Backtesting
A method in quantitative investing that uses historical data to simulate the performance of a trading strategy, in order to evaluate the strategy's effectiveness and risk.
Timeline (last 90 days)
Always backtest it yourself; blindly trusting any AI agent or bot is the most costly mistake the author has made.
Backtesting uses historical data to evaluate a trading strategy, and a typical process includes selecting an asset, choosing a historical time period, applying the strategy rules, simulating historical trades and analyzing the results.
A successful backtest does not prove future profitability; a strategy may be over-optimized to fit historical data, that is, overfitted.
A backtest that ignores transaction costs produces unrealistic results; commissions, spreads, slippage and other fees must be included in the simulation.
普通回测允许开发者反复调参直到曲线好看，极易产生过拟合，策略只是记住了历史噪声而非真实规律。
When evaluating a model, the sample period should cover multiple market states, including low-volatility periods, crisis periods, and sector rotation periods; validation only during bull markets cannot reflect real deployment performance
All Facts (6)
Always backtest it yourself; blindly trusting any AI agent or bot is the most costly mistake the author has made.
50%UnverifiedBacktesting uses historical data to evaluate a trading strategy, and a typical process includes selecting an asset, choosing a historical time period, applying the strategy rules, simulating historical trades and analyzing the results.
50%UnverifiedA successful backtest does not prove future profitability; a strategy may be over-optimized to fit historical data, that is, overfitted.
50%UnverifiedA backtest that ignores transaction costs produces unrealistic results; commissions, spreads, slippage and other fees must be included in the simulation.
50%Unverified普通回测允许开发者反复调参直到曲线好看，极易产生过拟合，策略只是记住了历史噪声而非真实规律。
50%UnverifiedWhen evaluating a model, the sample period should cover multiple market states, including low-volatility periods, crisis periods, and sector rotation periods; validation only during bull markets cannot reflect real deployment performance
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