Assessing how well your trading system performs is critical to maintain steady results across market cycles. Many traders focus exclusively on profits, but a truly effective system must also be stable, repeatable, and suitable for your risk profile.
Begin by gathering a meaningful number of trades. Too few trades may be distorted by market anomalies, so target a minimum of 30–50 trades, preferably more, to get a meaningful statistical picture.
After compiling your trade history, review the essential statistics. Look at the win rate, which is the proportion of profitable entries, but don’t assume a high win rate means success. A strategy with fewer winners can still be lucrative if your winners significantly outpace your losers.
Therefore, you must determine the profit-to-loss ratio, which is net profit relative to net loss. A profit factor above 1.5 is widely accepted as strong.
A critical additional metric the expected value, which tells you the average amount you can expect to make or lose per dollar risked. Use this formula: multiply your success rate by your average profit and subtract the loss rate multiplied by the average loss. If the expectancy is above zero means your strategy is mathematically profitable.
Review your drawdowns as well. Even profitable systems can experience temporarily negative runs. Understand the maximum drawdown and the recovery duration. If the recovery period is excessive, تریدینگ پروفسور it may be too taxing for your discipline.
Additionally verify how consistent your results are across different market conditions. A well-designed system should perform adequately in trending, ranging, and volatile markets.
Past performance analysis helps, but it’s insufficient. Test your system on live data with micro-sized trades to see how it behaves under actual market conditions.
Record every trade to record your decisions, emotions, and any deviations from your rules.
End by evaluating against a baseline against a standard comparison, like passive index investing. If your system doesn’t consistently outperform the benchmark after deducting commissions and slippage, it may lack practical value.
Continuously optimize your approach, but prevent curve-fitting to noise. The goal is not to chase perfection, but to follow a consistent, data-driven framework that you can stick with over the long term.