Advanced guide to avoiding common pitfalls, ensuring data quality, and building realistic backtests that translate to live trading success.
Include transaction costs, slippage, and realistic fill assumptions. Backtests should be conservative - if it works with conservative assumptions, it's more likely to work live.
A strategy must work in bull markets, bear markets, and sideways markets. Test at least 2-3 years including 2022 bear market.
Never optimize on all your data. Hold back 20-30% for validation testing. If it fails out-of-sample, you overfit.
Using future information to make past decisions. Example: Using tomorrow's close to make today's trade.
Stratify prevents this: All indicators calculate using only prior data.
Testing only on stocks that survived (not delisted). Ignoring failures inflates results.
Solution: Test on current AND delisted stocks, or use broad ETFs (SPY, QQQ).
Tweaking parameters until perfect fit on one dataset. Fails on new data.
Solution: Keep strategies simple (<5 parameters), validate out-of-sample.
Forgetting commissions and slippage. A 100-trade strategy with 0.2% costs loses 20% to fees!
Solution: Always include realistic costs (0.1-0.2% per trade minimum).
