Remove deprecated app_new.py and consolidate main application logic into main.py
- 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
@@ -47,6 +47,21 @@ from .error_handling import (
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get_error_message,
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create_error_annotation
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)
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from .chart_data import (
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get_supported_symbols,
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get_supported_timeframes,
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get_market_statistics,
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check_data_availability,
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create_data_status_indicator
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)
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from .chart_creation import (
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create_candlestick_chart,
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create_strategy_chart,
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create_error_chart,
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create_basic_chart,
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create_indicator_chart,
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create_chart_with_indicators
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)
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# Layer imports with error handling
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from .layers.base import (
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@@ -89,6 +104,9 @@ __all__ = [
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"create_strategy_chart",
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"create_empty_chart",
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"create_error_chart",
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"create_basic_chart",
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"create_indicator_chart",
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"create_chart_with_indicators",
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# Data integration
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"MarketDataIntegrator",
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@@ -109,7 +127,7 @@ __all__ = [
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"get_error_message",
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"create_error_annotation",
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# Utility functions
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# Data-related utility functions
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"get_supported_symbols",
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"get_supported_timeframes",
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"get_market_statistics",
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@@ -135,362 +153,5 @@ __all__ = [
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"BaseSubplotLayer",
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"RSILayer",
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"MACDLayer",
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# Convenience functions
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"create_basic_chart",
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"create_indicator_chart",
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"create_chart_with_indicators"
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]
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# Initialize logger
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from utils.logger import get_logger
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logger = get_logger("charts")
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def create_candlestick_chart(symbol: str, timeframe: str, days_back: int = 7, **kwargs) -> go.Figure:
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"""
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Create a candlestick chart with enhanced data integration.
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Args:
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symbol: Trading pair (e.g., 'BTC-USDT')
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timeframe: Timeframe (e.g., '1h', '1d')
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days_back: Number of days to look back
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**kwargs: Additional chart parameters
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Returns:
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Plotly figure with candlestick chart
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"""
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builder = ChartBuilder()
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# Check data quality first
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data_quality = builder.check_data_quality(symbol, timeframe)
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if not data_quality['available']:
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logger.warning(f"Data not available for {symbol} {timeframe}: {data_quality['message']}")
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return builder._create_error_chart(f"No data available: {data_quality['message']}")
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if not data_quality['sufficient_for_indicators']:
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logger.warning(f"Insufficient data for indicators: {symbol} {timeframe}")
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# Use enhanced data fetching
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try:
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candles = builder.fetch_market_data_enhanced(symbol, timeframe, days_back)
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if not candles:
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return builder._create_error_chart(f"No market data found for {symbol} {timeframe}")
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# Prepare data for charting
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df = prepare_chart_data(candles)
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if df.empty:
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return builder._create_error_chart("Failed to prepare chart data")
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# Create chart with data quality info
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fig = builder._create_candlestick_with_volume(df, symbol, timeframe)
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# Add data quality annotation if data is stale
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if not data_quality['is_recent']:
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age_hours = data_quality['data_age_minutes'] / 60
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fig.add_annotation(
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text=f"⚠️ Data is {age_hours:.1f}h old",
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xref="paper", yref="paper",
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x=0.02, y=0.98,
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showarrow=False,
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bgcolor="rgba(255,193,7,0.8)",
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bordercolor="orange",
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borderwidth=1
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)
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logger.debug(f"Created enhanced candlestick chart for {symbol} {timeframe} with {len(candles)} candles")
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return fig
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except Exception as e:
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logger.error(f"Error creating enhanced candlestick chart: {e}")
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return builder._create_error_chart(f"Chart creation failed: {str(e)}")
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def create_strategy_chart(symbol: str, timeframe: str, strategy_name: str, **kwargs):
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"""
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Convenience function to create a strategy-specific chart.
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Args:
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symbol: Trading pair
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timeframe: Timeframe
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strategy_name: Name of the strategy configuration
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**kwargs: Additional parameters
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Returns:
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Plotly Figure object with strategy indicators
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"""
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builder = ChartBuilder()
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return builder.create_strategy_chart(symbol, timeframe, strategy_name, **kwargs)
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def get_supported_symbols():
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"""Get list of symbols that have data in the database."""
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builder = ChartBuilder()
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candles = builder.fetch_market_data("BTC-USDT", "1m", days_back=1) # Test query
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if candles:
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from database.operations import get_database_operations
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from utils.logger import get_logger
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logger = get_logger("default_logger")
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try:
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db = get_database_operations(logger)
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with db.market_data.get_session() as session:
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from sqlalchemy import text
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result = session.execute(text("SELECT DISTINCT symbol FROM market_data ORDER BY symbol"))
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return [row[0] for row in result]
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except Exception:
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pass
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return ['BTC-USDT', 'ETH-USDT'] # Fallback
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def get_supported_timeframes():
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"""Get list of timeframes that have data in the database."""
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builder = ChartBuilder()
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candles = builder.fetch_market_data("BTC-USDT", "1m", days_back=1) # Test query
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if candles:
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from database.operations import get_database_operations
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from utils.logger import get_logger
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logger = get_logger("default_logger")
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try:
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db = get_database_operations(logger)
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with db.market_data.get_session() as session:
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from sqlalchemy import text
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result = session.execute(text("SELECT DISTINCT timeframe FROM market_data ORDER BY timeframe"))
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return [row[0] for row in result]
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except Exception:
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pass
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return ['5s', '1m', '15m', '1h'] # Fallback
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def get_market_statistics(symbol: str, timeframe: str = "1h", days_back: int = 1):
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"""Calculate market statistics from recent data over a specified period."""
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builder = ChartBuilder()
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candles = builder.fetch_market_data(symbol, timeframe, days_back=days_back)
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if not candles:
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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'}
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import pandas as pd
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df = pd.DataFrame(candles)
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latest = df.iloc[-1]
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current_price = float(latest['close'])
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# Calculate change over the period
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if len(df) > 1:
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price_period_ago = float(df.iloc[0]['open'])
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change_percent = ((current_price - price_period_ago) / price_period_ago) * 100
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else:
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change_percent = 0
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from .utils import format_price, format_volume
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# Determine label for period (e.g., "24h", "7d", "1h")
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if days_back == 1/24:
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period_label = "1h"
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elif days_back == 4/24:
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period_label = "4h"
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elif days_back == 6/24:
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period_label = "6h"
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elif days_back == 12/24:
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period_label = "12h"
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elif days_back < 1: # For other fractional days, show as hours
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period_label = f"{int(days_back * 24)}h"
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elif days_back == 1:
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period_label = "24h" # Keep 24h for 1 day for clarity
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else:
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period_label = f"{days_back}d"
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return {
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'Price': format_price(current_price, decimals=2),
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f'Change ({period_label})': f"{'+' if change_percent >= 0 else ''}{change_percent:.2f}%",
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f'Volume ({period_label})': format_volume(df['volume'].sum()),
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f'High ({period_label})': format_price(df['high'].max(), decimals=2),
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f'Low ({period_label})': format_price(df['low'].min(), decimals=2)
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}
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def check_data_availability(symbol: str, timeframe: str):
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"""Check data availability for a symbol and timeframe."""
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from datetime import datetime, timezone, timedelta
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from database.operations import get_database_operations
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from utils.logger import get_logger
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try:
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logger = get_logger("charts_data_check")
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db = get_database_operations(logger)
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latest_candle = db.market_data.get_latest_candle(symbol, timeframe)
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if latest_candle:
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latest_time = latest_candle['timestamp']
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time_diff = datetime.now(timezone.utc) - latest_time.replace(tzinfo=timezone.utc)
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return {
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'has_data': True,
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'latest_timestamp': latest_time,
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'time_since_last': time_diff,
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'is_recent': time_diff < timedelta(hours=1),
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'message': f"Latest data: {latest_time.strftime('%Y-%m-%d %H:%M:%S UTC')}"
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}
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else:
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return {
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'has_data': False,
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'latest_timestamp': None,
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'time_since_last': None,
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'is_recent': False,
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'message': f"No data available for {symbol} {timeframe}"
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}
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except Exception as e:
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return {
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'has_data': False,
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'latest_timestamp': None,
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'time_since_last': None,
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'is_recent': False,
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'message': f"Error checking data: {str(e)}"
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}
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def create_data_status_indicator(symbol: str, timeframe: str):
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"""Create a data status indicator for the dashboard."""
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status = check_data_availability(symbol, timeframe)
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if status['has_data']:
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if status['is_recent']:
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icon, color, status_text = "🟢", "#27ae60", "Real-time Data"
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else:
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icon, color, status_text = "🟡", "#f39c12", "Delayed Data"
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else:
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icon, color, status_text = "🔴", "#e74c3c", "No Data"
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return f'<span style="color: {color}; font-weight: bold;">{icon} {status_text}</span><br><small>{status["message"]}</small>'
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def create_error_chart(error_message: str):
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"""Create an error chart with error message."""
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builder = ChartBuilder()
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return builder._create_error_chart(error_message)
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def create_basic_chart(symbol: str, data: list,
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indicators: list = None,
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error_handling: bool = True) -> 'go.Figure':
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"""
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Create a basic chart with error handling.
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Args:
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symbol: Trading symbol
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data: OHLCV data as list of dictionaries
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indicators: List of indicator configurations
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error_handling: Whether to use comprehensive error handling
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Returns:
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Plotly figure with chart or error display
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"""
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try:
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from plotly import graph_objects as go
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# Initialize chart builder
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builder = ChartBuilder()
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if error_handling:
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# Use error-aware chart creation
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error_handler = ChartErrorHandler()
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is_valid = error_handler.validate_data_sufficiency(data, indicators=indicators or [])
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if not is_valid:
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# Create error chart
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fig = go.Figure()
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error_msg = error_handler.get_user_friendly_message()
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fig.add_annotation(create_error_annotation(error_msg, position='center'))
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fig.update_layout(
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title=f"Chart Error - {symbol}",
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xaxis={'visible': False},
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yaxis={'visible': False},
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template='plotly_white',
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height=400
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)
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return fig
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# Create chart normally
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return builder.create_candlestick_chart(data, symbol=symbol, indicators=indicators or [])
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except Exception as e:
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# Fallback error chart
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from plotly import graph_objects as go
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fig = go.Figure()
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fig.add_annotation(create_error_annotation(
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f"Chart creation failed: {str(e)}",
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position='center'
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))
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fig.update_layout(
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title=f"Chart Error - {symbol}",
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template='plotly_white',
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height=400
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)
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return fig
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def create_indicator_chart(symbol: str, data: list,
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indicator_type: str, **params) -> 'go.Figure':
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"""
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Create a chart focused on a specific indicator.
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Args:
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symbol: Trading symbol
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data: OHLCV data
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indicator_type: Type of indicator ('sma', 'ema', 'bollinger_bands', 'rsi', 'macd')
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**params: Indicator parameters
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Returns:
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Plotly figure with indicator chart
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"""
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try:
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# Map indicator types to configurations
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indicator_map = {
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'sma': {'type': 'sma', 'parameters': {'period': params.get('period', 20)}},
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'ema': {'type': 'ema', 'parameters': {'period': params.get('period', 20)}},
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'bollinger_bands': {
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'type': 'bollinger_bands',
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'parameters': {
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'period': params.get('period', 20),
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'std_dev': params.get('std_dev', 2)
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}
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},
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'rsi': {'type': 'rsi', 'parameters': {'period': params.get('period', 14)}},
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'macd': {
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'type': 'macd',
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'parameters': {
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'fast_period': params.get('fast_period', 12),
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'slow_period': params.get('slow_period', 26),
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'signal_period': params.get('signal_period', 9)
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}
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}
|
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}
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if indicator_type not in indicator_map:
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raise ValueError(f"Unknown indicator type: {indicator_type}")
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indicator_config = indicator_map[indicator_type]
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return create_basic_chart(symbol, data, indicators=[indicator_config])
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except Exception as e:
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return create_basic_chart(symbol, data, indicators=[]) # Fallback to basic chart
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def create_chart_with_indicators(symbol: str, timeframe: str,
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overlay_indicators: List[str] = None,
|
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subplot_indicators: List[str] = None,
|
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days_back: int = 7, **kwargs) -> go.Figure:
|
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"""
|
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Create a chart with dynamically selected indicators.
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|
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Args:
|
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symbol: Trading pair (e.g., 'BTC-USDT')
|
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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
|
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**kwargs: Additional chart parameters
|
||||
|
||||
Returns:
|
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Plotly figure with selected indicators
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||||
"""
|
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builder = ChartBuilder()
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return builder.create_chart_with_indicators(
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symbol, timeframe, overlay_indicators, subplot_indicators, days_back, **kwargs
|
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)
|
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|
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def initialize_indicator_manager():
|
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# Implementation of initialize_indicator_manager function
|
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pass
|
||||
]
|
||||
213
components/charts/chart_creation.py
Normal file
213
components/charts/chart_creation.py
Normal file
@@ -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
141
components/charts/chart_data.py
Normal file
@@ -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>'
|
||||
Reference in New Issue
Block a user