Conclusion
Every live account finished the day with positive realized P&L. That sounds cleaner than the day actually was.
The six accounts combined for +¥313, with 25 winning exits and 14 losing exits. The overall win rate was 64.1%, but the payoff ratio was only 0.84. The average winner was about ¥37.5 while the average loser was about ¥44.6, so the day still depended on winning often enough.
The concentration was more interesting. LLMBridgeTrader made +¥125 from one trade and ML_ScoreAnalyst made +¥85 from one trade. Those two trades alone produced +¥210, roughly two-thirds of the entire day’s realized profit.
Then there was GateGrid AI: 29 exits, 17 winners, and only +¥2 at the end. I looked at that line twice. A positive result is a positive result, but it is also a good reminder that win rate and activity can hide weak exit economics.
Bot Results
■ GateGrid AI +¥2Record: 17W / 12LWin rate: 58.6%Gross profit: +¥421Gross loss: -¥419Payoff ratio: 0.71Max loss: -¥120
■ BoundSniper Bot +¥54Record: 2W / 0LWin rate: 100.0%Gross profit: +¥54Gross loss: ¥0Payoff ratio: N/AMax loss: None
■ LLMBridgeTrader +¥125Record: 1W / 0LWin rate: 100.0%Gross profit: +¥125Gross loss: ¥0Payoff ratio: N/AMax loss: None
■ ML_ScoreAnalyst +¥85Record: 1W / 0LWin rate: 100.0%Gross profit: +¥85Gross loss: ¥0Payoff ratio: N/AMax loss: None
■ MAribbonTrader +¥35Record: 2W / 2LWin rate: 50.0%Gross profit: +¥241Gross loss: -¥206Payoff ratio: 1.17Max loss: -¥161Open position at cutoff: GBPCHF- long, -¥22 unrealized
■ BoundSniper Bot2 +¥12Record: 2W / 0LWin rate: 100.0%Gross profit: +¥12Gross loss: ¥0Payoff ratio: N/AMax loss: None
■ Total +¥313Record: 25W / 14LWin rate: 64.1%Gross profit: +¥938Gross loss: -¥625Payoff ratio: 0.84Max loss: -¥161
Today’s Theme: A Green Account Is Not the Same as a Good Exit Structure
The headline result was broad. All six accounts were positive on realized P&L.
The underlying distributions were not broad at all. GateGrid generated hundreds of yen on both sides and barely escaped flat, while two systems each produced one clean winner and carried most of the portfolio profit. MAribbonTrader lost half of its closed trades but still kept a payoff ratio above 1.
That is why I keep coming back to exits in these experiments.
For the LLM-driven bots, I do not only want to know whether the model correctly called BUY or SELL. I want to know whether it recognized when the original idea had stopped working, when a profitable position deserved more room, and when HOLD should become CLOSE.
The MT5 statement gives me executions, prices, stop labels and P&L. It does not contain the full Ollama or Qwen reasoning from the moment of each decision. So I can judge the outcome of an exit, but I cannot honestly attribute every close to the LLM itself without the internal decision logs.
That distinction matters today.
GateGrid AI: 17 Wins Were Barely Enough
GateGrid AI finished at +¥2 from 29 closed legs. It made +¥421 on winners and lost ¥419 on losers.
The payoff ratio of 0.71 is the problem. Its average winning exit was about ¥24.8, while its average losing exit was about ¥34.9. A 58.6% win rate was just enough to keep the day above zero.
The close bursts show how quickly one bad leg can change the picture. Around 11:08, the report recorded -¥120, +¥64, +¥43, +¥17 and -¥10 in the same few seconds. That group finished only slightly negative, but the -¥120 leg did most of the damage before the other positions compensated.
GateGrid’s design uses a quantitative gate before the local LLM layer, with CatBoost filtering candidates and Ollama adding a second judgment layer. The interesting question is what happened after those filters had already approved exposure.
Did the model still consider the position structure recoverable? Did a mechanical grid-management rule keep it open? Was the exit simply late? The MT5 statement cannot answer that by itself.
For the next log review, I would start with the moments around the -¥120 exit and the other losing close clusters. Entry filtering is only half of this experiment if the expensive errors are being created later.
BoundSniper Bot: The Relay Did Its Job
BoundSniper Bot closed two USDJPY- shorts for +¥28 and +¥26.
There were no losing trades, so a payoff ratio cannot be calculated. More importantly, BoundSniper is not supposed to act like an LLM trader. TradingView makes the signal decision and the Bot’s job is to transport that instruction into MT5.
That changes what I want to evaluate.
Today I care about whether the entries and exits were executed cleanly and whether the upstream TradingView signal translated into the expected MT5 behavior. Both trades closed positive, and there is no obvious execution problem in the statement.
Two trades are nowhere near enough to judge the signal logic, but as a transport layer the day looks uneventful. For this Bot, uneventful is often a good result.
LLMBridgeTrader: A Profitable Stop Exit Is the Most Interesting Line
LLMBridgeTrader took one EURUSD- short at 1.15759 and closed at 1.15680 for +¥125.
The close is labeled [sl 1.15680]. That caught my attention because the stop-related exit finished well inside profitable territory.
LLMBridgeTrader has the widest AI authority in this group. The model can propose OPEN, HOLD, CLOSE and REVERSE, together with confidence, setup type, SL/TP levels and reasons. On a day like this, the entry direction was right, but the exit is the part I would inspect first.
If the stop had been moved into profit as the trade developed, then the management layer successfully turned an open gain into a protected result. What I cannot tell from this statement is whether that stop adjustment came directly from the LLM, from a trailing mechanism, or from another fixed risk rule.
That internal log is worth matching to the MT5 timestamp. The realized result is good; the experiment is figuring out why it was good.
ML_ScoreAnalyst: One Score, One Winner
ML_ScoreAnalyst opened a GBPJPY- short at 215.203 and closed at 215.118 for +¥85.
The exit is again stop-labeled. The Bot itself is much simpler than the LLM systems: it detects a candidate and uses CatBoost to score whether the setup deserves an entry.
That means today is mainly an entry-selection sample. The model found one short candidate that moved in the intended direction, and the position ultimately closed in profit.
What the MT5 report does not show is the actual CatBoost score, the threshold at the time, or the feature values that produced ENTER. Without those records, I would not call this evidence that the model is calibrated correctly. It is one successful sample.
Still, +¥85 from one trade with no visible sequence of re-entries is a clean result.
MAribbonTrader: The Best Payoff Ratio Came With the Worst Loss
MAribbonTrader finished at +¥35 from four closed trades: +¥106, -¥45, -¥161 and +¥135.
The -¥161 stop was the number that made me hesitate. It was the largest realized loss across all six accounts.
Yet MAribbonTrader’s payoff ratio was 1.17. Its average winner was ¥120.5 and its average loser was ¥103. That is a healthier shape than GateGrid’s distribution, even with only a 50% win rate.
This Bot is the most discretionary-looking AI experiment in the group. Qwen reads a chart package containing the MAribbon structure, higher-timeframe context, support and resistance, ranges and other visual information before producing decisions such as WAIT, BUY, SELL or EXIT.
Two losing trades were stop-labeled. The +¥135 NZDJPY- close carried the MAribbon-LLM comment instead, which makes that exit especially worth checking against the AI log. The MT5 record alone does not prove that Qwen explicitly chose EXIT, but the timestamp gives me a precise place to look.
There was also one GBPCHF- long still open at the report cutoff with -¥22 unrealized P&L. I have excluded that from all realized performance figures.
For MAribbonTrader, I would rather see a 50% win rate with payoff above 1 than a beautiful hit rate built on tiny winners and occasional large stops. The sample is small, but the shape is encouraging.
BoundSniper Bot2: Positive, but Too Small to Read Much Into
BoundSniper Bot2 added +¥12 from two USDJPY- longs, +¥5 and +¥7.
It uses the same basic relay idea as BoundSniper Bot but follows a different upstream indicator. That makes the comparison interesting over a long sample, not over two trades.
Both variants finished positive today. The original BoundSniper made +¥54 from shorts, while Bot2 made +¥12 from longs.
For now, I would log the difference and move on. There is not enough evidence here to say which signal source is better.
Summary
The pleasant part of Aug. 14 is easy to see: every account closed positive on realized P&L.
The more useful part is underneath that headline. The portfolio payoff ratio was still below 1, GateGrid needed 17 winning exits just to finish at +¥2, and two single-trade systems produced most of the actual money.
The next step is not to celebrate the six green numbers. I want to match the profitable stop exits and the larger losing legs back to the LLM and management logs and see who actually made the exit decision.
A green day is nice. Knowing why it stayed green is more useful.
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