How to Backtest Your Own Trading Strategy (Without Coding)

"This approach feels like it works" — the problem is, "feels" isn't evidence. Backtesting applies your rules to historical data to see whether they actually made money, instead of finding out with real cash.

What a backtest measures

A meaningful backtest gives you at least four numbers:

• Win rate: the share of trades that were profitable.
• Profit factor / expectancy: average win ÷ average loss. Low win rate with a high ratio still makes money.
• Max drawdown (MDD): the largest peak-to-trough drop — it decides whether you can actually hold on.
• Sharpe ratio: return per unit of volatility taken on.

Looking only at "total return" is the classic beginner trap. A strategy with a great annual return but a 60% drawdown is one you'd never survive to recovery in real life.

Three steps to a clean backtest

1. Write the rules as explicit conditions: not "buy when it's low," but decidable conditions like "RSI < 35 AND close change > 1.5%" (see how to write buy rules).
2. Use long-enough data that includes delisted stocks: testing only survivors overstates returns (survivorship bias).
3. Don't overfit: tuning parameters to a perfect past usually just memorizes noise. Hold out a slice of data for out-of-sample checks.

You don't need to code

Backtesting used to require Python. Now you can build buy/take-profit/stop-loss rules in a visual strategy builder and backtest them in one click on your own watchlist — win rate, profit factor, max drawdown — before risking anything.

Summary

Backtesting isn't about finding a "perfect strategy" — it's about knowing a rule's temperament before you bet on it. Validate first, execute second.