Backtesting Sample Size Analysis: Evaluating 110 Trades for Statistical Significance

The user’s backtest of 110 trades (51 wins,59 losses;46% win rate, +51% return) raises questions about statistical significance. Per external sources,30 trades are the baseline for basic statistical testing [1][3][5], while100-200 trades are recommended for robustness to distinguish edge from luck [2][4]. The user’s sample exceeds the baseline but falls at the lower end of the robust range. Additional factors like trade independence, market regime diversity, and statistical validation are critical to confirm an edge [5].

Cross-domain connections: The central limit theorem justifies the30-trade baseline [5], but real-world trading requires larger samples to mitigate overfitting risks [2][4]. A strategy’s edge depends not just on sample size but on trade quality (independence) and adaptability across market conditions [5].

Risks: The110-trade sample may not account for regime shifts, leading to overfitting [2][4]. Lack of statistical validation (e.g., Monte Carlo simulations) increases uncertainty about luck vs. skill [1].
Opportunities: Conducting additional tests (T-tests, Monte Carlo) and forward testing can validate the strategy’s edge [1][2]. Diversifying trades across market regimes reduces overfitting risk [5].

Minimum sample size guidelines vary:30 (baseline),50 (ideal basic),100-200 (robust). The user’s sample is sufficient for basic analysis but needs further validation. Critical missing data includes trade independence, market regime coverage, risk-reward ratio, and statistical test results.