ml4t-diagnostic 0.1.0b18

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.

Documentation: https://ml4trading.io/docs/diagnostic/

Part of the ML4T Library Ecosystem

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

Together they cover data infrastructure, feature engineering, modeling, 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 (RAS, PBO, FDR)
• Combinatorial purged cross-validation (CPCV) with calendar-aware splitting
• Feature importance analysis (MDI, PFI, MDA, SHAP) with consensus ranking
• Trade-level diagnostics with SHAP-based error pattern discovery
• Backtest reporting: BacktestProfile, report metadata, and template-based HTML tearsheets
• Portfolio analysis: 16 performance metrics (Sharpe, Sortino, Calmar, VaR, CVaR, ...)
• Systematic feature selection with IC, importance, correlation, and drift filtering
• 65+ Plotly visualizations with 4 themes (default, dark, print, presentation)

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[backtest]  # ml4t-backtest bridge
pip install ml4t-diagnostic[dashboard]  # Streamlit dashboard
pip install ml4t-diagnostic[all]  # Everything

Quick Start

Signal Analysis

from ml4t.diagnostic import analyze_signal

result = analyze_signal(
    factor=factor_data,  # date, asset, factor
    prices=price_data,   # date, asset, price
    periods=(1, 5, 21),
)

print(f"IC (1D): {result.ic['1D']:.4f}")
print(f"IC t-stat (1D): {result.ic_t_stat['1D']:.2f}")
print(f"Q5-Q1 spread (1D): {result.spread['1D']:.2%}")

Backtest Tear Sheet

from ml4t.diagnostic.visualization.backtest import generate_backtest_tearsheet

html = generate_backtest_tearsheet(
    trades=trades_df,
    returns=daily_returns,
    metrics={"sharpe": 1.5, "max_drawdown": -0.15},
    template="hedge_fund",    # or "quant_trader", "risk_manager", "full"
    theme="default",          # or "dark", "print", "presentation"
    output_path="report.html",
    n_trials=100,             # for DSR multiple-testing correction
)

Backtest Reporting From ml4t-backtest

from ml4t.diagnostic.integration import (
    BacktestReportMetadata,
    generate_tearsheet_from_result,
)

html = generate_tearsheet_from_result(
    result=backtest_result,
    template="risk_manager",
    report_metadata=BacktestReportMetadata(
        strategy_name="ETF Momentum",
        benchmark_name="SPY",
        evaluation_window="2018-01-01 to 2025-12-31",
    ),
    output_path="backtest_result_report.html",
)

Deflated Sharpe Ratio

from ml4t.diagnostic.evaluation.stats import deflated_sharpe_ratio

# Accounts for multiple testing
dsr_result = deflated_sharpe_ratio(
    returns=strategy_returns,
    benchmark_sharpe=0.0,
    n_trials=100,
)

print(f"Sharpe: {dsr_result.sharpe_ratio:.2f}")
print(f"Deflated Sharpe: {dsr_result.deflated_sharpe:.2f}")
print(f"Significant: {dsr_result.is_significant}")

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

Calendar-aware splitting with trading-day gaps:

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

# Walk-forward with purging (gaps in trading days, not calendar days)
cv = WalkForwardCV(n_splits=5, train_size=252, test_size=63, purge_days=21)

# Combinatorial purged CV (de Prado)
cpcv = CombinatorialCV(n_groups=6, n_test_groups=2, purge_days=5)

# Calendar-aware: "4W" = 20 trading sessions, not 28 calendar days
cv = WalkForwardCV(n_splits=5, train_size="52W", test_size="4W", calendar="NYSE")

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

Backtest Tear Sheets

The tearsheet pipeline supports direct rendering from normalized surfaces, BacktestResult, or saved run artifacts.

Four presets covering different analysis needs:

Template	Focus	Sections
quant_trader	Trade-level analysis	overview, trading, performance, validation, ML, factors
hedge_fund	Performance and costs	overview, performance, trading, validation, factors, ML
risk_manager	Statistical credibility	overview, validation, performance, trading, factors, ML
full	Comprehensive presentation	overview, performance, trading, validation, factors, ML

Object-oriented API for custom tearsheets:

from ml4t.diagnostic.visualization.backtest import BacktestTearsheet
from ml4t.diagnostic.integration import BacktestReportMetadata

tearsheet = BacktestTearsheet(template="quant_trader", theme="dark")
tearsheet.add_profile(profile)
tearsheet.add_report_metadata(
    BacktestReportMetadata(strategy_name="ETF Momentum", benchmark_name="SPY")
)
tearsheet.enable_section("shap_errors")
html = tearsheet.generate(output_path="report.html")

Portfolio Analysis

from ml4t.diagnostic.evaluation import PortfolioAnalysis

pa = PortfolioAnalysis(daily_returns)
metrics = pa.compute_metrics()

print(f"Sharpe: {metrics.sharpe_ratio:.2f}")
print(f"Sortino: {metrics.sortino_ratio:.2f}")
print(f"Max Drawdown: {metrics.max_drawdown:.2%}")
print(f"VaR (95%): {metrics.value_at_risk:.2%}")

Available metrics: sharpe_ratio, sortino_ratio, calmar_ratio, omega_ratio, tail_ratio, max_drawdown, annual_return, annual_volatility, value_at_risk, conditional_var, stability_of_timeseries, alpha_beta, information_ratio, up_down_capture, and more.

Feature Selection

Systematic multi-criteria feature filtering:

from ml4t.diagnostic import FeatureSelector

selector = FeatureSelector()
report = selector.run_pipeline(
    ic_results=ic_results,
    importance_results=importance_results,
    correlation_matrix=corr_matrix,
)

selected = selector.get_selected_features()
print(f"Selected {len(selected)} features from {report.initial_count}")

Steps: IC filtering, importance filtering, correlation filtering, drift filtering.

Feature Importance

from ml4t.diagnostic.metrics 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}")

Fold-aware SHAP for walk-forward CV models:

from ml4t.diagnostic.evaluation.trade_shap import compute_fold_shap

# Compute SHAP values across walk-forward folds
aligned_features, shap_values = compute_fold_shap(
    boosters=fold_models,        # {fold_id: booster}
    predictions_df=predictions,
    features_df=features,
    feature_names=feature_names,
)

Documentation

• Docs Site — deployed documentation
• Backtest Tearsheets — BacktestResult, artifact, and profile-driven reporting
• Book Guide — chapter and case-study map
• Workflows — end-to-end analysis patterns
• Validation Tiers — four-tier diagnostic framework
• Cross-Validation — CPCV and walk-forward splitting
• CV Configuration — JSON/YAML config and fold persistence
• Feature Diagnostics — importance and interaction analysis
• Feature Selection — systematic multi-criteria selection
• Statistical Tests — DSR, RAS, PBO, HAC
• Trade Analysis — trade-level diagnostics and SHAP

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
• Calendar-aware: NYSE, CME, crypto calendars for trading-day gaps
• 65+ visualizations: Plotly-based with 4 themes (default, dark, print, presentation)
• PDF/HTML export: Institutional-grade tear sheets
• Type-safe: 0 type diagnostics (ty/Astral), full type annotations
• 4,978 tests: Comprehensive test coverage

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/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."
• Bailey, D., et al. (2016). "The Probability of Backtest Overfitting."
• Lopez de Prado, M. (2020). "Combinatorial Purged Cross-Validation."

License

MIT License - see LICENSE for details.