opendeviation-eval-metrics

name	opendeviation-eval-metrics
description	evaluating open deviation bar signal quality, computing Sharpe ratios with non-IID bars, running PSR/DSR/MinTRL statistical tests, or assessing.
allowed-tools	Read, Grep, Glob, Bash

Open Deviation Bar Evaluation Metrics

Machine-readable reference + computation scripts for state-of-the-art metrics evaluating open deviation bar (ODB, brim-to-brim price-based sampling) data.

Cross-reference: Project-level experiment catalogue at signal-archaeology skill in opendeviationbar-patterns repo — contains 10 BHR-validated experiments with auditable SQL.

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:

• Evaluating ML model performance on open deviation bar data
• Computing Sharpe ratios with non-IID bar sequences
• Running Walk-Forward Optimization metric analysis
• Calculating PSR, DSR, or MinTRL statistical tests
• Generating evaluation reports from fold results

Quick Start

# Compute metrics from predictions + actuals
python scripts/compute_metrics.py --predictions preds.npy --actuals actuals.npy --timestamps ts.npy

# Generate full evaluation report
python scripts/generate_report.py --results folds.jsonl --output report.md

Metric Tiers

Tier	Purpose	Metrics	Compute
Primary (5)	Research decisions	weekly_sharpe, hit_rate, cumulative_pnl, n_bars, positive_sharpe_rate	Per-fold + aggregate
Secondary/Risk (5)	Additional context	max_drawdown, bar_sharpe, return_per_bar, profit_factor, cv_fold_returns	Per-fold
ML Quality (3)	Prediction health	ic, prediction_autocorr, is_collapsed	Per-fold
Diagnostic (5)	Final validation	psr, dsr, autocorr_lag1, effective_n, binomial_pvalue	Aggregate only
Extended Risk (5)	Deep risk analysis	var_95, cvar_95, omega_ratio, sortino_ratio, ulcer_index	Per-fold (optional)

Why Open Deviation Bars Need Special Treatment

Open deviation bars violate standard IID assumptions:

1. Variable duration: Bars form based on price movement, not time
2. Autocorrelation: High-volatility periods cluster bars → temporal correlation
3. Non-constant information: More bars during volatility = more information per day

Canonical solution: Daily aggregation via _group_by_day() before Sharpe calculation.

References

Core Reference Files

Topic	Reference File
Sharpe Ratio Calculations	sharpe-formulas.md
Risk Metrics (VaR, Omega, Ulcer)	risk-metrics.md
ML Prediction Quality (IC, Autocorr)	ml-prediction-quality.md
Crypto Market Considerations	crypto-markets.md
Temporal Aggregation Rules	temporal-aggregation.md
JSON Schema for Metrics	metrics-schema.md
Anti-Patterns (Transaction Costs)	anti-patterns.md
SOTA 2025-2026 (SHAP, BOCPD, etc.)	sota-2025-2026.md
Beyond Hit Rate (BHR) Framework	beyond-hit-rate.md
Worked Examples (BTC, EUR/USD)	worked-examples.md
Structured Logging (NDJSON)	structured-logging.md

Related Skills

Skill	Relationship
sharpe-ratio-non-iid-corrections	ρ-corrected PSR, DSR, MinTRL, pFDR, oFDR — full non-IID framework
adaptive-wfo-epoch	Uses weekly_sharpe, psr, dsr for WFE calculation

Dependencies

pip install -r requirements.txt
# Or: pip install numpy>=1.24 pandas>=2.0 scipy>=1.10

Key Formulas

Daily-Aggregated Sharpe (Primary Metric)

def weekly_sharpe(pnl: np.ndarray, timestamps: np.ndarray) -> float:
    """Sharpe with daily aggregation for open deviation bars."""
    daily_pnl = _group_by_day(pnl, timestamps)  # Sum PnL per calendar day
    if len(daily_pnl) < 2 or np.std(daily_pnl) == 0:
        return 0.0
    daily_sharpe = np.mean(daily_pnl) / np.std(daily_pnl)
    # For crypto (7-day week): sqrt(7). For equities: sqrt(5)
    return daily_sharpe * np.sqrt(7)  # Crypto default

Information Coefficient (Prediction Quality)

from scipy.stats import spearmanr

def information_coefficient(predictions: np.ndarray, actuals: np.ndarray) -> float:
    """Spearman rank IC - captures magnitude alignment."""
    ic, _ = spearmanr(predictions, actuals)
    return ic  # Range: [-1, 1]. >0.02 acceptable, >0.05 good, >0.10 excellent

Probabilistic Sharpe Ratio (Statistical Validation)

from scipy.stats import norm

def psr(sharpe: float, se: float, benchmark: float = 0.0) -> float:
    """P(true Sharpe > benchmark)."""
    return norm.cdf((sharpe - benchmark) / se)

Annualization Factors

Market	Daily → Weekly	Daily → Annual	Rationale
Crypto (24/7)	sqrt(7) = 2.65	sqrt(365) = 19.1	7 trading days/week
Equity	sqrt(5) = 2.24	sqrt(252) = 15.9	5 trading days/week

NEVER use sqrt(252) for crypto markets.

CRITICAL: Session Filter Changes Annualization

View	Filter	days_per_week	Rationale
Session-filtered (London-NY)	Weekdays 08:00-16:00	sqrt(5)	Trading like equities
All-bars (unfiltered)	None	sqrt(7)	Full 24/7 crypto

Using sqrt(7) for session-filtered data overstates Sharpe by ~18%!

See crypto-markets.md for detailed rationale.

Dual-View Metrics

For comprehensive analysis, compute metrics with BOTH views:

1. Session-filtered (London 08:00 to NY 16:00): Primary strategy evaluation
2. All-bars: Regime detection, data quality diagnostics

Academic References

Concept	Citation
Deflated Sharpe Ratio	Bailey & López de Prado (2014)
Sharpe SE with Non-Normality	Mertens (2002)
Statistics of Sharpe Ratios	Lo (2002)
Omega Ratio	Keating & Shadwick (2002)
Ulcer Index	Peter Martin (1987)

Beyond Hit Rate (BHR) Framework

Hit rate is a necessary but insufficient metric. Always supplement with outcome predictability metrics. See beyond-hit-rate.md for the full framework.

Minimum Viable Signal Evaluation

Every signal MUST be evaluated with at least:

1. One sequence structure test: entropy, LZC, or runs test on the W/L sequence
2. One temporal decay test: CUSUM on equity curve or rolling hit rate
3. One regime awareness test: per-session hit rate or HMM decomposition

A signal that passes all three is robust. A signal with only high hit rate is noise.

Outcome Predictability Index (OPI)

OPI = 0.25 * (1 - LZC_norm) + 0.25 * |z_runs| + 0.25 * Var(HR_per_regime) + 0.25 * AUC_meta

Higher OPI = more predictable win/loss timing. A 45% HR signal with OPI=0.8 is more valuable than a 65% HR signal with OPI=0.1.

Decision Framework

Go Criteria (Research)

go_criteria:
  - positive_sharpe_rate > 0.55
  - mean_weekly_sharpe > 0
  - cv_fold_returns < 1.5
  - bhr_sequence_test_passes: true # At least one of: entropy, LZC, runs test significant
  - bhr_cusum_verdict: "ALIVE" # No recent regime break

Publication Criteria

publication_criteria:
  - binomial_pvalue < 0.05
  - psr > 0.85
  - dsr > 0.50 # If n_trials > 1
  - bhr_lzc_shuffle_z < -2.0 # W/L sequence has genuine structure
  - bhr_alpha_halflife > 200 # Edge persists for 200+ trades

Scripts

Script	Purpose
scripts/compute_metrics.py	Compute all metrics from predictions/actuals
scripts/generate_report.py	Generate Markdown report from fold results
scripts/validate_schema.py	Validate metrics JSON against schema

Remediations (2026-01-19 Multi-Agent Audit)

The following fixes were applied based on a 12-subagent adversarial audit:

Issue	Root Cause	Fix	Source
weekly_sharpe=0	Constant predictions	Model collapse detection + architecture fix	model-expert
IC=None	Zero variance predictions	Return 1.0 for constant (semantically correct)	model-expert
prediction_autocorr=NaN	Division by zero	Guard for std < 1e-10, return 1.0	model-expert
Ulcer Index divide-by-zero	Peak equity = 0	Guard with np.where(peak > 1e-10, ...)	risk-analyst
Omega/Profit Factor unreliable	Too few samples	min_days parameter (default: 5)	robustness-analyst
BiLSTM mean collapse	Architecture too small	hidden_size: 16→48, dropout: 0.5→0.3	model-expert
profit_factor=1.0 (n_bars=0)	Early return wrong value	Return NaN when no data to compute ratio	risk-analyst

Model Collapse Detection

# ALWAYS check for model collapse after prediction
pred_std = np.std(predictions)
if pred_std < 1e-6:
    logger.warning(
        f"Constant predictions detected (std={pred_std:.2e}). "
        "Model collapsed to mean - check architecture."
    )

Recommended BiLSTM Architecture

# BEFORE (causes collapse on open deviation bars)
HIDDEN_SIZE = 16
DROPOUT = 0.5

# AFTER (prevents collapse)
HIDDEN_SIZE = 48  # Triple capacity
DROPOUT = 0.3     # Less aggressive regularization

See reference docs for complete implementation details.

---

Troubleshooting

Issue	Cause	Solution
weekly_sharpe is 0	Constant predictions	Check for model collapse, increase hidden_size
IC returns None	Zero variance in predictions	Model collapsed - check architecture
prediction_autocorr is NaN	Division by zero	Guard for std < 1e-10 in autocorr calculation
Ulcer Index divide error	Peak equity is zero	Add guard: np.where(peak > 1e-10, ...)
profit_factor = 1.0	No bars processed	Return NaN when n_bars is 0
Sharpe inflated 18%	Wrong annualization for data	Use sqrt(5) for session-filtered, sqrt(7) for 24/7
PSR/DSR not computed	Missing scipy	Install: pip install scipy
Timestamps not parsed	Wrong format	Ensure Unix timestamps, not datetime strings

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.