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Trading & Risk

Backtesting

Pomegra Learn

Backtesting

Before you trade your edge with real money, you need evidence that it works. Backtesting is the process of running your trading rules against historical market data to see how they would have performed. A well-designed backtest reveals whether your strategy has a statistical edge, what your expected drawdowns are, and where the setup breaks down under stress. Without this validation, you are gambling with your capital.

Many traders skip backtesting because they believe their setup is "obvious" or because they are impatient to start trading live. This is a mistake. Backtesting is not about proving you are right—it is about identifying and quantifying your true edge before you risk real money. Some setups that feel obvious fail consistently under backtesting. Others that seem marginal show a solid statistical edge over a large sample of trades. Backtesting reveals the truth.

In this chapter, we cover how to conduct a rigorous backtest without falling into common traps. You'll learn to avoid overfitting (tuning your rules to fit past data perfectly, only to fail in the future), to account for slippage and commissions (real costs that eat into your edge), and to interpret backtest results honestly. We'll show you how to test a complete trading system: entry signals, exit rules, position sizing, and stop-loss placement. You'll also learn to conduct robustness tests—proving that your edge persists across different market conditions and time periods.

Why This Matters​

Backtesting is where hope meets reality. A trader might have a vague conviction that stocks break out of consolidation patterns, so they chase every breakout they see live. A proper backtest will show whether that conviction is justified. Perhaps the win rate is only 35%, but the average winner is 3 times the size of the average loser, yielding a positive expectancy. Or perhaps the setup fails entirely in sideways markets and has suffered 30% drawdowns. These insights cannot come from gut feel—they come from data.

What You Will Learn​

  • How to set up a backtest: defining entry signals, exit rules, position size, and stop-loss levels
  • How to avoid overfitting: the difference between in-sample and out-of-sample testing
  • How to account for slippage, commissions, and gaps in realistic simulation
  • Key metrics: win rate, average winner/loser, profit factor, maximum drawdown, and recovery factor
  • Robustness testing: proving your edge works across different symbols, time periods, and market regimes
  • When a backtest is reliable and when to distrust the results

How to Read This Chapter​

Start with the fundamentals: what constitutes a complete backtest, and what mistakes to avoid. The articles on overfitting and slippage are critical because many traders delude themselves with unrealistic backtests that fail the moment they trade live. Once you understand the pitfalls, move into the practical articles on setting up a test, interpreting results, and conducting robustness checks. Read carefully and be skeptical of your own results. The traders who profit are those who test conservatively and trade conservatively.

By the end of this chapter, you'll know how to validate your edge and approach live trading with evidence, not hope.

Articles in this chapter​