A backtest is a simulation of a trading strategy on historical price data. The strategy is fed past bars in order, generates entries and exits as it would have at the time, and the resulting trades are aggregated into an equity curve and a set of performance statistics.
Backtests are the cheapest and fastest way to falsify a strategy idea — most ideas die in the backtest, which is a good thing. But a profitable backtest is necessary, not sufficient. The hard problems are look-ahead bias, overfitting, survivorship bias, and the gap between historical fill prices and what a real broker would have given you.
Honest backtest discipline: hold out an unseen out-of-sample period, never look at it during development, run the final strategy on it once, and accept whatever number comes out. Walk-forward optimization formalizes this loop.
How Noon Barbari uses Backtest
Every concept here is implemented in the platform. Open the relevant docs or tool to see it in action.
How backtests work here →Related terms
- Backtesting
Walk-forward optimization
Optimize on a rolling in-sample window, validate on the next out-of-sample slice.
- Backtesting
Out-of-sample
Data the strategy was not allowed to see during parameter selection.
- Backtesting
In-sample
The portion of history used to fit the strategy's parameters.
- Backtesting
Overfitting
Fitting a strategy so closely to past data that it captures noise, not signal.