Vectorbt Walk-Forward: Implementing Purged
What if your backtest results are lying to you because your training data peeked at the future? This is the silent killer of algorithmic strategies, but...
What if your backtest results are lying to you because your training data peeked at the future? This is the silent killer of algorithmic strategies, but vectorbt offers a specific solution to stop this leakage before it ruins your edge. Most traders run standard backtests and assume the numbers are real. In reality, financial time series data violates the independence assumption required by traditional statistical methods. When you test a strategy on data that overlaps with its training period, you create look-ahead bias. This leads to inflated performance metrics that vanish the moment you go live. Key fact: Standard k-fold cross-validation fails in finance because it assumes observations are independently and identically distributed (IID), which is rarely true for time series data where labels depend on future events. This article explores how to implement purged k-fold cross-validation to generate realistic performance distributions. We will move beyond single-point estimates and build robust validation pipelines that account for market structure and temporal dependencies.