Bottom Line
The six-bot portfolio closed August 11 at -¥55, with 32 wins and 22 losses. A 59.3% win rate does not look bad on its own. The uncomfortable number was the 0.66 payoff ratio: the average winner was about ¥36, while the average loser was ¥55.
GateGrid AI made the problem easiest to see. It won 23 of 43 closed trades and still lost ¥295 because its average loss was much larger than its average win. The -¥156 largest loss made me stop for a second. This was not a day where entry accuracy completely failed; the damage came from what happened after positions were already open.
Results by Bot
■ BoundSniper Bot +¥89Record: 3W / 0L (Win rate 100.0%)Gross profit: +¥89Gross loss: ¥0Payoff ratio: N/A (no losing trades)Max loss: ¥0
■ LLMBridgeTrader ¥0Record: 0W / 0L (Win rate N/A)Gross profit: ¥0Gross loss: ¥0Payoff ratio: N/AMax loss: ¥0
■ GateGrid AI -¥295Record: 23W / 20L (Win rate 53.5%)Gross profit: +¥777Gross loss: -¥1,072Payoff ratio: 0.63Max loss: -¥156
■ ML_ScoreAnalyst ¥0Record: 0W / 0L (Win rate N/A)Gross profit: ¥0Gross loss: ¥0Payoff ratio: N/AMax loss: ¥0
■ MAribbonTrader +¥86Record: 4W / 2L (Win rate 66.7%)Gross profit: +¥224Gross loss: -¥138Payoff ratio: 0.81Max loss: -¥112
■ BoundSniper Bot2 +¥65Record: 2W / 0L (Win rate 100.0%)Gross profit: +¥65Gross loss: ¥0Payoff ratio: N/A (no losing trades)Max loss: ¥0
■ Total -¥55Record: 32W / 22L (Win rate 59.3%)Gross profit: +¥1,155Gross loss: -¥1,210Payoff ratio: 0.66Max loss: -¥156
Today’s Theme: The Exit Can Undo a Decent Entry Rate
GateGrid is designed as a multi-stage decision system rather than a simple always-on grid. Its design notes describe a CatBoost gate, local LLM judgment through Ollama, volatility and session filters, and position management. That makes its live result interesting for a reason beyond the ¥295 loss: a system built to filter entries still ended up with an unfavorable payoff structure.
The MT5 statement does not include the corresponding AI_SKIP, OLLAMA_HOLD, prompt response, or model reasoning log for each trade, so I cannot say which LLM decision caused a particular loss. What the execution record does show is enough to raise the exit question. GateGrid’s average winning trade was about ¥33.8, while its average losing trade was ¥53.6. With a 0.63 payoff ratio, it would need a win rate around 61% just to offset that asymmetry before other costs. It delivered 53.5%.
One cluster was especially ugly. Around 14:25, four positions were closed for -¥8, -¥56, -¥76 and -¥101, a combined -¥241. That is the kind of sequence I want to match against the decision log next: did the model keep the basket alive because its original thesis still looked valid, or did the exit mechanism simply react too late? The statement alone cannot answer that, but it tells me exactly where to look.
Bot Notes
BoundSniper Bot
BoundSniper finished 3W / 0L for +¥89. The individual exits were +¥52, +¥22 and +¥15, so there was no single oversized winner carrying the result.
This bot does not predict the market itself. TradingView generates the instructions and BoundSniper acts as the execution bridge into MT5, which means I read this result differently from the LLM-driven systems. The statement confirms three profitable completed entry/exit pairs; evaluating signal quality or exit reasoning further would require the TradingView signal log alongside the MT5 fills.
For today, the execution result is clean. I would not extrapolate much from three trades, though.
LLMBridgeTrader
There was no trade statement for LLMBridgeTrader on August 11, so it is recorded as no trades.
This is the bot where the LLM has the broadest decision authority: OPEN, HOLD, CLOSE and REVERSE, along with proposed SL and TP distances. Because no live trades were supplied today, there is no evidence to grade its entry or exit decisions. A zero is not a good day or a bad day here; it is simply no sample.
GateGrid AI
GateGrid produced 23 wins and 20 losses, yet finished at -¥295. That combination is more useful than a simple losing-day label because it isolates the structural issue.
Gross profit reached +¥777, but gross loss expanded to -¥1,072. The payoff ratio was only 0.63, and the largest individual loss was -¥156. Several profitable basket closes show that the strategy can recover mixed positions, but the losing baskets were too expensive when that recovery failed.
For an ML-plus-LLM system, this is where I want the next experiment to focus. Entry filters can become more selective, but if HOLD and eventual exit behavior allow average losses to grow faster than average winners, better entries alone may not repair the expectancy. My suspicion is the exit side, although I would want the Ollama decision log before calling that settled.
ML_ScoreAnalyst
No trade statement was supplied for ML_ScoreAnalyst, so the bot is recorded at ¥0 with no trades.
Unlike the LLM bots, ML_ScoreAnalyst uses CatBoost scoring without an LLM layer. That makes it useful as a comparison group when enough live samples accumulate: a relatively deterministic score threshold against systems where language models also interpret context.
There is nothing to compare from August 11 itself, so I am leaving the result untouched rather than filling the gap with assumptions.
MAribbonTrader
MAribbonTrader finished 4W / 2L for +¥86, trading GBPCAD in the supplied statement. Its result was positive, but the shape of the P&L was uneven.
The six exits were +¥3, -¥26, +¥2, +¥5, -¥112 and +¥214. That final +¥214 take-profit changed the whole day; without it, the bot would have been at -¥128. Seeing the position finally run that far was encouraging, but it also exposes a dependency I do not want to ignore.
The payoff ratio was only 0.81 because several wins were tiny while the two losses averaged ¥69. Still, the 66.7% win rate was high enough to make the combination profitable, and one exit did exactly what a chart-reading system should sometimes do: stay with a move long enough for a large winner to emerge.
MAribbonTrader uses Qwen to interpret chart imagery and supporting context such as moving-average structure and higher-timeframe information. The MT5 statement records the result but not the corresponding visual judgment or EXIT explanation. Matching that +¥214 trade and the -¥112 loss back to the stored AI reasoning should be far more valuable than merely celebrating the net +¥86.
BoundSniper Bot2
BoundSniper Bot2 closed 2W / 0L for +¥65, with winners of +¥44 and +¥21.
Like the original BoundSniper, this version is primarily an execution bridge, with a different indicator supplying the TradingView signal. Both variants ended positive on the same day, but the sample is too small to decide which signal source is superior.
The useful part is that they give the LLM experiments a simple benchmark: external rule-based signals can be compared with systems where AI has more freedom over entries and exits.
Closing Thoughts
A portfolio can post a 59.3% win rate and still lose money. August 11 was a small loss in yen, but a useful live experiment because the reason was visible in the distribution rather than hidden in the final number.
The next comparison I care about is not “Can an LLM pick direction better?” It is whether giving the model control over HOLD and EXIT can keep average losses from outrunning average wins. GateGrid made that weakness visible today, while MAribbon showed the opposite possibility with one trade that was allowed to run. That tension is probably more interesting than the -¥55 itself.
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