Trading

Backtesting: Definition

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Simple Definition

Testing a trading rule on past market data to see how it would have behaved — before risking any real money.

Why It Matters

Backtesting replays a strategy against historical prices to estimate how it might have performed. It's a reality check, not a crystal ball: markets change, and a rule that looked brilliant on the last ten years can still fail tomorrow. Backtests are also easy to fool yourself with — through look-ahead bias (using data the strategy couldn't have known yet), survivorship bias (testing only on companies that survived), and overfitting. A backtest describes the past; it does not predict the future.

Key Points

  • Replays a strategy on historical data to estimate past behavior
  • Common traps: look-ahead bias, survivorship bias, and overfitting to old data
  • Strong past results never guarantee future results

Learn More

Foundation Lesson

Backtesting: Testing a Strategy on History

Get a complete explanation with examples, key takeaways, and a quiz to test your knowledge.

Related Terms

Common Questions

Testing a trading rule on past market data to see how it would have behaved — before risking any real money. Backtesting replays a strategy against historical prices to estimate how it might have performed. It's a reality check, not a crystal ball: markets change, and a rule that looked brilliant on the last ten years can still fail tomorrow.

Backtesting replays a strategy against historical prices to estimate how it might have performed. It's a reality check, not a crystal ball: markets change, and a rule that looked brilliant on the last ten years can still fail tomorrow. Backtests are also easy to fool yourself with — through look-ahead bias (using data the strategy couldn't have known yet), survivorship bias (testing only on companies that survived), and overfitting. A backtest describes the past; it does not predict the future.

Replays a strategy on historical data to estimate past behavior

Common traps: look-ahead bias, survivorship bias, and overfitting to old data

Strong past results never guarantee future results