I am currently running four MT5 automated trading bots (GateGrid AI, BoundSniper, LLMBridgeTrader, and MLScore GF-T4) simultaneously to verify their real-world behavior and performance.
I have compiled the operational results for July 9 and 10.
The harsh reality revealed by these two days of testing is that “no matter how high the win rate is, a single costly exit (stop loss) can destroy the portfolio.”
Even though the overall win rate exceeded 60% on both days, they both ended with negative balances. Through a detailed review, I will delve into the challenges facing the current system.
July 9 Analysis: The Day One Bad Exit Decided Everything
[Overall Performance]
* Total Trades: 3 (2 Wins, 1 Loss)
* Win Rate: 66.7%
* Realized Profit/Loss: -481 JPY (-496 JPY including MLScore’s unrealized loss)
A Fatal Blow from GateGrid AI
While BoundSniper (+4 JPY) and LLMBridgeTrader (+57 JPY) steadily accumulated small profits, GateGrid AI suffered a massive loss of -542 JPY in a single trade.
A Warning from a 0.06 Payoff Ratio
The total profit for the entire portfolio was a mere +61 JPY, whereas a single loss amounted to -542 JPY.
The 66.7% win rate is entirely meaningless here. This extreme payoff ratio (risk-reward ratio) serves as a strong warning to the system that the “retreat rules” are too slow when an idea turns out to be wrong.
July 10 Analysis: The Contrast Between Planned Take-Profits and Late Stop-Losses
[Overall Performance]
* Total Trades: 20 (12 Wins, 8 Losses)
* Win Rate: 60.0%
* Realized Profit/Loss: -259 JPY
Trading volume increased on this day, clearly highlighting the differences in the “quality of exits” among the bots.
Two Bots Shining with Great Exits
* MLScore GF-T4 GB
Successfully executed clean take-profits by precisely hitting pre-set targets (TP) twice, earning +483 JPY.
* LLMBridgeTrader
Despite having 1 win and 1 loss, it significantly extended its profits (+39 JPY) against its losses (-23 JPY), demonstrating a very healthy risk-reward payoff ratio of 1.70.
Two Bots Dragging Down the Portfolio with “Small Profits, Large Losses”
* GateGrid AI
Despite a winning record of 7 wins and 6 losses, it totaled -602 JPY. Extremely small profits, such as +8 JPY, stood out against large stop-losses, peaking at -289 JPY.
* BoundSniper
Suffered significant damage of -380 JPY on its first trade. Although it attempted to recover with two subsequent wins, it couldn’t fully pay off the initial debt and ended at -156 JPY.
Conclusion: What We Need Isn’t “New Predictions,” but “Rules to Accept Losses Cheaply”
The biggest lesson from these two days is that “planned exits beat frequent decisions.”
The AI models are already provided with sufficient market information, such as volatility, spreads, and the direction of higher timeframes, and their entry win rates are by no means bad.
However, in the current system (especially GateGrid AI), the decision to recognize that an entry idea has “died” and to retreat is made far too late.
The most crucial aspect of future system improvements is not giving the AI more information to increase entry accuracy.
“How to abandon a wrong idea as cheaply (with as shallow a wound) as possible”
Strictly enforcing this exit rule is the top priority for surviving in the market.
This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
