Conclusion
The five-bot book finished August 10 at -¥294, with 11 wins and 13 losses. Gross profit was +¥590 against -¥884 in gross losses, giving the whole group a 45.8% win rate and a 0.79 payoff ratio.
But the daily total hides the main story. BoundSniper took one -¥301 loss; remove that single trade from the arithmetic and the book would have ended at +¥7. I stared at that -¥301 longer than I did at the final -¥294, because it says more about the day than the hit rate does.
Bot-by-bot results
The GateGrid account contains both “GateGrid AI” and “GateGrid v4” execution comments on August 10, so I am treating them as one GateGrid line for this five-bot daily comparison.
■ GateGrid AI -132 yenRecord: 6W / 7L (Win rate 46.2%)Gross profit: +143 yenGross loss: -275 yenPayoff ratio: 0.61Max loss: -90 yen
■ BoundSniper -315 yenRecord: 2W / 2L (Win rate 50.0%)Gross profit: +33 yenGross loss: -348 yenPayoff ratio: 0.09Max loss: -301 yen
■ LLMBridgeTrader -78 yenRecord: 0W / 1L (Win rate 0.0%)Gross profit: 0 yenGross loss: -78 yenPayoff ratio: 0.00Max loss: -78 yen
■ ML_ScoreAnalyst +411 yenRecord: 2W / 0L (Win rate 100.0%)Gross profit: +411 yenGross loss: 0 yenPayoff ratio: N/A (no losing trades)Max loss: 0 yen
■ MAribbonTrader -180 yenRecord: 1W / 3L (Win rate 25.0%)Gross profit: +3 yenGross loss: -183 yenPayoff ratio: 0.05Max loss: -65 yen
■ Total -294 yenRecord: 11W / 13L (Win rate 45.8%)Gross profit: +590 yenGross loss: -884 yenPayoff ratio: 0.79Max loss: -301 yen
Today’s theme: exits mattered more than entries
There is one limitation in today’s material that matters for an LLM trading experiment. The MT5 statement tells me when positions opened and closed, the execution prices, P/L, and comments such as [sl] or [tp], but it does not contain the actual model prompt, confidence score, HOLD/CLOSE reasoning, or the text returned by the LLM.
So I can evaluate the behavior of the exits, but I should not invent a story about why the model made them. For LLMBridgeTrader and MAribbonTrader in particular, the next layer of analysis needs the model decision log beside the MT5 execution log. That missing link is becoming part of the experiment itself.
GateGrid AI: many small exits, but losses were still heavier
GateGrid finished at -132 yen with six winners and seven losers. A 46.2% win rate is not especially alarming on its own, but the 0.61 payoff ratio shows the real problem: the average winner was smaller than the average loser.
The morning sequence illustrates it well. Two USDJPY positions closed within seconds for +50 and -21 yen, then a later position produced -90 yen, the largest GateGrid loss of the day. In the v4 portion of the account, several small profits appeared, but the final short basket closed at +20, -67 and -78 yen almost immediately after entry.
That last cluster bothers me more than the win rate. The system was capable of cutting positions quickly, yet “quick” did not automatically mean “cheap”; the losing legs were still large enough to erase several earlier winners. The exit mechanism is active, but the payoff distribution says it is not balanced yet.
BoundSniper: 50% wins and a 0.09 payoff ratio
BoundSniper is not an LLM trader. It is an execution bridge for TradingView signals, so there is no reason to blame an AI model for the direction of these trades.
Still, its numbers are the clearest warning of the day. It went 2W / 2L, which looks harmless at first glance, but gross profit was only +33 yen against -348 yen of gross losses. The payoff ratio fell to 0.09.
One position closed for -301 yen, while the two winners were only +10 and +23 yen. The first daily close also realized -47 yen after including the -19 yen swap. This is exactly why I do not want to judge an automated system by win rate alone: a 50% hit rate can still leave a deeply asymmetric loss profile.
For BoundSniper, the question is less about prediction and more about what the TradingView strategy permits before an exit arrives. The bridge did its job; the loss budget around the signal logic needs the attention.
LLMBridgeTrader: one trade, and the exit log matters
LLMBridgeTrader had one EURUSD trade. It sold at 1.15479 at 16:45:14 and closed at 1.15528 at 17:30:05, ending at -78 yen.
That is roughly 4.9 pips against the short over about 45 minutes. The loss itself is small enough to be controlled, but one trade tells me almost nothing about entry quality.
What I do want to know is what happened during those 45 minutes. LLMBridgeTrader is designed to choose among OPEN, HOLD, CLOSE and REVERSE, so the interesting data is whether the model kept returning HOLD while the trade deteriorated, switched to CLOSE at the right moment, or was closed by another safety condition. The MT5 statement alone does not answer that, and I would rather leave that blank than manufacture a neat explanation.
ML_ScoreAnalyst: the best result also had the most interesting exit
ML_ScoreAnalyst was the only clear winner, finishing at +411 yen from two GBPJPY trades. The first closed at TP for +300 yen.
The second is more interesting. It entered long at 213.485 and later closed via an [sl 213.596] execution for +111 yen. Seeing a stop-loss label attached to a profitable trade made me look twice; whatever moved or maintained that stop, the practical result was that the exit protected profit rather than turning the trade back into a loser.
There were no losing trades, so the payoff ratio cannot be calculated meaningfully yet. A 100% win rate from two samples also does not prove much, but the exit shape was clean: one target win and one protected-profit stop.
MAribbonTrader: the stop worked, then the Bot kept coming back
MAribbonTrader traded GBPCAD four times, all from the sell side. The first closed for +3 yen, followed by three losses of -65, -58 and -60 yen.
The good part is that the maximum individual loss stayed at -65 yen. The hard exit prevented a BoundSniper-style single loss from appearing. The bad part is the repetition: after being stopped, the Bot returned to essentially the same directional idea several times and accumulated -180 yen.
For an LLM chart-reading Bot, this is where EXIT and WAIT need to be considered together. A stop can end one bad trade correctly, but if the model immediately interprets the same market structure as another valid sell, the portfolio-level exit has not really happened. I suspect the improvement belongs somewhere around post-stop regime recognition, although one day is not enough to prove it.
Closing thoughts
August 10 was not a simple “AI bots lost” day. ML_ScoreAnalyst actually covered a large part of the damage, GateGrid kept most individual losses moderate, and MAribbon’s hard stops did cap each attempt.
The uncomfortable number came from somewhere else: loss concentration. One -301 yen trade changed the sign of the entire five-bot book, while another Bot lost through repeated smaller attempts. The next thing I want from these logs is not a prettier win rate; I want to see how each system behaves immediately after the market tells it that its first idea was wrong.
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