backtest-reviewlisted

# Backtest Review Review any strategy artifact in this order. Do not skip steps; most fatal flaws live in step 2. ## 1. Inventory before judging Identify: universe, rebalance frequency, signal definition, execution assumptions, cost model, in-sample vs out-of-sample split, number of variants tried. If any of these are unstated, ask before analyzing. ## 2. Look-ahead sweep (kills most strategies) Check every data access for information that would not exist at decision time: - Full-sample normalization (z-scores, min-max) computed across the whole history - Signals using close prices of day t to trade day t - Fundamentals joined without point-in-time availability dates - Survivorship: universe built from today's index membership - Targets shifted the wrong direction (`df.shift(-1)` used as feature instead of label) Report each finding with file and line references. ## 3. Multiple testing honesty Ask: how many total variants were tried before this one? Apply these rules of thumb: - Under ~5 trials: judge the raw statistics - 5-50 trials: require out-of-sample confirmation on data never touched during iteration - 50+: demand explicit deflation analysis (deflated Sharpe ratio, Bailey & Lopez de Prado); a Sharpe under 1 is presumptively noise at this search scale ## 4. Distribution sanity Compute and report: skewness, kurtosis, max drawdown, longest drawdown duration, lag-1 autocorrelation of returns. - |autocorrelation| significant at sqrt(n) > 2 → suspect smoothed marks