Why Most Backtests Lie

Every quant has been here: your backtest shows a 3.2 Sharpe ratio, beautiful equity curve, minimal drawdown. You go live. It bleeds.

The Usual Suspects

1. Overfitting

If you have 50 parameters and 500 data points, you're not finding alpha — you're finding noise. The market doesn't care about your polynomial regression on the third derivative of RSI.

2. Survivorship Bias

Your universe of stocks only includes companies that survived to today. The ones that went bankrupt? Not in your dataset. Your "buy the dip" strategy looks great when every dip eventually recovered — because you're only looking at survivors.

3. Look-Ahead Bias

Using data that wouldn't have been available at the time of the trade. Earnings revisions, restated financials, adjusted prices — all contaminate your signal.

What I Do Differently

Walk-forward validation. Never train and test on the same window. The engine splits data into expanding windows, trains on the past, and tests on the unseen future. Every single time.

for window in walk_forward_windows(data, train_size=252, test_size=63):
    model.fit(window.train)
    predictions = model.predict(window.test)
    results.append(evaluate(predictions, window.test.returns))

Regime awareness. A strategy that works in a trending market will get chopped up in a range-bound one. The pipeline detects the current regime and only runs strategies suited to it.

Transaction cost modeling. Slippage, spread, and market impact. If your edge disappears after costs, it was never an edge.