The Ultimate Guide to Backtesting Trading Strategies
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조회 28회 작성일 25-11-14 12:36
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Backtesting a trading strategy is a crucial step before risking real money in the markets.
It allows you to evaluate how your strategy would have performed in the past using historical data.
Your backtest’s accuracy hinges on how clearly you articulate your trading logic.
This includes entry and exit rules, position sizing, stop loss and take profit levels, and any indicators or conditions you rely on.
The more specific and objective your rules are, the more reliable your backtest will be.
Your results are only as good as the data you feed into your model.
Use data that is accurate, adjusted for splits and dividends, and includes bid ask spreads if you're trading frequently.
Inaccurate or survivorship-biased data will distort your performance metrics.
Your backtest must span multiple economic cycles to reveal true robustness.
Avoid platforms that assume flawless execution at perfect prices.
Slippage, fees, and آرش وداد order fill delays must be baked into your model.
Your backtest must reflect the friction of actual trading.
Always account for liquidity risk when modeling execution.
Include these costs in your model to get a true picture of potential profitability.
Test your strategy across different volatility regimes and economic cycles.
Market regime shifts expose hidden weaknesses.
Test across at least five to ten years of data, and if possible, include periods of high volatility and low liquidity.
A strategy that survives multiple market regimes has a higher probability of future success.
Over-optimization is the silent killer of trading systems.
The result is a strategy that collapses when faced with new data.
Simplicity improves generalization and reduces overfitting risk.
Use out of sample testing to validate your results.
This prevents data snooping and ensures your edge is real.
Profit alone doesn’t tell the full story.
Analyze risk-adjusted returns and behavioral patterns.
Win rate without context is misleading.
Positive expectancy matters more than frequency.
Are profits coming from a few big winners or many small ones.
Document every assumption, data source, and parameter used.
Reproducibility is the foundation of scientific trading.
Finally, remember that backtesting is not a guarantee of future success.
What worked in 2010 may fail in 2025.
It tells you what could have happened, not what will happen.
Live trading reveals psychological and execution realities no backtest can capture