Backtesting Basics: Testing an Idea Against History
Posted: Sun Oct 04, 2026 10:28 am
Backtesting Basics: Testing an Idea Against History
Backtesting means applying your rules to past data to see how they would have performed. Done carefully, it filters out weak ideas before they cost real money.
The manual version works for beginners. Scroll back on a chart, find each instance of your setup according to your written rules, record the entry, stop, target, and result, and continue forward bar by bar without looking ahead. Cover the right side of the chart if necessary.
Record a sufficient number of trades, at least 100 if possible, across different market conditions, including trends, ranges, and high-volatility periods. A test limited to one favorable stretch proves little.
Include costs. Subtract the spread and any commission from each trade. Many strategies that look profitable fall below zero after realistic costs.
Calculate metrics beyond total profit: win rate, average win and loss in R, expectancy, maximum drawdown, and the longest losing streak.
Be honest about limits. A backtest is an estimate. It cannot capture changing conditions, emotional pressure, or execution problems like slippage.
Treat positive results as a reason to move to forward testing on demo or very small live size, not as proof.
Automated backtesting tools exist and are faster, but they require clear rules and careful data, and they make overfitting easier.
Practical step: manually backtest fifty occurrences of your main setup, with costs included, and calculate expectancy.
Backtesting means applying your rules to past data to see how they would have performed. Done carefully, it filters out weak ideas before they cost real money.
The manual version works for beginners. Scroll back on a chart, find each instance of your setup according to your written rules, record the entry, stop, target, and result, and continue forward bar by bar without looking ahead. Cover the right side of the chart if necessary.
Record a sufficient number of trades, at least 100 if possible, across different market conditions, including trends, ranges, and high-volatility periods. A test limited to one favorable stretch proves little.
Include costs. Subtract the spread and any commission from each trade. Many strategies that look profitable fall below zero after realistic costs.
Calculate metrics beyond total profit: win rate, average win and loss in R, expectancy, maximum drawdown, and the longest losing streak.
Be honest about limits. A backtest is an estimate. It cannot capture changing conditions, emotional pressure, or execution problems like slippage.
Treat positive results as a reason to move to forward testing on demo or very small live size, not as proof.
Automated backtesting tools exist and are faster, but they require clear rules and careful data, and they make overfitting easier.
Practical step: manually backtest fifty occurrences of your main setup, with costs included, and calculate expectancy.