Conclusion The Aug. 20 run finished at +1,236 yen across the six currently documented Bot instances. Four finished positive and two negative. The result looks comfortable at first glance, but the interesting part was not the total. It was how differently the Bots lost. GateGrid AI ended slightly negative at -35 yen, yet its worst realized loss was only -19 yen. MAribbonTrader lost -261 yen with a maximum loss of -127 yen and a payoff ratio of just 0.36. I stopped for a moment at that -127 yen because its average winning trade was only about 32 yen. That gap matters more to me than the headline win rate. BoundSniper, BoundSniper Bot2, LLMBridgeTrader and ML_ScoreAnalyst recorded no realized losing trades in the statement. That is a strong day, but not proof that their risk structure is solved. A zero-loss sample also means the payoff ratio cannot be calculated yet. Bot Results ■ GateGrid AI -35 yenRecord: 9W / 15L (Win rate 37.5%)Gross profit: +147 yenGross loss: -182 yenPayoff ratio: 1.35Max loss: -19 yen ■ BoundSniper Bot +590 yenRecord: 13W / 0L (Win rate 100.0%)Gross profit: +590 yenGross loss: 0 yenPayoff ratio: N/A, no losing tradesMax loss: 0 yen ■ LLMBridgeTrader +509 yenRecord: 4W / 0L (Win rate 100.0%)Gross profit: +509 yenGross loss: 0 yenPayoff ratio: N/A, no losing tradesMax loss: 0 yen ■ ML_ScoreAnalyst +173 yenRecord: 2W / 0L (Win rate 100.0%)Gross profit: +173 yenGross loss: 0 yenPayoff ratio: N/A, no losing tradesMax loss: 0 yen ■ MAribbonTrader -261 yenRecord: 3W / 4L (Win rate 42.9%)Gross profit: +95 yenGross loss: -356 yenPayoff ratio: 0.36Max loss: -127 yen ■ BoundSniper Bot2 +260 yenRecord: 2W / 0L (Win rate 100.0%)Gross profit: +260 yenGross loss: 0 yenPayoff ratio: N/A, no losing tradesMax loss: 0 yen ■ Total +1,236 yenRecord: 33W / 19L (Win rate 63.5%)Gross profit: +1,774 yenGross loss: -538 yenPayoff ratio: 1.90Max loss: -127 yen The broker statement also contains seven profitable bb_pullback_rider exits totaling +328 yen on the same account as LLMBridgeTrader. I left those trades outside this Bot roster because that strategy is not one of the six runners described in the current operating memo. Today’s Theme: The Exit Is Where the Bots Separate These Bots do not make decisions in the same way. BoundSniper mainly executes TradingView signals, ML_ScoreAnalyst scores candidates with CatBoost, GateGrid adds multiple gates including local-AI judgment, and MAribbonTrader asks Qwen to interpret chart structure closer to discretionary trading. LLMBridgeTrader goes further and lets the LLM consider OPEN, HOLD, CLOSE and REVERSE, along with SL and TP proposals. That makes the exit particularly interesting. Entry accuracy alone cannot explain whether the LLM is useful. If the model reads the chart correctly but keeps a bad trade too long, closes winners too early, or proposes an asymmetric stop structure, the final P/L will expose it. Today gave a clean example of that difference. GateGrid AI GateGrid lost more often than it won, with 9 winners against 15 losers. On win rate alone, 37.5% looks weak. But its average winner was roughly 16 yen while its average loser was about 12 yen, producing a 1.35 payoff ratio. More importantly, the largest realized loss was only -19 yen. Seeing fifteen losses and still ending at only -35 yen is not comfortable, but it is a very different problem from an uncontrolled tail loss. The entry gate may have been too permissive for the conditions, or the grid created too many marginal attempts. I cannot establish the cause from the broker statement alone. The exit and loss containment, however, did not blow up. One detail worth checking later is configuration drift: today’s statement labels the GateGrid v4 executions on USDJPY-, while the operating description documents GateGrid AI as an EURUSD system. The two sources do not explain that difference. BoundSniper Bot BoundSniper produced 13 winners from 13 completed trades and +590 yen. Since this Bot does not predict the market itself, I read this less as an “AI was right” result and more as a strong day for the TradingView signal plus execution chain. The exits were also consistently positive. The largest realized win was +138 yen, while no losing close appeared in the statement. Still, 100% is a dangerous number to get excited about. There is no losing trade here, so there is no payoff ratio and no evidence from this single day about what happens when the TradingView exit arrives late. Today tells me the pipeline worked. It does not tell me the worst-case behavior yet. LLMBridgeTrader LLMBridgeTrader was the most interesting positive result for the LLM experiment. Four EURUSD trades closed for +127, +127, +125 and +130 yen, totaling +509 yen. The broker comments on those exits are shown as stop-related closes. That suggests profit was ultimately realized through stop handling, but the statement alone cannot tell me whether the LLM itself decided to exit, whether a Bot-side rule moved the stop, or exactly how the HOLD/CLOSE logic contributed. That distinction is worth preserving in the logs. For an LLM that is allowed to choose OPEN, HOLD, CLOSE and REVERSE, I want to know not just that a trade made +130 yen, but why the position was still held five minutes earlier and why it was no longer held at the end. Today’s P/L is excellent. The next useful evidence is the decision trace. ML_ScoreAnalyst ML_ScoreAnalyst completed two GBPJPY trades, both winners, for +173 yen in total. The two exits were +84 and +89 yen, which is unusually consistent. This Bot has a narrower job than the LLM systems. CatBoost scores an entry candidate and the surrounding safety logic decides whether to send the order. With only two trades, there is little to say statistically, but there was no obvious sign of the model taking low-quality entries and then relying on a large stop to escape. The sample is simply too small. I would rather keep collecting score, time-of-day and volatility context than raise confidence because of a 2-for-2 day. MAribbonTrader MAribbonTrader is where today’s result changes tone. It won three trades and lost four, so the 42.9% win rate is not disastrous by itself. The problem is the size distribution. The three winners totaled only +95 yen. The four losers totaled -356 yen. Average win was about +32 yen, while average loss was -89 yen, leaving a payoff ratio of 0.36. With that structure, a modest improvement in entry accuracy will not fix much. The -125 and -127 yen losses stood out. Again, this is the part that bothers me more than the number of losing trades. MAribbonTrader is designed to feed chart images, MAribbon structure, higher-timeframe context, support/resistance and other visual context into Qwen, then let the AI return BUY, SELL, WAIT or EXIT. That makes the exit decision central to the experiment. Maybe the issue is the initial stop width. Maybe it is holding through a setup invalidation that a discretionary trader would have abandoned earlier. I do not have enough evidence from the statement to choose between those explanations yet. There was also an AUDJPY position still open at the end of the report with -31 yen unrealized P/L, which I did not include in the realized performance statistics. BoundSniper Bot2 Bot2 closed two USDJPY trades for +138 and +122 yen. That is +260 yen with no realized loss. Because the core logic is described as the same BoundSniper execution architecture with a different referenced indicator, this creates a useful comparison. The infrastructure can remain largely fixed while the upstream signal source changes. Two trades are nowhere near enough to rank the indicators. But keeping the two variants separate in the logs may eventually tell us whether one generates cleaner exits rather than merely more entries. Summary The day was profitable, but the result I want to carry forward is not +1,236 yen. GateGrid showed that a low win rate can stay manageable when individual losses remain small, while MAribbon showed the opposite problem: a few winners cannot compensate when the losing side is almost three times larger on average. The LLMBridge result is promising, especially because all four completed EURUSD trades ended with similar profits. Still, I want the next analysis to connect those results to the model’s actual HOLD, CLOSE and stop-adjustment logs. Today the machines did not mainly differ in whether they could find a trade. They differed in what happened after they were already in one. 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