quant-backtesting-info
The Quant's GauntletWhy most trading strategies fail, and how to build one that works. A visual guide to rigorous backtesting. The 7 Deadly Sins of BacktestingA flawed backtest is a factory for false hope. These common biases create the illusion of profitability where none exists. Avoiding them is the first and most critical step. 😈
OverfittingMistaking historical noise for a real signal. The strategy perfectly "memorizes" the past but fails in the future. 🔮
Look-Ahead BiasUsing information in the simulation that would not have been available at the time of the trade decision. 👻
Survivorship BiasTesting only on stocks that "survived," ignoring the bankrupt companies where the strategy would have failed. 💸
Ignoring CostsForgetting that commissions and slippage can turn a "profitable" high-frequency strategy into a losing one. The Validation GauntletFinancial data is not random. Standard validation methods create "data leakage," leading to unrealistically smooth equity curves. A Walk-Forward Analysis (WFA) respects the timeline, revealing a more honest, and often harsher, reality. Illustrative equity curves. The flawed backtest shows dangerously optimistic results due to data leakage. The Bet Sizing CrucibleA good signal is useless without smart risk management. Bet sizing determines how much capital to risk per trade. As the chart shows, this single choice has a profound impact on growth, volatility, and the risk of ruin. Illustrative curves. Full Kelly maximizes growth but with extreme volatility. Fixed Fractional is conservative. Half Kelly offers a balance. The Final Judgement: Performance DashboardTotal return is a vanity metric. A robust strategy is judged by its risk-adjusted performance. These key metrics provide a holistic view of a strategy's true character. Sharpe Ratio 1.25 Return per unit of risk Max Drawdown -22% Worst peak-to-trough loss Calmar Ratio 0.95 Return vs. drawdown Profit Factor 1.80 Gross wins / Gross losses The Quant's Process: From Idea to ExecutionData IntegritySource high-quality, point-in-time data that includes delisted assets to eliminate survivorship and look-ahead bias. Rigorous ValidationUse Walk-Forward Analysis or Purged CV. Never use standard K-Fold CV on time-series data. This is the primary defense against overfitting. Smart Bet SizingImplement a formal risk management model like Fixed Fractional or Fractional Kelly. A signal is not a strategy without bet sizing. Execution SimulationModel realistic transaction costs. A backtest that ignores commissions and slippage is a fantasy. Holistic EvaluationAnalyze a full suite of performance metrics. Look beyond total return to understand the true risk-adjusted character of the strategy. |
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