Conclusion

The week ended at -2,007 yen, and the uncomfortable part is that the bots were not simply “bad at trading.” They found plenty of winning trades. GateGrid AI even had a day with a 93.3% win rate, which sounds almost too clean. But that number did not protect the portfolio.

The real problem was exit quality. Across the week, several bots showed the same pattern: small wins piled up, then one oversized loss cut through the progress. I do not think the lesson is “the entries failed.” The sharper lesson is that an automated strategy can be directionally right often enough and still lose money if it does not know when the original idea has expired.

Bot-by-bot weekly performance

■ GateGrid AIMain pair: GBPUSDWeekly theme: high win rate, weak loss controlNotable result: 93.3% win rate on June 30Key loss: -729 yen on June 29Main issue: one large grid loss erased many small wins

■ BoundSniper BotMain pair: USDJPYWeekly theme: execution was fine, signal-side exit risk was notNotable result: positive day on July 2 despite only 25.0% win rateKey loss: -771 yen on June 30Main issue: one late exit damaged the full portfolio

■ LLMBridgeTraderMain pair: EURUSDWeekly theme: strong planning when right, slow CLOSE when wrongNotable result: 6 wins out of 6 on July 1, +710 yenKey issue: holding losing ideas too long on weaker daysMain issue: AI needs better judgment for switching from HOLD to CLOSE

■ MLScore GF-T4Main pair: GBPJPYWeekly theme: low trade count, but open risk mattersNotable result: 0 yen realized on July 3Open risk: -211 yen unrealized loss on July 3Main issue: realized P/L alone did not show the actual account risk

■ Weekly totalPeriod: June 29–July 3Total realized result: -2,007 yenMain theme: exit discipline mattered more than entry accuracyMost uncomfortable pattern: high win rate did not prevent lossesNext focus: max-loss rules, earlier exits, and stricter trade invalidation

Today’s, or rather this week’s, theme

This week made the win rate feel a little dangerous. It is an easy number to like. It gives a sense of control. But the logs kept showing the same contradiction: the bots were often right, yet the account still moved in the wrong direction.

June 29 was the first warning. GateGrid AI had an 80.0% win rate and still finished at -400 yen because one -729 yen loss overpowered the smaller wins. I stopped on that number for a moment, because it is the kind of trade that makes every clean entry before it feel smaller than it looked.

June 30 made the point even harder. GateGrid AI produced 14 wins and only 1 loss, ending at +442 yen. But BoundSniper took a -771 yen hit, and the whole portfolio closed at -974 yen. That is the week in one sentence: one bot can behave well, and another bot’s exit can still decide the day.

GateGrid AI

GateGrid AI gave the clearest example of the win-rate trap. On some days it looked almost too good. A 93.3% win rate on June 30 is not something I want to dismiss. The CatBoost gate and Ollama judgment layer were clearly finding trades that could close green.

But the bad days were not small. June 29 had the -729 yen loss. July 2 ended with GateGrid AI down -845 yen despite winning 15 out of 23 trades. The problem was not a lack of winning trades. It was the size of the losing side.

For a grid-style bot, this is the oldest problem in the room: where do you give up? GateGrid AI is designed to avoid low-quality entries, and that still matters. But this week showed that “not entering badly” is only half the job. The other half is cutting the structure before the grid becomes a stubborn position.

BoundSniper Bot

BoundSniper Bot is simpler in design. It does not predict the market by itself. TradingView sends the signal, the webhook path passes it through, and MT5 executes. So when BoundSniper has a bad result, I look less at the execution engine and more at the signal and exit rules sitting upstream.

The contrast was sharp. On July 2, BoundSniper had only a 25.0% win rate, but still ended slightly positive at +14 yen because the payoff ratio was strong. That was a useful reminder: a low win rate is not automatically bad if the losses are controlled and the winners have room.

Then there was June 30. The -771 yen loss was too large for the role this bot should be playing in the portfolio. It felt less like a normal loss and more like a rule boundary being too loose. The fix is probably not in the webhook layer. It is in the TradingView-side stop, exit, or invalidation logic.

LLMBridgeTrader

LLMBridgeTrader had the most interesting week from an AI-experiment point of view. On July 1, it went 6 for 6 and made +710 yen. That is the version of the bot I want to study carefully, because the AI was not only entering. It was managing position actions through OPEN, HOLD, CLOSE, and sometimes REVERSE logic.

But the same freedom can cut both ways. On weaker days, the bot seemed too willing to keep holding after the trade idea had started to fail. This is where LLM trading becomes less about prediction and more about self-correction.

The main question for LLMBridgeTrader is not “can the model find a setup?” It can. The question is whether it can admit the setup is no longer valid. That is a harder judgment, and probably the one that matters more in live trading.

MLScore GF-T4

MLScore GF-T4 did not dominate the week by trade count, but it gave an important reminder on July 3. The realized P/L was 0 yen, which looks harmless on a closed-trade report. But there was a -211 yen unrealized loss sitting in the open position.

That is not just a footnote. In automated trading, open risk is still part of the result, even if the statement does not force you to count it yet. A system can look flat or even green in realized terms while carrying risk that will land in the next day’s report.

I do not want to overjudge the bot from one open position. Still, it changes how I want to write these logs. From now on, realized P/L alone is not enough. Open positions need to be treated as part of the daily and weekly story.

Summary

The week did not say, “the bots cannot win.” It said something more annoying: they can win often and still lose overall. That is a harder problem, because it means the entry layer is not useless. It is just not enough.

The next upgrade should not chase a prettier win rate. It should focus on max-loss limits, faster invalidation, and stricter exit rules. GateGrid AI needs clearer grid surrender conditions. BoundSniper needs tighter signal-side damage control. LLMBridgeTrader needs a better way to switch from HOLD to CLOSE when the market stops agreeing. MLScore GF-T4 needs open-risk visibility baked into the review.

The week’s loss was -2,007 yen. Small in scale, maybe. But the lesson was not small at all.

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