Backtesting

Backtesting evaluates an investment strategy by applying it to historical market data.

Backtesting is the process of checking the performance of an investment strategy by applying it to past market data before using it with real money. Just like trying a newly created recipe before serving it to customers, it simulates whether the profit would have been made if the investment had been made according to this strategy in the past. It is a basic tool for quantitative investment that verifies the strategy with data rather than intuition, and as the number of investment strategies using AI increases, it has also become important as a procedure for verifying its performance. Proper backtesting involves checking not only returns but also risk indicators such as maximum drawdown.

The most common pitfall is over-optimization. If you refine your strategy to fit only past data, your backtest performance will be spectacular, but it will easily fall apart in actual practice. The fundamental limitation of this tool is that past performance does not guarantee the future.

✅ Why it matters

• You can check the feasibility of yits strategy with data before betting real money
• It allows you to understand risk characteristics such as the maximum decline as well as the rate of return
• It creates investment habits based on evidence, not intuition

⚠️ Limits and debates

• Strategies that are overly tailored to past data are likely to fail in practice
• Past performance does not guarantee future profits
• If realistic conditions such as transaction costs and liquidity are omitted, results will be inflated