Systematic Trading: Backtesting and Strategy Development

Module: Fintech AI | Difficulty: Advanced

Backtesting Framework

Walk-Forward Analysis

1. Train on , test on
2. Train on , test on
3. Repeat

Common Pitfalls

| Pitfall | Description | Solution | |---------|-------------|----------| | Look-ahead | Using future data | Strict point-in-time | | Survivorship | Only live stocks | Include delisted | | Overfitting | Too many parameters | Cross-validation |

import numpy as np

class WalkForwardBacktest:
    def __init__(self, model, train_window, test_window):
        self.model = model; self.train_w = train_window; self.test_w = test_window
    def run(self, data, start_date):
        results = []
        for i in range(start_date, len(data) - self.train_w - self.test_w, self.test_w):
            train = data[i:i+self.train_w]
            test = data[i+self.train_w:i+self.train_w+self.test_w]
            self.model.fit(train)
            pred = self.model.predict(test)
            results.append({'date': test.index[-1], 'pred': pred})
        return results

Research Insight: Walk-forward analysis is essential for realistic backtesting because it respects temporal ordering. The key insight is that out-of-sample performance is typically 30-50% worse than in-sample performance, so realistic expectations are crucial.