DAY 26: How to Handle Performance Drops of EA — Flexible Logic Adjustments and Risk Hedging

Last time (DAY 25), we reconfirmed the limits of EAs and talked about the dangers of overconfidence and complete neglect.
Today, under the theme of “What to do when an EA's performance declines,” we will organize concrete methods and cautions.
Cases like “an EA that had been stable suddenly starts losing” or “consecutive losses cause anxiety” are by no means rare.
How you confront these situations can significantly affect the eventual performance of your operations.

1. Main causes of performance decline

1. Major changes in market environment

   • When the market shifts from a trend-dominated environment to a ranging one, or when volatility suddenly rises or falls.

   • An EA's logic may not continue to perform well just because it has been effective for a while. When it enters a weakness phase, drawdown tends to widen.

2. Divergence from backtest assumptions

   • If the actual market state in spreads and volatility differs greatly from what was assumed in backtests.

   • During events like the COVID shock or rapid changes in monetary policy, market moves can deviate from past data, causing unexpected losses.

3. Logic becoming obsolete

   • If you leave an EA with the same settings for a long time, changes in market participant behavior can make previously effective methods less functional.

   • Without regular maintenance and validation, you may realize too late when it’s already overdue.

2. Specific countermeasures

1. Pause temporarily and analyze causes

   • If performance clearly deteriorates, it may be necessary to decisively pause the EA.

   • To isolate the cause, re-run backtests and forward tests, compare with other EAs running in parallel, and determine whether the deterioration is due to “market environment” or “logic flaws.”

2. Fine-tuning parameters

   • There are many tunable parameters in an EA, such as stop-loss width, take-profit width, and trailing settings.

   • Due to market changes, small adjustments like “make stop-loss a bit smaller” or “delay trailing activation” can ease losses in some cases.

   • Large changes risk overfitting, so the trick is to test gradually and carefully.

3. Logic updates (adding filters, etc.)

   • If you are comfortable with programming or can contact the developer, evolving the logic itself is an option.

   • Adding filters to adapt to new market environments (volatility filters, economic indicator filters, etc.) may help avoid weak areas.

4. Risk hedging through portfolio management

   • If you already operate with multiple EAs, some may underperform while others compensate.

   • A major risk-reduction step is not to concentrate all capital in a single EA.

   • One approach is to pause the underperforming EA and rebalance the portfolio by continuing the well-performing ones.

5. Clarifying loss tolerance thresholds

   • Setting numeric rules like “pause after a certain number of consecutive losses” or “revert to a backtest mode when drawdown reaches a certain % of funds” reduces the risk of emotionally driven large changes.

   • If a pre-set loss line is breached, it’s important to calmly reanalyze rather than reacting impulsively.

3. Considerations for psychology

• When an EA underperforms for a while, you may be tempted to switch everything back to manual
  When you’re not winning as expected, you may feel that discretion might be better.
  However, remember why you introduced an EA in the first place (to reduce mental burden and to build objective data through automation), and avoid abruptly relinquishing everything.

• Don’t tinker with the EA emotionally
  Pausing and analyzing causes are important, but rushing to overhaul the logic can break continuity with past validation data, potentially forcing you to rebuild from scratch.
  Try small changes and verify them with new backtests and forward tests to aim for a more stable restart.

4. Today’s summary and next week's preview

• EA performance declines can be caused by market changes and logic obsolescence, among other factors.

• If performance worsens, pause temporarily and gradually perform cause analysis, small parameter tweaks, and logic updates to minimize losses.

• Using portfolio management and clear loss-tolerance thresholds helps prevent being driven by emotional impulses.

Next time (DAY 27) the theme will be “EA implementation success and failure cases (conceptual only),” introducing examples of how EA adoption grew funds steadily and, unfortunately, cases of failure, in a conceptual form. Understanding where the differences lie will give you hints for your own operations.

Introduction to the EAs I sell

For those wondering how to respond when performance drops during operation, how to choose EAs and build logic, please also consider the EAs I sell as a reference.
https://www.gogojungle.co.jp/users/147322/products

If you clearly understand “in what scenarios they perform well and when they tend to underperform,” you’ll be able to respond more calmly during downturns.

In the next article, we will conceptually compare successful and failed EA implementations and learn the key points.
Please click “Read more” to deepen your understanding.