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Performance Analysis

Performance Analysis¶

Performance attribution, strategy optimization, parameter sensitivity, and continuous improvement.

Performance Attribution¶

Return Decomposition¶

# Analyze sources of returns
def performance_attribution(returns, factors):
    """Decompose returns by factors"""
    from sklearn.linear_model import LinearRegression

    model = LinearRegression()
    model.fit(factors, returns)

    explained = model.predict(factors)
    residual = returns - explained

    return {
        'Explained': explained,
        'Residual': residual,
        'Factor Loadings': model.coef_
    }

Strategy Optimization¶

Parameter Optimization¶

from scipy.optimize import minimize

def objective(params):
    # params: [window1, window2, threshold]
    # Calculate strategy performance
    return -sharpe_ratio  # Minimize negative Sharpe

result = minimize(objective, x0=[20, 50, 0.02], method='Nelder-Mead')
optimal_params = result.x

Grid Search¶

from itertools import product

# Test parameter combinations
windows = [10, 20, 30, 50]
thresholds = [0.01, 0.02, 0.03, 0.05]

best_sharpe = -np.inf
best_params = None

for window, threshold in product(windows, thresholds):
    sharpe = test_strategy(window, threshold)
    if sharpe > best_sharpe:
        best_sharpe = sharpe
        best_params = (window, threshold)

Parameter Sensitivity¶

Sensitivity Analysis¶

def sensitivity_analysis(base_params, ranges):
    """Test parameter sensitivity"""
    results = []

    for param_name, param_range in ranges.items():
        for value in param_range:
            params = base_params.copy()
            params[param_name] = value
            performance = test_strategy(**params)
            results.append({
                'param': param_name,
                'value': value,
                'performance': performance
            })

    return pd.DataFrame(results)

Continuous Improvement¶

Performance Monitoring¶

# Track performance over time
def track_performance(returns, window=30):
    """Rolling performance metrics"""
    rolling_sharpe = returns.rolling(window).apply(
        lambda x: x.mean() / x.std() * np.sqrt(252)
    )
    return rolling_sharpe

Monitor performance regularly
Identify underperforming periods
Adjust parameters carefully
Test changes before implementing

Key Takeaways¶

Performance Attribution: Understand return sources
Optimization: Find best parameters
Sensitivity: Test parameter robustness
Continuous Improvement: Monitor and refine

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