Adaptive Wfo Epoch

Adaptive Walk-Forward Epoch Selection (AWFES)

Machine-readable reference for adaptive epoch selection within Walk-Forward Optimization (WFO). Optimizes training epochs per-fold using Walk-Forward Efficiency (WFE) as the objective.

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

When to Use This Skill

Use this skill when:

• Selecting optimal training epochs for ML models in WFO
• Avoiding overfitting via Walk-Forward Efficiency metrics
• Implementing per-fold adaptive epoch selection
• Computing efficient frontiers for epoch-performance trade-offs
• Carrying epoch priors across WFO folds

Quick Start

from adaptive_wfo_epoch import AWFESConfig, compute_efficient_frontier

# Generate epoch candidates from search bounds and granularity
config = AWFESConfig.from_search_space(
    min_epoch=100,
    max_epoch=2000,
    granularity=5,  # Number of frontier points
)
# config.epoch_configs → [100, 211, 447, 945, 2000] (log-spaced)

# Per-fold epoch sweep
for fold in wfo_folds:
    epoch_metrics = []
    for epoch in config.epoch_configs:
        is_sharpe, oos_sharpe = train_and_evaluate(fold, epochs=epoch)
        wfe = config.compute_wfe(is_sharpe, oos_sharpe, n_samples=len(fold.train))
        epoch_metrics.append({"epoch": epoch, "wfe": wfe, "is_sharpe": is_sharpe})

    # Select from efficient frontier
    selected_epoch = compute_efficient_frontier(epoch_metrics)

    # Carry forward to next fold as prior
    prior_epoch = selected_epoch

Methodology Overview

What This Is

Per-fold adaptive epoch selection where:

1. Train models across a range of epochs (e.g., 400, 800, 1000, 2000)
2. Compute WFE = OOS_Sharpe / IS_Sharpe for each epoch count
3. Find the "efficient frontier" - epochs maximizing WFE vs training cost
4. Select optimal epoch from frontier for OOS evaluation
5. Carry forward as prior for next fold

What This Is NOT

• NOT early stopping: Early stopping monitors validation loss continuously; this evaluates discrete candidates post-hoc
• NOT Bayesian optimization: No surrogate model; direct evaluation of all candidates
• NOT nested cross-validation: Uses temporal WFO, not shuffled splits

Academic Foundations

See references/academic-foundations.md for full literature review.

Core Formula: Walk-Forward Efficiency

def compute_wfe(
    is_sharpe: float,
    oos_sharpe: float,
    n_samples: int | None = None,
) -> float | None:
    """Walk-Forward Efficiency - measures performance transfer.

    WFE = OOS_Sharpe / IS_Sharpe

    Interpretation (guidelines, not hard thresholds):
    - WFE ≥ 0.70: Excellent transfer (low overfitting)
    - WFE 0.50-0.70: Good transfer
    - WFE 0.30-0.50: Moderate transfer (investigate)
    - WFE < 0.30: Severe overfitting (likely reject)

    The IS_Sharpe minimum is derived from signal-to-noise ratio,
    not a fixed magic number. See compute_is_sharpe_threshold().

    Reference: Pardo (2008) "The Evaluation and Optimization of Trading Strategies"
    """
    # Data-driven threshold: IS_Sharpe must exceed 2σ noise floor
    min_is_sharpe = compute_is_sharpe_threshold(n_samples) if n_samples else 0.1

    if abs(is_sharpe) < min_is_sharpe:
        return None
    return oos_sharpe / is_sharpe

Principled Configuration Framework

All parameters are derived from first principles or data characteristics. AWFESConfig provides unified configuration with log-spaced epoch generation, Bayesian variance derivation from search space, and market-specific annualization factors.

See references/configuration-framework.md for the full AWFESConfig class and compute_is_sharpe_threshold() implementation.

Guardrails (Principled Guidelines)

• G1: WFE Thresholds - 0.30 (reject), 0.50 (warning), 0.70 (target) based on practitioner consensus
• G2: IS_Sharpe Minimum - Data-driven threshold: 2/sqrt(n) adapts to sample size
• G3: Stability Penalty - Adaptive threshold derived from WFE variance prevents epoch churn
• G4: DSR Adjustment - Deflated Sharpe corrects for epoch selection multiplicity via Gumbel distribution

See references/guardrails.md for full implementations of all guardrails.

WFE Aggregation Methods

Under the null hypothesis, WFE follows a Cauchy distribution (no defined mean). Always prefer median or pooled methods:

• Pooled WFE: Precision-weighted by sample size (best for variable fold sizes)
• Median WFE: Robust to outliers (best for suspected regime changes)
• Weighted Mean: Inverse-variance weighting (best for homogeneous folds)

See references/wfe-aggregation.md for implementations and selection guide.

Efficient Frontier Algorithm

Pareto-optimal epoch selection: an epoch is on the frontier if no other epoch dominates it (better WFE AND lower training time). The AdaptiveEpochSelector class maintains state across folds with adaptive stability penalties.

See references/efficient-frontier.md for the full algorithm and carry-forward mechanism.

Anti-Patterns

CRITICAL: Never use expanding window for range bar ML training. See references/anti-patterns.md for the full analysis (Section 7).

Decision Tree

See references/epoch-selection-decision-tree.md for the full practitioner decision tree.

Start
  │
  ├─ IS_Sharpe > compute_is_sharpe_threshold(n)? ──NO──> Mark WFE invalid, use fallback
  │         │                                            (threshold = 2/√n, adapts to sample size)
  │        YES
  │         │
  ├─ Compute WFE for each epoch
  │         │
  ├─ Any WFE > 0.30? ──NO──> REJECT all epochs (severe overfit)
  │         │                (guideline, not hard threshold)
  │        YES
  │         │
  ├─ Compute efficient frontier
  │         │
  ├─ Apply AdaptiveStabilityPenalty
  │         │ (threshold derived from WFE variance)
  └─> Return selected epoch

Integration with rangebar-eval-metrics

This skill extends rangebar-eval-metrics:

Recommended Workflow

1. Compute base metrics using rangebar-eval-metrics:compute_metrics.py
2. Feed to AWFES for epoch selection with sharpe_tw as primary signal
3. Validate with psr > 0.85 and dsr > 0.50 before deployment
4. Monitor is_collapsed and prediction_autocorr for model health

OOS Application Phase

AWFES uses Nested WFO with three data splits per fold (Train 60% / Val 20% / Test 20%) with 6% embargo gaps at each boundary. The per-fold workflow: epoch sweep on train, WFE computation on validation, Bayesian update, final model training on train+val, evaluation on test.

See references/oos-workflow.md for the complete workflow with diagrams, BayesianEpochSelector class, and apply_awfes_to_test() implementation. Also see references/oos-application.md for the extended reference.

Epoch Smoothing Methods

Bayesian updating (recommended) provides principled, uncertainty-aware smoothing. Alternatives include EMA and SMA. Initialization via AWFESConfig.from_search_space() derives variances from the epoch range automatically.

See references/epoch-smoothing-methods.md for all methods, formulas, and initialization strategies. See references/epoch-smoothing.md for extended mathematical analysis.

OOS Metrics Specification

Three-tier metric hierarchy for test evaluation:

• Tier 1 (Primary): sharpe_tw, hit_rate, cumulative_pnl, positive_sharpe_folds, wfe_test
• Tier 2 (Risk): max_drawdown, calmar_ratio, profit_factor, cvar_10pct
• Tier 3 (Statistical): psr, dsr, binomial_pvalue, hac_ttest_pvalue

See references/oos-metrics-implementation.md for full metric tables, compute_oos_metrics(), and fold aggregation code. See references/oos-metrics.md for threshold justifications.

Look-Ahead Bias Prevention

CRITICAL (v3 fix): TEST must use prior_bayesian_epoch (from prior folds only), NOT val_optimal_epoch. The Bayesian update happens AFTER test evaluation, ensuring information flows only from past to present.

See references/look-ahead-bias-v3.md for the v3 fix details, embargo requirements, validation checklist, and anti-patterns. See references/look-ahead-bias.md for detailed examples.

References

Related Skills

Full Citations

• Bailey, D. H., & López de Prado, M. (2014). The deflated Sharpe ratio: Correcting for selection bias, backtest overfitting and non-normality. The Journal of Portfolio Management, 40(5), 94-107.
• Bischl, B., et al. (2023). Multi-Objective Hyperparameter Optimization in Machine Learning. ACM Transactions on Evolutionary Learning and Optimization.
• López de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. Chapter 7.
• Nomura, M., & Ono, I. (2021). Warm Starting CMA-ES for Hyperparameter Optimization. AAAI Conference on Artificial Intelligence.
• Pardo, R. E. (2008). The Evaluation and Optimization of Trading Strategies, 2nd Edition. John Wiley & Sons.

Post-Execution Reflection

After this skill completes, check before closing:

1. Did the command succeed? — If not, fix the instruction or error table that caused the failure.
2. Did parameters or output change? — If the underlying tool's interface drifted, update Usage examples and Parameters table to match.
3. Was a workaround needed? — If you had to improvise (different flags, extra steps), update this SKILL.md so the next invocation doesn't need the same workaround.

Only update if the issue is real and reproducible — not speculative.