snippets/vbt-topics/vbt-custom-metrics-analysis.md

Custom metrics in VectorBT PRO, which is a powerful feature for extending portfolio analysis beyond the built-in metrics.Now let me provide a comprehensive elaboration on custom metrics in VectorBT PRO:

Custom Metrics in VectorBT PRO

Custom metrics in VectorBT PRO provide a powerful way to extend portfolio analysis beyond the built-in metrics. They allow you to calculate domain-specific metrics, implement proprietary performance measures, or create metrics tailored to your specific trading strategies.

1. Understanding the Metrics System

Built-in Metrics Structure

VectorBT PRO uses a configuration-based approach where metrics are stored in Portfolio.metrics as a HybridConfig:

# View all available metrics
print(vbt.Portfolio.metrics)

# Get specific metric configuration
print(vbt.Portfolio.metrics['sharpe_ratio'])

Metric Configuration Structure

Each metric is defined as a dictionary with specific keys:

metric_config = {
    'title': 'My Custom Metric',        # Display name
    'calc_func': calculation_function,   # Function to calculate the metric
    'tags': ['custom', 'risk'],         # Tags for filtering
    'apply_to_timedelta': False,        # Whether to convert to timedelta
    'agg_func': None,                   # Aggregation function
    'resolve_calc_func': True,          # Whether to resolve attributes
    # ... other configuration options
}

2. Creating Custom Metrics

Method 1: Simple Function-Based Metrics

# Add a simple custom metric
vbt.Portfolio.metrics['total_bars'] = dict(
    title='Total Bars',
    calc_func=lambda self: len(self.wrapper.index)
)

# Add skewness and kurtosis
vbt.Portfolio.metrics['skew'] = dict(
    title='Skew', 
    calc_func='returns.skew'
)
vbt.Portfolio.metrics['kurtosis'] = dict(
    title='Kurtosis', 
    calc_func='returns.kurtosis'
)

Method 2: Complex Custom Calculations

# Custom metric with multiple parameters
def total_return_no_fees(self, orders):
    """Calculate total return without fees"""
    return (self.total_profit + orders.fees.sum()) / self.get_init_cash() * 100

vbt.Portfolio.metrics['total_return_no_fees'] = dict(
    title='Total Return (No Fees) [%]',
    calc_func=total_return_no_fees,
    resolve_orders=True  # Automatically resolve orders parameter
)

Method 3: Using Lambda Functions with Settings

# PnL in dollar terms (for futures trading)
vbt.Portfolio.metrics['pnl_dollars'] = dict(
    title='PnL ($)',
    calc_func=lambda self, settings: (self.value.iloc[-1] - self.value.iloc[0]) * 50,
    resolve_calc_func=False  # Don't resolve attributes automatically
)

3. Advanced Custom Metrics

Quantile-Based Metrics

def value_at_risk_custom(returns, confidence_level=0.05):
    """Custom VaR calculation"""
    return returns.quantile(confidence_level)

vbt.Portfolio.metrics['custom_var'] = dict(
    title='Custom VaR (5%)',
    calc_func=value_at_risk_custom,
    resolve_returns=True,
    confidence_level=0.05
)

Multi-Component Metrics

def comprehensive_trade_stats(trades):
    """Return multiple trade statistics"""
    return {
        'long_trades': trades.direction_long.count(),
        'short_trades': trades.direction_short.count(),
        'long_pnl': trades.direction_long.pnl.sum(),
        'short_pnl': trades.direction_short.pnl.sum(),
        'avg_trade_duration': trades.duration.mean()
    }

vbt.Portfolio.metrics['trade_breakdown'] = dict(
    title='Trade Breakdown',
    calc_func=comprehensive_trade_stats,
    resolve_trades=True
)

Time-Based Metrics

def monthly_returns_volatility(returns):
    """Calculate monthly returns volatility"""
    monthly_returns = returns.resample('M').sum()
    return monthly_returns.std() * np.sqrt(12)

vbt.Portfolio.metrics['monthly_vol'] = dict(
    title='Monthly Volatility',
    calc_func=monthly_returns_volatility,
    resolve_returns=True
)

4. Metric Resolution and Parameters

Automatic Parameter Resolution

VectorBT PRO can automatically resolve portfolio attributes as parameters:

# These parameters will be automatically resolved:
vbt.Portfolio.metrics['custom_metric'] = dict(
    title='Custom Metric',
    calc_func=lambda returns, trades, orders: calculation_logic(returns, trades, orders),
    resolve_returns=True,    # Passes self.returns
    resolve_trades=True,     # Passes self.trades
    resolve_orders=True      # Passes self.orders
)

Common Resolvable Parameters

• self - The portfolio instance
• returns - Portfolio returns
• trades - Trade records
• orders - Order records
• drawdowns - Drawdown records
• value - Portfolio value
• close - Close prices
• init_cash - Initial cash
• total_profit - Total profit
• wrapper - Array wrapper (for index/column info)

5. Global vs Instance-Level Metrics

Global Metrics (Class-Level)

# Add to all future Portfolio instances
vbt.Portfolio.metrics['my_metric'] = metric_config

# Or modify settings globally
vbt.settings.portfolio['stats']['metrics'] = list(vbt.Portfolio.metrics.items()) + [
    ('my_metric', metric_config)
]

Instance-Level Metrics

# Add to specific portfolio instance
pf._metrics['my_metric'] = metric_config

# Then use it
pf.stats(['my_metric'])

6. Using Custom Metrics

Basic Usage

# Calculate specific custom metrics
pf.stats(['total_bars', 'skew', 'kurtosis'])

# Calculate all metrics including custom ones
pf.stats('all')

# Filter by tags
pf.stats(tags=['custom'])

Advanced Usage with Settings

# Use custom metrics in optimization
results = []
for param in parameter_combinations:
    pf = vbt.Portfolio.from_signals(close, entries, exits, **param)
    stats = pf.stats(['total_return', 'sharpe_ratio', 'my_custom_metric'])
    results.append(stats)

# Create comparison DataFrame
comparison_df = pd.DataFrame(results)

7. Real-World Examples

Futures Trading Metrics

# Point-based P&L for futures
vbt.Portfolio.metrics['pnl_points'] = dict(
    title='P&L (Points)',
    calc_func=lambda self: (self.value.iloc[-1] - self.value.iloc[0]) / self.close.iloc[0] * 10000
)

# Risk-adjusted return for futures
vbt.Portfolio.metrics['risk_adjusted_return'] = dict(
    title='Risk Adjusted Return',
    calc_func=lambda self, returns: self.total_return / returns.std() * np.sqrt(252),
    resolve_returns=True
)

Intraday Strategy Metrics

# Time-of-day analysis
def intraday_performance(orders):
    """Analyze performance by hour of day"""
    order_df = orders.records_readable
    order_df['hour'] = order_df.index.hour
    return order_df.groupby('hour')['PnL'].mean()

vbt.Portfolio.metrics['hourly_performance'] = dict(
    title='Hourly Performance',
    calc_func=intraday_performance,
    resolve_orders=True
)

Market Regime Metrics

def regime_performance(returns, benchmark_returns):
    """Performance in different market regimes"""
    bull_mask = benchmark_returns > benchmark_returns.quantile(0.6)
    bear_mask = benchmark_returns < benchmark_returns.quantile(0.4)
    
    return {
        'bull_return': returns[bull_mask].mean(),
        'bear_return': returns[bear_mask].mean(),
        'bull_sharpe': returns[bull_mask].mean() / returns[bull_mask].std() * np.sqrt(252),
        'bear_sharpe': returns[bear_mask].mean() / returns[bear_mask].std() * np.sqrt(252)
    }

vbt.Portfolio.metrics['regime_analysis'] = dict(
    title='Market Regime Analysis',
    calc_func=regime_performance,
    resolve_returns=True,
    resolve_bm_returns=True
)

8. Best Practices

1. Naming Conventions

• Use descriptive names: monthly_volatility instead of mv
• Include units in title: 'Max Drawdown [%]'
• Use consistent naming patterns

2. Error Handling

def robust_metric(returns):
    """Metric with error handling"""
    try:
        if len(returns) < 2:
            return np.nan
        return returns.std() * np.sqrt(252)
    except Exception as e:
        print(f"Error calculating metric: {e}")
        return np.nan

3. Performance Optimization

# Use vectorized operations
def efficient_metric(returns):
    """Efficient vectorized calculation"""
    return returns.rolling(30).std().mean()

# Avoid loops when possible
def inefficient_metric(returns):
    """Avoid this approach"""
    results = []
    for i in range(len(returns)):
        results.append(some_calculation(returns.iloc[i]))
    return np.mean(results)

4. Documentation

vbt.Portfolio.metrics['documented_metric'] = dict(
    title='Well Documented Metric',
    calc_func=lambda returns: returns.std() * np.sqrt(252),
    resolve_returns=True,
    tags=['custom', 'risk', 'volatility'],
    # Add description in comments or docstrings
)

9. Common Pitfalls and Solutions

Pitfall 1: Metric Not Available After Creation

# ❌ Wrong: Metric added after portfolio creation
pf = vbt.Portfolio.from_signals(...)
vbt.Portfolio.metrics['my_metric'] = metric_config
pf.stats(['my_metric'])  # KeyError!

# ✅ Correct: Add metric before portfolio creation
vbt.Portfolio.metrics['my_metric'] = metric_config
pf = vbt.Portfolio.from_signals(...)
pf.stats(['my_metric'])  # Works!

Pitfall 2: Incorrect Parameter Resolution

# ❌ Wrong: Using external variables
portfolio_instance = some_portfolio
vbt.Portfolio.metrics['bad_metric'] = dict(
    calc_func=lambda self: portfolio_instance.total_return  # External reference
)

# ✅ Correct: Using self parameter
vbt.Portfolio.metrics['good_metric'] = dict(
    calc_func=lambda self: self.total_return  # Self reference
)

Pitfall 3: Missing Error Handling

# ❌ Wrong: No error handling
def risky_metric(trades):
    return trades.pnl.sum() / trades.duration.mean()  # Division by zero possible

# ✅ Correct: With error handling
def safe_metric(trades):
    if len(trades) == 0 or trades.duration.mean() == 0:
        return np.nan
    return trades.pnl.sum() / trades.duration.mean()

Custom metrics in VectorBT PRO provide unlimited flexibility to analyze your trading strategies exactly how you need. They integrate seamlessly with the existing stats system and can be used in optimization, comparison, and reporting workflows.

I'll provide a comprehensive analysis of VectorBT PRO's pf.trades analysis capabilities, with a focus on the specific metrics you mentioned.# Comprehensive VectorBT PRO pf.trades Analysis

The pf.trades object in VectorBT PRO provides extensive capabilities for analyzing trading performance. Here's a comprehensive guide focusing on directional analysis, temporal patterns, and advanced trade analytics.

1. Basic Trade Analysis

Trade Counts by Direction

# Basic trade counts
total_trades = pf.trades.count()
long_trades = pf.trades.direction_long.count()
short_trades = pf.trades.direction_short.count()

print(f"Total trades: {total_trades}")
print(f"Long trades: {long_trades}")
print(f"Short trades: {short_trades}")

# Alternative using records
trade_records = pf.trades.records_readable
direction_counts = trade_records['Direction'].value_counts()
print(f"\nDirection breakdown:\n{direction_counts}")

P&L Analysis by Direction

# Total P&L by direction
long_pnl = pf.trades.direction_long.pnl.sum()
short_pnl = pf.trades.direction_short.pnl.sum()
total_pnl = pf.trades.pnl.sum()

print(f"Long P&L: {long_pnl:.2f}")
print(f"Short P&L: {short_pnl:.2f}")
print(f"Total P&L: {total_pnl:.2f}")

# P&L statistics by direction
long_stats = pf.trades.direction_long.pnl.describe()
short_stats = pf.trades.direction_short.pnl.describe()

2. Daily P&L Analysis

Daily P&L Calculation

# Method 1: Using trade records with date grouping
trade_records = pf.trades.records_readable
trade_records['exit_date'] = trade_records.index.date

# Daily P&L overall
daily_pnl = trade_records.groupby('exit_date')['PnL'].sum()

# Daily P&L by direction
daily_pnl_by_direction = trade_records.groupby(['exit_date', 'Direction'])['PnL'].sum().unstack(fill_value=0)

print("Daily P&L by Direction:")
print(daily_pnl_by_direction.head())

Daily P&L for Each Direction

# Separate long and short daily P&L
long_trades_records = trade_records[trade_records['Direction'] == 'Long']
short_trades_records = trade_records[trade_records['Direction'] == 'Short']

daily_long_pnl = long_trades_records.groupby('exit_date')['PnL'].sum()
daily_short_pnl = short_trades_records.groupby('exit_date')['PnL'].sum()

# Combine into comprehensive daily analysis
daily_analysis = pd.DataFrame({
    'Total_PnL': daily_pnl,
    'Long_PnL': daily_long_pnl,
    'Short_PnL': daily_short_pnl,
    'Long_Trades': long_trades_records.groupby('exit_date').size(),
    'Short_Trades': short_trades_records.groupby('exit_date').size()
}).fillna(0)

print("Daily Trade Analysis:")
print(daily_analysis.head())

3. Hourly P&L Analysis by Exit Time

Hourly P&L by Direction

# Extract hour from exit time
trade_records = pf.trades.records_readable
trade_records['exit_hour'] = trade_records.index.hour

# Hourly P&L analysis
hourly_pnl_analysis = trade_records.groupby(['exit_hour', 'Direction']).agg({
    'PnL': ['sum', 'mean', 'count'],
    'Return': ['mean', 'std']
}).round(4)

print("Hourly P&L Analysis by Direction:")
print(hourly_pnl_analysis)

# Separate analysis for each direction
hourly_long_pnl = trade_records[trade_records['Direction'] == 'Long'].groupby('exit_hour')['PnL'].agg(['sum', 'mean', 'count'])
hourly_short_pnl = trade_records[trade_records['Direction'] == 'Short'].groupby('exit_hour')['PnL'].agg(['sum', 'mean', 'count'])

print("\nHourly Long P&L:")
print(hourly_long_pnl)

print("\nHourly Short P&L:")
print(hourly_short_pnl)

Advanced Hourly Analysis

# Create comprehensive hourly performance matrix
def hourly_performance_analysis(trades_records):
    """Comprehensive hourly performance analysis"""
    
    # Add time components
    trades_records['exit_hour'] = trades_records.index.hour
    trades_records['entry_hour'] = pd.to_datetime(trades_records['Entry Index']).dt.hour
    
    # Hourly exit analysis
    hourly_stats = trades_records.groupby(['exit_hour', 'Direction']).agg({
        'PnL': ['sum', 'mean', 'count', 'std'],
        'Return': ['mean', 'std'],
        'Size': 'mean'
    }).round(4)
    
    return hourly_stats

hourly_performance = hourly_performance_analysis(trade_records)

4. Day of Week Analysis

P&L by Day of Week and Direction

# Add day of week analysis
trade_records['exit_day_of_week'] = trade_records.index.day_name()
trade_records['exit_weekday'] = trade_records.index.weekday  # 0=Monday, 6=Sunday

# Day of week P&L analysis
dow_analysis = trade_records.groupby(['exit_day_of_week', 'Direction']).agg({
    'PnL': ['sum', 'mean', 'count'],
    'Return': ['mean', 'std'],
    'Size': 'mean'
}).round(4)

print("Day of Week Analysis:")
print(dow_analysis)

# Pivot for easier viewing
dow_pivot = trade_records.pivot_table(
    index='exit_day_of_week',
    columns='Direction',
    values='PnL',
    aggfunc=['sum', 'mean', 'count'],
    fill_value=0
)

print("\nDay of Week Pivot Analysis:")
print(dow_pivot)

Advanced Day of Week Patterns

# Create comprehensive day of week analysis
def day_of_week_analysis(trades_records):
    """Comprehensive day of week performance analysis"""
    
    # Ensure we have day names in proper order
    day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
    
    trades_records['exit_day_name'] = trades_records.index.day_name()
    
    # Group by day and direction
    dow_stats = trades_records.groupby(['exit_day_name', 'Direction']).agg({
        'PnL': ['sum', 'mean', 'count', 'std'],
        'Return': ['mean', 'std'],
        'Size': 'mean',
        'Entry Fees': 'mean',
        'Exit Fees': 'mean'
    }).round(4)
    
    # Reorder by day
    dow_stats = dow_stats.reindex(day_order, level=0)
    
    return dow_stats

dow_comprehensive = day_of_week_analysis(trade_records)

5. Advanced Temporal Analysis

Combined Time Pattern Analysis

# Create comprehensive time pattern analysis
def comprehensive_time_analysis(pf):
    """Complete temporal analysis of trades"""
    
    trades_records = pf.trades.records_readable
    
    # Add all time components
    trades_records['exit_hour'] = trades_records.index.hour
    trades_records['exit_day_name'] = trades_records.index.day_name()
    trades_records['exit_month'] = trades_records.index.month
    trades_records['exit_date'] = trades_records.index.date
    
    # 1. Hourly analysis
    hourly_stats = trades_records.groupby(['exit_hour', 'Direction']).agg({
        'PnL': ['sum', 'mean', 'count'],
        'Return': ['mean', 'std']
    }).round(4)
    
    # 2. Daily analysis
    daily_stats = trades_records.groupby(['exit_day_name', 'Direction']).agg({
        'PnL': ['sum', 'mean', 'count'],
        'Return': ['mean', 'std']
    }).round(4)
    
    # 3. Monthly analysis
    monthly_stats = trades_records.groupby(['exit_month', 'Direction']).agg({
        'PnL': ['sum', 'mean', 'count'],
        'Return': ['mean', 'std']
    }).round(4)
    
    # 4. Combined hour-day analysis
    hour_day_stats = trades_records.groupby(['exit_day_name', 'exit_hour', 'Direction']).agg({
        'PnL': ['sum', 'mean', 'count']
    }).round(4)
    
    return {
        'hourly': hourly_stats,
        'daily': daily_stats,
        'monthly': monthly_stats,
        'hour_day': hour_day_stats
    }

# Execute comprehensive analysis
time_analysis = comprehensive_time_analysis(pf)

# Display results
print("=== HOURLY ANALYSIS ===")
print(time_analysis['hourly'])
print("\n=== DAILY ANALYSIS ===")
print(time_analysis['daily'])
print("\n=== MONTHLY ANALYSIS ===")
print(time_analysis['monthly'])

6. Custom Metrics for Trade Analysis

Custom Trade Metrics

# Add custom metrics to Portfolio for directional analysis
vbt.Portfolio.metrics['long_trade_count'] = dict(
    title='Long Trade Count',
    calc_func=lambda trades: trades.direction_long.count(),
    resolve_trades=True
)

vbt.Portfolio.metrics['short_trade_count'] = dict(
    title='Short Trade Count',
    calc_func=lambda trades: trades.direction_short.count(),
    resolve_trades=True
)

vbt.Portfolio.metrics['long_pnl_total'] = dict(
    title='Long P&L Total',
    calc_func=lambda trades: trades.direction_long.pnl.sum(),
    resolve_trades=True
)

vbt.Portfolio.metrics['short_pnl_total'] = dict(
    title='Short P&L Total',
    calc_func=lambda trades: trades.direction_short.pnl.sum(),
    resolve_trades=True
)

# Temporal metrics
vbt.Portfolio.metrics['best_hour_pnl'] = dict(
    title='Best Hour P&L',
    calc_func=lambda trades: trades.records_readable.groupby(trades.records_readable.index.hour)['PnL'].sum().max(),
    resolve_trades=True
)

vbt.Portfolio.metrics['worst_hour_pnl'] = dict(
    title='Worst Hour P&L',
    calc_func=lambda trades: trades.records_readable.groupby(trades.records_readable.index.hour)['PnL'].sum().min(),
    resolve_trades=True
)

7. Performance Analysis Functions

Comprehensive Trade Performance Function

def analyze_trade_performance(pf):
    """Comprehensive trade performance analysis"""
    
    trades = pf.trades
    records = trades.records_readable
    
    # Basic directional statistics
    direction_stats = {
        'Long': {
            'count': trades.direction_long.count(),
            'total_pnl': trades.direction_long.pnl.sum(),
            'avg_pnl': trades.direction_long.pnl.mean(),
            'win_rate': trades.direction_long.win_rate,
            'profit_factor': trades.direction_long.profit_factor
        },
        'Short': {
            'count': trades.direction_short.count(),
            'total_pnl': trades.direction_short.pnl.sum(),
            'avg_pnl': trades.direction_short.pnl.mean(),
            'win_rate': trades.direction_short.win_rate,
            'profit_factor': trades.direction_short.profit_factor
        }
    }
    
    # Temporal analysis
    records['exit_hour'] = records.index.hour
    records['exit_day'] = records.index.day_name()
    records['exit_date'] = records.index.date
    
    # Hourly P&L by direction
    hourly_pnl = records.groupby(['exit_hour', 'Direction'])['PnL'].agg(['sum', 'mean', 'count'])
    
    # Daily P&L by direction
    daily_pnl = records.groupby(['exit_day', 'Direction'])['PnL'].agg(['sum', 'mean', 'count'])
    
    # Date-based P&L
    date_pnl = records.groupby(['exit_date', 'Direction'])['PnL'].agg(['sum', 'mean', 'count'])
    
    # Best/worst performing times
    best_hours = records.groupby(['exit_hour', 'Direction'])['PnL'].sum().groupby('Direction').idxmax()
    worst_hours = records.groupby(['exit_hour', 'Direction'])['PnL'].sum().groupby('Direction').idxmin()
    
    return {
        'direction_stats': direction_stats,
        'hourly_pnl': hourly_pnl,
        'daily_pnl': daily_pnl,
        'date_pnl': date_pnl,
        'best_hours': best_hours,
        'worst_hours': worst_hours
    }

# Execute analysis
performance_analysis = analyze_trade_performance(pf)

8. Visualization Functions

Trade Performance Visualization

def plot_trade_performance(pf):
    """Create comprehensive trade performance plots"""
    
    import plotly.graph_objects as go
    from plotly.subplots import make_subplots
    
    records = pf.trades.records_readable
    records['exit_hour'] = records.index.hour
    records['exit_day'] = records.index.day_name()
    
    # Create subplots
    fig = make_subplots(
        rows=2, cols=2,
        subplot_titles=['Hourly P&L by Direction', 'Daily P&L by Direction', 
                       'P&L Distribution', 'Cumulative P&L by Direction'],
        specs=[[{"secondary_y": True}, {"secondary_y": True}],
               [{"secondary_y": False}, {"secondary_y": False}]]
    )
    
    # Hourly P&L
    hourly_long = records[records['Direction'] == 'Long'].groupby('exit_hour')['PnL'].sum()
    hourly_short = records[records['Direction'] == 'Short'].groupby('exit_hour')['PnL'].sum()
    
    fig.add_trace(go.Bar(x=hourly_long.index, y=hourly_long.values, name='Long Hourly', marker_color='green'), row=1, col=1)
    fig.add_trace(go.Bar(x=hourly_short.index, y=hourly_short.values, name='Short Hourly', marker_color='red'), row=1, col=1)
    
    # Daily P&L
    day_order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
    daily_long = records[records['Direction'] == 'Long'].groupby('exit_day')['PnL'].sum().reindex(day_order, fill_value=0)
    daily_short = records[records['Direction'] == 'Short'].groupby('exit_day')['PnL'].sum().reindex(day_order, fill_value=0)
    
    fig.add_trace(go.Bar(x=daily_long.index, y=daily_long.values, name='Long Daily', marker_color='lightgreen'), row=1, col=2)
    fig.add_trace(go.Bar(x=daily_short.index, y=daily_short.values, name='Short Daily', marker_color='lightcoral'), row=1, col=2)
    
    # P&L Distribution
    fig.add_trace(go.Histogram(x=records[records['Direction'] == 'Long']['PnL'], name='Long Distribution', opacity=0.7), row=2, col=1)
    fig.add_trace(go.Histogram(x=records[records['Direction'] == 'Short']['PnL'], name='Short Distribution', opacity=0.7), row=2, col=1)
    
    # Cumulative P&L
    long_cumulative = records[records['Direction'] == 'Long']['PnL'].cumsum()
    short_cumulative = records[records['Direction'] == 'Short']['PnL'].cumsum()
    
    fig.add_trace(go.Scatter(y=long_cumulative.values, mode='lines', name='Long Cumulative', line=dict(color='green')), row=2, col=2)
    fig.add_trace(go.Scatter(y=short_cumulative.values, mode='lines', name='Short Cumulative', line=dict(color='red')), row=2, col=2)
    
    fig.update_layout(height=800, title_text="Comprehensive Trade Analysis")
    return fig

# Create visualization
# trade_plot = plot_trade_performance(pf)
# trade_plot.show()

9. Advanced Analytics

Trade Streaks and Patterns

def analyze_trade_patterns(pf):
    """Analyze trade patterns and streaks"""
    
    trades = pf.trades
    records = trades.records_readable
    
    # Winning and losing streaks
    winning_streaks = trades.winning_streak.records_readable
    losing_streaks = trades.losing_streak.records_readable
    
    # Pattern analysis
    patterns = {
        'longest_winning_streak': winning_streaks['Duration'].max() if len(winning_streaks) > 0 else 0,
        'longest_losing_streak': losing_streaks['Duration'].max() if len(losing_streaks) > 0 else 0,
        'avg_winning_streak': winning_streaks['Duration'].mean() if len(winning_streaks) > 0 else 0,
        'avg_losing_streak': losing_streaks['Duration'].mean() if len(losing_streaks) > 0 else 0,
    }
    
    # Direction-specific patterns
    long_patterns = analyze_direction_patterns(trades.direction_long)
    short_patterns = analyze_direction_patterns(trades.direction_short)
    
    return {
        'overall_patterns': patterns,
        'long_patterns': long_patterns,
        'short_patterns': short_patterns
    }

def analyze_direction_patterns(direction_trades):
    """Analyze patterns for specific direction"""
    
    if direction_trades.count() == 0:
        return {}
    
    return {
        'total_trades': direction_trades.count(),
        'win_rate': direction_trades.win_rate,
        'profit_factor': direction_trades.profit_factor,
        'avg_winner': direction_trades.winning.pnl.mean() if direction_trades.winning.count() > 0 else 0,
        'avg_loser': direction_trades.losing.pnl.mean() if direction_trades.losing.count() > 0 else 0,
        'largest_winner': direction_trades.pnl.max(),
        'largest_loser': direction_trades.pnl.min(),
        'total_pnl': direction_trades.pnl.sum()
    }

# Execute pattern analysis
pattern_analysis = analyze_trade_patterns(pf)

10. Summary Report Function

Comprehensive Trade Report

def generate_trade_report(pf):
    """Generate comprehensive trade analysis report"""
    
    print("="*80)
    print("COMPREHENSIVE TRADE ANALYSIS REPORT")
    print("="*80)
    
    # Basic Statistics
    trades = pf.trades
    total_trades = trades.count()
    long_trades = trades.direction_long.count()
    short_trades = trades.direction_short.count()
    
    print(f"\n📊 BASIC STATISTICS")
    print(f"Total Trades: {total_trades}")
    print(f"Long Trades: {long_trades} ({long_trades/total_trades*100:.1f}%)")
    print(f"Short Trades: {short_trades} ({short_trades/total_trades*100:.1f}%)")
    
    # P&L Analysis
    print(f"\n💰 P&L ANALYSIS")
    print(f"Total P&L: ${trades.pnl.sum():.2f}")
    print(f"Long P&L: ${trades.direction_long.pnl.sum():.2f}")
    print(f"Short P&L: ${trades.direction_short.pnl.sum():.2f}")
    print(f"Average P&L per Trade: ${trades.pnl.mean():.2f}")
    
    # Temporal Analysis
    records = trades.records_readable
    records['exit_hour'] = records.index.hour
    records['exit_day'] = records.index.day_name()
    
    print(f"\n⏰ TEMPORAL ANALYSIS")
    
    # Best/Worst Hours
    hourly_pnl = records.groupby('exit_hour')['PnL'].sum()
    best_hour = hourly_pnl.idxmax()
    worst_hour = hourly_pnl.idxmin()
    
    print(f"Best Hour: {best_hour}:00 (${hourly_pnl[best_hour]:.2f})")
    print(f"Worst Hour: {worst_hour}:00 (${hourly_pnl[worst_hour]:.2f})")
    
    # Best/Worst Days
    daily_pnl = records.groupby('exit_day')['PnL'].sum()
    best_day = daily_pnl.idxmax()
    worst_day = daily_pnl.idxmin()
    
    print(f"Best Day: {best_day} (${daily_pnl[best_day]:.2f})")
    print(f"Worst Day: {worst_day} (${daily_pnl[worst_day]:.2f})")
    
    # Direction Performance
    print(f"\n📈 DIRECTION PERFORMANCE")
    if long_trades > 0:
        print(f"Long Win Rate: {trades.direction_long.win_rate:.2%}")
        print(f"Long Profit Factor: {trades.direction_long.profit_factor:.2f}")
    
    if short_trades > 0:
        print(f"Short Win Rate: {trades.direction_short.win_rate:.2%}")
        print(f"Short Profit Factor: {trades.direction_short.profit_factor:.2f}")
    
    print("="*80)

# Generate report
generate_trade_report(pf)

This comprehensive analysis framework provides all the tools needed to analyze pf.trades with particular focus on:

1. Direction-specific analysis - Separate analysis for long and short trades
2. Daily P&L patterns - Understanding daily performance patterns
3. Hourly P&L by direction - Identifying optimal trading hours for each direction
4. Day of week analysis - Finding the best/worst days for different directions
5. Custom metrics - Extending the analysis with domain-specific metrics
6. Visualization tools - Creating comprehensive performance visualizations
7. Pattern recognition - Identifying winning/losing streaks and patterns
8. Comprehensive reporting - Generating detailed performance reports

The framework is designed to be modular, allowing you to pick and choose the specific analyses most relevant to your trading strategy evaluation needs.