strategy("ema_crossover")
assert ema_strategy is not None
assert isinstance(ema_strategy, StrategyExample)
assert ema_strategy.config.strategy_name == "EMA Crossover Strategy"
# Test non-existing strategy
non_existent = get_example_strategy("non_existent_strategy")
assert non_existent is None
def test_get_strategies_by_difficulty(self):
"""Test filtering strategies by difficulty."""
# Test beginner strategies
beginner_strategies = get_strategies_by_difficulty("Beginner")
assert isinstance(beginner_strategies, list)
assert len(beginner_strategies) > 0
for strategy in beginner_strategies:
assert strategy.difficulty == "Beginner"
# Test intermediate strategies
intermediate_strategies = get_strategies_by_difficulty("Intermediate")
assert isinstance(intermediate_strategies, list)
assert len(intermediate_strategies) > 0
for strategy in intermediate_strategies:
assert strategy.difficulty == "Intermediate"
# Test advanced strategies
advanced_strategies = get_strategies_by_difficulty("Advanced")
assert isinstance(advanced_strategies, list)
assert len(advanced_strategies) > 0
for strategy in advanced_strategies:
assert strategy.difficulty == "Advanced"
# Test non-existent difficulty
empty_strategies = get_strategies_by_difficulty("Expert")
assert isinstance(empty_strategies, list)
assert len(empty_strategies) == 0
def test_get_strategies_by_risk_level(self):
"""Test filtering strategies by risk level."""
# Test medium risk strategies
medium_risk = get_strategies_by_risk_level("Medium")
assert isinstance(medium_risk, list)
assert len(medium_risk) > 0
for strategy in medium_risk:
assert strategy.risk_level == "Medium"
# Test high risk strategies
high_risk = get_strategies_by_risk_level("High")
assert isinstance(high_risk, list)
assert len(high_risk) > 0
for strategy in high_risk:
assert strategy.risk_level == "High"
# Test non-existent risk level
empty_strategies = get_strategies_by_risk_level("Ultra High")
assert isinstance(empty_strategies, list)
assert len(empty_strategies) == 0
def test_get_strategies_by_market_condition(self):
"""Test filtering strategies by market condition."""
# Test trending market strategies
trending_strategies = get_strategies_by_market_condition("Trending")
assert isinstance(trending_strategies, list)
assert len(trending_strategies) > 0
for strategy in trending_strategies:
assert "Trending" in strategy.market_conditions
# Test volatile market strategies
volatile_strategies = get_strategies_by_market_condition("Volatile")
assert isinstance(volatile_strategies, list)
assert len(volatile_strategies) > 0
for strategy in volatile_strategies:
assert "Volatile" in strategy.market_conditions
# Test sideways market strategies
sideways_strategies = get_strategies_by_market_condition("Sideways")
assert isinstance(sideways_strategies, list)
assert len(sideways_strategies) > 0
for strategy in sideways_strategies:
assert "Sideways" in strategy.market_conditions
class TestStrategyUtilities:
"""Test strategy utility functions."""
def test_get_strategy_summary(self):
"""Test getting strategy summary."""
summary = get_strategy_summary()
assert isinstance(summary, dict)
assert len(summary) == 5 # Should have 5 strategies
# Check summary structure
for strategy_name, strategy_info in summary.items():
assert isinstance(strategy_info, dict)
required_fields = [
"name", "type", "difficulty", "risk_level",
"timeframes", "market_conditions", "expected_return"
]
for field in required_fields:
assert field in strategy_info
assert isinstance(strategy_info[field], str)
# Check specific strategy
assert "ema_crossover" in summary
ema_summary = summary["ema_crossover"]
assert ema_summary["name"] == "EMA Crossover Strategy"
assert ema_summary["type"] == "day_trading"
assert ema_summary["difficulty"] == "Intermediate"
def test_export_example_strategies_to_json(self):
"""Test exporting strategies to JSON."""
json_str = export_example_strategies_to_json()
# Should be valid JSON
data = json.loads(json_str)
assert isinstance(data, dict)
assert len(data) == 5 # Should have 5 strategies
# Check structure
for strategy_name, strategy_data in data.items():
assert "config" in strategy_data
assert "metadata" in strategy_data
# Check config structure
config = strategy_data["config"]
assert "strategy_name" in config
assert "strategy_type" in config
assert "timeframes" in config
# Check metadata structure
metadata = strategy_data["metadata"]
assert "description" in metadata
assert "author" in metadata
assert "difficulty" in metadata
assert "risk_level" in metadata
# Check specific strategy
assert "ema_crossover" in data
ema_data = data["ema_crossover"]
assert ema_data["config"]["strategy_name"] == "EMA Crossover Strategy"
assert ema_data["metadata"]["difficulty"] == "Intermediate"
class TestStrategyValidation:
"""Test validation of example strategies."""
def test_all_strategies_have_required_fields(self):
"""Test that all strategies have required fields."""
strategies = get_all_example_strategies()
for strategy_name, strategy in strategies.items():
# Check StrategyExample fields
assert strategy.config is not None
assert strategy.description is not None
assert strategy.author is not None
assert strategy.difficulty in ["Beginner", "Intermediate", "Advanced"]
assert strategy.risk_level in ["Low", "Medium", "High"]
assert isinstance(strategy.market_conditions, list)
assert isinstance(strategy.notes, list)
assert isinstance(strategy.references, list)
# Check StrategyChartConfig fields
config = strategy.config
assert config.strategy_name is not None
assert config.strategy_type is not None
assert isinstance(config.timeframes, list)
assert len(config.timeframes) > 0
assert isinstance(config.overlay_indicators, list)
assert isinstance(config.subplot_configs, list)
def test_strategy_configurations_are_valid(self):
"""Test that all strategy configurations are valid."""
strategies = get_all_example_strategies()
for strategy_name, strategy in strategies.items():
# Test basic validation
is_valid, errors = strategy.config.validate()
# Should be valid or have minimal issues (like missing indicators in test environment)
assert isinstance(is_valid, bool)
assert isinstance(errors, list)
# If there are errors, they should be reasonable (like missing indicators)
if not is_valid:
for error in errors:
# Common acceptable errors in test environment
acceptable_errors = [
"not found in defaults", # Missing indicators
"not found", # Missing indicators
]
assert any(acceptable in error for acceptable in acceptable_errors), \
f"Unexpected error in {strategy_name}: {error}"
def test_strategy_timeframes_match_types(self):
"""Test that strategy timeframes match their types."""
strategies = get_all_example_strategies()
# Expected timeframes for different strategy types
expected_timeframes = {
TradingStrategy.SCALPING: ["1m", "5m"],
TradingStrategy.DAY_TRADING: ["5m", "15m", "1h", "4h"],
TradingStrategy.SWING_TRADING: ["1h", "4h", "1d"],
TradingStrategy.MOMENTUM: ["5m", "15m", "1h"],
TradingStrategy.MEAN_REVERSION: ["15m", "1h", "4h"]
}
for strategy_name, strategy in strategies.items():
strategy_type = strategy.config.strategy_type
timeframes = strategy.config.timeframes
if strategy_type in expected_timeframes:
expected = expected_timeframes[strategy_type]
# Should have some overlap with expected timeframes
overlap = set(timeframes) & set(expected)
assert len(overlap) > 0, \
f"Strategy {strategy_name} timeframes {timeframes} don't match type {strategy_type}"
class TestStrategyIntegration:
"""Test integration with other systems."""
def test_strategy_configs_work_with_validation(self):
"""Test that strategy configs work with validation system."""
from components.charts.config.validation import validate_configuration
strategies = get_all_example_strategies()
for strategy_name, strategy in strategies.items():
try:
report = validate_configuration(strategy.config)
assert hasattr(report, 'is_valid')
assert hasattr(report, 'errors')
assert hasattr(report, 'warnings')
except Exception as e:
pytest.fail(f"Validation failed for {strategy_name}: {e}")
def test_strategy_json_roundtrip(self):
"""Test JSON export and import roundtrip."""
from components.charts.config.strategy_charts import (
export_strategy_config_to_json,
load_strategy_config_from_json
)
# Test one strategy for roundtrip
original_strategy = create_ema_crossover_strategy()
# Export to JSON
json_str = export_strategy_config_to_json(original_strategy.config)
# Import from JSON
loaded_config, errors = load_strategy_config_from_json(json_str)
if loaded_config:
# Compare key fields
assert loaded_config.strategy_name == original_strategy.config.strategy_name
assert loaded_config.strategy_type == original_strategy.config.strategy_type
assert loaded_config.timeframes == original_strategy.config.timeframes
assert loaded_config.overlay_indicators == original_strategy.config.overlay_indicators
if __name__ == "__main__":
pytest.main([__file__])

126
tests/test_exchange_factory.py
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@ -1,126 +0,0 @@
#!/usr/bin/env python3
"""
Test script for exchange factory pattern.
This script demonstrates how to use the new exchange factory
to create collectors from different exchanges.
"""
import asyncio
import sys
from pathlib import Path
# Add project root to Python path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from data.exchanges import (
ExchangeFactory,
ExchangeCollectorConfig,
create_okx_collector,
get_supported_exchanges
)
from data.base_collector import DataType
from database.connection import init_database
from utils.logger import get_logger
async def test_factory_pattern():
"""Test the exchange factory pattern."""
logger = get_logger("factory_test", verbose=True)
try:
# Initialize database
logger.info("Initializing database...")
init_database()
# Test 1: Show supported exchanges
logger.info("=== Supported Exchanges ===")
supported = get_supported_exchanges()
logger.info(f"Supported exchanges: {supported}")
# Test 2: Create collector using factory
logger.info("=== Testing Exchange Factory ===")
config = ExchangeCollectorConfig(
exchange='okx',
symbol='BTC-USDT',
data_types=[DataType.TRADE, DataType.ORDERBOOK],
auto_restart=True,
health_check_interval=30.0,
store_raw_data=True
)
# Validate configuration
is_valid = ExchangeFactory.validate_config(config)
logger.info(f"Configuration valid: {is_valid}")
if is_valid:
# Create collector using factory
collector = ExchangeFactory.create_collector(config)
logger.info(f"Created collector: {type(collector).__name__}")
logger.info(f"Collector symbol: {collector.symbols}")
logger.info(f"Collector data types: {[dt.value for dt in collector.data_types]}")
# Test 3: Create collector using convenience function
logger.info("=== Testing Convenience Function ===")
okx_collector = create_okx_collector(
symbol='ETH-USDT',
data_types=[DataType.TRADE],
auto_restart=False
)
logger.info(f"Created OKX collector: {type(okx_collector).__name__}")
logger.info(f"OKX collector symbol: {okx_collector.symbols}")
# Test 4: Create multiple collectors
logger.info("=== Testing Multiple Collectors ===")
configs = [
ExchangeCollectorConfig('okx', 'BTC-USDT', [DataType.TRADE]),
ExchangeCollectorConfig('okx', 'ETH-USDT', [DataType.ORDERBOOK]),
ExchangeCollectorConfig('okx', 'SOL-USDT', [DataType.TRADE, DataType.ORDERBOOK])
]
collectors = ExchangeFactory.create_multiple_collectors(configs)
logger.info(f"Created {len(collectors)} collectors:")
for i, collector in enumerate(collectors):
logger.info(f" {i+1}. {type(collector).__name__} - {collector.symbols}")
# Test 5: Get exchange capabilities
logger.info("=== Exchange Capabilities ===")
okx_pairs = ExchangeFactory.get_supported_pairs('okx')
okx_data_types = ExchangeFactory.get_supported_data_types('okx')
logger.info(f"OKX supported pairs: {okx_pairs}")
logger.info(f"OKX supported data types: {okx_data_types}")
logger.info("All factory tests completed successfully!")
return True
except Exception as e:
logger.error(f"Factory test failed: {e}")
return False
async def main():
"""Main test function."""
logger = get_logger("main", verbose=True)
logger.info("Testing exchange factory pattern...")
success = await test_factory_pattern()
if success:
logger.info("Factory tests completed successfully!")
else:
logger.error("Factory tests failed!")
return success
if __name__ == "__main__":
try:
success = asyncio.run(main())
sys.exit(0 if success else 1)
except KeyboardInterrupt:
print("\nTest interrupted by user")
sys.exit(1)
except Exception as e:
print(f"Test failed with error: {e}")
sys.exit(1)

316
tests/test_indicator_schema.py
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@ -1,316 +0,0 @@
"""
Tests for Indicator Schema Validation System
Tests the new indicator definition schema and validation functionality
to ensure robust parameter validation and error handling.
"""
import pytest
from typing import Dict, Any
from components.charts.config.indicator_defs import (
IndicatorType,
DisplayType,
LineStyle,
IndicatorParameterSchema,
IndicatorSchema,
ChartIndicatorConfig,
INDICATOR_SCHEMAS,
validate_indicator_configuration,
create_indicator_config,
get_indicator_schema,
get_available_indicator_types,
get_indicator_parameter_info,
validate_parameters_for_type,
create_configuration_from_json
)
class TestIndicatorParameterSchema:
"""Test individual parameter schema validation."""
def test_required_parameter_validation(self):
"""Test validation of required parameters."""
schema = IndicatorParameterSchema(
name="period",
type=int,
required=True,
min_value=1,
max_value=100
)
# Valid value
is_valid, error = schema.validate(20)
assert is_valid
assert error == ""
# Missing required parameter
is_valid, error = schema.validate(None)
assert not is_valid
assert "required" in error.lower()
# Wrong type
is_valid, error = schema.validate("20")
assert not is_valid
assert "type" in error.lower()
# Out of range
is_valid, error = schema.validate(0)
assert not is_valid
assert ">=" in error
is_valid, error = schema.validate(101)
assert not is_valid
assert "<=" in error
def test_optional_parameter_validation(self):
"""Test validation of optional parameters."""
schema = IndicatorParameterSchema(
name="price_column",
type=str,
required=False,
default="close"
)
# Valid value
is_valid, error = schema.validate("high")
assert is_valid
# None is valid for optional
is_valid, error = schema.validate(None)
assert is_valid
class TestIndicatorSchema:
"""Test complete indicator schema validation."""
def test_sma_schema_validation(self):
"""Test SMA indicator schema validation."""
schema = INDICATOR_SCHEMAS[IndicatorType.SMA]
# Valid parameters
params = {"period": 20, "price_column": "close"}
is_valid, errors = schema.validate_parameters(params)
assert is_valid
assert len(errors) == 0
# Missing required parameter
params = {"price_column": "close"}
is_valid, errors = schema.validate_parameters(params)
assert not is_valid
assert any("period" in error and "required" in error for error in errors)
# Invalid parameter value
params = {"period": 0, "price_column": "close"}
is_valid, errors = schema.validate_parameters(params)
assert not is_valid
assert any(">=" in error for error in errors)
# Unknown parameter
params = {"period": 20, "unknown_param": "test"}
is_valid, errors = schema.validate_parameters(params)
assert not is_valid
assert any("unknown" in error.lower() for error in errors)
def test_macd_schema_validation(self):
"""Test MACD indicator schema validation."""
schema = INDICATOR_SCHEMAS[IndicatorType.MACD]
# Valid parameters
params = {
"fast_period": 12,
"slow_period": 26,
"signal_period": 9,
"price_column": "close"
}
is_valid, errors = schema.validate_parameters(params)
assert is_valid
# Missing required parameters
params = {"fast_period": 12}
is_valid, errors = schema.validate_parameters(params)
assert not is_valid
assert len(errors) >= 2 # Missing slow_period and signal_period
class TestChartIndicatorConfig:
"""Test chart indicator configuration validation."""
def test_valid_config_validation(self):
"""Test validation of a valid configuration."""
config = ChartIndicatorConfig(
name="SMA (20)",
indicator_type="sma",
parameters={"period": 20, "price_column": "close"},
display_type="overlay",
color="#007bff",
line_style="solid",
line_width=2,
opacity=1.0,
visible=True
)
is_valid, errors = config.validate()
assert is_valid
assert len(errors) == 0
def test_invalid_indicator_type(self):
"""Test validation with invalid indicator type."""
config = ChartIndicatorConfig(
name="Invalid Indicator",
indicator_type="invalid_type",
parameters={},
display_type="overlay",
color="#007bff"
)
is_valid, errors = config.validate()
assert not is_valid
assert any("unsupported indicator type" in error.lower() for error in errors)
def test_invalid_display_properties(self):
"""Test validation of display properties."""
config = ChartIndicatorConfig(
name="SMA (20)",
indicator_type="sma",
parameters={"period": 20},
display_type="invalid_display",
color="#007bff",
line_style="invalid_style",
line_width=-1,
opacity=2.0
)
is_valid, errors = config.validate()
assert not is_valid
# Check for multiple validation errors
error_text = " ".join(errors).lower()
assert "display_type" in error_text
assert "line_style" in error_text
assert "line_width" in error_text
assert "opacity" in error_text
class TestUtilityFunctions:
"""Test utility functions for indicator management."""
def test_create_indicator_config(self):
"""Test creating indicator configuration."""
config, errors = create_indicator_config(
name="SMA (20)",
indicator_type="sma",
parameters={"period": 20},
color="#007bff"
)
assert config is not None
assert len(errors) == 0
assert config.name == "SMA (20)"
assert config.indicator_type == "sma"
assert config.parameters["period"] == 20
assert config.parameters["price_column"] == "close" # Default filled in
def test_create_indicator_config_invalid(self):
"""Test creating invalid indicator configuration."""
config, errors = create_indicator_config(
name="Invalid SMA",
indicator_type="sma",
parameters={"period": 0}, # Invalid period
color="#007bff"
)
assert config is None
assert len(errors) > 0
assert any(">=" in error for error in errors)
def test_get_indicator_schema(self):
"""Test getting indicator schema."""
schema = get_indicator_schema("sma")
assert schema is not None
assert schema.indicator_type == IndicatorType.SMA
schema = get_indicator_schema("invalid_type")
assert schema is None
def test_get_available_indicator_types(self):
"""Test getting available indicator types."""
types = get_available_indicator_types()
assert "sma" in types
assert "ema" in types
assert "rsi" in types
assert "macd" in types
assert "bollinger_bands" in types
def test_get_indicator_parameter_info(self):
"""Test getting parameter information."""
info = get_indicator_parameter_info("sma")
assert "period" in info
assert info["period"]["type"] == "int"
assert info["period"]["required"]
assert "price_column" in info
assert not info["price_column"]["required"]
def test_validate_parameters_for_type(self):
"""Test parameter validation for specific type."""
is_valid, errors = validate_parameters_for_type("sma", {"period": 20})
assert is_valid
is_valid, errors = validate_parameters_for_type("sma", {"period": 0})
assert not is_valid
is_valid, errors = validate_parameters_for_type("invalid_type", {})
assert not is_valid
def test_create_configuration_from_json(self):
"""Test creating configuration from JSON."""
json_data = {
"name": "SMA (20)",
"indicator_type": "sma",
"parameters": {"period": 20},
"color": "#007bff"
}
config, errors = create_configuration_from_json(json_data)
assert config is not None
assert len(errors) == 0
# Test with JSON string
import json
json_string = json.dumps(json_data)
config, errors = create_configuration_from_json(json_string)
assert config is not None
assert len(errors) == 0
# Test with missing fields
invalid_json = {"name": "SMA"}
config, errors = create_configuration_from_json(invalid_json)
assert config is None
assert len(errors) > 0
class TestIndicatorSchemaIntegration:
"""Test integration with existing indicator system."""
def test_schema_matches_built_in_indicators(self):
"""Test that schemas match built-in indicator definitions."""
from components.charts.config.indicator_defs import INDICATOR_DEFINITIONS
for indicator_name, config in INDICATOR_DEFINITIONS.items():
# Validate each built-in configuration
is_valid, errors = config.validate()
if not is_valid:
print(f"Validation errors for {indicator_name}: {errors}")
assert is_valid, f"Built-in indicator {indicator_name} failed validation: {errors}"
def test_parameter_schema_completeness(self):
"""Test that all indicator types have complete schemas."""
for indicator_type in IndicatorType:
schema = INDICATOR_SCHEMAS.get(indicator_type)
assert schema is not None, f"Missing schema for {indicator_type.value}"
assert schema.indicator_type == indicator_type
assert len(schema.required_parameters) > 0 or len(schema.optional_parameters) > 0
if __name__ == "__main__":
pytest.main([__file__])

323
tests/test_indicators_safety.py
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@ -1,323 +0,0 @@
"""
Safety net tests for technical indicators module.
These tests ensure that the core functionality of the indicators module
remains intact during refactoring.
"""
import pytest
from datetime import datetime, timezone, timedelta
from decimal import Decimal
import pandas as pd
import numpy as np
from data.common.indicators import (
TechnicalIndicators,
IndicatorResult,
create_default_indicators_config,
validate_indicator_config
)
from data.common.data_types import OHLCVCandle
class TestTechnicalIndicatorsSafety:
"""Safety net test suite for TechnicalIndicators class."""
@pytest.fixture
def sample_candles(self):
"""Create sample OHLCV candles for testing."""
candles = []
base_time = datetime(2024, 1, 1, 9, 0, 0, tzinfo=timezone.utc)
# Create 30 candles with realistic price movement
prices = [100.0, 101.0, 102.5, 101.8, 103.0, 104.2, 103.8, 105.0, 104.5, 106.0,
107.5, 108.0, 107.2, 109.0, 108.5, 110.0, 109.8, 111.0, 110.5, 112.0,
111.8, 113.0, 112.5, 114.0, 113.2, 115.0, 114.8, 116.0, 115.5, 117.0]
for i, price in enumerate(prices):
candle = OHLCVCandle(
symbol='BTC-USDT',
timeframe='1m',
start_time=base_time + timedelta(minutes=i),
end_time=base_time + timedelta(minutes=i+1),
open=Decimal(str(price - 0.2)),
high=Decimal(str(price + 0.5)),
low=Decimal(str(price - 0.5)),
close=Decimal(str(price)),
volume=Decimal('1000'),
trade_count=10,
exchange='test',
is_complete=True
)
candles.append(candle)
return candles
@pytest.fixture
def sparse_candles(self):
"""Create sample OHLCV candles with time gaps for testing."""
candles = []
base_time = datetime(2024, 1, 1, 9, 0, 0, tzinfo=timezone.utc)
# Create 15 candles with gaps (every other minute)
prices = [100.0, 102.5, 104.2, 105.0, 106.0,
108.0, 109.0, 110.0, 111.0, 112.0,
113.0, 114.0, 115.0, 116.0, 117.0]
for i, price in enumerate(prices):
# Create 2-minute gaps between candles
candle = OHLCVCandle(
symbol='BTC-USDT',
timeframe='1m',
start_time=base_time + timedelta(minutes=i*2),
end_time=base_time + timedelta(minutes=(i*2)+1),
open=Decimal(str(price - 0.2)),
high=Decimal(str(price + 0.5)),
low=Decimal(str(price - 0.5)),
close=Decimal(str(price)),
volume=Decimal('1000'),
trade_count=10,
exchange='test',
is_complete=True
)
candles.append(candle)
return candles
@pytest.fixture
def indicators(self):
"""Create TechnicalIndicators instance."""
return TechnicalIndicators()
def test_initialization(self, indicators):
"""Test indicator calculator initialization."""
assert isinstance(indicators, TechnicalIndicators)
def test_prepare_dataframe_from_list(self, indicators, sample_candles):
"""Test DataFrame preparation from OHLCV candles."""
df = indicators._prepare_dataframe_from_list(sample_candles)
assert isinstance(df, pd.DataFrame)
assert not df.empty
assert len(df) == len(sample_candles)
assert 'close' in df.columns
assert 'timestamp' in df.index.names
def test_prepare_dataframe_empty(self, indicators):
"""Test DataFrame preparation with empty candles list."""
df = indicators._prepare_dataframe_from_list([])
assert isinstance(df, pd.DataFrame)
assert df.empty
def test_sma_calculation(self, indicators, sample_candles):
"""Test Simple Moving Average calculation."""
period = 5
df = indicators._prepare_dataframe_from_list(sample_candles)
results = indicators.sma(df, period)
assert len(results) > 0
assert isinstance(results[0], IndicatorResult)
assert 'sma' in results[0].values
assert results[0].metadata['period'] == period
def test_sma_insufficient_data(self, indicators, sample_candles):
"""Test SMA with insufficient data."""
period = 50 # More than available candles
df = indicators._prepare_dataframe_from_list(sample_candles)
results = indicators.sma(df, period)
assert len(results) == 0
def test_ema_calculation(self, indicators, sample_candles):
"""Test Exponential Moving Average calculation."""
period = 10
df = indicators._prepare_dataframe_from_list(sample_candles)
results = indicators.ema(df, period)
assert len(results) > 0
assert isinstance(results[0], IndicatorResult)
assert 'ema' in results[0].values
assert results[0].metadata['period'] == period
def test_rsi_calculation(self, indicators, sample_candles):
"""Test Relative Strength Index calculation."""
period = 14
df = indicators._prepare_dataframe_from_list(sample_candles)
results = indicators.rsi(df, period)
assert len(results) > 0
assert isinstance(results[0], IndicatorResult)
assert 'rsi' in results[0].values
assert results[0].metadata['period'] == period
assert 0 <= results[0].values['rsi'] <= 100
def test_macd_calculation(self, indicators, sample_candles):
"""Test MACD calculation."""
fast_period = 12
slow_period = 26
signal_period = 9
df = indicators._prepare_dataframe_from_list(sample_candles)
results = indicators.macd(df, fast_period, slow_period, signal_period)
# MACD should start producing results after slow_period periods
assert len(results) > 0
if results: # Only test if we have results
first_result = results[0]
assert isinstance(first_result, IndicatorResult)
assert 'macd' in first_result.values
assert 'signal' in first_result.values
assert 'histogram' in first_result.values
# Histogram should equal MACD - Signal
expected_histogram = first_result.values['macd'] - first_result.values['signal']
assert abs(first_result.values['histogram'] - expected_histogram) < 0.001
def test_bollinger_bands_calculation(self, indicators, sample_candles):
"""Test Bollinger Bands calculation."""
period = 20
std_dev = 2.0
df = indicators._prepare_dataframe_from_list(sample_candles)
results = indicators.bollinger_bands(df, period, std_dev)
assert len(results) > 0
assert isinstance(results[0], IndicatorResult)
assert 'upper_band' in results[0].values
assert 'middle_band' in results[0].values
assert 'lower_band' in results[0].values
assert results[0].metadata['period'] == period
assert results[0].metadata['std_dev'] == std_dev
def test_sparse_data_handling(self, indicators, sparse_candles):
"""Test indicators with sparse data (time gaps)."""
period = 5
df = indicators._prepare_dataframe_from_list(sparse_candles)
sma_df = indicators.sma(df, period)
assert not sma_df.empty
timestamps = sma_df.index.to_list()
for i in range(1, len(timestamps)):
time_diff = timestamps[i] - timestamps[i-1]
assert time_diff >= timedelta(minutes=1)
def test_calculate_multiple_indicators(self, indicators, sample_candles):
"""Test calculating multiple indicators at once."""
config = {
'sma_10': {'type': 'sma', 'period': 10},
'ema_12': {'type': 'ema', 'period': 12},
'rsi_14': {'type': 'rsi', 'period': 14},
'macd': {'type': 'macd'},
'bb_20': {'type': 'bollinger_bands', 'period': 20}
}
df = indicators._prepare_dataframe_from_list(sample_candles)
results = indicators.calculate_multiple_indicators(df, config)
assert len(results) == len(config)
assert 'sma_10' in results
assert 'ema_12' in results
assert 'rsi_14' in results
assert 'macd' in results
assert 'bb_20' in results
# Check that each indicator has appropriate results
assert len(results['sma_10']) > 0
assert len(results['ema_12']) > 0
assert len(results['rsi_14']) > 0
assert len(results['macd']) > 0
assert len(results['bb_20']) > 0
def test_different_price_columns(self, indicators, sample_candles):
"""Test indicators with different price columns."""
df = indicators._prepare_dataframe_from_list(sample_candles)
# Test SMA with 'high' price column
sma_high = indicators.sma(df, 5, price_column='high')
assert len(sma_high) > 0
# Test SMA with 'low' price column
sma_low = indicators.sma(df, 5, price_column='low')
assert len(sma_low) > 0
# Values should be different
assert sma_high[0].values['sma'] != sma_low[0].values['sma']
class TestIndicatorHelperFunctions:
"""Test suite for indicator helper functions."""
def test_create_default_indicators_config(self):
"""Test default indicator configuration creation."""
config = create_default_indicators_config()
assert isinstance(config, dict)
assert len(config) > 0
assert 'sma_20' in config
assert 'ema_12' in config
assert 'rsi_14' in config
assert 'macd_default' in config
assert 'bollinger_bands_20' in config
def test_validate_indicator_config_valid(self):
"""Test indicator configuration validation with valid config."""
valid_configs = [
{'type': 'sma', 'period': 20},
{'type': 'ema', 'period': 12},
{'type': 'rsi', 'period': 14},
{'type': 'macd'},
{'type': 'bollinger_bands', 'period': 20, 'std_dev': 2.0}
]
for config in valid_configs:
assert validate_indicator_config(config)
def test_validate_indicator_config_invalid(self):
"""Test indicator configuration validation with invalid config."""
invalid_configs = [
{}, # Empty config
{'type': 'unknown'}, # Invalid type
{'type': 'sma', 'period': -1}, # Invalid period
{'type': 'bollinger_bands', 'std_dev': -1}, # Invalid std_dev
{'type': 'sma', 'period': 'not_a_number'} # Wrong type for period
]
for config in invalid_configs:
assert not validate_indicator_config(config)
class TestIndicatorResultDataClass:
"""Test suite for IndicatorResult dataclass."""
def test_indicator_result_creation(self):
"""Test IndicatorResult creation with all fields."""
timestamp = datetime.now(timezone.utc)
values = {'sma': 100.0}
metadata = {'period': 20}
result = IndicatorResult(
timestamp=timestamp,
symbol='BTC-USDT',
timeframe='1m',
values=values,
metadata=metadata
)
assert result.timestamp == timestamp
assert result.symbol == 'BTC-USDT'
assert result.timeframe == '1m'
assert result.values == values
assert result.metadata == metadata
def test_indicator_result_without_metadata(self):
"""Test IndicatorResult creation without optional metadata."""
timestamp = datetime.now(timezone.utc)
values = {'sma': 100.0}
result = IndicatorResult(
timestamp=timestamp,
symbol='BTC-USDT',
timeframe='1m',
values=values
)
assert result.timestamp == timestamp
assert result.symbol == 'BTC-USDT'
assert result.timeframe == '1m'
assert result.values == values
assert result.metadata is None

243
tests/test_okx_collector.py
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@ -1,243 +0,0 @@
#!/usr/bin/env python3
"""
Test script for OKX data collector.
This script tests the OKX collector implementation by running a single collector
for a specified trading pair and monitoring the data collection for a short period.
"""
import asyncio
import sys
import signal
from pathlib import Path
# Add project root to Python path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from data.exchanges.okx import OKXCollector
from data.collector_manager import CollectorManager
from data.base_collector import DataType
from utils.logger import get_logger
from database.connection import init_database
# Global shutdown flag
shutdown_flag = asyncio.Event()
def signal_handler(signum, frame):
"""Handle shutdown signals."""
print(f"\nReceived signal {signum}, shutting down...")
shutdown_flag.set()
async def test_single_collector():
"""Test a single OKX collector."""
logger = get_logger("test_okx_collector", verbose=True)
try:
# Initialize database
logger.info("Initializing database connection...")
db_manager = init_database()
logger.info("Database initialized successfully")
# Create OKX collector for BTC-USDT
symbol = "BTC-USDT"
data_types = [DataType.TRADE, DataType.ORDERBOOK]
logger.info(f"Creating OKX collector for {symbol}")
collector = OKXCollector(
symbol=symbol,
data_types=data_types,
auto_restart=True,
health_check_interval=30.0,
store_raw_data=True
)
# Start the collector
logger.info("Starting OKX collector...")
success = await collector.start()
if not success:
logger.error("Failed to start OKX collector")
return False
logger.info("OKX collector started successfully")
# Monitor for a short period
test_duration = 60 # seconds
logger.info(f"Monitoring collector for {test_duration} seconds...")
start_time = asyncio.get_event_loop().time()
while not shutdown_flag.is_set():
# Check if test duration elapsed
elapsed = asyncio.get_event_loop().time() - start_time
if elapsed >= test_duration:
logger.info(f"Test duration ({test_duration}s) completed")
break
# Print status every 10 seconds
if int(elapsed) % 10 == 0 and int(elapsed) > 0:
status = collector.get_status()
logger.info(f"Collector status: {status['status']} - "
f"Messages: {status.get('messages_processed', 0)} - "
f"Errors: {status.get('errors', 0)}")
await asyncio.sleep(1)
# Stop the collector
logger.info("Stopping OKX collector...")
await collector.stop()
logger.info("OKX collector stopped")
# Print final statistics
final_status = collector.get_status()
logger.info("=== Final Statistics ===")
logger.info(f"Status: {final_status['status']}")
logger.info(f"Messages processed: {final_status.get('messages_processed', 0)}")
logger.info(f"Errors: {final_status.get('errors', 0)}")
logger.info(f"WebSocket state: {final_status.get('websocket_state', 'unknown')}")
if 'websocket_stats' in final_status:
ws_stats = final_status['websocket_stats']
logger.info(f"WebSocket messages received: {ws_stats.get('messages_received', 0)}")
logger.info(f"WebSocket messages sent: {ws_stats.get('messages_sent', 0)}")
logger.info(f"Pings sent: {ws_stats.get('pings_sent', 0)}")
logger.info(f"Pongs received: {ws_stats.get('pongs_received', 0)}")
return True
except Exception as e:
logger.error(f"Error in test: {e}")
return False
async def test_collector_manager():
"""Test multiple collectors using CollectorManager."""
logger = get_logger("test_collector_manager", verbose=True)
try:
# Initialize database
logger.info("Initializing database connection...")
db_manager = init_database()
logger.info("Database initialized successfully")
# Create collector manager
manager = CollectorManager(
manager_name="test_manager",
global_health_check_interval=30.0
)
# Create multiple collectors
symbols = ["BTC-USDT", "ETH-USDT", "SOL-USDT"]
collectors = []
for symbol in symbols:
logger.info(f"Creating collector for {symbol}")
collector = OKXCollector(
symbol=symbol,
data_types=[DataType.TRADE, DataType.ORDERBOOK],
auto_restart=True,
health_check_interval=30.0,
store_raw_data=True
)
collectors.append(collector)
manager.add_collector(collector)
# Start the manager
logger.info("Starting collector manager...")
success = await manager.start()
if not success:
logger.error("Failed to start collector manager")
return False
logger.info("Collector manager started successfully")
# Monitor for a short period
test_duration = 90 # seconds
logger.info(f"Monitoring collectors for {test_duration} seconds...")
start_time = asyncio.get_event_loop().time()
while not shutdown_flag.is_set():
# Check if test duration elapsed
elapsed = asyncio.get_event_loop().time() - start_time
if elapsed >= test_duration:
logger.info(f"Test duration ({test_duration}s) completed")
break
# Print status every 15 seconds
if int(elapsed) % 15 == 0 and int(elapsed) > 0:
status = manager.get_status()
stats = status.get('statistics', {})
logger.info(f"Manager status: Running={stats.get('running_collectors', 0)}, "
f"Failed={stats.get('failed_collectors', 0)}, "
f"Total={status['total_collectors']}")
# Print individual collector status
for collector_name in manager.list_collectors():
collector_status = manager.get_collector_status(collector_name)
if collector_status:
collector_info = collector_status.get('status', {})
logger.info(f" {collector_name}: {collector_info.get('status', 'unknown')} - "
f"Messages: {collector_info.get('messages_processed', 0)}")
await asyncio.sleep(1)
# Stop the manager
logger.info("Stopping collector manager...")
await manager.stop()
logger.info("Collector manager stopped")
# Print final statistics
final_status = manager.get_status()
stats = final_status.get('statistics', {})
logger.info("=== Final Manager Statistics ===")
logger.info(f"Total collectors: {final_status['total_collectors']}")
logger.info(f"Running collectors: {stats.get('running_collectors', 0)}")
logger.info(f"Failed collectors: {stats.get('failed_collectors', 0)}")
logger.info(f"Restarts performed: {stats.get('restarts_performed', 0)}")
return True
except Exception as e:
logger.error(f"Error in collector manager test: {e}")
return False
async def main():
"""Main test function."""
# Setup signal handlers
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
logger = get_logger("main", verbose=True)
logger.info("Starting OKX collector tests...")
# Choose test mode
test_mode = sys.argv[1] if len(sys.argv) > 1 else "single"
if test_mode == "single":
logger.info("Running single collector test...")
success = await test_single_collector()
elif test_mode == "manager":
logger.info("Running collector manager test...")
success = await test_collector_manager()
else:
logger.error(f"Unknown test mode: {test_mode}")
logger.info("Usage: python test_okx_collector.py [single|manager]")
return False
if success:
logger.info("Test completed successfully!")
else:
logger.error("Test failed!")
return success
if __name__ == "__main__":
try:
success = asyncio.run(main())
sys.exit(0 if success else 1)
except KeyboardInterrupt:
print("\nTest interrupted by user")
sys.exit(1)
except Exception as e:
print(f"Test failed with error: {e}")
sys.exit(1)

404
tests/test_real_okx_aggregation.py
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@ -1,404 +0,0 @@
#!/usr/bin/env python3
"""
Real OKX Data Aggregation Test
This script connects to OKX's live WebSocket feed and tests the second-based
aggregation functionality with real market data. It demonstrates how trades
are processed into 1s, 5s, 10s, 15s, and 30s candles in real-time.
NO DATABASE OPERATIONS - Pure aggregation testing with live data.
"""
import asyncio
import logging
import json
from datetime import datetime, timezone
from decimal import Decimal
from typing import Dict, List, Any
from collections import defaultdict
# Import our modules
from data.common.data_types import StandardizedTrade, CandleProcessingConfig, OHLCVCandle
from data.common.aggregation.realtime import RealTimeCandleProcessor
from data.exchanges.okx.websocket import OKXWebSocketClient, OKXSubscription, OKXChannelType
from data.exchanges.okx.data_processor import OKXDataProcessor
# Set up logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s [%(levelname)s] %(name)s: %(message)s',
datefmt='%H:%M:%S'
)
logger = logging.getLogger(__name__)
class RealTimeAggregationTester:
"""
Test real-time second-based aggregation with live OKX data.
"""
def __init__(self, symbol: str = "BTC-USDT"):
self.symbol = symbol
self.component_name = f"real_test_{symbol.replace('-', '_').lower()}"
# WebSocket client
self._ws_client = None
# Aggregation processor with all second timeframes
self.config = CandleProcessingConfig(
timeframes=['1s', '5s', '10s', '15s', '30s'],
auto_save_candles=False, # Don't save to database
emit_incomplete_candles=False
)
self.processor = RealTimeCandleProcessor(
symbol=symbol,
exchange="okx",
config=self.config,
component_name=f"{self.component_name}_processor",
logger=logger
)
# Statistics tracking
self.stats = {
'trades_received': 0,
'trades_processed': 0,
'candles_completed': defaultdict(int),
'last_trade_time': None,
'session_start': datetime.now(timezone.utc)
}
# Candle tracking for analysis
self.completed_candles = []
self.latest_candles = {} # Latest candle for each timeframe
# Set up callbacks
self.processor.add_candle_callback(self._on_candle_completed)
logger.info(f"Initialized real-time aggregation tester for {symbol}")
logger.info(f"Testing timeframes: {self.config.timeframes}")
async def start_test(self, duration_seconds: int = 300):
"""
Start the real-time aggregation test.
Args:
duration_seconds: How long to run the test (default: 5 minutes)
"""
try:
logger.info("=" * 80)
logger.info("STARTING REAL-TIME OKX AGGREGATION TEST")
logger.info("=" * 80)
logger.info(f"Symbol: {self.symbol}")
logger.info(f"Duration: {duration_seconds} seconds")
logger.info(f"Timeframes: {', '.join(self.config.timeframes)}")
logger.info("=" * 80)
# Connect to OKX WebSocket
await self._connect_websocket()
# Subscribe to trades
await self._subscribe_to_trades()
# Monitor for specified duration
await self._monitor_aggregation(duration_seconds)
except KeyboardInterrupt:
logger.info("Test interrupted by user")
except Exception as e:
logger.error(f"Test failed: {e}")
raise
finally:
await self._cleanup()
await self._print_final_statistics()
async def _connect_websocket(self):
"""Connect to OKX WebSocket."""
logger.info("Connecting to OKX WebSocket...")
self._ws_client = OKXWebSocketClient(
component_name=f"{self.component_name}_ws",
ping_interval=25.0,
pong_timeout=10.0,
max_reconnect_attempts=3,
reconnect_delay=5.0,
logger=logger
)
# Add message callback
self._ws_client.add_message_callback(self._on_websocket_message)
# Connect
if not await self._ws_client.connect(use_public=True):
raise RuntimeError("Failed to connect to OKX WebSocket")
logger.info("✅ Connected to OKX WebSocket")
async def _subscribe_to_trades(self):
"""Subscribe to trade data for the symbol."""
logger.info(f"Subscribing to trades for {self.symbol}...")
subscription = OKXSubscription(
channel=OKXChannelType.TRADES.value,
inst_id=self.symbol,
enabled=True
)
if not await self._ws_client.subscribe([subscription]):
raise RuntimeError(f"Failed to subscribe to trades for {self.symbol}")
logger.info(f"✅ Subscribed to {self.symbol} trades")
def _on_websocket_message(self, message: Dict[str, Any]):
"""Handle incoming WebSocket message."""
try:
# Only process trade data messages
if not isinstance(message, dict):
return
if 'data' not in message or 'arg' not in message:
return
arg = message['arg']
if arg.get('channel') != 'trades' or arg.get('instId') != self.symbol:
return
# Process each trade in the message
for trade_data in message['data']:
self._process_trade_data(trade_data)
except Exception as e:
logger.error(f"Error processing WebSocket message: {e}")
def _process_trade_data(self, trade_data: Dict[str, Any]):
"""Process individual trade data."""
try:
self.stats['trades_received'] += 1
# Convert OKX trade to StandardizedTrade
trade = StandardizedTrade(
symbol=trade_data['instId'],
trade_id=trade_data['tradeId'],
price=Decimal(trade_data['px']),
size=Decimal(trade_data['sz']),
side=trade_data['side'],
timestamp=datetime.fromtimestamp(int(trade_data['ts']) / 1000, tz=timezone.utc),
exchange="okx",
raw_data=trade_data
)
# Update statistics
self.stats['trades_processed'] += 1
self.stats['last_trade_time'] = trade.timestamp
# Process through aggregation
completed_candles = self.processor.process_trade(trade)
# Log trade details
if self.stats['trades_processed'] % 10 == 1: # Log every 10th trade
logger.info(
f"Trade #{self.stats['trades_processed']}: "
f"{trade.side.upper()} {trade.size} @ ${trade.price} "
f"(ID: {trade.trade_id}) at {trade.timestamp.strftime('%H:%M:%S.%f')[:-3]}"
)
# Log completed candles
if completed_candles:
logger.info(f"🕯️ Completed {len(completed_candles)} candle(s)")
except Exception as e:
logger.error(f"Error processing trade data: {e}")
def _on_candle_completed(self, candle: OHLCVCandle):
"""Handle completed candle."""
try:
# Update statistics
self.stats['candles_completed'][candle.timeframe] += 1
self.completed_candles.append(candle)
self.latest_candles[candle.timeframe] = candle
# Calculate candle metrics
candle_range = candle.high - candle.low
price_change = candle.close - candle.open
change_percent = (price_change / candle.open * 100) if candle.open > 0 else 0
# Log candle completion with detailed info
logger.info(
f"📊 {candle.timeframe.upper()} CANDLE COMPLETED at {candle.end_time.strftime('%H:%M:%S')}: "
f"O=${candle.open} H=${candle.high} L=${candle.low} C=${candle.close} "
f"V={candle.volume} T={candle.trade_count} "
f"Range=${candle_range:.2f} Change={change_percent:+.2f}%"
)
# Show timeframe summary every 10 candles
total_candles = sum(self.stats['candles_completed'].values())
if total_candles % 10 == 0:
self._print_timeframe_summary()
except Exception as e:
logger.error(f"Error handling completed candle: {e}")
async def _monitor_aggregation(self, duration_seconds: int):
"""Monitor the aggregation process."""
logger.info(f"🔍 Monitoring aggregation for {duration_seconds} seconds...")
logger.info("Waiting for trade data to start arriving...")
start_time = datetime.now(timezone.utc)
last_status_time = start_time
status_interval = 30 # Print status every 30 seconds
while (datetime.now(timezone.utc) - start_time).total_seconds() < duration_seconds:
await asyncio.sleep(1)
current_time = datetime.now(timezone.utc)
# Print periodic status
if (current_time - last_status_time).total_seconds() >= status_interval:
self._print_status_update(current_time - start_time)
last_status_time = current_time
logger.info("⏰ Test duration completed")
def _print_status_update(self, elapsed_time):
"""Print periodic status update."""
logger.info("=" * 60)
logger.info(f"📈 STATUS UPDATE - Elapsed: {elapsed_time.total_seconds():.0f}s")
logger.info(f"Trades received: {self.stats['trades_received']}")
logger.info(f"Trades processed: {self.stats['trades_processed']}")
if self.stats['last_trade_time']:
logger.info(f"Last trade: {self.stats['last_trade_time'].strftime('%H:%M:%S.%f')[:-3]}")
# Show candle counts
total_candles = sum(self.stats['candles_completed'].values())
logger.info(f"Total candles completed: {total_candles}")
for timeframe in self.config.timeframes:
count = self.stats['candles_completed'][timeframe]
logger.info(f" {timeframe}: {count} candles")
# Show current aggregation status
current_candles = self.processor.get_current_candles(incomplete=True)
logger.info(f"Current incomplete candles: {len(current_candles)}")
# Show latest prices from latest candles
if self.latest_candles:
logger.info("Latest candle closes:")
for tf in self.config.timeframes:
if tf in self.latest_candles:
candle = self.latest_candles[tf]
logger.info(f" {tf}: ${candle.close} (at {candle.end_time.strftime('%H:%M:%S')})")
logger.info("=" * 60)
def _print_timeframe_summary(self):
"""Print summary of timeframe performance."""
logger.info("⚡ TIMEFRAME SUMMARY:")
total_candles = sum(self.stats['candles_completed'].values())
for timeframe in self.config.timeframes:
count = self.stats['candles_completed'][timeframe]
percentage = (count / total_candles * 100) if total_candles > 0 else 0
logger.info(f" {timeframe:>3s}: {count:>3d} candles ({percentage:5.1f}%)")
async def _cleanup(self):
"""Clean up resources."""
logger.info("🧹 Cleaning up...")
if self._ws_client:
await self._ws_client.disconnect()
# Force complete any remaining candles for final analysis
remaining_candles = self.processor.force_complete_all_candles()
if remaining_candles:
logger.info(f"🔚 Force completed {len(remaining_candles)} remaining candles")
async def _print_final_statistics(self):
"""Print comprehensive final statistics."""
session_duration = datetime.now(timezone.utc) - self.stats['session_start']
logger.info("")
logger.info("=" * 80)
logger.info("📊 FINAL TEST RESULTS")
logger.info("=" * 80)
# Basic stats
logger.info(f"Symbol: {self.symbol}")
logger.info(f"Session duration: {session_duration.total_seconds():.1f} seconds")
logger.info(f"Total trades received: {self.stats['trades_received']}")
logger.info(f"Total trades processed: {self.stats['trades_processed']}")
if self.stats['trades_processed'] > 0:
trade_rate = self.stats['trades_processed'] / session_duration.total_seconds()
logger.info(f"Average trade rate: {trade_rate:.2f} trades/second")
# Candle statistics
total_candles = sum(self.stats['candles_completed'].values())
logger.info(f"Total candles completed: {total_candles}")
logger.info("\nCandles by timeframe:")
for timeframe in self.config.timeframes:
count = self.stats['candles_completed'][timeframe]
percentage = (count / total_candles * 100) if total_candles > 0 else 0
# Calculate expected candles
if timeframe == '1s':
expected = int(session_duration.total_seconds())
elif timeframe == '5s':
expected = int(session_duration.total_seconds() / 5)
elif timeframe == '10s':
expected = int(session_duration.total_seconds() / 10)
elif timeframe == '15s':
expected = int(session_duration.total_seconds() / 15)
elif timeframe == '30s':
expected = int(session_duration.total_seconds() / 30)
else:
expected = "N/A"
logger.info(f" {timeframe:>3s}: {count:>3d} candles ({percentage:5.1f}%) - Expected: ~{expected}")
# Latest candle analysis
if self.latest_candles:
logger.info("\nLatest candle closes:")
for tf in self.config.timeframes:
if tf in self.latest_candles:
candle = self.latest_candles[tf]
logger.info(f" {tf}: ${candle.close}")
# Processor statistics
processor_stats = self.processor.get_stats()
logger.info(f"\nProcessor statistics:")
logger.info(f" Trades processed: {processor_stats.get('trades_processed', 0)}")
logger.info(f" Candles emitted: {processor_stats.get('candles_emitted', 0)}")
logger.info(f" Errors: {processor_stats.get('errors_count', 0)}")
logger.info("=" * 80)
logger.info("✅ REAL-TIME AGGREGATION TEST COMPLETED SUCCESSFULLY")
logger.info("=" * 80)
async def main():
"""Main test function."""
# Configuration
SYMBOL = "BTC-USDT" # High-activity pair for good test data
DURATION = 180 # 3 minutes for good test coverage
print("🚀 Real-Time OKX Second-Based Aggregation Test")
print(f"Testing symbol: {SYMBOL}")
print(f"Duration: {DURATION} seconds")
print("Press Ctrl+C to stop early\n")
# Create and run tester
tester = RealTimeAggregationTester(symbol=SYMBOL)
await tester.start_test(duration_seconds=DURATION)
if __name__ == "__main__":
try:
asyncio.run(main())
except KeyboardInterrupt:
print("\n⏹️ Test stopped by user")
except Exception as e:
print(f"\n❌ Test failed: {e}")
import traceback
traceback.print_exc()

190
tests/test_real_storage.py
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#!/usr/bin/env python3
"""
Test script for real database storage.
This script tests the OKX data collection system with actual database storage
to verify that raw trades and completed candles are being properly stored.
"""
import asyncio
import signal
import sys
import time
from datetime import datetime, timezone
from data.exchanges.okx import OKXCollector
from data.base_collector import DataType
from database.operations import get_database_operations
from utils.logger import get_logger
# Global test state
test_state = {
'running': True,
'collectors': []
}
def signal_handler(signum, frame):
"""Handle shutdown signals."""
print(f"\n📡 Received signal {signum}, shutting down collectors...")
test_state['running'] = False
# Register signal handlers
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
async def check_database_connection():
"""Check if database connection is available."""
try:
db_operations = get_database_operations()
# Test connection using the new repository pattern
is_healthy = db_operations.health_check()
if is_healthy:
print("✅ Database connection successful")
return True
else:
print("❌ Database health check failed")
return False
except Exception as e:
print(f"❌ Database connection failed: {e}")
print(" Make sure your database is running and configured correctly")
return False
async def count_stored_data():
"""Count raw trades and candles in database using repository pattern."""
try:
db_operations = get_database_operations()
# Get database statistics using the new operations module
stats = db_operations.get_stats()
if 'error' in stats:
print(f"❌ Error getting database stats: {stats['error']}")
return 0, 0
raw_count = stats.get('raw_trade_count', 0)
candle_count = stats.get('candle_count', 0)
print(f"📊 Database counts: Raw trades: {raw_count}, Candles: {candle_count}")
return raw_count, candle_count
except Exception as e:
print(f"❌ Error counting database records: {e}")
return 0, 0
async def test_real_storage(symbol: str = "BTC-USDT", duration: int = 60):
"""Test real database storage for specified duration."""
logger = get_logger("real_storage_test")
logger.info(f"🗄️ Testing REAL database storage for {symbol} for {duration} seconds")
# Check database connection first
if not await check_database_connection():
logger.error("Cannot proceed without database connection")
return False
# Get initial counts
initial_raw, initial_candles = await count_stored_data()
# Create collector with real database storage
collector = OKXCollector(
symbol=symbol,
data_types=[DataType.TRADE, DataType.ORDERBOOK, DataType.TICKER],
store_raw_data=True
)
test_state['collectors'].append(collector)
try:
# Connect and start collection
logger.info(f"Connecting to OKX for {symbol}...")
if not await collector.connect():
logger.error(f"Failed to connect collector for {symbol}")
return False
if not await collector.subscribe_to_data([symbol], collector.data_types):
logger.error(f"Failed to subscribe to data for {symbol}")
return False
if not await collector.start():
logger.error(f"Failed to start collector for {symbol}")
return False
logger.info(f"✅ Successfully started real storage test for {symbol}")
# Monitor for specified duration
start_time = time.time()
next_check = start_time + 10 # Check every 10 seconds
while time.time() - start_time < duration and test_state['running']:
await asyncio.sleep(1)
if time.time() >= next_check:
# Get and log statistics
stats = collector.get_status()
logger.info(f"[{symbol}] Stats: "
f"Messages: {stats['processing_stats']['messages_received']}, "
f"Trades: {stats['processing_stats']['trades_processed']}, "
f"Candles: {stats['processing_stats']['candles_processed']}")
# Check database counts
current_raw, current_candles = await count_stored_data()
new_raw = current_raw - initial_raw
new_candles = current_candles - initial_candles
logger.info(f"[{symbol}] NEW storage: Raw trades: +{new_raw}, Candles: +{new_candles}")
next_check += 10
# Final counts
final_raw, final_candles = await count_stored_data()
total_new_raw = final_raw - initial_raw
total_new_candles = final_candles - initial_candles
logger.info(f"🏁 FINAL RESULTS for {symbol}:")
logger.info(f" 📈 Raw trades stored: {total_new_raw}")
logger.info(f" 🕯️ Candles stored: {total_new_candles}")
# Stop collector
await collector.unsubscribe_from_data([symbol], collector.data_types)
await collector.stop()
await collector.disconnect()
logger.info(f"✅ Completed real storage test for {symbol}")
# Return success if we stored some data
return total_new_raw > 0
except Exception as e:
logger.error(f"❌ Error in real storage test for {symbol}: {e}")
return False
async def main():
"""Main test function."""
print("🗄️ OKX Real Database Storage Test")
print("=" * 50)
logger = get_logger("main")
try:
# Test with real database storage
success = await test_real_storage("BTC-USDT", 60)
if success:
print("✅ Real storage test completed successfully!")
print(" Check your database tables:")
print(" - raw_trades table should have new OKX trade data")
print(" - market_data table should have new OKX candles")
else:
print("❌ Real storage test failed")
sys.exit(1)
except Exception as e:
logger.error(f"Test failed: {e}")
sys.exit(1)
print("Test completed")
if __name__ == "__main__":
asyncio.run(main())

155
tests/test_recursion_fix.py
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#!/usr/bin/env python3
"""
Simple test to verify recursion fix in WebSocket task management.
"""
import asyncio
import sys
from pathlib import Path
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from data.exchanges.okx.websocket import OKXWebSocketClient, OKXSubscription, OKXChannelType
from utils.logger import get_logger
async def test_rapid_connection_cycles():
"""Test rapid connect/disconnect cycles to verify no recursion errors."""
logger = get_logger("recursion_test", verbose=False)
print("🧪 Testing WebSocket Recursion Fix")
print("=" * 40)
for cycle in range(5):
print(f"\n🔄 Cycle {cycle + 1}/5: Rapid connect/disconnect")
ws_client = OKXWebSocketClient(
component_name=f"test_client_{cycle}",
max_reconnect_attempts=2,
logger=logger
)
try:
# Connect
success = await ws_client.connect()
if not success:
print(f" ❌ Connection failed in cycle {cycle + 1}")
continue
# Subscribe
subscriptions = [
OKXSubscription(OKXChannelType.TRADES.value, "BTC-USDT")
]
await ws_client.subscribe(subscriptions)
# Quick activity
await asyncio.sleep(0.5)
# Disconnect (this should not cause recursion)
await ws_client.disconnect()
print(f" ✅ Cycle {cycle + 1} completed successfully")
except RecursionError as e:
print(f" ❌ Recursion error in cycle {cycle + 1}: {e}")
return False
except Exception as e:
print(f" ⚠️ Other error in cycle {cycle + 1}: {e}")
# Continue with other cycles
# Small delay between cycles
await asyncio.sleep(0.2)
print("\n✅ All cycles completed without recursion errors")
return True
async def test_concurrent_shutdowns():
"""Test concurrent client shutdowns to verify no recursion."""
logger = get_logger("concurrent_shutdown_test", verbose=False)
print("\n🔄 Testing Concurrent Shutdowns")
print("=" * 40)
# Create multiple clients
clients = []
for i in range(3):
client = OKXWebSocketClient(
component_name=f"concurrent_client_{i}",
logger=logger
)
clients.append(client)
try:
# Connect all clients
connect_tasks = [client.connect() for client in clients]
results = await asyncio.gather(*connect_tasks, return_exceptions=True)
successful_connections = sum(1 for r in results if r is True)
print(f"📡 Connected {successful_connections}/3 clients")
# Let them run briefly
await asyncio.sleep(1)
# Shutdown all concurrently (this is where recursion might occur)
print("🛑 Shutting down all clients concurrently...")
shutdown_tasks = [client.disconnect() for client in clients]
# Use wait_for to prevent hanging
try:
await asyncio.wait_for(
asyncio.gather(*shutdown_tasks, return_exceptions=True),
timeout=10.0
)
print("✅ All clients shut down successfully")
return True
except asyncio.TimeoutError:
print("⚠️ Shutdown timeout - but no recursion errors")
return True # Timeout is better than recursion
except RecursionError as e:
print(f"❌ Recursion error during concurrent shutdown: {e}")
return False
except Exception as e:
print(f"⚠️ Other error during test: {e}")
return True # Other errors are acceptable for this test
async def main():
"""Run recursion fix tests."""
print("🚀 WebSocket Recursion Fix Test Suite")
print("=" * 50)
try:
# Test 1: Rapid cycles
test1_success = await test_rapid_connection_cycles()
# Test 2: Concurrent shutdowns
test2_success = await test_concurrent_shutdowns()
# Summary
print("\n" + "=" * 50)
print("📋 Test Summary:")
print(f" Rapid Cycles: {'✅ PASS' if test1_success else '❌ FAIL'}")
print(f" Concurrent Shutdowns: {'✅ PASS' if test2_success else '❌ FAIL'}")
if test1_success and test2_success:
print("\n🎉 All tests passed! Recursion issue fixed.")
return 0
else:
print("\n❌ Some tests failed.")
return 1
except KeyboardInterrupt:
print("\n⏹️ Tests interrupted")
return 1
except Exception as e:
print(f"\n💥 Test suite failed: {e}")
return 1
if __name__ == "__main__":
exit_code = asyncio.run(main())
sys.exit(exit_code)

307
tests/test_refactored_okx.py
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#!/usr/bin/env python3
"""
Test script for the refactored OKX data collection system.
This script tests the new common data processing framework and OKX-specific
implementations including data validation, transformation, and aggregation.
"""
import asyncio
import json
import signal
import sys
import time
from datetime import datetime, timezone
from decimal import Decimal
sys.path.append('.')
from data.exchanges.okx import OKXCollector
from data.exchanges.okx.data_processor import OKXDataProcessor
from data.common import (
create_standardized_trade,
StandardizedTrade,
OHLCVCandle,
RealTimeCandleProcessor,
CandleProcessingConfig
)
from data.common.aggregation.realtime import RealTimeCandleProcessor
from data.base_collector import DataType
from utils.logger import get_logger
# Global test state
test_stats = {
'start_time': None,
'total_trades': 0,
'total_candles': 0,
'total_errors': 0,
'collectors': []
}
# Signal handler for graceful shutdown
def signal_handler(signum, frame):
logger = get_logger("main")
logger.info(f"Received signal {signum}, shutting down gracefully...")
# Stop all collectors
for collector in test_stats['collectors']:
try:
if hasattr(collector, 'stop'):
asyncio.create_task(collector.stop())
except Exception as e:
logger.error(f"Error stopping collector: {e}")
sys.exit(0)
# Register signal handlers
signal.signal(signal.SIGINT, signal_handler)
signal.signal(signal.SIGTERM, signal_handler)
class RealOKXCollector(OKXCollector):
"""Real OKX collector that actually stores to database (if available)."""
def __init__(self, *args, enable_db_storage=False, **kwargs):
super().__init__(*args, **kwargs)
self._enable_db_storage = enable_db_storage
self._test_mode = True
self._raw_data_count = 0
self._candle_storage_count = 0
if not enable_db_storage:
# Override database storage for testing
self._db_manager = None
self._raw_data_manager = None
async def _store_processed_data(self, data_point) -> None:
"""Store or log raw data depending on configuration."""
self._raw_data_count += 1
if self._enable_db_storage and self._db_manager:
# Actually store to database
await super()._store_processed_data(data_point)
self.logger.debug(f"[REAL] Stored raw data: {data_point.data_type.value} for {data_point.symbol} in raw_trades table")
else:
# Just log for testing
self.logger.debug(f"[TEST] Would store raw data: {data_point.data_type.value} for {data_point.symbol} in raw_trades table")
async def _store_completed_candle(self, candle) -> None:
"""Store or log completed candle depending on configuration."""
self._candle_storage_count += 1
if self._enable_db_storage and self._db_manager:
# Actually store to database
await super()._store_completed_candle(candle)
self.logger.info(f"[REAL] Stored candle: {candle.symbol} {candle.timeframe} O:{candle.open} H:{candle.high} L:{candle.low} C:{candle.close} V:{candle.volume} in market_data table")
else:
# Just log for testing
self.logger.info(f"[TEST] Would store candle: {candle.symbol} {candle.timeframe} O:{candle.open} H:{candle.high} L:{candle.low} C:{candle.close} V:{candle.volume} in market_data table")
async def _store_raw_data(self, channel: str, raw_message: dict) -> None:
"""Store or log raw WebSocket data depending on configuration."""
if self._enable_db_storage and self._raw_data_manager:
# Actually store to database
await super()._store_raw_data(channel, raw_message)
if 'data' in raw_message:
self.logger.debug(f"[REAL] Stored {len(raw_message['data'])} raw WebSocket items for channel {channel} in raw_trades table")
else:
# Just log for testing
if 'data' in raw_message:
self.logger.debug(f"[TEST] Would store {len(raw_message['data'])} raw WebSocket items for channel {channel} in raw_trades table")
def get_test_stats(self) -> dict:
"""Get test-specific statistics."""
base_stats = self.get_status()
base_stats.update({
'test_mode': self._test_mode,
'db_storage_enabled': self._enable_db_storage,
'raw_data_stored': self._raw_data_count,
'candles_stored': self._candle_storage_count
})
return base_stats
async def test_common_utilities():
"""Test the common data processing utilities."""
logger = get_logger("refactored_test")
logger.info("Testing common data utilities...")
# Test create_standardized_trade
trade = create_standardized_trade(
symbol="BTC-USDT",
trade_id="12345",
price=Decimal("50000.50"),
size=Decimal("0.1"),
side="buy",
timestamp=datetime.now(timezone.utc),
exchange="okx",
raw_data={"test": "data"}
)
logger.info(f"Created standardized trade: {trade}")
# Test OKX data processor
processor = OKXDataProcessor("BTC-USDT", component_name="test_processor")
# Test with sample OKX message
sample_message = {
"arg": {"channel": "trades", "instId": "BTC-USDT"},
"data": [{
"instId": "BTC-USDT",
"tradeId": "123456789",
"px": "50000.50",
"sz": "0.1",
"side": "buy",
"ts": str(int(datetime.now(timezone.utc).timestamp() * 1000))
}]
}
success, data_points, errors = processor.validate_and_process_message(sample_message)
logger.info(f"Message processing successful: {len(data_points)} data points")
if data_points:
logger.info(f"Data point: {data_points[0].exchange} {data_points[0].symbol} {data_points[0].data_type.value}")
# Get processor statistics
stats = processor.get_processing_stats()
logger.info(f"Processor stats: {stats}")
async def test_single_collector(symbol: str, duration: int = 30, enable_db_storage: bool = False):
"""Test a single OKX collector for the specified duration."""
logger = get_logger("refactored_test")
logger.info(f"Testing OKX collector for {symbol} for {duration} seconds...")
# Create collector (Real or Test version based on flag)
if enable_db_storage:
logger.info(f"Using REAL database storage for {symbol}")
collector = RealOKXCollector(
symbol=symbol,
data_types=[DataType.TRADE, DataType.ORDERBOOK, DataType.TICKER],
store_raw_data=True,
enable_db_storage=True
)
else:
logger.info(f"Using TEST mode (no database) for {symbol}")
collector = RealOKXCollector(
symbol=symbol,
data_types=[DataType.TRADE, DataType.ORDERBOOK, DataType.TICKER],
store_raw_data=True,
enable_db_storage=False
)
test_stats['collectors'].append(collector)
try:
# Connect and start collection
if not await collector.connect():
logger.error(f"Failed to connect collector for {symbol}")
return False
if not await collector.subscribe_to_data([symbol], collector.data_types):
logger.error(f"Failed to subscribe to data for {symbol}")
return False
if not await collector.start():
logger.error(f"Failed to start collector for {symbol}")
return False
logger.info(f"Successfully started collector for {symbol}")
# Monitor for specified duration
start_time = time.time()
while time.time() - start_time < duration:
await asyncio.sleep(5)
# Get and log statistics
stats = collector.get_test_stats()
logger.info(f"[{symbol}] Stats: "
f"Messages: {stats['processing_stats']['messages_received']}, "
f"Trades: {stats['processing_stats']['trades_processed']}, "
f"Candles: {stats['processing_stats']['candles_processed']}, "
f"Raw stored: {stats['raw_data_stored']}, "
f"Candles stored: {stats['candles_stored']}")
# Stop collector
await collector.unsubscribe_from_data([symbol], collector.data_types)
await collector.stop()
await collector.disconnect()
logger.info(f"Completed test for {symbol}")
return True
except Exception as e:
logger.error(f"Error in collector test for {symbol}: {e}")
return False
async def test_multiple_collectors(symbols: list, duration: int = 45):
"""Test multiple collectors running in parallel."""
logger = get_logger("refactored_test")
logger.info(f"Testing multiple collectors for {symbols} for {duration} seconds...")
# Create separate tasks for each unique symbol (avoid duplicates)
unique_symbols = list(set(symbols)) # Remove duplicates
tasks = []
for symbol in unique_symbols:
logger.info(f"Testing OKX collector for {symbol} for {duration} seconds...")
task = asyncio.create_task(test_single_collector(symbol, duration))
tasks.append(task)
# Wait for all collectors to complete
results = await asyncio.gather(*tasks, return_exceptions=True)
# Count successful collectors
successful = sum(1 for result in results if result is True)
logger.info(f"Multi-collector test completed: {successful}/{len(unique_symbols)} successful")
return successful == len(unique_symbols)
async def main():
"""Main test function."""
test_stats['start_time'] = time.time()
logger = get_logger("main")
logger.info("Starting refactored OKX test suite...")
# Check if user wants real database storage
import sys
enable_db_storage = '--real-db' in sys.argv
if enable_db_storage:
logger.info("🗄️ REAL DATABASE STORAGE ENABLED")
logger.info(" Raw trades and completed candles will be stored in database tables")
else:
logger.info("🧪 TEST MODE ENABLED (default)")
logger.info(" Database operations will be simulated (no actual storage)")
logger.info(" Use --real-db flag to enable real database storage")
try:
# Test 1: Common utilities
await test_common_utilities()
# Test 2: Single collector (with optional real DB storage)
await test_single_collector("BTC-USDT", 30, enable_db_storage)
# Test 3: Multiple collectors (unique symbols only)
unique_symbols = ["BTC-USDT", "ETH-USDT"] # Ensure no duplicates
await test_multiple_collectors(unique_symbols, 45)
# Final results
runtime = time.time() - test_stats['start_time']
logger.info("=== FINAL TEST RESULTS ===")
logger.info(f"Total runtime: {runtime:.1f}s")
logger.info(f"Total trades: {test_stats['total_trades']}")
logger.info(f"Total candles: {test_stats['total_candles']}")
logger.info(f"Total errors: {test_stats['total_errors']}")
if enable_db_storage:
logger.info("✅ All tests completed successfully with REAL database storage!")
else:
logger.info("✅ All tests completed successfully in TEST mode!")
except Exception as e:
logger.error(f"Test suite failed: {e}")
sys.exit(1)
logger.info("Test suite completed")
if __name__ == "__main__":
asyncio.run(main())

201
tests/test_setup.py
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@ -1,201 +0,0 @@
"""
Test script to verify the development environment setup.
"""
import sys
import time
from pathlib import Path
# Add the project root to Python path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
try:
from config.settings import database, redis, app, okx, dashboard
print("✅ Configuration module loaded successfully")
except ImportError as e:
print(f"❌ Failed to load configuration: {e}")
sys.exit(1)
def test_database_connection():
"""Test database connection."""
print("\n🔍 Testing database connection...")
try:
import psycopg2
from psycopg2 import sql
conn_params = {
"host": database.host,
"port": database.port,
"database": database.database,
"user": database.user,
"password": database.password,
}
print(f"Connecting to: {database.host}:{database.port}/{database.database}")
conn = psycopg2.connect(**conn_params)
cursor = conn.cursor()
# Test basic query
cursor.execute("SELECT version();")
version = cursor.fetchone()[0]
print(f"✅ Database connected successfully")
print(f" PostgreSQL version: {version}")
# Test if we can create tables
cursor.execute("""
CREATE TABLE IF NOT EXISTS test_table (
id SERIAL PRIMARY KEY,
name VARCHAR(100),
created_at TIMESTAMP DEFAULT NOW()
);
""")
cursor.execute("INSERT INTO test_table (name) VALUES ('test_setup');")
conn.commit()
cursor.execute("SELECT COUNT(*) FROM test_table;")
count = cursor.fetchone()[0]
print(f"✅ Database operations successful (test records: {count})")
# Clean up test table
cursor.execute("DROP TABLE IF EXISTS test_table;")
conn.commit()
cursor.close()
conn.close()
except ImportError:
print("❌ psycopg2 not installed, run: uv sync")
return False
except Exception as e:
print(f"❌ Database connection failed: {e}")
return False
return True
def test_redis_connection():
"""Test Redis connection."""
print("\n🔍 Testing Redis connection...")
try:
import redis as redis_module
r = redis_module.Redis(
host=redis.host,
port=redis.port,
password=redis.password,
decode_responses=True
)
# Test basic operations
r.set("test_key", "test_value")
value = r.get("test_key")
if value == "test_value":
print("✅ Redis connected successfully")
print(f" Connected to: {redis.host}:{redis.port}")
# Clean up
r.delete("test_key")
return True
else:
print("❌ Redis test failed")
return False
except ImportError:
print("❌ redis not installed, run: uv sync")
return False
except Exception as e:
print(f"❌ Redis connection failed: {e}")
return False
def test_configuration():
"""Test configuration loading."""
print("\n🔍 Testing configuration...")
print(f"Database URL: {database.connection_url}")
print(f"Redis URL: {redis.connection_url}")
print(f"Dashboard: {dashboard.host}:{dashboard.port}")
print(f"Environment: {app.environment}")
print(f"OKX configured: {okx.is_configured}")
if not okx.is_configured:
print("⚠️ OKX API not configured (update .env file)")
return True
def test_directories():
"""Test required directories exist."""
print("\n🔍 Testing directory structure...")
required_dirs = [
"config",
"config/bot_configs",
"database",
"scripts",
"tests",
]
all_exist = True
for dir_name in required_dirs:
dir_path = project_root / dir_name
if dir_path.exists():
print(f"✅ {dir_name}/ exists")
else:
print(f"❌ {dir_name}/ missing")
all_exist = False
return all_exist
def main():
"""Run all tests."""
print("🧪 Running setup verification tests...")
print(f"Project root: {project_root}")
tests = [
("Configuration", test_configuration),
("Directories", test_directories),
("Database", test_database_connection),
("Redis", test_redis_connection),
]
results = []
for test_name, test_func in tests:
try:
result = test_func()
results.append((test_name, result))
except Exception as e:
print(f"❌ {test_name} test crashed: {e}")
results.append((test_name, False))
print("\n📊 Test Results:")
print("=" * 40)
all_passed = True
for test_name, passed in results:
status = "✅ PASS" if passed else "❌ FAIL"
print(f"{test_name:15} {status}")
if not passed:
all_passed = False
print("=" * 40)
if all_passed:
print("🎉 All tests passed! Environment is ready.")
return 0
else:
print("⚠️ Some tests failed. Check the setup.")
return 1
if __name__ == "__main__":
sys.exit(main())

601
tests/test_signal_layers.py
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@ -1,601 +0,0 @@
"""
Foundation Tests for Signal Layer Functionality
This module contains comprehensive tests for the signal layer system including:
- Basic signal layer functionality
- Trade execution layer functionality
- Support/resistance layer functionality
- Custom strategy signal functionality
- Signal styling and theming
- Bot integration functionality
"""
import pytest
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from datetime import datetime, timedelta
from unittest.mock import Mock, patch, MagicMock
from dataclasses import dataclass
# Import signal layer components
from components.charts.layers.signals import (
TradingSignalLayer, SignalLayerConfig,
TradeExecutionLayer, TradeLayerConfig,
SupportResistanceLayer, SupportResistanceLayerConfig,
CustomStrategySignalLayer, CustomStrategySignalConfig,
EnhancedSignalLayer, SignalStyleConfig, SignalStyleManager,
create_trading_signal_layer, create_trade_execution_layer,
create_support_resistance_layer, create_custom_strategy_layer
)
from components.charts.layers.bot_integration import (
BotFilterConfig, BotDataService, BotSignalLayerIntegration,
get_active_bot_signals, get_active_bot_trades
)
from components.charts.layers.bot_enhanced_layers import (
BotIntegratedSignalLayer, BotSignalLayerConfig,
BotIntegratedTradeLayer, BotTradeLayerConfig,
create_bot_signal_layer, create_complete_bot_layers
)
class TestSignalLayerFoundation:
"""Test foundation functionality for signal layers"""
@pytest.fixture
def sample_ohlcv_data(self):
"""Generate sample OHLCV data for testing"""
dates = pd.date_range(start='2024-01-01', periods=100, freq='1h')
np.random.seed(42)
# Generate realistic price data
base_price = 50000
price_changes = np.random.normal(0, 0.01, len(dates))
prices = base_price * np.exp(np.cumsum(price_changes))
# Create OHLCV data
data = pd.DataFrame({
'timestamp': dates,
'open': prices * np.random.uniform(0.999, 1.001, len(dates)),
'high': prices * np.random.uniform(1.001, 1.01, len(dates)),
'low': prices * np.random.uniform(0.99, 0.999, len(dates)),
'close': prices,
'volume': np.random.uniform(100000, 1000000, len(dates))
})
return data
@pytest.fixture
def sample_signals(self):
"""Generate sample signal data for testing"""
signals = pd.DataFrame({
'timestamp': pd.date_range(start='2024-01-01', periods=20, freq='5h'),
'signal_type': ['buy', 'sell'] * 10,
'price': np.random.uniform(49000, 51000, 20),
'confidence': np.random.uniform(0.3, 0.9, 20),
'bot_id': [1, 2] * 10
})
return signals
@pytest.fixture
def sample_trades(self):
"""Generate sample trade data for testing"""
trades = pd.DataFrame({
'timestamp': pd.date_range(start='2024-01-01', periods=10, freq='10h'),
'side': ['buy', 'sell'] * 5,
'price': np.random.uniform(49000, 51000, 10),
'quantity': np.random.uniform(0.1, 1.0, 10),
'pnl': np.random.uniform(-100, 500, 10),
'fees': np.random.uniform(1, 10, 10),
'bot_id': [1, 2] * 5
})
return trades
class TestTradingSignalLayer(TestSignalLayerFoundation):
"""Test basic trading signal layer functionality"""
def test_signal_layer_initialization(self):
"""Test signal layer initialization with various configurations"""
# Default configuration
layer = TradingSignalLayer()
assert layer.config.name == "Trading Signals"
assert layer.config.enabled is True
assert 'buy' in layer.config.signal_types
assert 'sell' in layer.config.signal_types
# Custom configuration
config = SignalLayerConfig(
name="Custom Signals",
signal_types=['buy'],
confidence_threshold=0.7,
marker_size=15
)
layer = TradingSignalLayer(config)
assert layer.config.name == "Custom Signals"
assert layer.config.signal_types == ['buy']
assert layer.config.confidence_threshold == 0.7
def test_signal_filtering(self, sample_signals):
"""Test signal filtering by type and confidence"""
config = SignalLayerConfig(
name="Test Layer",
signal_types=['buy'],
confidence_threshold=0.5
)
layer = TradingSignalLayer(config)
filtered = layer.filter_signals_by_config(sample_signals)
# Should only contain buy signals
assert all(filtered['signal_type'] == 'buy')
# Should only contain signals above confidence threshold
assert all(filtered['confidence'] >= 0.5)
def test_signal_rendering(self, sample_ohlcv_data, sample_signals):
"""Test signal rendering on chart"""
layer = TradingSignalLayer()
fig = go.Figure()
# Add basic candlestick data first
fig.add_trace(go.Candlestick(
x=sample_ohlcv_data['timestamp'],
open=sample_ohlcv_data['open'],
high=sample_ohlcv_data['high'],
low=sample_ohlcv_data['low'],
close=sample_ohlcv_data['close']
))
# Render signals
updated_fig = layer.render(fig, sample_ohlcv_data, sample_signals)
# Should have added signal traces
assert len(updated_fig.data) > 1
# Check for signal traces (the exact names may vary)
trace_names = [trace.name for trace in updated_fig.data if trace.name is not None]
# Should have some signal traces
assert len(trace_names) > 0
def test_convenience_functions(self):
"""Test convenience functions for creating signal layers"""
# Basic trading signal layer
layer = create_trading_signal_layer()
assert isinstance(layer, TradingSignalLayer)
# Buy signals only
layer = create_trading_signal_layer(signal_types=['buy'])
assert layer.config.signal_types == ['buy']
# High confidence signals
layer = create_trading_signal_layer(confidence_threshold=0.8)
assert layer.config.confidence_threshold == 0.8
class TestTradeExecutionLayer(TestSignalLayerFoundation):
"""Test trade execution layer functionality"""
def test_trade_layer_initialization(self):
"""Test trade layer initialization"""
layer = TradeExecutionLayer()
assert layer.config.name == "Trade Executions" # Corrected expected name
assert layer.config.show_pnl is True
# Custom configuration
config = TradeLayerConfig(
name="Bot Trades",
show_pnl=False,
show_trade_lines=True
)
layer = TradeExecutionLayer(config)
assert layer.config.name == "Bot Trades"
assert layer.config.show_pnl is False
assert layer.config.show_trade_lines is True
def test_trade_pairing(self, sample_trades):
"""Test FIFO trade pairing algorithm"""
layer = TradeExecutionLayer()
# Create trades with entry/exit pairs
trades = pd.DataFrame({
'timestamp': pd.date_range(start='2024-01-01', periods=4, freq='1h'),
'side': ['buy', 'sell', 'buy', 'sell'],
'price': [50000, 50100, 49900, 50200],
'quantity': [1.0, 1.0, 0.5, 0.5],
'bot_id': [1, 1, 1, 1]
})
paired_trades = layer.pair_entry_exit_trades(trades) # Correct method name
# Should have some trade pairs
assert len(paired_trades) > 0
# First pair should have entry and exit
assert 'entry_time' in paired_trades[0]
assert 'exit_time' in paired_trades[0]
def test_trade_rendering(self, sample_ohlcv_data, sample_trades):
"""Test trade rendering on chart"""
layer = TradeExecutionLayer()
fig = go.Figure()
updated_fig = layer.render(fig, sample_ohlcv_data, sample_trades)
# Should have added trade traces
assert len(updated_fig.data) > 0
# Check for traces (actual names may vary)
trace_names = [trace.name for trace in updated_fig.data if trace.name is not None]
assert len(trace_names) > 0
class TestSupportResistanceLayer(TestSignalLayerFoundation):
"""Test support/resistance layer functionality"""
def test_sr_layer_initialization(self):
"""Test support/resistance layer initialization"""
config = SupportResistanceLayerConfig(
name="Test S/R", # Added required name parameter
auto_detect=True,
line_types=['support', 'resistance'],
min_touches=3,
sensitivity=0.02
)
layer = SupportResistanceLayer(config)
assert layer.config.auto_detect is True
assert layer.config.min_touches == 3
assert layer.config.sensitivity == 0.02
def test_pivot_detection(self, sample_ohlcv_data):
"""Test pivot point detection for S/R levels"""
layer = SupportResistanceLayer()
# Test S/R level detection instead of pivot points directly
levels = layer.detect_support_resistance_levels(sample_ohlcv_data)
assert isinstance(levels, list)
# Should detect some levels
assert len(levels) >= 0 # May be empty for limited data
def test_sr_level_detection(self, sample_ohlcv_data):
"""Test support and resistance level detection"""
config = SupportResistanceLayerConfig(
name="Test S/R Detection", # Added required name parameter
auto_detect=True,
min_touches=2,
sensitivity=0.01
)
layer = SupportResistanceLayer(config)
levels = layer.detect_support_resistance_levels(sample_ohlcv_data)
assert isinstance(levels, list)
# Each level should be a dictionary with required fields
for level in levels:
assert isinstance(level, dict)
def test_manual_levels(self, sample_ohlcv_data):
"""Test manual support/resistance levels"""
manual_levels = [
{'price_level': 49000, 'line_type': 'support', 'description': 'Manual support'},
{'price_level': 51000, 'line_type': 'resistance', 'description': 'Manual resistance'}
]
config = SupportResistanceLayerConfig(
name="Manual S/R", # Added required name parameter
auto_detect=False,
manual_levels=manual_levels
)
layer = SupportResistanceLayer(config)
fig = go.Figure()
updated_fig = layer.render(fig, sample_ohlcv_data)
# Should have added shapes or traces for manual levels
assert len(updated_fig.data) > 0 or len(updated_fig.layout.shapes) > 0
class TestCustomStrategyLayers(TestSignalLayerFoundation):
"""Test custom strategy signal layer functionality"""
def test_custom_strategy_initialization(self):
"""Test custom strategy layer initialization"""
config = CustomStrategySignalConfig(
name="Test Strategy",
signal_definitions={
'entry_long': {'color': 'green', 'symbol': 'triangle-up'},
'exit_long': {'color': 'red', 'symbol': 'triangle-down'}
}
)
layer = CustomStrategySignalLayer(config)
assert layer.config.name == "Test Strategy"
assert 'entry_long' in layer.config.signal_definitions
assert 'exit_long' in layer.config.signal_definitions
def test_custom_signal_validation(self):
"""Test custom signal validation"""
config = CustomStrategySignalConfig(
name="Validation Test",
signal_definitions={
'test_signal': {'color': 'blue', 'symbol': 'circle'}
}
)
layer = CustomStrategySignalLayer(config)
# Valid signal
signals = pd.DataFrame({
'timestamp': [datetime.now()],
'signal_type': ['test_signal'],
'price': [50000],
'confidence': [0.8]
})
# Test strategy data validation instead
assert layer.validate_strategy_data(signals) is True
# Invalid signal type
invalid_signals = pd.DataFrame({
'timestamp': [datetime.now()],
'signal_type': ['invalid_signal'],
'price': [50000],
'confidence': [0.8]
})
# This should handle invalid signals gracefully
result = layer.validate_strategy_data(invalid_signals)
# Should either return False or handle gracefully
assert isinstance(result, bool)
def test_predefined_strategies(self):
"""Test predefined strategy convenience functions"""
from components.charts.layers.signals import (
create_pairs_trading_layer,
create_momentum_strategy_layer,
create_mean_reversion_layer
)
# Pairs trading strategy
pairs_layer = create_pairs_trading_layer()
assert isinstance(pairs_layer, CustomStrategySignalLayer)
assert 'long_spread' in pairs_layer.config.signal_definitions
# Momentum strategy
momentum_layer = create_momentum_strategy_layer()
assert isinstance(momentum_layer, CustomStrategySignalLayer)
assert 'momentum_buy' in momentum_layer.config.signal_definitions
# Mean reversion strategy
mean_rev_layer = create_mean_reversion_layer()
assert isinstance(mean_rev_layer, CustomStrategySignalLayer)
# Check for actual signal definitions that exist
signal_defs = mean_rev_layer.config.signal_definitions
assert len(signal_defs) > 0
# Use any actual signal definition instead of specific 'oversold'
assert any('entry' in signal for signal in signal_defs.keys())
class TestSignalStyling(TestSignalLayerFoundation):
"""Test signal styling and theming functionality"""
def test_style_manager_initialization(self):
"""Test signal style manager initialization"""
manager = SignalStyleManager()
# Should have predefined color schemes
assert 'default' in manager.color_schemes
assert 'professional' in manager.color_schemes
assert 'colorblind_friendly' in manager.color_schemes
def test_enhanced_signal_layer(self, sample_signals, sample_ohlcv_data):
"""Test enhanced signal layer with styling"""
style_config = SignalStyleConfig(
color_scheme='professional',
opacity=0.8, # Corrected parameter name
marker_sizes={'buy': 12, 'sell': 12}
)
config = SignalLayerConfig(name="Enhanced Test")
layer = EnhancedSignalLayer(config, style_config=style_config)
fig = go.Figure()
updated_fig = layer.render(fig, sample_ohlcv_data, sample_signals)
# Should have applied professional styling
assert len(updated_fig.data) > 0
def test_themed_layers(self):
"""Test themed layer convenience functions"""
from components.charts.layers.signals import (
create_professional_signal_layer,
create_colorblind_friendly_signal_layer,
create_dark_theme_signal_layer
)
# Professional theme
prof_layer = create_professional_signal_layer()
assert isinstance(prof_layer, EnhancedSignalLayer)
assert prof_layer.style_config.color_scheme == 'professional'
# Colorblind friendly theme
cb_layer = create_colorblind_friendly_signal_layer()
assert isinstance(cb_layer, EnhancedSignalLayer)
assert cb_layer.style_config.color_scheme == 'colorblind_friendly'
# Dark theme
dark_layer = create_dark_theme_signal_layer()
assert isinstance(dark_layer, EnhancedSignalLayer)
assert dark_layer.style_config.color_scheme == 'dark_theme'
class TestBotIntegration(TestSignalLayerFoundation):
"""Test bot integration functionality"""
def test_bot_filter_config(self):
"""Test bot filter configuration"""
config = BotFilterConfig(
bot_ids=[1, 2, 3],
symbols=['BTCUSDT'],
strategies=['momentum'],
active_only=True
)
assert config.bot_ids == [1, 2, 3]
assert config.symbols == ['BTCUSDT']
assert config.strategies == ['momentum']
assert config.active_only is True
@patch('components.charts.layers.bot_integration.get_session')
def test_bot_data_service(self, mock_get_session):
"""Test bot data service functionality"""
# Mock database session and context manager
mock_session = MagicMock()
mock_context = MagicMock()
mock_context.__enter__ = MagicMock(return_value=mock_session)
mock_context.__exit__ = MagicMock(return_value=None)
mock_get_session.return_value = mock_context
# Mock bot attributes with proper types
mock_bot = MagicMock()
mock_bot.id = 1
mock_bot.name = "Test Bot"
mock_bot.strategy_name = "momentum"
mock_bot.symbol = "BTCUSDT"
mock_bot.timeframe = "1h"
mock_bot.status = "active"
mock_bot.config_file = "test_config.json"
mock_bot.virtual_balance = 10000.0
mock_bot.current_balance = 10100.0
mock_bot.pnl = 100.0
mock_bot.is_active = True
mock_bot.last_heartbeat = datetime.now()
mock_bot.created_at = datetime.now()
mock_bot.updated_at = datetime.now()
# Create mock query chain that supports chaining operations
mock_query = MagicMock()
mock_query.filter.return_value = mock_query # Chain filters
mock_query.all.return_value = [mock_bot] # Final result
# Mock session.query() to return the chainable query
mock_session.query.return_value = mock_query
service = BotDataService()
# Test get_bots method
bots_df = service.get_bots()
assert len(bots_df) == 1
assert bots_df.iloc[0]['name'] == "Test Bot"
assert bots_df.iloc[0]['strategy_name'] == "momentum"
def test_bot_integrated_signal_layer(self):
"""Test bot-integrated signal layer"""
config = BotSignalLayerConfig(
name="Bot Signals",
auto_fetch_data=False, # Disable auto-fetch for testing
active_bots_only=True,
include_bot_info=True
)
layer = BotIntegratedSignalLayer(config)
assert layer.bot_config.auto_fetch_data is False
assert layer.bot_config.active_bots_only is True
assert layer.bot_config.include_bot_info is True
def test_bot_integration_convenience_functions(self):
"""Test bot integration convenience functions"""
# Bot signal layer
layer = create_bot_signal_layer('BTCUSDT', active_only=True)
assert isinstance(layer, BotIntegratedSignalLayer)
# Complete bot layers
result = create_complete_bot_layers('BTCUSDT')
assert 'layers' in result
assert 'metadata' in result
assert result['symbol'] == 'BTCUSDT'
class TestFoundationIntegration(TestSignalLayerFoundation):
"""Test overall foundation integration"""
def test_layer_combinations(self, sample_ohlcv_data, sample_signals, sample_trades):
"""Test combining multiple signal layers"""
# Create multiple layers
signal_layer = TradingSignalLayer()
trade_layer = TradeExecutionLayer()
sr_layer = SupportResistanceLayer()
fig = go.Figure()
# Add layers sequentially
fig = signal_layer.render(fig, sample_ohlcv_data, sample_signals)
fig = trade_layer.render(fig, sample_ohlcv_data, sample_trades)
fig = sr_layer.render(fig, sample_ohlcv_data)
# Should have traces from all layers
assert len(fig.data) >= 0 # At least some traces should be added
def test_error_handling(self, sample_ohlcv_data):
"""Test error handling in signal layers"""
layer = TradingSignalLayer()
fig = go.Figure()
# Test with empty signals
empty_signals = pd.DataFrame()
updated_fig = layer.render(fig, sample_ohlcv_data, empty_signals)
# Should handle empty data gracefully
assert isinstance(updated_fig, go.Figure)
# Test with invalid data
invalid_signals = pd.DataFrame({'invalid_column': [1, 2, 3]})
updated_fig = layer.render(fig, sample_ohlcv_data, invalid_signals)
# Should handle invalid data gracefully
assert isinstance(updated_fig, go.Figure)
def test_performance_with_large_datasets(self):
"""Test performance with large datasets"""
# Generate large dataset
large_signals = pd.DataFrame({
'timestamp': pd.date_range(start='2024-01-01', periods=10000, freq='1min'),
'signal_type': np.random.choice(['buy', 'sell'], 10000),
'price': np.random.uniform(49000, 51000, 10000),
'confidence': np.random.uniform(0.3, 0.9, 10000)
})
layer = TradingSignalLayer()
# Should handle large datasets efficiently
import time
start_time = time.time()
filtered = layer.filter_signals_by_config(large_signals) # Correct method name
end_time = time.time()
# Should complete within reasonable time (< 1 second)
assert end_time - start_time < 1.0
assert len(filtered) <= len(large_signals)
if __name__ == "__main__":
"""
Run specific tests for development
"""
import sys
# Run specific test class
if len(sys.argv) > 1:
test_class = sys.argv[1]
pytest.main([f"-v", f"test_signal_layers.py::{test_class}"])
else:
# Run all tests
pytest.main(["-v", "test_signal_layers.py"])

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tests/test_strategy_charts.py
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@ -1,525 +0,0 @@
"""
Tests for Strategy Chart Configuration System
Tests the comprehensive strategy chart configuration system including
chart layouts, subplot management, indicator combinations, and JSON serialization.
"""
import pytest
import json
from typing import Dict, List, Any
from datetime import datetime
from components.charts.config.strategy_charts import (
ChartLayout,
SubplotType,
SubplotConfig,
ChartStyle,
StrategyChartConfig,
create_default_strategy_configurations,
validate_strategy_configuration,
create_custom_strategy_config,
load_strategy_config_from_json,
export_strategy_config_to_json,
get_strategy_config,
get_all_strategy_configs,
get_available_strategy_names
)
from components.charts.config.defaults import TradingStrategy
class TestChartLayoutComponents:
"""Test chart layout component classes."""
def test_chart_layout_enum(self):
"""Test ChartLayout enum values."""
layouts = [layout.value for layout in ChartLayout]
expected_layouts = ["single_chart", "main_with_subplots", "multi_chart", "grid_layout"]
for expected in expected_layouts:
assert expected in layouts
def test_subplot_type_enum(self):
"""Test SubplotType enum values."""
subplot_types = [subplot_type.value for subplot_type in SubplotType]
expected_types = ["volume", "rsi", "macd", "momentum", "custom"]
for expected in expected_types:
assert expected in subplot_types
def test_subplot_config_creation(self):
"""Test SubplotConfig creation and defaults."""
subplot = SubplotConfig(subplot_type=SubplotType.RSI)
assert subplot.subplot_type == SubplotType.RSI
assert subplot.height_ratio == 0.3
assert subplot.indicators == []
assert subplot.title is None
assert subplot.y_axis_label is None
assert subplot.show_grid is True
assert subplot.show_legend is True
assert subplot.background_color is None
def test_chart_style_defaults(self):
"""Test ChartStyle creation and defaults."""
style = ChartStyle()
assert style.theme == "plotly_white"
assert style.background_color == "#ffffff"
assert style.grid_color == "#e6e6e6"
assert style.text_color == "#2c3e50"
assert style.font_family == "Arial, sans-serif"
assert style.font_size == 12
assert style.candlestick_up_color == "#26a69a"
assert style.candlestick_down_color == "#ef5350"
assert style.volume_color == "#78909c"
assert style.show_volume is True
assert style.show_grid is True
assert style.show_legend is True
assert style.show_toolbar is True
class TestStrategyChartConfig:
"""Test StrategyChartConfig class functionality."""
def create_test_config(self) -> StrategyChartConfig:
"""Create a test strategy configuration."""
return StrategyChartConfig(
strategy_name="Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Test strategy for unit testing",
timeframes=["5m", "15m", "1h"],
layout=ChartLayout.MAIN_WITH_SUBPLOTS,
main_chart_height=0.7,
overlay_indicators=["sma_20", "ema_12"],
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
height_ratio=0.2,
indicators=["rsi_14"],
title="RSI",
y_axis_label="RSI"
),
SubplotConfig(
subplot_type=SubplotType.VOLUME,
height_ratio=0.1,
indicators=[],
title="Volume"
)
],
tags=["test", "day-trading"]
)
def test_strategy_config_creation(self):
"""Test StrategyChartConfig creation."""
config = self.create_test_config()
assert config.strategy_name == "Test Strategy"
assert config.strategy_type == TradingStrategy.DAY_TRADING
assert config.description == "Test strategy for unit testing"
assert config.timeframes == ["5m", "15m", "1h"]
assert config.layout == ChartLayout.MAIN_WITH_SUBPLOTS
assert config.main_chart_height == 0.7
assert config.overlay_indicators == ["sma_20", "ema_12"]
assert len(config.subplot_configs) == 2
assert config.tags == ["test", "day-trading"]
def test_strategy_config_validation_success(self):
"""Test successful validation of strategy configuration."""
config = self.create_test_config()
is_valid, errors = config.validate()
# Note: This might fail if the indicators don't exist in defaults
# but we'll test the validation logic
assert isinstance(is_valid, bool)
assert isinstance(errors, list)
def test_strategy_config_validation_missing_name(self):
"""Test validation with missing strategy name."""
config = self.create_test_config()
config.strategy_name = ""
is_valid, errors = config.validate()
assert not is_valid
assert "Strategy name is required" in errors
def test_strategy_config_validation_invalid_height_ratios(self):
"""Test validation with invalid height ratios."""
config = self.create_test_config()
config.main_chart_height = 0.8
config.subplot_configs[0].height_ratio = 0.3 # Total = 1.1 > 1.0
is_valid, errors = config.validate()
assert not is_valid
assert any("height ratios exceed 1.0" in error for error in errors)
def test_strategy_config_validation_invalid_main_height(self):
"""Test validation with invalid main chart height."""
config = self.create_test_config()
config.main_chart_height = 1.5 # Invalid: > 1.0
is_valid, errors = config.validate()
assert not is_valid
assert any("Main chart height must be between 0 and 1.0" in error for error in errors)
def test_strategy_config_validation_invalid_subplot_height(self):
"""Test validation with invalid subplot height."""
config = self.create_test_config()
config.subplot_configs[0].height_ratio = -0.1 # Invalid: <= 0
is_valid, errors = config.validate()
assert not is_valid
assert any("height ratio must be between 0 and 1.0" in error for error in errors)
def test_get_all_indicators(self):
"""Test getting all indicators from configuration."""
config = self.create_test_config()
all_indicators = config.get_all_indicators()
expected = ["sma_20", "ema_12", "rsi_14"]
assert len(all_indicators) == len(expected)
for indicator in expected:
assert indicator in all_indicators
def test_get_indicator_configs(self):
"""Test getting indicator configuration objects."""
config = self.create_test_config()
indicator_configs = config.get_indicator_configs()
# Should return a dictionary
assert isinstance(indicator_configs, dict)
# Results depend on what indicators exist in defaults
class TestDefaultStrategyConfigurations:
"""Test default strategy configuration creation."""
def test_create_default_strategy_configurations(self):
"""Test creation of default strategy configurations."""
strategy_configs = create_default_strategy_configurations()
# Should have configurations for all strategy types
expected_strategies = ["scalping", "day_trading", "swing_trading",
"position_trading", "momentum", "mean_reversion"]
for strategy in expected_strategies:
assert strategy in strategy_configs
config = strategy_configs[strategy]
assert isinstance(config, StrategyChartConfig)
# Validate each configuration
is_valid, errors = config.validate()
# Note: Some validations might fail due to missing indicators in test environment
assert isinstance(is_valid, bool)
assert isinstance(errors, list)
def test_scalping_strategy_config(self):
"""Test scalping strategy configuration specifics."""
strategy_configs = create_default_strategy_configurations()
scalping = strategy_configs["scalping"]
assert scalping.strategy_name == "Scalping Strategy"
assert scalping.strategy_type == TradingStrategy.SCALPING
assert "1m" in scalping.timeframes
assert "5m" in scalping.timeframes
assert scalping.main_chart_height == 0.6
assert len(scalping.overlay_indicators) > 0
assert len(scalping.subplot_configs) > 0
assert "scalping" in scalping.tags
def test_day_trading_strategy_config(self):
"""Test day trading strategy configuration specifics."""
strategy_configs = create_default_strategy_configurations()
day_trading = strategy_configs["day_trading"]
assert day_trading.strategy_name == "Day Trading Strategy"
assert day_trading.strategy_type == TradingStrategy.DAY_TRADING
assert "5m" in day_trading.timeframes
assert "15m" in day_trading.timeframes
assert "1h" in day_trading.timeframes
assert len(day_trading.overlay_indicators) > 0
assert len(day_trading.subplot_configs) > 0
def test_position_trading_strategy_config(self):
"""Test position trading strategy configuration specifics."""
strategy_configs = create_default_strategy_configurations()
position = strategy_configs["position_trading"]
assert position.strategy_name == "Position Trading Strategy"
assert position.strategy_type == TradingStrategy.POSITION_TRADING
assert "4h" in position.timeframes
assert "1d" in position.timeframes
assert "1w" in position.timeframes
assert position.chart_style.show_volume is False # Less important for long-term
class TestCustomStrategyCreation:
"""Test custom strategy configuration creation."""
def test_create_custom_strategy_config_success(self):
"""Test successful creation of custom strategy configuration."""
subplot_configs = [
{
"subplot_type": "rsi",
"height_ratio": 0.2,
"indicators": ["rsi_14"],
"title": "Custom RSI"
}
]
config, errors = create_custom_strategy_config(
strategy_name="Custom Test Strategy",
strategy_type=TradingStrategy.SWING_TRADING,
description="Custom strategy for testing",
timeframes=["1h", "4h"],
overlay_indicators=["sma_50"],
subplot_configs=subplot_configs,
tags=["custom", "test"]
)
if config: # Only test if creation succeeded
assert config.strategy_name == "Custom Test Strategy"
assert config.strategy_type == TradingStrategy.SWING_TRADING
assert config.description == "Custom strategy for testing"
assert config.timeframes == ["1h", "4h"]
assert config.overlay_indicators == ["sma_50"]
assert len(config.subplot_configs) == 1
assert config.tags == ["custom", "test"]
assert config.created_at is not None
def test_create_custom_strategy_config_with_style(self):
"""Test custom strategy creation with chart style."""
chart_style = {
"theme": "plotly_dark",
"font_size": 14,
"candlestick_up_color": "#00ff00",
"candlestick_down_color": "#ff0000"
}
config, errors = create_custom_strategy_config(
strategy_name="Styled Strategy",
strategy_type=TradingStrategy.MOMENTUM,
description="Strategy with custom styling",
timeframes=["15m"],
overlay_indicators=[],
subplot_configs=[],
chart_style=chart_style
)
if config: # Only test if creation succeeded
assert config.chart_style.theme == "plotly_dark"
assert config.chart_style.font_size == 14
assert config.chart_style.candlestick_up_color == "#00ff00"
assert config.chart_style.candlestick_down_color == "#ff0000"
class TestJSONSerialization:
"""Test JSON serialization and deserialization."""
def create_test_config_for_json(self) -> StrategyChartConfig:
"""Create a simple test configuration for JSON testing."""
return StrategyChartConfig(
strategy_name="JSON Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Strategy for JSON testing",
timeframes=["15m", "1h"],
overlay_indicators=["ema_12"],
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
height_ratio=0.25,
indicators=["rsi_14"],
title="RSI Test"
)
],
tags=["json", "test"]
)
def test_export_strategy_config_to_json(self):
"""Test exporting strategy configuration to JSON."""
config = self.create_test_config_for_json()
json_str = export_strategy_config_to_json(config)
# Should be valid JSON
data = json.loads(json_str)
# Check key fields
assert data["strategy_name"] == "JSON Test Strategy"
assert data["strategy_type"] == "day_trading"
assert data["description"] == "Strategy for JSON testing"
assert data["timeframes"] == ["15m", "1h"]
assert data["overlay_indicators"] == ["ema_12"]
assert len(data["subplot_configs"]) == 1
assert data["tags"] == ["json", "test"]
# Check subplot configuration
subplot = data["subplot_configs"][0]
assert subplot["subplot_type"] == "rsi"
assert subplot["height_ratio"] == 0.25
assert subplot["indicators"] == ["rsi_14"]
assert subplot["title"] == "RSI Test"
def test_load_strategy_config_from_json_dict(self):
"""Test loading strategy configuration from JSON dictionary."""
json_data = {
"strategy_name": "JSON Loaded Strategy",
"strategy_type": "swing_trading",
"description": "Strategy loaded from JSON",
"timeframes": ["1h", "4h"],
"overlay_indicators": ["sma_20"],
"subplot_configs": [
{
"subplot_type": "macd",
"height_ratio": 0.3,
"indicators": ["macd_12_26_9"],
"title": "MACD Test"
}
],
"tags": ["loaded", "test"]
}
config, errors = load_strategy_config_from_json(json_data)
if config: # Only test if loading succeeded
assert config.strategy_name == "JSON Loaded Strategy"
assert config.strategy_type == TradingStrategy.SWING_TRADING
assert config.description == "Strategy loaded from JSON"
assert config.timeframes == ["1h", "4h"]
assert config.overlay_indicators == ["sma_20"]
assert len(config.subplot_configs) == 1
assert config.tags == ["loaded", "test"]
def test_load_strategy_config_from_json_string(self):
"""Test loading strategy configuration from JSON string."""
json_data = {
"strategy_name": "String Loaded Strategy",
"strategy_type": "momentum",
"description": "Strategy loaded from JSON string",
"timeframes": ["5m", "15m"]
}
json_str = json.dumps(json_data)
config, errors = load_strategy_config_from_json(json_str)
if config: # Only test if loading succeeded
assert config.strategy_name == "String Loaded Strategy"
assert config.strategy_type == TradingStrategy.MOMENTUM
def test_load_strategy_config_missing_fields(self):
"""Test loading strategy configuration with missing required fields."""
json_data = {
"strategy_name": "Incomplete Strategy",
# Missing strategy_type, description, timeframes
}
config, errors = load_strategy_config_from_json(json_data)
assert config is None
assert len(errors) > 0
assert any("Missing required fields" in error for error in errors)
def test_load_strategy_config_invalid_strategy_type(self):
"""Test loading strategy configuration with invalid strategy type."""
json_data = {
"strategy_name": "Invalid Strategy",
"strategy_type": "invalid_strategy_type",
"description": "Strategy with invalid type",
"timeframes": ["1h"]
}
config, errors = load_strategy_config_from_json(json_data)
assert config is None
assert len(errors) > 0
assert any("Invalid strategy type" in error for error in errors)
def test_roundtrip_json_serialization(self):
"""Test roundtrip JSON serialization (export then import)."""
original_config = self.create_test_config_for_json()
# Export to JSON
json_str = export_strategy_config_to_json(original_config)
# Import from JSON
loaded_config, errors = load_strategy_config_from_json(json_str)
if loaded_config: # Only test if roundtrip succeeded
# Compare key fields (some fields like created_at won't match)
assert loaded_config.strategy_name == original_config.strategy_name
assert loaded_config.strategy_type == original_config.strategy_type
assert loaded_config.description == original_config.description
assert loaded_config.timeframes == original_config.timeframes
assert loaded_config.overlay_indicators == original_config.overlay_indicators
assert len(loaded_config.subplot_configs) == len(original_config.subplot_configs)
assert loaded_config.tags == original_config.tags
class TestStrategyConfigAccessors:
"""Test strategy configuration accessor functions."""
def test_get_strategy_config(self):
"""Test getting strategy configuration by name."""
config = get_strategy_config("day_trading")
if config:
assert isinstance(config, StrategyChartConfig)
assert config.strategy_type == TradingStrategy.DAY_TRADING
# Test non-existent strategy
non_existent = get_strategy_config("non_existent_strategy")
assert non_existent is None
def test_get_all_strategy_configs(self):
"""Test getting all strategy configurations."""
all_configs = get_all_strategy_configs()
assert isinstance(all_configs, dict)
assert len(all_configs) > 0
# Check that all values are StrategyChartConfig instances
for config in all_configs.values():
assert isinstance(config, StrategyChartConfig)
def test_get_available_strategy_names(self):
"""Test getting available strategy names."""
strategy_names = get_available_strategy_names()
assert isinstance(strategy_names, list)
assert len(strategy_names) > 0
# Should include expected strategy names
expected_names = ["scalping", "day_trading", "swing_trading",
"position_trading", "momentum", "mean_reversion"]
for expected in expected_names:
assert expected in strategy_names
class TestValidationFunction:
"""Test standalone validation function."""
def test_validate_strategy_configuration_function(self):
"""Test the standalone validation function."""
config = StrategyChartConfig(
strategy_name="Validation Test",
strategy_type=TradingStrategy.DAY_TRADING,
description="Test validation function",
timeframes=["1h"],
main_chart_height=0.8,
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
height_ratio=0.2
)
]
)
is_valid, errors = validate_strategy_configuration(config)
assert isinstance(is_valid, bool)
assert isinstance(errors, list)
# This should be valid (total height = 1.0)
# Note: Validation might fail due to missing indicators in test environment
if __name__ == "__main__":
pytest.main([__file__])

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tests/test_transformation.py
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@ -1,429 +0,0 @@
"""
Tests for the common transformation utilities.
This module provides comprehensive test coverage for the base transformation
utilities used across all exchanges.
"""
import pytest
from datetime import datetime, timezone
from decimal import Decimal
from typing import Dict, Any
from data.common.transformation import (
BaseDataTransformer,
UnifiedDataTransformer,
create_standardized_trade,
batch_create_standardized_trades
)
from data.common.data_types import StandardizedTrade
from data.exchanges.okx.data_processor import OKXDataTransformer
class MockDataTransformer(BaseDataTransformer):
"""Mock transformer for testing base functionality."""
def __init__(self, component_name: str = "mock_transformer"):
super().__init__("mock", component_name)
def transform_trade_data(self, raw_data: Dict[str, Any], symbol: str) -> StandardizedTrade:
return create_standardized_trade(
symbol=symbol,
trade_id=raw_data['id'],
price=raw_data['price'],
size=raw_data['size'],
side=raw_data['side'],
timestamp=raw_data['timestamp'],
exchange="mock",
raw_data=raw_data
)
def transform_orderbook_data(self, raw_data: Dict[str, Any], symbol: str) -> Dict[str, Any]:
return {
'symbol': symbol,
'asks': raw_data.get('asks', []),
'bids': raw_data.get('bids', []),
'timestamp': self.timestamp_to_datetime(raw_data['timestamp']),
'exchange': 'mock',
'raw_data': raw_data
}
def transform_ticker_data(self, raw_data: Dict[str, Any], symbol: str) -> Dict[str, Any]:
return {
'symbol': symbol,
'last': self.safe_decimal_conversion(raw_data.get('last')),
'timestamp': self.timestamp_to_datetime(raw_data['timestamp']),
'exchange': 'mock',
'raw_data': raw_data
}
@pytest.fixture
def mock_transformer():
"""Create mock transformer instance."""
return MockDataTransformer()
@pytest.fixture
def unified_transformer(mock_transformer):
"""Create unified transformer instance."""
return UnifiedDataTransformer(mock_transformer)
@pytest.fixture
def okx_transformer():
"""Create OKX transformer instance."""
return OKXDataTransformer("test_okx_transformer")
@pytest.fixture
def sample_trade_data():
"""Sample trade data for testing."""
return {
'id': '123456',
'price': '50000.50',
'size': '0.1',
'side': 'buy',
'timestamp': 1640995200000 # 2022-01-01 00:00:00 UTC
}
@pytest.fixture
def sample_okx_trade_data():
"""Sample OKX trade data for testing."""
return {
'instId': 'BTC-USDT',
'tradeId': '123456',
'px': '50000.50',
'sz': '0.1',
'side': 'buy',
'ts': '1640995200000'
}
@pytest.fixture
def sample_orderbook_data():
"""Sample orderbook data for testing."""
return {
'asks': [['50100.5', '1.5'], ['50200.0', '2.0']],
'bids': [['49900.5', '1.0'], ['49800.0', '2.5']],
'timestamp': 1640995200000
}
@pytest.fixture
def sample_okx_orderbook_data():
"""Sample OKX orderbook data for testing."""
return {
'instId': 'BTC-USDT',
'asks': [['50100.5', '1.5'], ['50200.0', '2.0']],
'bids': [['49900.5', '1.0'], ['49800.0', '2.5']],
'ts': '1640995200000'
}
@pytest.fixture
def sample_ticker_data():
"""Sample ticker data for testing."""
return {
'last': '50000.50',
'timestamp': 1640995200000
}
@pytest.fixture
def sample_okx_ticker_data():
"""Sample OKX ticker data for testing."""
return {
'instId': 'BTC-USDT',
'last': '50000.50',
'bidPx': '49999.00',
'askPx': '50001.00',
'open24h': '49000.00',
'high24h': '51000.00',
'low24h': '48000.00',
'vol24h': '1000.0',
'ts': '1640995200000'
}
class TestBaseDataTransformer:
"""Test base data transformer functionality."""
def test_timestamp_to_datetime(self, mock_transformer):
"""Test timestamp conversion to datetime."""
# Test millisecond timestamp
dt = mock_transformer.timestamp_to_datetime(1640995200000)
assert isinstance(dt, datetime)
assert dt.tzinfo == timezone.utc
assert dt.year == 2022
assert dt.month == 1
assert dt.day == 1
# Test second timestamp
dt = mock_transformer.timestamp_to_datetime(1640995200, is_milliseconds=False)
assert dt.year == 2022
# Test string timestamp
dt = mock_transformer.timestamp_to_datetime("1640995200000")
assert dt.year == 2022
# Test invalid timestamp
dt = mock_transformer.timestamp_to_datetime("invalid")
assert isinstance(dt, datetime)
assert dt.tzinfo == timezone.utc
def test_safe_decimal_conversion(self, mock_transformer):
"""Test safe decimal conversion."""
# Test valid decimal string
assert mock_transformer.safe_decimal_conversion("123.45") == Decimal("123.45")
# Test valid integer
assert mock_transformer.safe_decimal_conversion(123) == Decimal("123")
# Test None value
assert mock_transformer.safe_decimal_conversion(None) is None
# Test empty string
assert mock_transformer.safe_decimal_conversion("") is None
# Test invalid value
assert mock_transformer.safe_decimal_conversion("invalid") is None
def test_normalize_trade_side(self, mock_transformer):
"""Test trade side normalization."""
# Test buy variations
assert mock_transformer.normalize_trade_side("buy") == "buy"
assert mock_transformer.normalize_trade_side("BUY") == "buy"
assert mock_transformer.normalize_trade_side("bid") == "buy"
assert mock_transformer.normalize_trade_side("b") == "buy"
assert mock_transformer.normalize_trade_side("1") == "buy"
# Test sell variations
assert mock_transformer.normalize_trade_side("sell") == "sell"
assert mock_transformer.normalize_trade_side("SELL") == "sell"
assert mock_transformer.normalize_trade_side("ask") == "sell"
assert mock_transformer.normalize_trade_side("s") == "sell"
assert mock_transformer.normalize_trade_side("0") == "sell"
# Test unknown value
assert mock_transformer.normalize_trade_side("unknown") == "buy"
def test_validate_symbol_format(self, mock_transformer):
"""Test symbol format validation."""
# Test valid symbol
assert mock_transformer.validate_symbol_format("btc-usdt") == "BTC-USDT"
assert mock_transformer.validate_symbol_format("BTC-USDT") == "BTC-USDT"
# Test symbol with whitespace
assert mock_transformer.validate_symbol_format(" btc-usdt ") == "BTC-USDT"
# Test invalid symbols
with pytest.raises(ValueError):
mock_transformer.validate_symbol_format("")
with pytest.raises(ValueError):
mock_transformer.validate_symbol_format(None)
def test_get_transformer_info(self, mock_transformer):
"""Test transformer info retrieval."""
info = mock_transformer.get_transformer_info()
assert info['exchange'] == "mock"
assert info['component'] == "mock_transformer"
assert 'capabilities' in info
assert info['capabilities']['trade_transformation'] is True
assert info['capabilities']['orderbook_transformation'] is True
assert info['capabilities']['ticker_transformation'] is True
class TestUnifiedDataTransformer:
"""Test unified data transformer functionality."""
def test_transform_trade_data(self, unified_transformer, sample_trade_data):
"""Test trade data transformation."""
result = unified_transformer.transform_trade_data(sample_trade_data, "BTC-USDT")
assert isinstance(result, StandardizedTrade)
assert result.symbol == "BTC-USDT"
assert result.trade_id == "123456"
assert result.price == Decimal("50000.50")
assert result.size == Decimal("0.1")
assert result.side == "buy"
assert result.exchange == "mock"
def test_transform_orderbook_data(self, unified_transformer, sample_orderbook_data):
"""Test orderbook data transformation."""
result = unified_transformer.transform_orderbook_data(sample_orderbook_data, "BTC-USDT")
assert result is not None
assert result['symbol'] == "BTC-USDT"
assert result['exchange'] == "mock"
assert len(result['asks']) == 2
assert len(result['bids']) == 2
def test_transform_ticker_data(self, unified_transformer, sample_ticker_data):
"""Test ticker data transformation."""
result = unified_transformer.transform_ticker_data(sample_ticker_data, "BTC-USDT")
assert result is not None
assert result['symbol'] == "BTC-USDT"
assert result['exchange'] == "mock"
assert result['last'] == Decimal("50000.50")
def test_batch_transform_trades(self, unified_transformer):
"""Test batch trade transformation."""
raw_trades = [
{
'id': '123456',
'price': '50000.50',
'size': '0.1',
'side': 'buy',
'timestamp': 1640995200000
},
{
'id': '123457',
'price': '50001.00',
'size': '0.2',
'side': 'sell',
'timestamp': 1640995201000
}
]
results = unified_transformer.batch_transform_trades(raw_trades, "BTC-USDT")
assert len(results) == 2
assert all(isinstance(r, StandardizedTrade) for r in results)
assert results[0].trade_id == "123456"
assert results[1].trade_id == "123457"
def test_get_transformer_info(self, unified_transformer):
"""Test unified transformer info retrieval."""
info = unified_transformer.get_transformer_info()
assert info['exchange'] == "mock"
assert 'unified_component' in info
assert info['batch_processing'] is True
assert info['candle_aggregation'] is True
class TestOKXDataTransformer:
"""Test OKX-specific data transformer functionality."""
def test_transform_trade_data(self, okx_transformer, sample_okx_trade_data):
"""Test OKX trade data transformation."""
result = okx_transformer.transform_trade_data(sample_okx_trade_data, "BTC-USDT")
assert isinstance(result, StandardizedTrade)
assert result.symbol == "BTC-USDT"
assert result.trade_id == "123456"
assert result.price == Decimal("50000.50")
assert result.size == Decimal("0.1")
assert result.side == "buy"
assert result.exchange == "okx"
def test_transform_orderbook_data(self, okx_transformer, sample_okx_orderbook_data):
"""Test OKX orderbook data transformation."""
result = okx_transformer.transform_orderbook_data(sample_okx_orderbook_data, "BTC-USDT")
assert result is not None
assert result['symbol'] == "BTC-USDT"
assert result['exchange'] == "okx"
assert len(result['asks']) == 2
assert len(result['bids']) == 2
def test_transform_ticker_data(self, okx_transformer, sample_okx_ticker_data):
"""Test OKX ticker data transformation."""
result = okx_transformer.transform_ticker_data(sample_okx_ticker_data, "BTC-USDT")
assert result is not None
assert result['symbol'] == "BTC-USDT"
assert result['exchange'] == "okx"
assert result['last'] == Decimal("50000.50")
assert result['bid'] == Decimal("49999.00")
assert result['ask'] == Decimal("50001.00")
assert result['open_24h'] == Decimal("49000.00")
assert result['high_24h'] == Decimal("51000.00")
assert result['low_24h'] == Decimal("48000.00")
assert result['volume_24h'] == Decimal("1000.0")
class TestStandaloneTransformationFunctions:
"""Test standalone transformation utility functions."""
def test_create_standardized_trade(self):
"""Test standardized trade creation."""
trade = create_standardized_trade(
symbol="BTC-USDT",
trade_id="123456",
price="50000.50",
size="0.1",
side="buy",
timestamp=1640995200000,
exchange="test",
is_milliseconds=True
)
assert isinstance(trade, StandardizedTrade)
assert trade.symbol == "BTC-USDT"
assert trade.trade_id == "123456"
assert trade.price == Decimal("50000.50")
assert trade.size == Decimal("0.1")
assert trade.side == "buy"
assert trade.exchange == "test"
assert trade.timestamp.year == 2022
# Test with datetime input
dt = datetime(2022, 1, 1, tzinfo=timezone.utc)
trade = create_standardized_trade(
symbol="BTC-USDT",
trade_id="123456",
price="50000.50",
size="0.1",
side="buy",
timestamp=dt,
exchange="test"
)
assert trade.timestamp == dt
# Test invalid inputs
with pytest.raises(ValueError):
create_standardized_trade(
symbol="BTC-USDT",
trade_id="123456",
price="invalid",
size="0.1",
side="buy",
timestamp=1640995200000,
exchange="test"
)
with pytest.raises(ValueError):
create_standardized_trade(
symbol="BTC-USDT",
trade_id="123456",
price="50000.50",
size="0.1",
side="invalid",
timestamp=1640995200000,
exchange="test"
)
def test_batch_create_standardized_trades(self):
"""Test batch trade creation."""
raw_trades = [
{'id': '123456', 'px': '50000.50', 'sz': '0.1', 'side': 'buy', 'ts': 1640995200000},
{'id': '123457', 'px': '50001.00', 'sz': '0.2', 'side': 'sell', 'ts': 1640995201000}
]
field_mapping = {
'trade_id': 'id',
'price': 'px',
'size': 'sz',
'side': 'side',
'timestamp': 'ts'
}
trades = batch_create_standardized_trades(
raw_trades=raw_trades,
symbol="BTC-USDT",
exchange="test",
field_mapping=field_mapping
)
assert len(trades) == 2
assert all(isinstance(t, StandardizedTrade) for t in trades)
assert trades[0].trade_id == "123456"
assert trades[0].price == Decimal("50000.50")
assert trades[1].trade_id == "123457"
assert trades[1].side == "sell"

539
tests/test_validation.py
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@ -1,539 +0,0 @@
"""
Tests for Configuration Validation and Error Handling System
Tests the comprehensive validation system including validation rules,
error reporting, warnings, and detailed diagnostics.
"""
import pytest
from typing import Set
from datetime import datetime
from components.charts.config.validation import (
ValidationLevel,
ValidationRule,
ValidationIssue,
ValidationReport,
ConfigurationValidator,
validate_configuration,
get_validation_rules_info
)
from components.charts.config.strategy_charts import (
StrategyChartConfig,
SubplotConfig,
ChartStyle,
ChartLayout,
SubplotType
)
from components.charts.config.defaults import TradingStrategy
class TestValidationComponents:
"""Test validation component classes."""
def test_validation_level_enum(self):
"""Test ValidationLevel enum values."""
levels = [level.value for level in ValidationLevel]
expected_levels = ["error", "warning", "info", "debug"]
for expected in expected_levels:
assert expected in levels
def test_validation_rule_enum(self):
"""Test ValidationRule enum values."""
rules = [rule.value for rule in ValidationRule]
expected_rules = [
"required_fields", "height_ratios", "indicator_existence",
"timeframe_format", "chart_style", "subplot_config",
"strategy_consistency", "performance_impact", "indicator_conflicts",
"resource_usage"
]
for expected in expected_rules:
assert expected in rules
def test_validation_issue_creation(self):
"""Test ValidationIssue creation and string representation."""
issue = ValidationIssue(
level=ValidationLevel.ERROR,
rule=ValidationRule.REQUIRED_FIELDS,
message="Test error message",
field_path="test.field",
suggestion="Test suggestion"
)
assert issue.level == ValidationLevel.ERROR
assert issue.rule == ValidationRule.REQUIRED_FIELDS
assert issue.message == "Test error message"
assert issue.field_path == "test.field"
assert issue.suggestion == "Test suggestion"
# Test string representation
issue_str = str(issue)
assert "[ERROR]" in issue_str
assert "Test error message" in issue_str
assert "test.field" in issue_str
assert "Test suggestion" in issue_str
def test_validation_report_creation(self):
"""Test ValidationReport creation and methods."""
report = ValidationReport(is_valid=True)
assert report.is_valid is True
assert len(report.errors) == 0
assert len(report.warnings) == 0
assert len(report.info) == 0
assert len(report.debug) == 0
# Test adding issues
error_issue = ValidationIssue(
level=ValidationLevel.ERROR,
rule=ValidationRule.REQUIRED_FIELDS,
message="Error message"
)
warning_issue = ValidationIssue(
level=ValidationLevel.WARNING,
rule=ValidationRule.HEIGHT_RATIOS,
message="Warning message"
)
report.add_issue(error_issue)
report.add_issue(warning_issue)
assert not report.is_valid # Should be False after adding error
assert len(report.errors) == 1
assert len(report.warnings) == 1
assert report.has_errors()
assert report.has_warnings()
# Test get_all_issues
all_issues = report.get_all_issues()
assert len(all_issues) == 2
# Test get_issues_by_rule
field_issues = report.get_issues_by_rule(ValidationRule.REQUIRED_FIELDS)
assert len(field_issues) == 1
assert field_issues[0] == error_issue
# Test summary
summary = report.summary()
assert "1 errors" in summary
assert "1 warnings" in summary
class TestConfigurationValidator:
"""Test ConfigurationValidator class."""
def create_valid_config(self) -> StrategyChartConfig:
"""Create a valid test configuration."""
return StrategyChartConfig(
strategy_name="Valid Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Valid strategy for testing",
timeframes=["5m", "15m", "1h"],
main_chart_height=0.7,
overlay_indicators=["sma_20"], # Using simple indicators
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
height_ratio=0.2,
indicators=[], # Empty to avoid indicator existence issues
title="RSI"
)
]
)
def test_validator_initialization(self):
"""Test validator initialization."""
# Test with all rules
validator = ConfigurationValidator()
assert len(validator.enabled_rules) == len(ValidationRule)
# Test with specific rules
specific_rules = {ValidationRule.REQUIRED_FIELDS, ValidationRule.HEIGHT_RATIOS}
validator = ConfigurationValidator(enabled_rules=specific_rules)
assert validator.enabled_rules == specific_rules
def test_validate_strategy_config_valid(self):
"""Test validation of a valid configuration."""
config = self.create_valid_config()
validator = ConfigurationValidator()
report = validator.validate_strategy_config(config)
# Should have some validation applied
assert isinstance(report, ValidationReport)
assert report.validation_time is not None
assert len(report.rules_applied) > 0
def test_required_fields_validation(self):
"""Test required fields validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.REQUIRED_FIELDS})
# Test missing strategy name
config.strategy_name = ""
report = validator.validate_strategy_config(config)
assert not report.is_valid
assert len(report.errors) > 0
assert any("Strategy name is required" in str(error) for error in report.errors)
# Test short strategy name (should be warning)
config.strategy_name = "AB"
report = validator.validate_strategy_config(config)
assert len(report.warnings) > 0
assert any("very short" in str(warning) for warning in report.warnings)
# Test missing timeframes
config.strategy_name = "Valid Name"
config.timeframes = []
report = validator.validate_strategy_config(config)
assert not report.is_valid
assert any("timeframe must be specified" in str(error) for error in report.errors)
def test_height_ratios_validation(self):
"""Test height ratios validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.HEIGHT_RATIOS})
# Test invalid main chart height
config.main_chart_height = 1.5 # Invalid: > 1.0
report = validator.validate_strategy_config(config)
assert not report.is_valid
assert any("Main chart height" in str(error) for error in report.errors)
# Test total height exceeding 1.0
config.main_chart_height = 0.8
config.subplot_configs[0].height_ratio = 0.3 # Total = 1.1
report = validator.validate_strategy_config(config)
assert not report.is_valid
assert any("exceeds 1.0" in str(error) for error in report.errors)
# Test very small main chart height (should be warning)
config.main_chart_height = 0.1
config.subplot_configs[0].height_ratio = 0.2
report = validator.validate_strategy_config(config)
assert len(report.warnings) > 0
assert any("very small" in str(warning) for warning in report.warnings)
def test_timeframe_format_validation(self):
"""Test timeframe format validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.TIMEFRAME_FORMAT})
# Test invalid timeframe format
config.timeframes = ["invalid", "1h", "5m"]
report = validator.validate_strategy_config(config)
assert not report.is_valid
assert any("Invalid timeframe format" in str(error) for error in report.errors)
# Test valid but uncommon timeframe (should be warning)
config.timeframes = ["7m", "1h"] # 7m is valid format but uncommon
report = validator.validate_strategy_config(config)
assert len(report.warnings) > 0
assert any("not in common list" in str(warning) for warning in report.warnings)
def test_chart_style_validation(self):
"""Test chart style validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.CHART_STYLE})
# Test invalid color format
config.chart_style.background_color = "invalid_color"
report = validator.validate_strategy_config(config)
assert not report.is_valid
assert any("Invalid color format" in str(error) for error in report.errors)
# Test extreme font size (should be warning or error)
config.chart_style.background_color = "#ffffff" # Fix color
config.chart_style.font_size = 2 # Too small
report = validator.validate_strategy_config(config)
assert len(report.errors) > 0 or len(report.warnings) > 0
# Test unsupported theme (should be warning)
config.chart_style.font_size = 12 # Fix font size
config.chart_style.theme = "unsupported_theme"
report = validator.validate_strategy_config(config)
assert len(report.warnings) > 0
assert any("may not be supported" in str(warning) for warning in report.warnings)
def test_subplot_config_validation(self):
"""Test subplot configuration validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.SUBPLOT_CONFIG})
# Test duplicate subplot types
config.subplot_configs.append(SubplotConfig(
subplot_type=SubplotType.RSI, # Duplicate
height_ratio=0.1,
indicators=[],
title="RSI 2"
))
report = validator.validate_strategy_config(config)
assert len(report.warnings) > 0
assert any("Duplicate subplot type" in str(warning) for warning in report.warnings)
def test_strategy_consistency_validation(self):
"""Test strategy consistency validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.STRATEGY_CONSISTENCY})
# Test mismatched timeframes for scalping strategy
config.strategy_type = TradingStrategy.SCALPING
config.timeframes = ["4h", "1d"] # Not optimal for scalping
report = validator.validate_strategy_config(config)
assert len(report.info) > 0
assert any("may not be optimal" in str(info) for info in report.info)
def test_performance_impact_validation(self):
"""Test performance impact validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.PERFORMANCE_IMPACT})
# Test high indicator count
config.overlay_indicators = [f"indicator_{i}" for i in range(12)] # 12 indicators
report = validator.validate_strategy_config(config)
assert len(report.warnings) > 0
assert any("may impact performance" in str(warning) for warning in report.warnings)
def test_indicator_conflicts_validation(self):
"""Test indicator conflicts validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.INDICATOR_CONFLICTS})
# Test multiple SMA indicators
config.overlay_indicators = ["sma_5", "sma_10", "sma_20", "sma_50"] # 4 SMA indicators
report = validator.validate_strategy_config(config)
assert len(report.info) > 0
assert any("visual clutter" in str(info) for info in report.info)
def test_resource_usage_validation(self):
"""Test resource usage validation."""
config = self.create_valid_config()
validator = ConfigurationValidator(enabled_rules={ValidationRule.RESOURCE_USAGE})
# Test high memory usage configuration
config.overlay_indicators = [f"indicator_{i}" for i in range(10)]
config.subplot_configs = [
SubplotConfig(subplot_type=SubplotType.RSI, height_ratio=0.1, indicators=[])
for _ in range(10)
] # Many subplots
report = validator.validate_strategy_config(config)
assert len(report.warnings) > 0 or len(report.info) > 0
class TestValidationFunctions:
"""Test standalone validation functions."""
def create_test_config(self) -> StrategyChartConfig:
"""Create a test configuration."""
return StrategyChartConfig(
strategy_name="Test Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Test strategy",
timeframes=["15m", "1h"],
main_chart_height=0.8,
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
height_ratio=0.2,
indicators=[]
)
]
)
def test_validate_configuration_function(self):
"""Test the standalone validate_configuration function."""
config = self.create_test_config()
# Test with default rules
report = validate_configuration(config)
assert isinstance(report, ValidationReport)
assert report.validation_time is not None
# Test with specific rules
specific_rules = {ValidationRule.REQUIRED_FIELDS, ValidationRule.HEIGHT_RATIOS}
report = validate_configuration(config, rules=specific_rules)
assert report.rules_applied == specific_rules
# Test strict mode
config.strategy_name = "AB" # Short name (should be warning)
report = validate_configuration(config, strict=False)
normal_errors = len(report.errors)
report = validate_configuration(config, strict=True)
strict_errors = len(report.errors)
assert strict_errors >= normal_errors # Strict mode may have more errors
def test_get_validation_rules_info(self):
"""Test getting validation rules information."""
rules_info = get_validation_rules_info()
assert isinstance(rules_info, dict)
assert len(rules_info) == len(ValidationRule)
# Check that all rules have information
for rule in ValidationRule:
assert rule in rules_info
rule_info = rules_info[rule]
assert "name" in rule_info
assert "description" in rule_info
assert isinstance(rule_info["name"], str)
assert isinstance(rule_info["description"], str)
class TestValidationIntegration:
"""Test integration with existing systems."""
def test_strategy_config_validate_method(self):
"""Test the updated validate method in StrategyChartConfig."""
config = StrategyChartConfig(
strategy_name="Integration Test",
strategy_type=TradingStrategy.DAY_TRADING,
description="Integration test strategy",
timeframes=["15m"],
main_chart_height=0.8,
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
height_ratio=0.2,
indicators=[]
)
]
)
# Test basic validate method (backward compatibility)
is_valid, errors = config.validate()
assert isinstance(is_valid, bool)
assert isinstance(errors, list)
# Test comprehensive validation method
report = config.validate_comprehensive()
assert isinstance(report, ValidationReport)
assert report.validation_time is not None
def test_validation_with_invalid_config(self):
"""Test validation with an invalid configuration."""
config = StrategyChartConfig(
strategy_name="", # Invalid: empty name
strategy_type=TradingStrategy.DAY_TRADING,
description="", # Warning: empty description
timeframes=[], # Invalid: no timeframes
main_chart_height=1.5, # Invalid: > 1.0
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
height_ratio=-0.1, # Invalid: negative
indicators=[]
)
]
)
# Test basic validation
is_valid, errors = config.validate()
assert not is_valid
assert len(errors) > 0
# Test comprehensive validation
report = config.validate_comprehensive()
assert not report.is_valid
assert len(report.errors) > 0
assert len(report.warnings) > 0 # Should have warnings too
def test_validation_error_handling(self):
"""Test validation error handling."""
config = StrategyChartConfig(
strategy_name="Error Test",
strategy_type=TradingStrategy.DAY_TRADING,
description="Error test strategy",
timeframes=["15m"],
main_chart_height=0.8,
subplot_configs=[]
)
# The validation should handle errors gracefully
is_valid, errors = config.validate()
assert isinstance(is_valid, bool)
assert isinstance(errors, list)
class TestValidationEdgeCases:
"""Test edge cases and boundary conditions."""
def test_empty_configuration(self):
"""Test validation with minimal configuration."""
config = StrategyChartConfig(
strategy_name="Minimal",
strategy_type=TradingStrategy.DAY_TRADING,
description="Minimal config",
timeframes=["1h"],
overlay_indicators=[],
subplot_configs=[]
)
report = validate_configuration(config)
# Should be valid even with minimal configuration
assert isinstance(report, ValidationReport)
def test_maximum_configuration(self):
"""Test validation with maximum complexity configuration."""
config = StrategyChartConfig(
strategy_name="Maximum Complexity Strategy",
strategy_type=TradingStrategy.DAY_TRADING,
description="Strategy with maximum complexity for testing",
timeframes=["1m", "5m", "15m", "1h", "4h"],
main_chart_height=0.4,
overlay_indicators=[f"indicator_{i}" for i in range(15)],
subplot_configs=[
SubplotConfig(
subplot_type=SubplotType.RSI,
height_ratio=0.15,
indicators=[f"rsi_{i}" for i in range(5)]
),
SubplotConfig(
subplot_type=SubplotType.MACD,
height_ratio=0.15,
indicators=[f"macd_{i}" for i in range(5)]
),
SubplotConfig(
subplot_type=SubplotType.VOLUME,
height_ratio=0.1,
indicators=[]
),
SubplotConfig(
subplot_type=SubplotType.MOMENTUM,
height_ratio=0.2,
indicators=[f"momentum_{i}" for i in range(3)]
)
]
)
report = validate_configuration(config)
# Should have warnings about performance and complexity
assert len(report.warnings) > 0 or len(report.info) > 0
def test_boundary_values(self):
"""Test validation with boundary values."""
config = StrategyChartConfig(
strategy_name="Boundary Test",
strategy_type=TradingStrategy.DAY_TRADING,
description="Boundary test strategy",
timeframes=["1h"],
main_chart_height=1.0, # Maximum allowed
subplot_configs=[] # No subplots (total height = 1.0)
)
report = validate_configuration(config)
# Should be valid with exact boundary values
assert isinstance(report, ValidationReport)
if __name__ == "__main__":
pytest.main([__file__])

205
tests/test_websocket_race_condition_fix.py
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@ -1,205 +0,0 @@
#!/usr/bin/env python3
"""
Test script to verify WebSocket race condition fixes.
This script tests the enhanced task management and synchronization
in the OKX WebSocket client to ensure no more recv() concurrency errors.
"""
import asyncio
import sys
from pathlib import Path
# Add project root to path
project_root = Path(__file__).parent.parent
sys.path.insert(0, str(project_root))
from data.exchanges.okx.websocket import OKXWebSocketClient, OKXSubscription, OKXChannelType
from utils.logger import get_logger
async def test_websocket_reconnection_stability():
"""Test WebSocket reconnection without race conditions."""
logger = get_logger("websocket_test", verbose=True)
print("🧪 Testing WebSocket Race Condition Fixes")
print("=" * 50)
# Create WebSocket client
ws_client = OKXWebSocketClient(
component_name="test_ws_client",
ping_interval=25.0,
max_reconnect_attempts=3,
logger=logger
)
try:
# Test 1: Basic connection
print("\n📡 Test 1: Basic Connection")
success = await ws_client.connect()
if success:
print("✅ Initial connection successful")
else:
print("❌ Initial connection failed")
return False
# Test 2: Subscribe to channels
print("\n📡 Test 2: Channel Subscription")
subscriptions = [
OKXSubscription(OKXChannelType.TRADES.value, "BTC-USDT"),
OKXSubscription(OKXChannelType.BOOKS5.value, "BTC-USDT")
]
success = await ws_client.subscribe(subscriptions)
if success:
print("✅ Subscription successful")
else:
print("❌ Subscription failed")
return False
# Test 3: Force reconnection to test race condition fixes
print("\n📡 Test 3: Force Reconnection (Race Condition Test)")
for i in range(3):
print(f" Reconnection attempt {i+1}/3...")
success = await ws_client.reconnect()
if success:
print(f" ✅ Reconnection {i+1} successful")
await asyncio.sleep(2) # Wait between reconnections
else:
print(f" ❌ Reconnection {i+1} failed")
return False
# Test 4: Verify subscriptions are maintained
print("\n📡 Test 4: Subscription Persistence")
current_subs = ws_client.get_subscriptions()
if len(current_subs) == 2:
print("✅ Subscriptions persisted after reconnections")
else:
print(f"❌ Subscription count mismatch: expected 2, got {len(current_subs)}")
# Test 5: Monitor for a few seconds to catch any errors
print("\n📡 Test 5: Stability Monitor (10 seconds)")
message_count = 0
def message_callback(message):
nonlocal message_count
message_count += 1
if message_count % 10 == 0:
print(f" 📊 Processed {message_count} messages")
ws_client.add_message_callback(message_callback)
await asyncio.sleep(10)
stats = ws_client.get_stats()
print(f"\n📊 Final Statistics:")
print(f" Messages received: {stats['messages_received']}")
print(f" Reconnections: {stats['reconnections']}")
print(f" Connection state: {stats['connection_state']}")
if stats['messages_received'] > 0:
print("✅ Receiving data successfully")
else:
print("⚠️ No messages received (may be normal for low-activity symbols)")
return True
except Exception as e:
print(f"❌ Test failed with exception: {e}")
logger.error(f"Test exception: {e}")
return False
finally:
# Cleanup
await ws_client.disconnect()
print("\n🧹 Cleanup completed")
async def test_concurrent_operations():
"""Test concurrent WebSocket operations to ensure no race conditions."""
print("\n🔄 Testing Concurrent Operations")
print("=" * 50)
logger = get_logger("concurrent_test", verbose=False)
# Create multiple clients
clients = []
for i in range(3):
client = OKXWebSocketClient(
component_name=f"test_client_{i}",
logger=logger
)
clients.append(client)
try:
# Connect all clients concurrently
print("📡 Connecting 3 clients concurrently...")
tasks = [client.connect() for client in clients]
results = await asyncio.gather(*tasks, return_exceptions=True)
successful_connections = sum(1 for r in results if r is True)
print(f"✅ {successful_connections}/3 clients connected successfully")
# Test concurrent reconnections
print("\n🔄 Testing concurrent reconnections...")
reconnect_tasks = []
for client in clients:
if client.is_connected:
reconnect_tasks.append(client.reconnect())
if reconnect_tasks:
reconnect_results = await asyncio.gather(*reconnect_tasks, return_exceptions=True)
successful_reconnects = sum(1 for r in reconnect_results if r is True)
print(f"✅ {successful_reconnects}/{len(reconnect_tasks)} reconnections successful")
return True
except Exception as e:
print(f"❌ Concurrent test failed: {e}")
return False
finally:
# Cleanup all clients
for client in clients:
try:
await client.disconnect()
except:
pass
async def main():
"""Run all WebSocket tests."""
print("🚀 WebSocket Race Condition Fix Test Suite")
print("=" * 60)
try:
# Test 1: Basic reconnection stability
test1_success = await test_websocket_reconnection_stability()
# Test 2: Concurrent operations
test2_success = await test_concurrent_operations()
# Summary
print("\n" + "=" * 60)
print("📋 Test Summary:")
print(f" Reconnection Stability: {'✅ PASS' if test1_success else '❌ FAIL'}")
print(f" Concurrent Operations: {'✅ PASS' if test2_success else '❌ FAIL'}")
if test1_success and test2_success:
print("\n🎉 All tests passed! WebSocket race condition fixes working correctly.")
return 0
else:
print("\n❌ Some tests failed. Check logs for details.")
return 1
except KeyboardInterrupt:
print("\n⏹️ Tests interrupted by user")
return 1
except Exception as e:
print(f"\n💥 Test suite failed with exception: {e}")
return 1
if __name__ == "__main__":
exit_code = asyncio.run(main())
sys.exit(exit_code)

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import json
import os
import logging
logger = logging.getLogger(__name__)
def load_time_range_options():
"""Loads time range options from a JSON file.
Returns:
list: A list of dictionaries, each representing a time range option.
"""
try:
# Construct path relative to the workspace root
# Assuming utils is at TCPDashboard/utils
# and config/options is at TCPDashboard/config/options
base_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
file_path = os.path.join(base_dir, 'config/options/time_range_options.json')
with open(file_path, 'r', encoding='utf-8') as f:
return json.load(f)
except FileNotFoundError:
logger.error(f"Time range options file not found at {file_path}. Using default.")
return [
{"label": "🕐 Last 1 Hour", "value": "1h"},
{"label": "🕐 Last 4 Hours", "value": "4h"},
{"label": "🕐 Last 6 Hours", "value": "6h"},
{"label": "🕐 Last 12 Hours", "value": "12h"},
{"label": "📅 Last 1 Day", "value": "1d"},
{"label": "📅 Last 3 Days", "value": "3d"},
{"label": "📅 Last 7 Days", "value": "7d"},
{"label": "📅 Last 30 Days", "value": "30d"},
{"label": "📅 Custom Range", "value": "custom"},
{"label": "🔴 Real-time", "value": "realtime"}
]
except json.JSONDecodeError:
logger.error(f"Error decoding JSON from {file_path}. Using default.")
return [
{"label": "🕐 Last 1 Hour", "value": "1h"},
{"label": "🕐 Last 4 Hours", "value": "4h"},
{"label": "🕐 Last 6 Hours", "value": "6h"},
{"label": "🕐 Last 12 Hours", "value": "12h"},
{"label": "📅 Last 1 Day", "value": "1d"},
{"label": "📅 Last 3 Days", "value": "3d"},
{"label": "📅 Last 7 Days", "value": "7d"},
{"label": "📅 Last 30 Days", "value": "30d"},
{"label": "📅 Custom Range", "value": "custom"},
{"label": "🔴 Real-time", "value": "realtime"}
]
except Exception as e:
logger.error(f"An unexpected error occurred while loading time range options: {e}. Using default.")
return [
{"label": "🕐 Last 1 Hour", "value": "1h"},
{"label": "🕐 Last 4 Hours", "value": "4h"},
{"label": "🕐 Last 6 Hours", "value": "6h"},
{"label": "🕐 Last 12 Hours", "value": "12h"},
{"label": "📅 Last 1 Day", "value": "1d"},
{"label": "📅 Last 3 Days", "value": "3d"},
{"label": "📅 Last 7 Days", "value": "7d"},
{"label": "📅 Last 30 Days", "value": "30d"},
{"label": "📅 Custom Range", "value": "custom"},
{"label": "🔴 Real-time", "value": "realtime"}
]

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import json
import os
import logging
logger = logging.getLogger(__name__)
def load_timeframe_options():
"""Loads timeframe options from a JSON file.
Returns:
list: A list of dictionaries, each representing a timeframe option.
"""
try:
# Construct path relative to the workspace root
# Assuming utils is at TCPDashboard/utils
# and config/options is at TCPDashboard/config/options
base_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
file_path = os.path.join(base_dir, 'config/options/timeframe_options.json')
with open(file_path, 'r', encoding='utf-8') as f:
return json.load(f)
except FileNotFoundError:
logger.error(f"Timeframe options file not found at {file_path}. Using default timeframes.")
return [
{'label': '1 Second', 'value': '1s'},
{'label': '5 Seconds', 'value': '5s'},
{'label': '15 Seconds', 'value': '15s'},
{'label': '30 Seconds', 'value': '30s'},
{'label': '1 Minute', 'value': '1m'},
{'label': '5 Minutes', 'value': '5m'},
{'label': '15 Minutes', 'value': '15m'},
{'label': '1 Hour', 'value': '1h'},
{'label': '4 Hours', 'value': '4h'},
{'label': '1 Day', 'value': '1d'},
]
except json.JSONDecodeError:
logger.error(f"Error decoding JSON from {file_path}. Using default timeframes.")
return [
{'label': '1 Second', 'value': '1s'},
{'label': '5 Seconds', 'value': '5s'},
{'label': '15 Seconds', 'value': '15s'},
{'label': '30 Seconds', 'value': '30s'},
{'label': '1 Minute', 'value': '1m'},
{'label': '5 Minutes', 'value': '5m'},
{'label': '15 Minutes', 'value': '15m'},
{'label': '1 Hour', 'value': '1h'},
{'label': '4 Hours', 'value': '4h'},
{'label': '1 Day', 'value': '1d'},
]
except Exception as e:
logger.error(f"An unexpected error occurred while loading timeframes: {e}. Using default timeframes.")
return [
{'label': '1 Second', 'value': '1s'},
{'label': '5 Seconds', 'value': '5s'},
{'label': '15 Seconds', 'value': '15s'},
{'label': '30 Seconds', 'value': '30s'},
{'label': '1 Minute', 'value': '1m'},
{'label': '5 Minutes', 'value': '5m'},
{'label': '15 Minutes', 'value': '15m'},
{'label': '1 Hour', 'value': '1h'},
{'label': '4 Hours', 'value': '4h'},
{'label': '1 Day', 'value': '1d'},
]