[AI Trading Log] Surviving FOMC and the Trap of High Win Rates:

Hello! I have been running a parallel test of five distinct MT5 automated trading bots to evaluate their real-world behaviors and AI decision-making processes. July 28 and 29 provided incredibly insightful data for the portfolio. On July 29, I deliberately left all five bots running through the highly volatile FOMC event instead of shutting them down. The data from these two days delivered a harsh reality check: Event risks like the FOMC do not blow up accounts; ordinary, poorly designed exits and payoff asymmetry are what truly destroy a portfolio.Overall Performance: A 64 percent Win Rate Cannot Outrun Bad ExitsLooking purely at the win rates, the portfolio seemed highly capable of predicting market direction. However, both days resulted in net realized losses.July 28: Total minus 64 JPY (Win Rate 63.6 percent)The fleet closed 7 winning trades and 4 losing trades, ending the day with a realized loss of 64 JPY. The root cause was glaringly obvious: the average winner brought in about 80.4 JPY, while the average loser wiped out 156.8 JPY. The AI successfully called the direction, but it paid far too much when those ideas were wrong.July 29 (FOMC): Total minus 333 JPY (Win Rate 64.3 percent)Despite the FOMC volatility, the damage remained contained with no single closed trade losing more than 314 JPY. The portfolio achieved 18 wins and 10 losses, yet the payoff ratio was a dismal 0.45. The average winner was about 78 JPY, while the average loser reached roughly 174 JPY. FOMC did not create an uncontrolled failure; it was the ordinary exit asymmetry that did most of the damage.Bot-by-Bot Breakdown: Different Brains, Same Exit StrugglesBecause each bot processes information and makes decisions differently, their results and failure points varied drastically.1. BoundSniper Bot: The Disciplined Execution LayerThis bot relays TradingView signals into MT5 and does not predict the market itself. It had no trades on July 28. On July 29, it was the undisputed MVP of the FOMC session, closing 7 wins and 1 loss for a profit of 284 JPY. It posted an incredible payoff ratio of 3.52, proving that fast, externally defined exits can keep risk incredibly small, even during severe market events.2. LLMBridgeTrader: The AI Planner’s HesitationThis AI operates with high autonomy, deciding whether to OPEN, HOLD, CLOSE, or REVERSE a position. On July 28, it closed flat at 0 JPY realized, though it carried an unrealized loss. On July 29, it secured a 105 JPY profit. It successfully protected several winners through stop-based exits, but it also hesitated on holding positions, resulting in late exits and large losses like a 295 JPY loss on July 28 and a 199 JPY loss on July 29.3. GateGrid AI: Small Wins Swallowed by Heavy ExitsThis hybrid bot uses a CatBoost probability gate and an Ollama review to filter entries. It fell victim to the classic “small win, massive loss” trap. On July 28, it lost 65 JPY, as four wins were wiped out by a single 252 JPY loss. On July 29, it lost 259 JPY with a terrible 0.42 payoff ratio. It proved that entry filtering alone cannot repair a fundamentally flawed exit profile.4. ML_ScoreAnalyst: Improving Risk-to-Reward BalanceThis fast bot evaluates confirmed GBPJPY breakouts using a CatBoost score. It had no trades on July 28. On July 29, it lost 69 JPY. Among the losing bots, it came the closest to a balanced risk-to-reward profile with a payoff ratio of 0.88, though its stops were still slightly heavier than its target profits.5. MAribbonTrader: Rich Context, Weakest PayoffsA chart-reading AI that sends MT5 screenshots and rich visual context to a local LLM for discretionary analysis. It won 1 JPY on July 28. On July 29, it lost 394 JPY. Despite receiving the richest visual context of all five bots, it exposed the weakest payoff structure at 0.12. A 60 percent win rate was useless when the average winner of 30 JPY was forced to absorb an average loss of 242 JPY. It highlighted that giving an AI more information does not automatically produce a better exit.Conclusion: The Flaw is Inside the System, Not the EventThe ultimate takeaway from these two days is that the systems were not defeated by a lack of winning trades or by market events. They were defeated by the massive distance between their normal profits and normal losses. The most critical part of an experimental trading model is the brief window of time between the setup weakening and the actual closure of the position. Moving forward, the primary focus must shift away from entry accuracy and prioritize strict exit discipline, ensuring the AI learns how to quickly and cheaply abandon bad ideas. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com/subscribe