A Reflection on Money Management in EA Portfolio Analysis
EA : Zyra
During my recent tests on Zyra EA, I aimed to delve deeper into the analysis of my strategies on 7 major currency pairs using Quant Analyzer. Here is the process I initially followed, along with the corrections I had to make upon reflection.
Step 1: Individual Simulations for Each Currency Pair
I conducted simulations for the following currency pairs:
EURUSD, AUDUSD, GBPUSD, USDCHF, USDCAD, USDJPY, and NZDUSD, all on the H1 timeframe between 2005 and 2025, with an initial capital of $5,000. The parameters used for each simulation were as follows:
- Number of EA in same time = 7
- Risk Percent = 50%
This represented a risk of approximately 0.714% per trade, corresponding to the distribution of risk across 7 strategies.
Step 2: Creating a Portfolio in Quant Analyzer
Once the 7 individual simulations were completed, I used Quant Analyzer to create a combined portfolio from the results. I then directly extracted the portfolio’s displayed performance to produce my initial screenshots.
However, upon further reflection, something didn’t seem right. The results appeared incorrect, and an important question arose: was global money management properly applied at the portfolio level?
Step 3: Identifying the Error
Upon deeper testing, I realized there was a methodological error:
The portfolio was displaying results based on the initial lot sizes from each individual backtest. However, in a real scenario, each trade in the portfolio should recalculate the lot size based on the evolving global capital to reflect accurate money management.
Step 4: Correction with Quant Analyzer
To correct this, I used the Money Management Simulation tool in Quant Analyzer. Here’s what this step achieved:
- Recalculated each trade in the portfolio, dynamically adjusting the lot sizes based on the global capital.
- Applied the actual risk of 0.714% per trade to all transactions, accounting for capital changes after each trade.
The corrected result was very different from the initial analysis. This demonstrates how crucial global money management is when evaluating an EA’s performance.
Before
After
The total profit shifts from $87,000 to $12,000,000.
Conclusion
This experience highlights the importance of verifying each step of analysis, especially when simulating complex portfolios with multiple strategies. An error in the application of money management can distort conclusions and lead to suboptimal decisions.
If you’d like to delve deeper into this method or have questions about using Quant Analyzer for money management, feel free to share your experiences in the comments!