At QuantMonitor.net, one of the core principles in building and testing trading strategies is optimization with a focus on the Sharpe ratio.
How does it work? We split the data into two parts – two-thirds for the in-sample training period and one-third reserved for out-of-sample validation. From the in-sample runs we select the configuration with the highest Sharpe ratio, and then we check its performance in the out-of-sample test.
Why Sharpe ratio? Because it’s a metric that combines both returns and volatility. A strategy might look highly profitable, but if it is extremely volatile, the Sharpe ratio will immediately penalize it. This naturally filters out overfitted and overly risky setups that only work on paper.
On the other hand, strategies with a high Sharpe ratio in the training period often show more stable performance over time. This means they have a greater chance of holding up outside the training data – in real-world trading.
And that’s the ultimate goal: to build strategies that are reliable, consistent, and robust.
👉 The best part is that you can try this yourself. Split your own data into in-sample and out-of-sample, optimize by Sharpe ratio, and see which results prove to be the most stable.
Sharpe ratio optimization
