ml4t-diagnostic 0.1.0a7

Comprehensive diagnostic and evaluation framework for quantitative finance ML workflows

ml4t-diagnostic

Statistical validation and diagnostics for quantitative trading strategies: signal analysis, backtest evaluation, and overfitting detection.

Part of the ML4T Library Ecosystem

This library is one of five interconnected libraries supporting the machine learning for trading workflow described in Machine Learning for Trading:

Each library addresses a distinct stage: data infrastructure, feature engineering, signal evaluation, strategy backtesting, and live deployment.

What This Library Does

Evaluating whether a signal or strategy has genuine predictive power requires statistical rigor. ml4t-diagnostic provides:

• Information coefficient (IC) analysis with HAC-adjusted standard errors
• Deflated Sharpe Ratio (DSR) and other multiple-testing corrections
• Combinatorial purged cross-validation (CPCV) for time series
• Feature importance analysis (MDI, PFI, MDA, SHAP)
• Trade-level diagnostics with SHAP-based error pattern discovery
• Portfolio performance metrics and tear sheets

The library implements methods from the academic finance literature, particularly those addressing backtest overfitting and false discovery in strategy research.

Installation

pip install ml4t-diagnostic

Optional dependencies:

pip install ml4t-diagnostic[ml]   # SHAP, importance analysis
pip install ml4t-diagnostic[viz]  # Plotly visualizations
pip install ml4t-diagnostic[all]  # Everything

Quick Start

Signal Analysis

from ml4t.diagnostic.evaluation import SignalAnalysis

analyzer = SignalAnalysis(
    signal=factor_data,
    returns=forward_returns,
    periods=[1, 5, 21],
)

# IC with HAC adjustment for autocorrelation
ic_result = analyzer.compute_ic_analysis()
print(f"IC: {ic_result.ic_mean:.4f}, HAC t-stat: {ic_result.hac_tstat:.2f}")

# Quantile returns
quantile_result = analyzer.compute_quantile_analysis()
print(f"Q5-Q1 spread: {quantile_result.spread:.2%}")

Deflated Sharpe Ratio

from ml4t.diagnostic.evaluation import stats

# Accounts for multiple testing
dsr_result = stats.compute_dsr(
    returns=strategy_returns,
    benchmark_sr=0.0,
    n_trials=100,
)

print(f"Sharpe: {dsr_result['sr']:.2f}")
print(f"Deflated Sharpe: {dsr_result['dsr']:.2f}")
print(f"Significant: {dsr_result['is_significant']}")

Feature Importance

from ml4t.diagnostic.evaluation import analyze_ml_importance

# Combines MDI, PFI, MDA, SHAP methods
results = analyze_ml_importance(model, X, y)
print(results.consensus_ranking)

Trade Diagnostics

from ml4t.diagnostic.evaluation import TradeAnalysis, TradeShapAnalyzer

analyzer = TradeAnalysis(trade_records)
worst_trades = analyzer.worst_trades(n=20)

# SHAP-based error pattern discovery
shap_analyzer = TradeShapAnalyzer(model, features_df, shap_values)
result = shap_analyzer.explain_worst_trades(worst_trades)

for pattern in result.error_patterns:
    print(f"Pattern: {pattern.hypothesis}")
    print(f"Potential savings: ${pattern.potential_impact:,.2f}")

Diagnostic Framework

Tier 1: Feature Analysis (Pre-Modeling)
├── Time series diagnostics (stationarity, ACF, volatility)
├── Distribution analysis (moments, normality, tails)
├── Feature importance (MDI, PFI, MDA, SHAP)
└── Feature interactions (conditional IC, H-stat)

Tier 2: Signal Analysis (Model Outputs)
├── IC analysis (time series, histogram, decay)
├── Quantile returns (spreads, monotonicity)
├── Turnover analysis
└── Multi-signal comparison

Tier 3: Backtest Analysis (Post-Modeling)
├── Trade analysis (win/loss, holding periods)
├── Statistical validity (DSR, RAS, PBO)
├── Trade-SHAP diagnostics
└── Excursion analysis (TP/SL optimization)

Tier 4: Portfolio Analysis (Production)
├── Performance metrics (Sharpe, Sortino, Calmar)
├── Drawdown analysis
├── Rolling metrics
└── Risk metrics (VaR, CVaR)

Statistical Methods

Method	Purpose
DSR (Deflated Sharpe)	Corrects for multiple testing bias
CPCV (Combinatorial Purged CV)	Leak-free time series validation
RAS (Rademacher Anti-Serum)	Backtest overfitting detection
PBO	Probability of backtest overfitting
HAC-adjusted IC	Autocorrelation-robust information coefficient
FDR Control	Multiple comparisons (Benjamini-Hochberg)

Cross-Validation

from ml4t.diagnostic.splitters import WalkForwardCV, CombinatorialCV
from ml4t.diagnostic.visualization import plot_cv_folds

# Walk-forward with purging
cv = WalkForwardCV(n_splits=5, train_size=252, test_size=63, purge_days=21)

# Visualize fold structure
fig = plot_cv_folds(cv, dates)
fig.show()

Technical Characteristics

• Polars-based: Native Polars DataFrames throughout
• HAC standard errors: Newey-West adjustment for autocorrelated data
• Time-aware validation: Purged and embargoed cross-validation splits

Related Libraries

• ml4t-data: Market data acquisition and storage
• ml4t-engineer: Feature engineering and technical indicators
• ml4t-backtest: Event-driven backtesting
• ml4t-live: Live trading with broker integration

Development

git clone https://github.com/applied-ai/ml4t-diagnostic.git
cd ml4t-diagnostic
uv sync
uv run pytest tests/ -q -n auto
uv run ty check

References

• Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley.
• Bailey, D., & Lopez de Prado, M. (2012). "The Sharpe Ratio Efficient Frontier."
• Bailey, D., et al. (2014). "The Deflated Sharpe Ratio."

License

MIT License - see LICENSE for details.