Conclusion

The five-bot portfolio finished the session with a realized gain of ¥624. Three bots were positive, one did not trade, and LLMBridgeTrader closed four positions without a single win.

The -¥195 stop on its first trade made me pause. The later losses shrank to -¥80, -¥16, and -¥5, so the bot did become quicker about getting out, but it never found a profitable exit during the reporting window. That makes this less of an entry contest and more of a test of when each system gives up on its original idea.

MAribbonTrader also needs a closer look despite ending positive. Its +¥316 closing trade appears to have come from a position carried into the day, and the three positions opened and closed on July 27 produced a combined -¥89 before swap. The account made money, but the fresh setups did not.

Win rate below includes the zero-yen GateGrid AI closure in the total number of closed trades. Gross profit, gross loss, and payoff ratio exclude swap.

Bot-by-bot results

■ GateGrid AI +384 yen　Market: EURUSD-　Record: 5W / 0L / 1 flat　Win rate: 83.3%　Gross profit: +384 yen　Gross loss: 0 yen　Payoff ratio: N/A, no losing trade　Max loss: 0 yen

■ BoundSniper Bot 0 yen　Trades: None reported　Record: N/A　Win rate: N/A　Gross profit: 0 yen　Gross loss: 0 yen　Payoff ratio: N/A　Max loss: N/A

■ LLMBridgeTrader -296 yen　Market: EURUSD-　Record: 0W / 4L　Win rate: 0.0%　Gross profit: 0 yen　Gross loss: -296 yen　Payoff ratio: N/A, no winning trade　Max loss: -195 yen　Unrealized P/L at cutoff: -16 yen

■ ML_ScoreAnalyst +300 yen　Market: GBPJPY-　Record: 1W / 0L　Win rate: 100.0%　Gross profit: +300 yen　Gross loss: 0 yen　Payoff ratio: N/A, no losing trade　Max loss: 0 yen

■ MAribbonTrader +236 yen　Market: GBPCAD-　Record: 2W / 2L　Win rate: 50.0%　Gross profit: +505 yen　Gross loss: -278 yen　Payoff ratio: 1.82　Max loss: -154 yen　Swap: +9 yen

■ Total +624 yen　Record: 8W / 6L / 1 flat　Win rate: 53.3%　Gross profit: +1,189 yen　Gross loss: -574 yen　Payoff ratio: 1.55　Max loss: -195 yen　Swap: +9 yen　Unrealized P/L at cutoff: -16 yen

Today’s theme: the MT5 report knows what happened, but not why

The execution report gives a clear sequence of entries, exits, stops, and take-profits. What it does not contain is the model context behind those actions: confidence, setup type, HOLD or CLOSE reasoning, chart state, or the inputs shown to the LLM.

That gap matters most for LLMBridgeTrader. Its design allows the model to choose OPEN, HOLD, CLOSE, and REVERSE while also proposing SL and TP distances. Looking only at the MT5 result, I cannot tell whether the -¥80 market exit was a sensible early escape from a broken setup or a late reaction after the model ignored an earlier warning.

The same issue exists in MAribbonTrader. The trading report shows the outcome, but not whether Qwen saw a first pullback to the long-term ribbon, a resistance retest, a narrowing channel, or a reason to switch from HOLD to EXIT. Without that join, a winning trade can look smarter than it was, and a losing trade can look worse than the decision that produced it.

GateGrid AI

GateGrid AI had the cleanest realized record of the day. It closed six short positions for five gains and one flat result, with no losing exit and no open exposure at the cutoff.

The closures came in groups. Two positions were closed around 14:30 for +¥92 and ¥0, while another pair was closed around 16:58 for +¥110 and +¥21. That looks like basket-level management rather than demanding that every grid layer reach an individual target, and it worked well in this session.

There is one configuration detail worth checking. The bot overview describes GateGrid AI as a GBPUSD system, but this live report records EURUSD- orders. The analysis here follows the actual account report, though the symbol difference should be confirmed before comparing the result with model thresholds or training data.

A payoff ratio cannot be calculated because there was no losing trade. That is a pleasant problem for one day, but six closures are not enough to judge the grid’s real downside. Its max-loss behavior remains untested in this sample.

BoundSniper Bot

No BoundSniper transaction details were included, so it is recorded as no trade. For a bridge bot, inactivity is not automatically a problem; it may simply mean TradingView sent no qualifying signal.

Still, its logs should distinguish between “no alert received,” “alert rejected,” and “order submission failed.” All three create an empty MT5 report, but they describe very different system states.

LLMBridgeTrader

LLMBridgeTrader was the weak point of the portfolio. Four completed EURUSD- trades lost -¥195, -¥80, -¥16, and -¥5, producing a 0% win rate and the day’s largest single loss.

The sequence is not entirely negative from a risk perspective. After the first stop, each completed loss became smaller, which may indicate that later CLOSE decisions reacted faster. I would not claim that from execution data alone, though; the market may simply have moved less.

The first buy was closed by a stop at -¥195. Two later buys were closed at market for -¥80 and -¥16, followed by a sell closed at -¥5. A new short was then opened at 23:00 and remained open with -¥16 of unrealized loss at the report cutoff.

For a bot with broad LLM discretion, the next useful comparison is not just BUY versus SELL accuracy. Each losing trade should be joined to the model’s confidence, setup label, original SL and TP proposal, every HOLD decision, and the final reason for CLOSE. The exit model is where the evidence is missing.

ML_ScoreAnalyst

ML_ScoreAnalyst took one GBPJPY- short and reached its take-profit for +¥300. It was a simple result: one scored entry, one predefined target, and no further exposure.

The fixed exit beat the adaptive LLM exits today, but the sample size is one. I would not promote the score threshold or TP setting on this result alone. What it does provide is a useful control case for the more flexible bots: a narrow decision system can be easier to evaluate because the path from signal to result is short.

MAribbonTrader

MAribbonTrader recorded two wins and two losses, with a payoff ratio of 1.82. That is the strongest measurable payoff ratio among the five bots, since the other profitable systems had no losing trades from which to calculate one.

The +¥316 close appears without a corresponding same-day entry and also earned +¥9 in swap, so it was probably a position carried into July 27. After that, the bot closed a fresh long for +¥189, then lost -¥154 and -¥124 on two more longs. Those three same-day round trips netted -¥89.

This split matters. The account-level result was +¥236, but the new setups had a difficult day. The -¥154 stop caught my eye because it arrived shortly after the profitable TP, followed almost immediately by another entry and another stop. That may be a re-entry filter problem, a ribbon-state problem, or just a bad patch of GBPCAD movement. The MT5 report cannot settle it.

The MAribbon logs should make it possible to check whether the two losing entries shared the same chart context. I would look first at long-term ribbon direction, distance to resistance, whether the move was still an impulse or already a correction, and why the second long was allowed only about ninety seconds after the previous stop.

Summary

The portfolio’s realized result was positive, but the most useful output from the day is not the ¥624. It is the contrast between systems whose exits were explicit and systems whose exits depended on changing model judgment.

The next improvement may not be a new prompt or another indicator. A shared trade ID connecting every LLM response, chart snapshot, position action, and MT5 execution would make the losses far more valuable. Right now the numbers are clear, while the decisions that created them are still partly hidden.

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