Open Deviation Bar Evaluation Metrics Skill

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.

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:

Variable duration: Bars form based on price movement, not time
Autocorrelation: High-volatility periods cluster bars → temporal correlation
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:

Session-filtered (London 08:00 to NY 16:00): Primary strategy evaluation
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:

One sequence structure test: entropy, LZC, or runs test on the W/L sequence
One temporal decay test: CUSUM on equity curve or rolling hit rate
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 |

Agent Skills: Open Deviation Bar Evaluation Metrics

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