Machine-readable reference + computation scripts for state-of-the-art metrics evaluating range bar (price-based sampling) data.
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
| 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 Range Bars Need Special Treatment
Range 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
| 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
| Worked Examples (BTC, EUR/USD) | worked-examples.md
| Structured Logging (NDJSON) | structured-logging.md
Related Skills
| 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 range 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
| 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
| 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:
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Session-filtered (London 08:00 to NY 16:00): Primary strategy evaluation
-
All-bars: Regime detection, data quality diagnostics
Academic References
| 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)
Decision Framework
Go Criteria (Research)
go_criteria:
- positive_sharpe_rate > 0.55
- mean_weekly_sharpe > 0
- cv_fold_returns < 1.5
- mean_hit_rate > 0.50
Publication Criteria
publication_criteria:
- binomial_pvalue < 0.05
- psr > 0.85
- dsr > 0.50 # If n_trials > 1
Scripts
| 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:
| 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 range 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.