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3.9 Enhance chart functionality with indicator management and data export features

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- Updated `ChartBuilder` to support dynamic indicator integration, allowing users to specify overlay and subplot indicators for enhanced chart analysis.
- Implemented a new `get_indicator_data` method in `MarketDataIntegrator` for fetching indicator data based on user configurations.
- Added `create_export_controls` in `chart_controls.py` to facilitate data export options (CSV/JSON) for user analysis.
- Enhanced error handling and logging throughout the chart and data analysis processes to improve reliability and user feedback.
- Updated documentation to reflect new features and usage guidelines for indicator management and data export functionalities.
This commit is contained in:
Vasily.onl
2025-06-06 12:57:35 +08:00
parent 8572a7a387
commit c121b469f0
10 changed files with 512 additions and 654 deletions
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41
dashboard/components/chart_controls.py
View File

@@ -181,4 +181,45 @@ def create_time_range_controls():
'padding': '15px',
'background-color': '#f0f8ff',
'margin-bottom': '20px'
})
def create_export_controls():
"""Create the data export control panel."""
return html.Div([
html.H5("💾 Data Export", style={'color': '#2c3e50', 'margin-bottom': '15px'}),
html.Button(
"Export to CSV",
id="export-csv-btn",
className="btn btn-primary",
style={
'background-color': '#28a745',
'color': 'white',
'border': 'none',
'padding': '8px 16px',
'border-radius': '4px',
'cursor': 'pointer',
'margin-right': '10px'
}
),
html.Button(
"Export to JSON",
id="export-json-btn",
className="btn btn-primary",
style={
'background-color': '#17a2b8',
'color': 'white',
'border': 'none',
'padding': '8px 16px',
'border-radius': '4px',
'cursor': 'pointer'
}
),
dcc.Download(id="download-chart-data")
], style={
'border': '1px solid #bdc3c7',
'border-radius': '8px',
'padding': '15px',
'background-color': '#f8f9fa',
'margin-bottom': '20px'
})

345
dashboard/components/data_analysis.py
View File

@@ -26,89 +26,54 @@ class VolumeAnalyzer:
self.db_manager = DatabaseManager()
self.db_manager.initialize()
def get_volume_statistics(self, symbol: str, timeframe: str = "1h", days_back: int = 7) -> Dict[str, Any]:
"""Calculate comprehensive volume statistics."""
def get_volume_statistics(self, df: pd.DataFrame) -> Dict[str, Any]:
"""Calculate comprehensive volume statistics from a DataFrame."""
try:
# Fetch recent market data
end_time = datetime.now(timezone.utc)
start_time = end_time - timedelta(days=days_back)
if df.empty or 'volume' not in df.columns:
return {'error': 'DataFrame is empty or missing volume column'}
# Calculate volume statistics
total_volume = df['volume'].sum()
avg_volume = df['volume'].mean()
volume_std = df['volume'].std()
with self.db_manager.get_session() as session:
from sqlalchemy import text
query = text("""
SELECT timestamp, open, high, low, close, volume, trades_count
FROM market_data
WHERE symbol = :symbol
AND timeframe = :timeframe
AND timestamp >= :start_time
AND timestamp <= :end_time
ORDER BY timestamp ASC
""")
result = session.execute(query, {
'symbol': symbol,
'timeframe': timeframe,
'start_time': start_time,
'end_time': end_time
})
candles = []
for row in result:
candles.append({
'timestamp': row.timestamp,
'open': float(row.open),
'high': float(row.high),
'low': float(row.low),
'close': float(row.close),
'volume': float(row.volume),
'trades_count': int(row.trades_count) if row.trades_count else 0
})
if not candles:
return {'error': 'No data available'}
df = pd.DataFrame(candles)
# Calculate volume statistics
total_volume = df['volume'].sum()
avg_volume = df['volume'].mean()
volume_std = df['volume'].std()
# Volume trend analysis
recent_volume = df['volume'].tail(10).mean() # Last 10 periods
older_volume = df['volume'].head(10).mean() # First 10 periods
volume_trend = "Increasing" if recent_volume > older_volume else "Decreasing"
# High volume periods (above 2 standard deviations)
high_volume_threshold = avg_volume + (2 * volume_std)
high_volume_periods = len(df[df['volume'] > high_volume_threshold])
# Volume-Price correlation
price_change = df['close'] - df['open']
volume_price_corr = df['volume'].corr(price_change.abs())
# Average trade size (volume per trade)
# Volume trend analysis
recent_volume = df['volume'].tail(10).mean() # Last 10 periods
older_volume = df['volume'].head(10).mean() # First 10 periods
volume_trend = "Increasing" if recent_volume > older_volume else "Decreasing"
# High volume periods (above 2 standard deviations)
high_volume_threshold = avg_volume + (2 * volume_std)
high_volume_periods = len(df[df['volume'] > high_volume_threshold])
# Volume-Price correlation
price_change = df['close'] - df['open']
volume_price_corr = df['volume'].corr(price_change.abs())
# Average trade size (volume per trade)
if 'trades_count' in df.columns:
df['avg_trade_size'] = df['volume'] / df['trades_count'].replace(0, 1)
avg_trade_size = df['avg_trade_size'].mean()
return {
'total_volume': total_volume,
'avg_volume': avg_volume,
'volume_std': volume_std,
'volume_trend': volume_trend,
'high_volume_periods': high_volume_periods,
'volume_price_correlation': volume_price_corr,
'avg_trade_size': avg_trade_size,
'max_volume': df['volume'].max(),
'min_volume': df['volume'].min(),
'volume_percentiles': {
'25th': df['volume'].quantile(0.25),
'50th': df['volume'].quantile(0.50),
'75th': df['volume'].quantile(0.75),
'95th': df['volume'].quantile(0.95)
}
else:
avg_trade_size = None # Not available
return {
'total_volume': total_volume,
'avg_volume': avg_volume,
'volume_std': volume_std,
'volume_trend': volume_trend,
'high_volume_periods': high_volume_periods,
'volume_price_correlation': volume_price_corr,
'avg_trade_size': avg_trade_size,
'max_volume': df['volume'].max(),
'min_volume': df['volume'].min(),
'volume_percentiles': {
'25th': df['volume'].quantile(0.25),
'50th': df['volume'].quantile(0.50),
'75th': df['volume'].quantile(0.75),
'95th': df['volume'].quantile(0.95)
}
}
except Exception as e:
logger.error(f"Volume analysis error: {e}")
@@ -122,120 +87,83 @@ class PriceMovementAnalyzer:
self.db_manager = DatabaseManager()
self.db_manager.initialize()
def get_price_movement_statistics(self, symbol: str, timeframe: str = "1h", days_back: int = 7) -> Dict[str, Any]:
"""Calculate comprehensive price movement statistics."""
def get_price_movement_statistics(self, df: pd.DataFrame) -> Dict[str, Any]:
"""Calculate comprehensive price movement statistics from a DataFrame."""
try:
# Fetch recent market data
end_time = datetime.now(timezone.utc)
start_time = end_time - timedelta(days=days_back)
if df.empty or not all(col in df.columns for col in ['open', 'high', 'low', 'close']):
return {'error': 'DataFrame is empty or missing required price columns'}
# Basic price statistics
current_price = df['close'].iloc[-1]
period_start_price = df['open'].iloc[0]
period_return = ((current_price - period_start_price) / period_start_price) * 100
with self.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:
return {'error': 'No data available'}
df = pd.DataFrame(candles)
# Basic price statistics
current_price = df['close'].iloc[-1]
period_start_price = df['open'].iloc[0]
period_return = ((current_price - period_start_price) / period_start_price) * 100
# Daily returns (percentage changes)
df['returns'] = df['close'].pct_change() * 100
df['returns'] = df['returns'].fillna(0)
# Volatility metrics
volatility = df['returns'].std()
avg_return = df['returns'].mean()
# Price range analysis
df['range'] = df['high'] - df['low']
df['range_pct'] = (df['range'] / df['open']) * 100
avg_range_pct = df['range_pct'].mean()
# Directional analysis
bullish_periods = len(df[df['close'] > df['open']])
bearish_periods = len(df[df['close'] < df['open']])
neutral_periods = len(df[df['close'] == df['open']])
total_periods = len(df)
bullish_ratio = (bullish_periods / total_periods) * 100 if total_periods > 0 else 0
# Price extremes
period_high = df['high'].max()
period_low = df['low'].min()
# Momentum indicators
# Simple momentum (current vs N periods ago)
momentum_periods = min(10, len(df) - 1)
if momentum_periods > 0:
momentum = ((current_price - df['close'].iloc[-momentum_periods-1]) / df['close'].iloc[-momentum_periods-1]) * 100
else:
momentum = 0
# Trend strength (linear regression slope)
if len(df) > 2:
x = np.arange(len(df))
slope, _ = np.polyfit(x, df['close'], 1)
trend_strength = slope / df['close'].mean() * 100 # Normalize by average price
else:
trend_strength = 0
return {
'current_price': current_price,
'period_return': period_return,
'volatility': volatility,
'avg_return': avg_return,
'avg_range_pct': avg_range_pct,
'bullish_periods': bullish_periods,
'bearish_periods': bearish_periods,
'neutral_periods': neutral_periods,
'bullish_ratio': bullish_ratio,
'period_high': period_high,
'period_low': period_low,
'momentum': momentum,
'trend_strength': trend_strength,
'return_percentiles': {
'5th': df['returns'].quantile(0.05),
'25th': df['returns'].quantile(0.25),
'75th': df['returns'].quantile(0.75),
'95th': df['returns'].quantile(0.95)
},
'max_gain': df['returns'].max(),
'max_loss': df['returns'].min(),
'positive_returns': len(df[df['returns'] > 0]),
'negative_returns': len(df[df['returns'] < 0])
}
# Daily returns (percentage changes)
df['returns'] = df['close'].pct_change() * 100
df['returns'] = df['returns'].fillna(0)
# Volatility metrics
volatility = df['returns'].std()
avg_return = df['returns'].mean()
# Price range analysis
df['range'] = df['high'] - df['low']
df['range_pct'] = (df['range'] / df['open']) * 100
avg_range_pct = df['range_pct'].mean()
# Directional analysis
bullish_periods = len(df[df['close'] > df['open']])
bearish_periods = len(df[df['close'] < df['open']])
neutral_periods = len(df[df['close'] == df['open']])
total_periods = len(df)
bullish_ratio = (bullish_periods / total_periods) * 100 if total_periods > 0 else 0
# Price extremes
period_high = df['high'].max()
period_low = df['low'].min()
# Momentum indicators
# Simple momentum (current vs N periods ago)
momentum_periods = min(10, len(df) - 1)
if momentum_periods > 0:
momentum = ((current_price - df['close'].iloc[-momentum_periods-1]) / df['close'].iloc[-momentum_periods-1]) * 100
else:
momentum = 0
# Trend strength (linear regression slope)
if len(df) > 2:
x = np.arange(len(df))
slope, _ = np.polyfit(x, df['close'], 1)
trend_strength = slope / df['close'].mean() * 100 # Normalize by average price
else:
trend_strength = 0
return {
'current_price': current_price,
'period_return': period_return,
'volatility': volatility,
'avg_return': avg_return,
'avg_range_pct': avg_range_pct,
'bullish_periods': bullish_periods,
'bearish_periods': bearish_periods,
'neutral_periods': neutral_periods,
'bullish_ratio': bullish_ratio,
'period_high': period_high,
'period_low': period_low,
'momentum': momentum,
'trend_strength': trend_strength,
'return_percentiles': {
'5th': df['returns'].quantile(0.05),
'25th': df['returns'].quantile(0.25),
'75th': df['returns'].quantile(0.75),
'95th': df['returns'].quantile(0.95)
},
'max_gain': df['returns'].max(),
'max_loss': df['returns'].min(),
'positive_returns': len(df[df['returns'] > 0]),
'negative_returns': len(df[df['returns'] < 0])
}
except Exception as e:
logger.error(f"Price movement analysis error: {e}")
@@ -718,4 +646,39 @@ def create_price_stats_display(stats: Dict[str, Any]) -> html.Div:
])
], p="md", shadow="sm")
], cols=3, spacing="md", style={'margin-top': '20px'})
], cols=3, spacing="md", style={'margin-top': '20px'})
def get_market_statistics(df: pd.DataFrame, symbol: str, timeframe: str) -> html.Div:
"""
Generate a comprehensive market statistics component from a DataFrame.
"""
try:
volume_analyzer = VolumeAnalyzer()
price_analyzer = PriceMovementAnalyzer()
volume_stats = volume_analyzer.get_volume_statistics(df)
price_stats = price_analyzer.get_price_movement_statistics(df)
if 'error' in volume_stats or 'error' in price_stats:
error_msg = volume_stats.get('error') or price_stats.get('error')
return html.Div(f"Error generating statistics: {error_msg}", style={'color': 'red'})
# Time range for display
start_date = df['timestamp'].min().strftime('%Y-%m-%d %H:%M')
end_date = df['timestamp'].max().strftime('%Y-%m-%d %H:%M')
days_back = (df['timestamp'].max() - df['timestamp'].min()).days
time_status = f"📅 Analysis Range: {start_date} to {end_date} (~{days_back} days)"
return html.Div([
html.H3("📊 Enhanced Market Statistics"),
html.P(
time_status,
style={'font-weight': 'bold', 'margin-bottom': '15px', 'color': '#4A4A4A', 'text-align': 'center', 'font-size': '1.1em'}
),
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 html.Div(f"Error generating statistics display: {e}", style={'color': 'red'})