We are going to explain strategy backtesting using first principles. This means breaking it down to the most fundamental truths and building up from there. We'll use simple language, analogies, and concrete examples.
First Principles:
- Historical data is a record of what happened in the past.
- A trading strategy is a set of rules for buying and selling.
- Simulating the strategy on past data helps predict future performance (but with limitations).
Step-by-step explanation:
What is backtesting?
- First principle: We can use past data to test ideas.
- Backtesting is like a science experiment for a trading strategy. You take the rules of your strategy and apply them to historical market data to see how well it would have done.
Why do we backtest?
- First principle: It's risky to try strategies with real money without testing.
- Imagine you have a new basketball shot. You wouldn't use it in a real game without practicing first. Backtesting is practice for a trading strategy.
Key components of backtesting:
- Strategy Rules: These are your instructions for trading. Example: "Buy a stock when its 50-day moving average goes above the 200-day moving average."
- Historical Data: Past prices of stocks (or other assets) over a period of time.
- Simulation: The computer runs through the historical data day by day, applying your rules to decide when to buy or sell.
What do we measure?
- Profit/Loss: How much money would the strategy have made or lost?
- Risk: How much did the value drop at its worst point? (Drawdown)
- Consistency: What percentage of trades were winners?
Limitations (Crucial for understanding):
- Past performance doesn't guarantee future results: Markets change. What worked in the past might not work in the future.
- Overfitting: If you tweak your strategy too much to fit past data, it might not work on new data (like memorizing answers to a practice test but failing the real test).
- Costs: Real trading has fees and slippage (the difference between expected price and actual price), which backtesting might ignore.
Example in Simple Terms:
Imagine you have a strategy for trading apples:
- Rule: Buy apples on Monday if the price was below $1 on Friday. Sell them on Wednesday.
- Historical Data: You have a diary of apple prices for the last year.
- Backtest: You go through each week in your diary and pretend to follow the rule. You record how much money you would have made or lost.
Results:
- Total profit: $50
- Worst week: Lost $10
- 70% of the trades were profitable.
But remember:
- This worked last year, but next year the apple market might change (e.g., a drought affects supply).
- You didn't account for the $0.10 fee per trade.
Why is this important?
- Minimize Risk: You don't want to lose real money on an untested idea.
- Improve Strategies: If the backtest shows losses, you can adjust the rules and test again.
Conclusion:
Backtesting is a tool to test trading strategies safely using historical data. It helps you understand potential profits and risks. But it's not a crystal ball — real-world trading involves uncertainties and costs that a backtest can't perfectly simulate.
