What Is Backtesting?

Backtesting is the process of applying a fixed trading strategy to historical market data and recording what would have happened if the rules had been followed exactly.

The emphasis is on fixed rules. If you change the strategy every time a historical trade loses, you are no longer testing one strategy.

What a Backtest Can Tell You

What a Backtest Cannot Tell You

Freeze the Strategy Before You Start

Before the first historical trade is reviewed, your Version 1 rules should already be written.

Now you are testing the strategy instead of redesigning it one chart at a time.

The Biggest Enemy: Hindsight Bias

Historical charts make everything look obvious because you can already see what happened next.

Use Bar Replay When Possible

One of the best ways to reduce hindsight bias is to hide future candles and reveal price one bar at a time.

Test Chronologically

Move through the market in order rather than jumping around looking for attractive examples.

This forces the strategy to experience quiet periods, ugly periods and losing streaks — not just the beautiful examples you would choose for a screenshot.

Never Cherry-Pick Only the Best Setups

Record Every Qualifying Signal

A proper backtest is not a gallery of favorite trades.

What Should You Record?

Measure Results in R-Multiples

R allows trades with different stop distances and account sizes to be compared consistently.

How Many Trades Should You Backtest?

There is no magic number, but very small samples can be misleading.

The more variable the strategy and market environment, the more valuable a larger sample becomes.

Test Across Different Market Conditions

A strategy that works only during one unusually strong trend may not be robust enough for general use.

Do Not Mix Markets Without Tracking Them Separately

You may later combine results, but separate tracking allows you to see whether one market is carrying the entire strategy.

Statistic #1: Win Rate

Win rate is important, but it means very little without average winner and average loser.

Statistic #2: Average Winner & Average Loser

A strategy can be profitable with a lower win rate if the average winner is sufficiently larger than the average loser.

Statistic #3: Expectancy

Expectancy estimates the average amount a strategy is expected to make or lose per trade across a large sample.

Statistic #4: Equity Curve

Add each trade’s R result in chronological order.

The equity curve shows not just whether the strategy made money, but how difficult the journey was.

Statistic #5: Maximum Drawdown

Maximum drawdown shows the largest decline from an equity peak to a later trough during the test.

A profitable strategy with extreme drawdown may still be unsuitable for your risk tolerance.

Statistic #6: Maximum Losing Streak

If your backtest shows five or six consecutive losses are normal, you should not be shocked when a similar sequence appears live.

Statistic #7: Profit Factor

Profit factor compares total winning profits with total losing losses.

Statistic #8: Trade Frequency

Two profitable strategies can feel completely different if one trades twice per month and another trades twice per day.

Break Results Down by Session

This may reveal that the core strategy works in both sessions but performs substantially better in one.

Break Results Down by Setup Type

Without setup labels, you might never discover that one variation is dragging down the rest.

Compare Long and Short Performance

Test Strategy Filters Separately

If you suspect a filter improves performance, test it objectively instead of simply assuming.

Compare results using a meaningful sample before changing the official strategy.

Beware of Overfitting

Overfitting happens when rules are adjusted so specifically to historical data that they may not generalize well to new market conditions.