Conclusion

July 23 ended with a realized profit of ¥574 across the five-bot lineup. Only three bots had closed trades in the supplied account statements, but two of them did enough: LLMBridgeTrader earned ¥398 and MAribbonTrader added ¥201, while GateGrid AI finished slightly negative at ¥25.

What caught my attention was not just the total. Several exits marked as stop-loss orders still closed in profit. That suggests the protective exit layer was doing more than limiting damage; it was also preserving gains after the market had already moved in the bot’s favor.

LLMBridgeTrader still held one EURUSD- short position at the end of the report with an unrealized loss of ¥38. That floating result is not included in the ¥574 realized total.

Bot Performance

■ LLMBridgeTrader +398 yenRecord: 4W / 1LWin rate: 80.0%Gross profit: +644 yenGross loss: -246 yenPayoff ratio: 0.65Max loss: -246 yen

■ MAribbonTrader +201 yenRecord: 2W / 0LWin rate: 100.0%Gross profit: +201 yenGross loss: 0 yenPayoff ratio: N/AMax loss: 0 yen

■ GateGrid AI -25 yenRecord: 8W / 3LWin rate: 72.7%Gross profit: +358 yenGross loss: -383 yenPayoff ratio: 0.35Max loss: -197 yen

■ BoundSniper Bot ±0 yenRecord: 0W / 0LWin rate: N/AGross profit: 0 yenGross loss: 0 yenPayoff ratio: N/AMax loss: N/A

■ ML_ScoreAnalyst ±0 yenRecord: 0W / 0LWin rate: N/AGross profit: 0 yenGross loss: 0 yenPayoff ratio: N/AMax loss: N/A

■ Total +574 yenRecord: 14W / 4LWin rate: 77.8%Gross profit: +1,203 yenGross loss: -629 yenPayoff ratio: 0.55Max loss: -246 yen

Today’s Theme: A Stop Is Not Always a Losing Exit

The account statement contains a useful detail. Four profitable LLMBridgeTrader exits were recorded with stop-related comments, and MAribbonTrader also closed its second trade through a stop-tagged order while still banking ¥41.

That ¥41 exit looked modest, but I liked it. A stop that closes above the original entry is no longer just an emergency brake. It becomes a mechanism for turning open profit into realized profit before the market has time to take it back.

The exact model reasoning is not included in the broker statement, so I cannot tell whether each stop adjustment came directly from the LLM, a trailing rule, or another risk-management layer. The execution result is clear, though: the profitable bots were able to leave the market with money still on the table.

LLMBridgeTrader: Four Winning Exits Absorbed One Full Loss

LLMBridgeTrader produced five completed EURUSD- trades: +¥261, +¥88, +¥131, -¥246, and +¥164. Seeing +¥398 from only five closed positions was the first number that made me pause.

The bot’s design gives the AI a wide decision space. It can propose BUY, SELL, or NONE, select OPEN, HOLD, CLOSE, or REVERSE, and return confidence, setup type, stop distance, target distance, and reasons for entry or exit. Risk checks then decide whether that plan is acceptable.

The day was not flawless. Its single ¥246 loss was also the largest loss across all five bots, and the payoff ratio was only 0.65. The average winning trade was smaller than the losing trade, so the result depended on maintaining a high hit rate.

Still, the sequence recovered well. Three early winners built ¥480, the loss removed ¥246, and the following ¥164 winner restored the daily result to ¥398. The exit process did not freeze after taking a hit, which matters for a system allowed to reassess positions through an LLM.

MAribbonTrader: Two Shorts, Two Profitable Closures

MAribbonTrader completed two GBPCAD- short trades and won both. The first reached a take-profit exit for ¥160, while the second closed through a stop-tagged order for another ¥41.

This bot gives Qwen a chart image containing the short- and long-term MA ribbons, higher-timeframe context, support and resistance areas, range boxes, crossings, and channel information. The model then returns WAIT, BUY, SELL, or EXIT together with its reasoning.

Two trades are far too few to prove an edge, and the payoff ratio cannot be calculated because there were no losing trades. Even so, this is the kind of small sample I would rather see: limited activity, no forced entry, and profit retained on both positions.

The second trade is the more interesting one for the experiment. It did not need to reach its original target to contribute. The exit layer found a way to close positively, although the broker report alone does not reveal whether that came from the visual model’s judgment or a mechanical stop update.

GateGrid AI: Gross Profit Was There, but the Exit Leakage Was Larger

GateGrid AI won eight of eleven closed trades and generated ¥358 in gross profit. On win rate alone, the day looked healthy. The problem was that three losses totaled ¥383, leaving the bot down ¥25.

The ¥197 loss was the one that bothered me. With an average win of only ¥44.75 and an average loss of roughly ¥127.67, the payoff ratio fell to 0.35. The bot needed almost three average winners to recover one average loss.

Before the last two exits, GateGrid AI was ahead by ¥161. The final two closures lost ¥43 and ¥143, removing ¥186 and turning a profitable session into a small negative one. Its entry filters found enough favorable movement to create real gross profit, but the closing sequence gave slightly more back.

GateGrid AI uses CatBoost as a quantitative gate before passing selected situations to Ollama. The local LLM then considers items such as spread, ATR, higher-timeframe trends, session, grid width, and recent performance. The broker statement does not contain the AI_SKIP, OLLAMA_HOLD, or decision-reason logs, so this report can evaluate the trade outcomes but not the exact rationale behind each entry.

There is also a logging detail worth checking. The system description identifies GateGrid AI as a GBPUSD strategy, while the supplied account statement shows EURUSD-. For this article, I have followed the live account statement.

BoundSniper Bot and ML_ScoreAnalyst

No closed trades from BoundSniper Bot or ML_ScoreAnalyst appeared in the supplied statements. I have therefore recorded both as flat for this daily comparison rather than assuming anything about their broader operational status.

BoundSniper is primarily an execution bridge. It carries TradingView signals through a webhook and local server into MT5, so its value is measured not only by strategy profit but also by execution accuracy, logging, and the absence of missed or duplicated orders.

ML_ScoreAnalyst uses a CatBoost score to filter GBPJPY breakout candidates. Since there were no trades to evaluate, the useful evidence for this day would be its skipped signals and score distribution, but those logs were not included in the account report.

Summary

The profitable side of July 23 came from two different AI designs. LLMBridgeTrader used a broad trade-planning framework and recovered after one large loss, while MAribbonTrader took only two positions and kept both positive.

GateGrid AI also found profitable moves, but its gross profit did not survive the full exit sequence. That contrast is useful. Entry quality created the opportunity, but the bots that finished ahead were the ones that converted open movement into closed profit.

The next step is to connect each broker-side exit with its model output, confidence, decision reason, and stop update history. I do not just want to know which bot made money. I want to know whether the way it kept that money can be reproduced.

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