quant-research

Adaptive epoch selection for Walk-Forward Optimization. TRIGGERS - WFO epoch, epoch selection, WFE optimization, overfitting epochs.

backtesting.py configuration for SQL oracle validation and range bar pattern backtesting. Use when running backtesting.py against ClickHouse SQL results, configuring Backtest() constructor, handling overlapping trades, multi-position mode, rolling quantile NaN handling, trade sorting, or oracle gate validation. TRIGGERS - backtesting.py, Backtest(), hedging, exclusive_orders, multi-position, overlapping trades, oracle validation, SQL vs Python, trade comparison, entry price mismatch, signal count mismatch, rolling quantile NaN, ExitTime sort, stats._trades, gen600_strategy, champion_strategy, gen300_strategy, barrier setup.

Multi-objective evolutionary optimization for per-metric percentile cutoffs and intersection-based config selection. TRIGGERS - ranking optimization, cutoff search, metric intersection, Optuna cutoffs, evolutionary search, percentile ranking, multi-objective ranking, config selection, survivor analysis, binding metrics, Pareto frontier cutoffs.

Production-grade DST-aware exchange session detection using the exchange_calendars library. Covers holiday detection, lunch break handling, vectorized session lookups, and the ExchangeConfig registry pattern for 10 global exchanges. Use this skill whenever the user needs to detect trading sessions, check market hours, handle DST transitions for exchanges, add session flags to DataFrames, detect holidays, handle lunch breaks (Tokyo, Hong Kong, Singapore), or mentions exchange_calendars, xcals, MIC codes, or trading hours. Also use when upgrading from simplified hour-range session detection (like zoneinfo + fixed hours) to production-grade exchange calendar support. TRIGGERS - exchange session, trading session, DST session, exchange calendar, market hours, lunch break, holiday detection, exchange_calendars, session detector, xcals, MIC code, trading hours, is market open, session flags, trading schedule.

Investigate why ODB bars are oversized, have zero duration, or show anomalous price ranges by forensically analyzing Parquet trade data and ClickHouse cache. Use this skill whenever the user asks about ODB bar anomalies, threshold overshoot, liquidation cascades, flash crashes in range bars, same-timestamp trade bursts, order book sweeps, matching engine batches, or why bars are bigger than expected. Also use when diagnosing data quality issues in the opendeviationbar_cache — the methodology distinguishes algorithm bugs from market microstructure phenomena. TRIGGERS - oversized bars, bar overshoot, zero duration bar, liquidation cascade, flash crash ODB, trade burst, same timestamp trades, matching engine batch, order book sweep, bar too large, threshold violation, microstructure anomaly, bar forensics, ODB data quality.

Use when evaluating open deviation bar signal quality, computing Sharpe ratios with non-IID bars, running PSR/DSR/MinTRL statistical tests, or assessing outcome predictability via the Beyond Hit Rate framework (entropy, CUSUM, runs test, Lempel-Ziv complexity, OPI score). Also use when someone reports only hit rate as evidence of signal quality — this skill provides the anti-pattern guidance and proper evaluation stack including temporal decay detection and regime break analysis.

Generalized Sharpe ratio inference under non-Normal serially correlated returns. Implements López de Prado, Lipton & Zoonekynd (2026): PSR, MinTRL, DSR, pFDR, oFDR, SFDR with ρ-corrected variance (Eq 2-3). Numba JIT for production speed. Use when computing Sharpe significance, minimum track record, false discovery rates, or deflated Sharpe ratios with autocorrelation correction.

ZigZag swing pattern classification framework for algorithmic trading. Covers the complete taxonomy of two-pivot (UP-DOWN, L₀→H₁→L₂) and three-pivot (UP-DOWN-UP, L₀→H₁→L₂→H₃) ZigZag patterns with EL/HL/LL base classes, 9 FD-binned variants, 9 three-pivot variants, 27-way extension, and market regime mapping. Use this skill whenever the user asks about zigzag patterns, swing classification, leg patterns, EL/HL/LL classification, higher-low/lower-low/equal-low patterns, pattern exhaustiveness proofs (why 9 or 27), Freedman-Diaconis binning for swing patterns, epsilon tolerance bands for price equality, normalized retracement z-scores, UP-DOWN or UP-DOWN-UP variant enumeration, market regime labels from swing structure, or any question about how many distinct zigzag configurations exist and what they mean. TRIGGERS - zigzag pattern, swing classification, EL HL LL, higher low lower low, pattern variants, leg classification, zigzag variants, UP-DOWN pattern, three-pivot, two-pivot, FD binning swing, tolerance band epsilon, retracement z-score, pattern exhaustiveness, 9 variants, 27 variants, swing regime, triangle compression, continuation impulse, rally failure.