backtesting
5 free lessons tagged backtesting across AI, Business. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
The Look-Ahead Problem: Your Model Already Knows
Backtesting a language model signal has a defect no other signal has. The model was trained on text from the period being tested, so it may already know what happened next, and it uses that knowledge even when instructed not to. This lesson establishes the problem from the published evidence, shows why prompting does not fix it, and covers what does.
Costs, Capacity, and a Protocol You Can Trust
The edge that survives statistics still has to survive trading. Spread, market impact and the square-root law, why every strategy has a capital ceiling, and the research protocol that makes a backtest worth believing.
Selection Bias and the Deflated Sharpe Ratio
The statistical core of backtest overfitting: why the best of many trials is inflated even when nothing works, how much to discount it, and why finance needs a far higher significance bar than the usual one.
The Biases That Break It Before Statistics
Look-ahead bias, survivorship bias, and point-in-time data. The errors that make a backtest wrong as a simulation, independent of any statistical question about whether the edge is real.
What a Backtest Actually Claims
A backtest is not a measurement of the past, it is a counterfactual about a world that never happened. Getting precise about that claim explains every way backtests mislead.