From Theory to Reality
This interactive workbench demonstrates why rigorous backtesting is non-negotiable in quantitative finance. Explore how common pitfalls, validation methods, and risk management choices transform a strategy from a "false discovery" into a potentially robust system.
The Seven Deadly Sins of Backtesting
Flawed backtests create the illusion of profitability. Understanding these common biases is the first step toward building a strategy that works in the real world, not just on paper.
Overfitting
Curve-fitting a model to historical noise instead of finding a true signal. The strategy perfectly memorizes the past but has no predictive power.
Look-Ahead Bias
Using information in the simulation that would not have been available at the time, like using a day's closing price to trade at that day's open.
Survivorship Bias
Testing only on assets that "survived" to the present, ignoring failed or delisted companies where the strategy would have lost money.
Ignoring Costs
Forgetting to model transaction costs like commissions and slippage, which can turn a profitable strategy into a losing one.
The Interactive Validation Lab
Financial data is not random. Standard cross-validation causes "data leakage," leading to inflated results. Select a method below to see how respecting the timeline produces a more realistic performance estimate.
The Bet Sizing Simulator
A good signal is useless without proper risk management. Bet sizing determines how much capital you risk per trade. Select a method to see its profound impact on portfolio growth, volatility, and risk of ruin.
Performance Dashboard
A holistic view of performance requires a suite of metrics. This dashboard reflects the results of the choices made in the Bet Sizing Simulator, revealing the trade-offs between return, risk, and volatility.
Sharpe Ratio
Max Drawdown
-18.5%
Calmar Ratio
0.98
Profit Factor
1.82