Backtesting & Statistics
5 articles
How to validate a strategy with data instead of faith: what a correct backtest is, hindsight bias, the metrics that matter (win rate, risk-reward, profit factor, max drawdown), and how to log your trades to know whether your edge is real.
What is backtesting and how do you validate a strategy with data?
Backtesting is testing your strategy against historical data before you risk real money. What it is, the hindsight bias that ruins home-made backtests, and how to do it honestly.
Which metrics actually matter when evaluating a strategy?
Win rate, risk-reward, profit factor and max drawdown: the four figures that define a strategy — and why win rate alone is the most misleading of them all.
Expectancy, Sharpe and Sortino: the metrics beyond win rate
Win rate tells you how often you are right, not whether you make money. Expectancy answers whether the edge exists; Sharpe and Sortino tell you what it cost to collect it.
What is forward testing and why isn't a backtest enough?
A backtest tells you how your strategy behaved in the past; forward testing tells you if it works when you don’t know the future. Demo vs live, what it reveals and how long to run it.
How do you log your trades to know whether your strategy works?
Every metric of a strategy comes from your data. What to record on each trade, why log in R, and how to turn the record into decisions that improve your trading.
