Conclusion
The six-bot lineup closed August 24 at -¥496. Across the confirmed exits, there were 27 wins, 37 losses and one break-even. Excluding the flat trade, the win rate was 42.2%. Gross profit reached +¥665, gross loss was -¥1,161, and the combined payoff ratio was 0.78.
The number that caught my attention was not the total loss. GateGrid AI produced 34 losing legs, yet its largest single loss was only -¥54 and its payoff ratio still came out at 1.27. ML_ScoreAnalyst, by contrast, needed only one losing trade to drop -¥251. That difference says more about the bots than the headline win rate does.
Bot Results
■ GateGrid AI -¥114Record: 22W / 34L / 1BEWin rate: 39.3%Gross profit: +¥535Gross loss: -¥649Payoff ratio: 1.27Max loss: -¥54
■ BoundSniper Bot +¥78Record: 4W / 0LWin rate: 100.0%Gross profit: +¥78Gross loss: ¥0Payoff ratio: N/AMax loss: ¥0
■ LLMBridgeTrader -¥192Record: 0W / 1LWin rate: 0.0%Gross profit: ¥0Gross loss: -¥192Payoff ratio: N/AMax loss: -¥192
■ ML_ScoreAnalyst -¥251Record: 0W / 1LWin rate: 0.0%Gross profit: ¥0Gross loss: -¥251Payoff ratio: N/AMax loss: -¥251
■ MAribbonTrader -¥69Record: 0W / 1LWin rate: 0.0%Gross profit: ¥0Gross loss: -¥69Payoff ratio: N/AMax loss: -¥69
■ BoundSniper Bot2 +¥52Record: 1W / 0LWin rate: 100.0%Gross profit: +¥52Gross loss: ¥0Payoff ratio: N/AMax loss: ¥0
■ Total -¥496Record: 27W / 37L / 1BEWin rate: 42.2%Gross profit: +¥665Gross loss: -¥1,161Payoff ratio: 0.78Max loss: -¥251
Today’s Theme: A Bot Can Lose Often Without Losing Big
GateGrid’s 39.3% win rate looks ugly at first glance. The average winning leg, however, was about ¥24.3 while the average losing leg was about ¥19.1. The winners were larger than the losers; there just were not enough of them.
That makes GateGrid a very different problem from ML_ScoreAnalyst or LLMBridgeTrader. GateGrid needs better selectivity. The other two need scrutiny around what happens when a trade is already wrong, because one bad exit was expensive enough to outweigh many small GateGrid losses.
This is why I do not want to rank these systems from win rate alone. The six bots deliberately use different decision structures: TradingView execution, LLM planning, CatBoost plus Ollama filtering, ML scoring, and chart-reading AI. The useful comparison is not only who wins, but how each architecture behaves when its thesis fails.
GateGrid AI
GateGrid was by far the busiest bot. It finished at -¥114 after 22 winning legs, 34 losing legs and one flat close. The max loss stayed at -¥54, while gross profit still reached +¥535 against -¥649 of losses.
I actually like part of that structure. A 1.27 payoff ratio means the exit size itself was not obviously broken. The harder question is why a bot designed to reject weak situations still generated so many losing legs.
GateGrid first uses CatBoost to screen candidates, then passes qualified situations to Ollama, where ATR, trend, session and other context can still lead to a HOLD decision. Its design explicitly emphasizes AI_SKIP and OLLAMA_HOLD when conditions are poor.
So I would inspect the entry gates before widening targets or stops. Were the thresholds too loose during a particular session? Did several grids restart under essentially the same market condition? The loss was controlled, but the bot may simply have been too willing to keep trying.
There is also a measurement issue worth keeping in mind. These are individual closed legs inside a grid strategy. A later version of this log should probably show both leg-level results and complete grid-cycle results, because 34 losing legs does not necessarily mean 34 independent failed ideas.
BoundSniper Bot
BoundSniper closed four profitable trades for +¥78. The individual gains were small, but all four finished on the right side.
That is consistent with what I want from this bot. BoundSniper itself is not trying to understand the market; it receives TradingView signals and executes them in MT5. The market intelligence is upstream, while the bot is responsible for getting the entry and exit instructions into the account correctly.
Four wins look good, but +¥78 is a useful reality check. One MLScore loss erased more than three times that amount. For BoundSniper, I would keep tracking execution quality and exit timing rather than celebrating a perfect win rate too early.
LLMBridgeTrader
LLMBridgeTrader finished at -¥192 from one EURUSD trade. There is not enough evidence here to judge the model’s entry accuracy, but one trade is enough to examine its exit process.
This bot gives the LLM unusually broad authority. It can return OPEN, HOLD, CLOSE or REVERSE, propose SL and TP distances, and explain both entry and exit reasoning. System-side risk checks still sit around those decisions.
That makes the final HOLD decisions especially interesting. If the model continued to defend the position while market evidence deteriorated, the problem may be the exit prompt or the context it receives while a position is open. If the stop was reached before the model had a realistic chance to reassess, the issue may lie elsewhere.
I would not touch the entry logic first. I would read the last few position-management logs.
ML_ScoreAnalyst
ML_ScoreAnalyst took one loss of -¥251, the largest single hit of the day. This one made me stop because GateGrid spent an entire active session accumulating losses and still finished with less than half that net damage.
The architecture is simpler than the LLM bots. ML_ScoreAnalyst detects candidates, scores them with CatBoost, and enters only above its configured threshold. The system also records scores and features so those trades can later be used for threshold testing and retraining.
There are two obvious suspects, and I would not choose between them yet. The model may have scored a weak breakout too highly, or the fixed exit geometry may have been poorly matched to that GBPJPY environment. One losing sample cannot settle it.
What it can settle is priority. A -¥251 loss deserves review before another small threshold adjustment elsewhere.
MAribbonTrader
MAribbonTrader closed one GBPJPY long at -¥69. The trade was short-lived, so I am more interested in whether BUY should have been WAIT than in the amount itself.
This bot is supposed to read a chart more like a discretionary trader. It combines the short and long MA ribbons with higher-timeframe context, support and resistance, range structure, channels and other visual information, then lets the AI choose BUY, SELL, WAIT or EXIT.
The stored decision log should tell us whether the setup really had enough room to move. Perhaps the entry was early. Perhaps the chart was acceptable and the stop simply got clipped. I would want to see the image and the reasoning before changing anything.
The loss was at least contained. At -¥69, it did not dominate the portfolio.
BoundSniper Bot2
BoundSniper Bot2 finished +¥52 from one winning trade. It uses the same basic bridge concept as BoundSniper but references a different indicator, giving the two bots a useful long-term A/B comparison.
One trade tells us almost nothing about which indicator is stronger. It does show a clean outcome: the signal was executed, the position survived long enough to move in the intended direction, and the exit banked the gain.
Summary
August 24 was a losing day, but the useful part is how unevenly that loss was produced. GateGrid lost repeatedly while keeping each individual hit small. ML_ScoreAnalyst and LLMBridgeTrader barely traded, yet their two losses together accounted for -¥443.
That is the comparison I want to keep following. The most dangerous bot may not be the one that loses most often. It may be the quiet one that is allowed to be wrong for too long.
② Substack Note
GateGrid AI went 22W / 34L / 1BE and still kept its max loss to just ¥54.
Its payoff ratio was 1.27.
ML_ScoreAnalyst traded once and lost ¥251.
Six-bot result for Aug. 24: -¥496.
That is the experiment I care about now: not only how often each bot is wrong, but how expensive one wrong decision is allowed to become
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
