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
@@ -5,10 +5,10 @@ Chart control components for the market data layout.
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from dash import html, dcc
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import dash_bootstrap_components as dbc
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from utils.logger import get_logger
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from utils.time_range_utils import load_time_range_options
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logger = get_logger("default_logger")
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def create_chart_config_panel(strategy_options, overlay_options, subplot_options):
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"""Create the chart configuration panel with add/edit UI."""
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return dbc.Card([
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@@ -74,18 +74,7 @@ def create_time_range_controls():
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html.Label("Quick Select:", className="form-label"),
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dcc.Dropdown(
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id='time-range-quick-select',
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options=[
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{'label': '🕐 Last 1 Hour', 'value': '1h'},
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{'label': '🕐 Last 4 Hours', 'value': '4h'},
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{'label': '🕐 Last 6 Hours', 'value': '6h'},
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{'label': '🕐 Last 12 Hours', 'value': '12h'},
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{'label': '📅 Last 1 Day', 'value': '1d'},
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{'label': '📅 Last 3 Days', 'value': '3d'},
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{'label': '📅 Last 7 Days', 'value': '7d'},
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{'label': '📅 Last 30 Days', 'value': '30d'},
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{'label': '📅 Custom Range', 'value': 'custom'},
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{'label': '🔴 Real-time', 'value': 'realtime'}
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],
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options=load_time_range_options(),
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value='7d',
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placeholder="Select time range",
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)
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@@ -4,9 +4,6 @@ Data analysis components for comprehensive market data analysis.
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from dash import html, dcc
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import dash_bootstrap_components as dbc
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import plotly.graph_objects as go
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import plotly.express as px
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from plotly.subplots import make_subplots
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import pandas as pd
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import numpy as np
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from datetime import datetime, timezone, timedelta
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@@ -14,7 +11,8 @@ from typing import Dict, Any, List, Optional
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from utils.logger import get_logger
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from database.connection import DatabaseManager
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from database.operations import DatabaseOperationError
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from database.operations import DatabaseOperationError, get_database_operations
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from config.constants.chart_constants import CHART_COLORS, UI_TEXT
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logger = get_logger("data_analysis")
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@@ -23,8 +21,7 @@ class VolumeAnalyzer:
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"""Analyze trading volume patterns and trends."""
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def __init__(self):
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self.db_manager = DatabaseManager()
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self.db_manager.initialize()
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pass
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def get_volume_statistics(self, df: pd.DataFrame) -> Dict[str, Any]:
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"""Calculate comprehensive volume statistics from a DataFrame."""
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@@ -32,69 +29,81 @@ class VolumeAnalyzer:
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if df.empty or 'volume' not in df.columns:
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return {'error': 'DataFrame is empty or missing volume column'}
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# Convert all relevant columns to float to avoid type errors with Decimal
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df = df.copy()
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numeric_cols = ['open', 'high', 'low', 'close', 'volume']
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for col in numeric_cols:
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if col in df.columns:
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df[col] = df[col].astype(float)
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if 'trades_count' in df.columns:
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df['trades_count'] = df['trades_count'].astype(float)
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df = self._ensure_numeric_cols(df)
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# Calculate volume statistics
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total_volume = df['volume'].sum()
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avg_volume = df['volume'].mean()
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volume_std = df['volume'].std()
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stats = {}
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stats.update(self._calculate_basic_volume_stats(df))
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stats.update(self._analyze_volume_trend(df))
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stats.update(self._identify_high_volume_periods(df, stats['avg_volume'], stats['volume_std']))
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stats.update(self._calculate_volume_price_correlation(df))
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stats.update(self._calculate_avg_trade_size(df))
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stats.update(self._calculate_volume_percentiles(df))
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# Volume trend analysis
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recent_volume = df['volume'].tail(10).mean() # Last 10 periods
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older_volume = df['volume'].head(10).mean() # First 10 periods
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volume_trend = "Increasing" if recent_volume > older_volume else "Decreasing"
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# High volume periods (above 2 standard deviations)
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high_volume_threshold = avg_volume + (2 * volume_std)
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high_volume_periods = len(df[df['volume'] > high_volume_threshold])
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# Volume-Price correlation
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price_change = df['close'] - df['open']
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volume_price_corr = df['volume'].corr(price_change.abs())
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# Average trade size (volume per trade)
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if 'trades_count' in df.columns:
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df['avg_trade_size'] = df['volume'] / df['trades_count'].replace(0, 1)
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avg_trade_size = df['avg_trade_size'].mean()
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else:
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avg_trade_size = None # Not available
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return {
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'total_volume': total_volume,
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'avg_volume': avg_volume,
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'volume_std': volume_std,
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'volume_trend': volume_trend,
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'high_volume_periods': high_volume_periods,
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'volume_price_correlation': volume_price_corr,
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'avg_trade_size': avg_trade_size,
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'max_volume': df['volume'].max(),
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'min_volume': df['volume'].min(),
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'volume_percentiles': {
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'25th': df['volume'].quantile(0.25),
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'50th': df['volume'].quantile(0.50),
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'75th': df['volume'].quantile(0.75),
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'95th': df['volume'].quantile(0.95)
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}
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}
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return stats
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except Exception as e:
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logger.error(f"Volume analysis error: {e}")
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return {'error': str(e)}
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def _ensure_numeric_cols(self, df: pd.DataFrame) -> pd.DataFrame:
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numeric_cols = ['open', 'high', 'low', 'close', 'volume']
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for col in numeric_cols:
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if col in df.columns:
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df[col] = df[col].astype(float)
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if 'trades_count' in df.columns:
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df['trades_count'] = df['trades_count'].astype(float)
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return df
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def _calculate_basic_volume_stats(self, df: pd.DataFrame) -> Dict[str, Any]:
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return {
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'total_volume': df['volume'].sum(),
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'avg_volume': df['volume'].mean(),
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'volume_std': df['volume'].std(),
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'max_volume': df['volume'].max(),
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'min_volume': df['volume'].min()
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}
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def _analyze_volume_trend(self, df: pd.DataFrame) -> Dict[str, Any]:
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recent_volume = df['volume'].tail(10).mean()
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older_volume = df['volume'].head(10).mean()
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volume_trend = "Increasing" if recent_volume > older_volume else "Decreasing"
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return {'volume_trend': volume_trend}
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def _identify_high_volume_periods(self, df: pd.DataFrame, avg_volume: float, volume_std: float) -> Dict[str, Any]:
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high_volume_threshold = avg_volume + (2 * volume_std)
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high_volume_periods = len(df[df['volume'] > high_volume_threshold])
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return {'high_volume_periods': high_volume_periods}
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def _calculate_volume_price_correlation(self, df: pd.DataFrame) -> Dict[str, Any]:
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price_change = df['close'] - df['open']
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volume_price_corr = df['volume'].corr(price_change.abs())
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return {'volume_price_correlation': volume_price_corr}
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def _calculate_avg_trade_size(self, df: pd.DataFrame) -> Dict[str, Any]:
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if 'trades_count' in df.columns:
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df['avg_trade_size'] = df['volume'] / df['trades_count'].replace(0, 1)
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avg_trade_size = df['avg_trade_size'].mean()
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else:
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avg_trade_size = None
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return {'avg_trade_size': avg_trade_size}
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def _calculate_volume_percentiles(self, df: pd.DataFrame) -> Dict[str, Any]:
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return {
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'volume_percentiles': {
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'25th': df['volume'].quantile(0.25),
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'50th': df['volume'].quantile(0.50),
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'75th': df['volume'].quantile(0.75),
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'95th': df['volume'].quantile(0.95)
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}
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}
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class PriceMovementAnalyzer:
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"""Analyze price movement patterns and statistics."""
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def __init__(self):
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self.db_manager = DatabaseManager()
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self.db_manager.initialize()
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pass
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def get_price_movement_statistics(self, df: pd.DataFrame) -> Dict[str, Any]:
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"""Calculate comprehensive price movement statistics from a DataFrame."""
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@@ -102,499 +111,317 @@ class PriceMovementAnalyzer:
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if df.empty or not all(col in df.columns for col in ['open', 'high', 'low', 'close']):
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return {'error': 'DataFrame is empty or missing required price columns'}
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# Convert all relevant columns to float to avoid type errors with Decimal
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df = df.copy()
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numeric_cols = ['open', 'high', 'low', 'close', 'volume']
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for col in numeric_cols:
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if col in df.columns:
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df[col] = df[col].astype(float)
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# Basic price statistics
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current_price = df['close'].iloc[-1]
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period_start_price = df['open'].iloc[0]
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period_return = ((current_price - period_start_price) / period_start_price) * 100
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df = self._ensure_numeric_cols(df)
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# Daily returns (percentage changes)
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df['returns'] = df['close'].pct_change() * 100
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df['returns'] = df['returns'].fillna(0)
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# Volatility metrics
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volatility = df['returns'].std()
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avg_return = df['returns'].mean()
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# Price range analysis
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df['range'] = df['high'] - df['low']
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df['range_pct'] = (df['range'] / df['open']) * 100
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avg_range_pct = df['range_pct'].mean()
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# Directional analysis
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bullish_periods = len(df[df['close'] > df['open']])
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bearish_periods = len(df[df['close'] < df['open']])
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neutral_periods = len(df[df['close'] == df['open']])
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total_periods = len(df)
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bullish_ratio = (bullish_periods / total_periods) * 100 if total_periods > 0 else 0
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# Price extremes
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period_high = df['high'].max()
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period_low = df['low'].min()
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# Momentum indicators
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# Simple momentum (current vs N periods ago)
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momentum_periods = min(10, len(df) - 1)
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if momentum_periods > 0:
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momentum = ((current_price - df['close'].iloc[-momentum_periods-1]) / df['close'].iloc[-momentum_periods-1]) * 100
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else:
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momentum = 0
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# Trend strength (linear regression slope)
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if len(df) > 2:
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x = np.arange(len(df))
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slope, _ = np.polyfit(x, df['close'], 1)
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trend_strength = slope / df['close'].mean() * 100 # Normalize by average price
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else:
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trend_strength = 0
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return {
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'current_price': current_price,
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'period_return': period_return,
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'volatility': volatility,
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'avg_return': avg_return,
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'avg_range_pct': avg_range_pct,
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'bullish_periods': bullish_periods,
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'bearish_periods': bearish_periods,
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'neutral_periods': neutral_periods,
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'bullish_ratio': bullish_ratio,
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'period_high': period_high,
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'period_low': period_low,
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'momentum': momentum,
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'trend_strength': trend_strength,
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'return_percentiles': {
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'5th': df['returns'].quantile(0.05),
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'25th': df['returns'].quantile(0.25),
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'75th': df['returns'].quantile(0.75),
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'95th': df['returns'].quantile(0.95)
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},
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'max_gain': df['returns'].max(),
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'max_loss': df['returns'].min(),
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'positive_returns': len(df[df['returns'] > 0]),
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'negative_returns': len(df[df['returns'] < 0])
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}
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stats = {}
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stats.update(self._calculate_basic_price_stats(df))
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stats.update(self._calculate_returns_and_volatility(df))
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stats.update(self._analyze_price_range(df))
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stats.update(self._analyze_directional_movement(df))
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stats.update(self._calculate_price_extremes(df))
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stats.update(self._calculate_momentum(df))
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stats.update(self._calculate_trend_strength(df))
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stats.update(self._calculate_return_percentiles(df))
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return stats
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except Exception as e:
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logger.error(f"Price movement analysis error: {e}")
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return {'error': str(e)}
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def _ensure_numeric_cols(self, df: pd.DataFrame) -> pd.DataFrame:
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numeric_cols = ['open', 'high', 'low', 'close', 'volume']
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for col in numeric_cols:
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if col in df.columns:
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df[col] = df[col].astype(float)
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return df
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def create_volume_analysis_chart(symbol: str, timeframe: str = "1h", days_back: int = 7) -> go.Figure:
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"""Create a comprehensive volume analysis chart."""
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try:
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analyzer = VolumeAnalyzer()
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# Fetch market data for chart
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db_manager = DatabaseManager()
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db_manager.initialize()
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end_time = datetime.now(timezone.utc)
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start_time = end_time - timedelta(days=days_back)
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with db_manager.get_session() as session:
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from sqlalchemy import text
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query = text("""
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SELECT timestamp, open, high, low, close, volume, trades_count
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FROM market_data
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WHERE symbol = :symbol
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AND timeframe = :timeframe
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AND timestamp >= :start_time
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AND timestamp <= :end_time
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ORDER BY timestamp ASC
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""")
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result = session.execute(query, {
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'symbol': symbol,
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'timeframe': timeframe,
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'start_time': start_time,
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'end_time': end_time
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})
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candles = []
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for row in result:
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candles.append({
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'timestamp': row.timestamp,
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'open': float(row.open),
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'high': float(row.high),
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'low': float(row.low),
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'close': float(row.close),
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'volume': float(row.volume),
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'trades_count': int(row.trades_count) if row.trades_count else 0
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})
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if not candles:
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fig = go.Figure()
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fig.add_annotation(text="No data available", xref="paper", yref="paper", x=0.5, y=0.5)
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return fig
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df = pd.DataFrame(candles)
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# Calculate volume moving average
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df['volume_ma'] = df['volume'].rolling(window=20, min_periods=1).mean()
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# Create subplots
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fig = make_subplots(
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rows=3, cols=1,
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subplot_titles=('Price Action', 'Volume Analysis', 'Volume vs Moving Average'),
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vertical_spacing=0.08,
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row_heights=[0.4, 0.3, 0.3]
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)
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# Price candlestick
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fig.add_trace(
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go.Candlestick(
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x=df['timestamp'],
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open=df['open'],
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high=df['high'],
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low=df['low'],
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close=df['close'],
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name='Price',
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increasing_line_color='#26a69a',
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decreasing_line_color='#ef5350'
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),
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row=1, col=1
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)
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# Volume bars with color coding
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colors = ['#26a69a' if close >= open else '#ef5350' for close, open in zip(df['close'], df['open'])]
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fig.add_trace(
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go.Bar(
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x=df['timestamp'],
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y=df['volume'],
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name='Volume',
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marker_color=colors,
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opacity=0.7
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),
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row=2, col=1
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)
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# Volume vs moving average
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fig.add_trace(
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go.Scatter(
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x=df['timestamp'],
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y=df['volume'],
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mode='lines',
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name='Volume',
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line=dict(color='#2196f3', width=1)
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),
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row=3, col=1
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)
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fig.add_trace(
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go.Scatter(
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x=df['timestamp'],
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y=df['volume_ma'],
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mode='lines',
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name='Volume MA(20)',
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line=dict(color='#ff9800', width=2)
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),
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row=3, col=1
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)
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# Update layout
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fig.update_layout(
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title=f'{symbol} Volume Analysis ({timeframe})',
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xaxis_rangeslider_visible=False,
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height=800,
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showlegend=True,
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template='plotly_white'
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)
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# Update y-axes
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fig.update_yaxes(title_text="Price", row=1, col=1)
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fig.update_yaxes(title_text="Volume", row=2, col=1)
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fig.update_yaxes(title_text="Volume", row=3, col=1)
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return fig
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except Exception as e:
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logger.error(f"Volume chart creation error: {e}")
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fig = go.Figure()
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fig.add_annotation(text=f"Error: {str(e)}", xref="paper", yref="paper", x=0.5, y=0.5)
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return fig
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def _calculate_basic_price_stats(self, df: pd.DataFrame) -> Dict[str, Any]:
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current_price = df['close'].iloc[-1]
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period_start_price = df['open'].iloc[0]
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period_return = ((current_price - period_start_price) / period_start_price) * 100
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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
|
||||
)
|
||||
])
|
||||
@@ -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),
|
||||
|
||||
Reference in New Issue
Block a user