Farm-level risk: correlating EA sleeves so they do not die together

Running multiple EAs in a single farm is a classic way to diversify, but it only works if they aren't all correlated. If your sleeves share the same underlying logic or market sensitivity, they will likely hit a drawdown at the same time, turning your diversification into a single point of failure.

To check your exposure, you can use a few different methods:

• Simple spreadsheets: Calculate the correlation coefficient of daily returns for each EA. If the value is consistently above 0.7, they are essentially the same strategy.
• Monitoring platforms: Tools like My EA Farm provide a centralized surface to track performance, though they don't always auto-calculate cross-sleeve correlation for you.
• Stress testing: Use QuantMogul to run Monte Carlo simulations on your joint drawdown profile. This helps visualize the tail risk of your entire portfolio.

Spreadsheets are manual and prone to human error, especially when your data set grows. Monitoring surfaces are great for visual health checks but often lack the deep statistical modeling needed to see hidden correlations during high volatility. Finally, Monte Carlo tools are powerful but can give you a false sense of security if the historical data you feed them is poor or regime-dependent.

The goal isn't to get zero correlation, which is nearly impossible in correlated asset classes anyway. It's to ensure your total farm drawdown doesn't exceed your risk tolerance when the market turns. Have you tried stress testing your entire farm as a single unit, or do you manage your sleeves mostly in isolation?