Simplicity Wins: Why Fixed Weighting is Best for Your EA Portfolio!

A beginner-friendly summary of the verification: “Simplicity Wins: Why Fixed Weighting is Best for Your EA Portfolio!”.

What’s the idea?

When you’re running multiple Expert Advisors (EAs) or trading various currency pairs, a big question is: how much capital should you allocate to each? Should you give every EA an equal slice of the pie, or try to dynamically adjust the weights based on market conditions, risk, or recent performance? This research project aimed to find out if more sophisticated, dynamic portfolio allocation methods could beat the simplest approach: fixed, equal weighting. We put several popular dynamic allocation strategies to the test:

• Inverse Volatility: Giving more weight to less volatile (i.e., less “swingy”) assets, hoping to smooth out overall portfolio returns.
• Risk Parity: Allocating capital so that each asset contributes equally to the total portfolio risk, aiming for a balanced risk profile.
• Minimum Variance: Trying to find the combination of assets that results in the lowest possible overall portfolio variance (or “wobbliness”), regardless of expected returns.
• Momentum: Leaning into assets that have performed well recently, hoping their positive trend continues.
• Mean-Variance Optimization: A more complex method that attempts to find the optimal balance between expected return and risk. The core question was: can any of these clever dynamic approaches outperform just sticking to a simple, fixed allocation for each component of our portfolio?

How I tested it

To keep things fair and realistic, we designed our tests with “no hindsight bias.” This means our allocation decisions were based only on information available at that specific moment, just like in live trading. We didn’t peek at future market data to make our choices! Our baseline was a fixed, equal risk allocation. Imagine you have four EAs; with fixed allocation, you’d simply give 25% of your capital to each. It’s straightforward and doesn’t try to predict anything. We then compared this fixed approach against the dynamic methods listed above, using a diverse basket of our EAs trading various FX pairs. To measure success, we focused on the Calmar Ratio. If you’re not familiar, the Calmar Ratio is a fantastic metric: it tells you how much compound annual growth you’re getting for every unit of maximum drawdown (the biggest peak-to-trough drop your portfolio experiences). In simpler terms, it shows how efficiently your strategy generates returns relative to the risk it takes. A higher Calmar Ratio is always better, indicating strong returns with controlled risk.

What happened?

The results were quite the eye-opener! Across the board, every single dynamic allocation method we tested failed to outperform the simple, fixed (equal) weighting. For our core set of currency pairs, the Calmar Ratios broke down like this:

• Fixed (Equal Weighting): Calmar Ratio 1.43
• Inverse Volatility: Calmar Ratio 1.03
• Momentum: Calmar Ratio 0.53
• Mean-Variance: Calmar Ratio 0.13 In other words, the fixed allocation gave us 1.43 units of return for every unit of maximum drawdown. Inverse Volatility was okay, but still noticeably worse. Momentum barely managed half that performance, meaning it took on a lot of risk for relatively little return. And Mean-Variance? It practically collapsed, delivering almost no risk-adjusted profit! The Risk Parity and Minimum Variance methods also fell short, showing similar underperformance compared to the fixed approach.

What I learned

This outcome might seem counter-intuitive. Aren’t more sophisticated methods supposed to be better? Well, not always, especially in the unpredictable world of FX trading. Here’s what we believe happened:

1. Our Components Were Already Well-Balanced: It seems the individual EAs or currency pairs we used in our portfolio were already doing a good job of diversifying risk on their own. They weren’t highly correlated, meaning they didn’t all move in lockstep. When you already have a naturally balanced portfolio, trying to “optimize” it further can sometimes do more harm than good.
2. Dynamic Adjustments Introduce Errors: Trying to constantly shift weights based on market data isn’t as easy as it sounds.

• Timing Errors: Predicting future market conditions (like volatility or momentum) is incredibly difficult. Even small errors in when and how much to rebalance can easily erode any potential gains. It’s like trying to perfectly time your turns on a constantly changing road – you might overcorrect and end up worse off!
• Estimation Errors: This was particularly problematic for complex methods like Mean-Variance. These strategies rely on estimating how different assets move together (their “covariance”). If these estimations are even slightly off (which they often are in volatile markets), the entire optimization can go haywire. Imagine trying to predict the exact path of a bunch of billiard balls based on imperfect information about their initial pushes and the friction on the table – it’s incredibly hard, and small errors compound quickly. So, the big takeaway from this research is that for the specific dynamic allocation methods we tested, simplicity won the day. Trying to “outsmart” the market with complex rebalancing strategies actually led to worse performance than just sticking to an equal, fixed allocation. This doesn’t mean all dynamic allocation is inherently bad, or that there aren’t other methods that might work. But for this group of popular techniques, the added complexity introduced more problems (timing and estimation errors) than benefits. We’ll definitely keep exploring other allocation approaches in future research, but for now, sometimes the simplest solution is indeed the best!