Why one backtest is not a conclusion
A backtest is one test of one hypothesis. Treating the result as a conclusion needs out-of-sample data, trading costs, and invalidation rules written in advance.
Multiple testing and the false discovery trap in factor research
When scanning thousands of candidate signals, traditional significance thresholds inevitably break down. Controlling the false discovery rate and verifying out-of-sample replicability separates genuine anomalies from statistical noise.
From backtest curves to live execution: slippage and capacity limits
Theoretical backtest gains quickly vanish in the frictions of live execution. Embedding market impact, turnover constraints, and liquidity ceilings directly into the research model is what keeps strategies viable.
Point-in-time data integrity and the defense against lookahead bias
Leaking future information into backtests is the most covert cause of live trading failure. Maintaining point-in-time data and tracking information availability timestamps is a non-negotiable quantitative baseline.
Combinatorial Purged Cross-Validation (CPCV) and Label-Overlap Leakage Defense
Financial time series exhibit serial correlation and overlapping event labels. Standard k-fold cross-validation causes severe information leakage; purging overlapping samples and embargoing post-test autocorrelation are required to construct unbiased evaluation distributions.