August 17, 2026 — Five MT5 bot families finished at -342 yen. The bigger lesson was not entry accuracy. It was what one position was allowed to do after Friday.
Conclusion
The five-bot run ended with a realized P/L of -342 yen.
That number looks like an ordinary losing day until I separate MAribbonTrader from its Friday carryover. Its three positions opened on Monday made +206 yen in total. The old GBPCHF position carried from Friday lost -682 yen in trading P/L, partly offset by +18 yen of swap.
That single carryover changed the entire shape of the day.
The -682 yen line made me stop for a second. The Monday entries were not the main problem. The position lifecycle was.
I changed MAribbonTrader so it will no longer carry positions over.
Bot Performance
■ GateGrid AI -120 yenRecord: 22W / 22L / 1 flatWin rate: 50.0% excluding the flat tradeGross profit: +406 yenGross loss: -526 yenPayoff ratio: 0.77Max loss: -89 yen
■ BoundSniper family +47 yenRecord: 3W / 0LWin rate: 100.0%Gross profit: +47 yenGross loss: 0 yenPayoff ratio: N/A, no losing tradesMax loss: N/A, no losing trades
■ LLMBridgeTrader +189 yenRecord: 1W / 0LWin rate: 100.0%Gross profit: +189 yenGross loss: 0 yenPayoff ratio: N/A, no losing tradesMax loss: N/A, no losing trades
■ ML_ScoreAnalyst 0 yenRecord: No tradesWin rate: N/AGross profit: 0 yenGross loss: 0 yenPayoff ratio: N/AMax loss: N/A
■ MAribbonTrader -458 yenRecord: 2W / 2LWin rate: 50.0%Gross profit: +254 yenGross loss: -730 yenSwap: +18 yenPayoff ratio: 0.35Max loss: -682 yen
■ Total -342 yenRecord: 28W / 24L / 1 flatWin rate: 53.8% excluding the flat tradeGross profit: +896 yenGross loss: -1,256 yenSwap: +18 yenPayoff ratio: 0.61Max loss: -682 yen
Today’s Theme: The Exit Rule Mattered More Than the Entry Model
MAribbonTrader is one of the bots where I give AI more room to read context. It looks at the chart, moving-average ribbons, higher-timeframe structure, support and resistance, ranges, and other information before deciding between BUY, SELL, WAIT and EXIT.
That makes entry quality interesting, but today the experiment exposed a more basic problem.
A sophisticated chart reader still needs hard rules around when a position is no longer allowed to exist.
The GBPCHF trade had survived from Friday into Monday. It finally closed at -682 yen before swap. Meanwhile, the three positions actually opened on Monday produced +71 yen on USDCAD, +183 yen on GBPJPY and -48 yen on EURJPY.
That is +206 yen from the new Monday trades.
I did not expect that contrast to be this clean. The AI’s Monday decisions were profitable as a group, but an older position overwhelmed them.
So I treated this less as a prompt problem and more as a system-design problem. I changed the Bot to prevent positions from being carried over.
GateGrid AI
GateGrid closed 45 positions, with 22 winners, 22 losers and one flat result. A 50% win rate does not look bad by itself, but the payoff ratio tells the less comfortable part of the story.
Average winners were smaller than average losers. The payoff ratio was 0.77.
That is why 22 wins against 22 losses still ended at -120 yen. There was no catastrophic hit here; the maximum single loss was only -89 yen. The leak was distributed across the exit profile.
GateGrid uses multiple filters rather than blindly opening a grid. CatBoost and local AI can help decide when not to enter, but this result says the post-entry side deserves the same attention. If the average losing leg remains larger than the average winner, better filtering alone may not fix the curve.
The cause may be exit timing, grid closure behavior, or the shape of the underlying entries. I do not have enough from the MT5 report alone to pin that down yet.
BoundSniper Family
The two BoundSniper variants produced three winners for a combined +47 yen.
There were no losing closes, so the payoff ratio cannot be evaluated yet. Three trades are also far too few to treat the 100% win rate as evidence of an edge.
This Bot family is different from the LLM-driven systems anyway. BoundSniper mainly carries TradingView instructions into MT5, so I judge it partly as an execution layer: did the intended trades reach MT5, and were they closed correctly?
On August 17, nothing in the realized results suggests an execution problem.
LLMBridgeTrader
LLMBridgeTrader had one EURUSD short and made +189 yen.
The entry was at 1.16137. The original order showed a stop around 1.16256 and a target around 1.15956, while the eventual exit was around 1.16018 with the closing record referencing a stop near 1.16016.
That looks like a position where the stop was eventually brought into profitable territory instead of simply waiting for the original target or loss limit. I like this exit much more than a high win-rate number.
There is one limitation in today’s material: the MT5 report does not contain the LLM’s actual reasoning log, so I cannot say whether the stop adjustment came directly from the model or from the Bot’s risk-management layer. That distinction is worth checking in the decision logs.
ML_ScoreAnalyst
ML_ScoreAnalyst made no trades.
For a scoring Bot, zero trades are not automatically a failure. It is designed to filter candidates and enter only when the CatBoost score clears its threshold.
There is nothing to calculate for payoff ratio or maximum loss today. The useful question is whether the lack of entries came from correctly rejecting weak setups or from a threshold that has become too restrictive. The MT5 report alone cannot answer that.
MAribbonTrader
MAribbonTrader ended at -458 yen, and the raw win rate was 50%.
The payoff ratio was only 0.35 because the two losses were heavily unbalanced against the winners. The -682 yen GBPCHF close did almost all the damage. Again, seeing that number next to +71 and +183 made the problem hard to ignore.
But removing the Friday carryover changes the picture.
Monday’s newly opened trades were two wins and one loss for +206 yen. The biggest new loss was only -48 yen. The old position was the outlier.
MAribbonTrader is supposed to use AI for chart context and discretionary-style decisions. After this result, I do not want the model to solve every risk problem by reasoning harder. Some boundaries should simply be code.
The new no-carry behavior is one of those boundaries.
Wrap-up
The five-bot portfolio lost money on August 17, but I came away less worried about Monday’s entries than the headline result suggests.
GateGrid needs a better balance between average winners and losers. LLMBridge showed a promising profitable exit. BoundSniper executed cleanly, ML_ScoreAnalyst stayed inactive, and MAribbon’s fresh Monday trades actually held up.
The trade that mattered most was already alive before Monday began.
Sometimes the best upgrade to an AI trader is not another model, another indicator, or a longer prompt. It is one boring rule that refuses to let an old mistake survive into the next session.
② Substack Note
MAribbonTrader gave me an uncomfortable result on Aug. 17.
Its new Monday trades were profitable as a group, but a GBPCHF position carried from Friday took a -682 yen trading loss and dragged the Bot to -458 yen realized.
Across the five bot families, the day finished at -342 yen.
The interesting part wasn’t the win rate. GateGrid was 22W/22L and still lost money because its payoff ratio was only 0.77.
I’ve now changed MAribbon so positions won’t be carried over. For this run, the biggest lesson came from the exit architecture, not the entry model.
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