The interesting part of August 13 was not that the six accounts lost ¥1,080. It was how concentrated that loss was.
BoundSniper, bb_pullback_rider, MLScore, and BoundSniper Bot2 combined for +¥218. GateGrid and MAribbon then produced -¥1,298 between them, turning the whole group negative. The systems are built very differently: GateGrid uses a multi-stage ML/LLM filtering concept, MAribbon asks Qwen to interpret chart context, while BoundSniper is mainly an execution bridge and MLScore relies on CatBoost scoring. Those design differences are exactly why I run them side by side.
What bothered me most was not the overall win rate. It was the shape of the losses. MAribbon had a single -¥282 trade and a payoff ratio of only 0.26. GateGrid did not suffer one catastrophic hit, but 31 closed positions slowly accumulated -¥897. Different paths, same problem: the exit side overwhelmed the winners.
Bot-by-Bot Performance
■ GateGrid AINet P/L: -¥897Record: 11W / 20LWin rate: 35.5%Gross profit: +¥367Gross loss: -¥1,264Payoff ratio: 0.53Max loss: -¥156
■ BoundSniper BotNet P/L: +¥38Record: 3W / 1LWin rate: 75.0%Gross profit: +¥52Gross loss: -¥14Payoff ratio: 1.24Max loss: -¥14
■ bb_pullback_riderNet P/L: +¥51Record: 2W / 0LWin rate: 100.0%Gross profit: +¥51Gross loss: ¥0Payoff ratio: N/AMax loss: N/A
■ ML_ScoreAnalystNet P/L: +¥152Record: 2W / 0LWin rate: 100.0%Gross profit: +¥152Gross loss: ¥0Payoff ratio: N/AMax loss: N/A
■ MAribbonTraderNet P/L: -¥401Record: 2W / 3LWin rate: 40.0%Gross profit: +¥83Gross loss: -¥484Payoff ratio: 0.26Max loss: -¥282
■ BoundSniper Bot2Net P/L: -¥23Record: 0W / 2LWin rate: 0.0%Gross profit: ¥0Gross loss: -¥23Payoff ratio: N/AMax loss: -¥19
■ TotalNet P/L: -¥1,080Record: 20W / 26LWin rate: 43.5%Gross profit: +¥705Gross loss: -¥1,785Payoff ratio: 0.51Max loss: -¥282
Today’s Theme: The Exit Matters More Than the Signal
I keep experimenting with different ways to decide whether to enter. CatBoost scores probabilities. GateGrid adds an Ollama layer and volatility/session filters. MAribbon gives Qwen chart images, moving-average ribbons, higher-timeframe context, support and resistance, and asks for something closer to discretionary judgment.
August 13 pushed me back toward a less glamorous question: once a position is open, how quickly does the system admit that the original idea is no longer working?
A good entry model can still lose if bad trades are allowed to become expensive. A mediocre entry model can survive surprisingly well if the exit cuts the wrong ideas early.
GateGrid AI: Many Small Decisions Added Up
GateGrid closed 31 positions and finished at -¥897. The win rate was 35.5%, but the bigger issue was the 0.53 payoff ratio. Average winning trades were about ¥33, while the average loss was roughly ¥63.
The live report also shows repeated grouped closures. This was not one freak trade destroying the day. Losses accumulated across multiple baskets, including -¥145, -¥128 and -¥156 exits. That pattern worries me more than a single abnormal event because it can be repeated by the system.
GateGrid was designed around filtering: CatBoost first, then local-LLM judgment, ATR, session conditions and other gates. The actual AI decision logs were not included in today’s report, so I cannot say whether Ollama approved poor setups or whether the market simply behaved badly after acceptable entries. What the P/L log does show is that filtering entries alone was not enough. The basket exit logic deserves at least as much attention as the entry gate.
BoundSniper: Small Sample, Clean Damage Control
BoundSniper made four trades, winning three and finishing at +¥38. The payoff ratio was 1.24 and the worst loss was only -¥14.
This Bot does not try to read the market itself. TradingView generates the signal and the local bridge handles delivery and execution in MT5. Because of that, I look at its result a little differently. I am less interested in whether the Bot predicted USDJPY correctly and more interested in whether the execution chain turned the upstream signals into controlled trades.
On this day, it did. One loss did not erase the three winners.
bb_pullback_rider: Two Wins, but Not Enough Evidence Yet
The bb_pullback_rider account closed two USDJPY trades for +¥31 and +¥20. That gives it +¥51 and a 100% daily win rate.
There is no matching architecture description for this name in the supplied six-Bot design memo, so I do not want to invent an explanation for why it worked. From the broker report alone, all I can say is that both positions were profitable and there was no losing trade from which to calculate a payoff ratio.
Two trades are also far too few to turn 100% into a meaningful claim. For now, this is a clean day rather than evidence of an edge.
ML_ScoreAnalyst: The Best Exit Pattern of the Day
MLScore produced only two GBPJPY trades, but both were winners: +¥81 and +¥71.
What caught my eye was the way they closed. Both exits are recorded with [sl ...] comments, yet the exit prices locked in positive P/L. Whatever adjustment led to those stop levels, the result was exactly what an exit mechanism is supposed to do: the trade moved in the right direction and profit was protected before the market could take it back.
The daily sample is tiny, so a 100% win rate means almost nothing by itself. Still, +¥152 from two trades with no loser is the cleanest result among the accounts today.
MAribbonTrader: This Is Where the Exit Question Gets Serious
MAribbon finished at -¥401 from five trades. Two wins produced only +¥83 in total, while three losses cost -¥484. The payoff ratio was 0.26.
The -¥282 USDCHF loss made me stop for a moment. It was the largest single loss across all six accounts, and it was almost four times the Bot’s best winner of +¥73.
This matters because MAribbon is one of the systems where AI judgment is supposed to contribute more than a simple signal. Qwen receives chart context such as the MA ribbons, higher-timeframe environment, support and resistance, ranges and channels, and the architecture can produce WAIT, BUY, SELL and EXIT decisions. The design goal is close to discretionary chart reading.
Today’s broker report does not contain the Qwen reasoning log, so I cannot blame the model for holding too long or choosing the wrong stop. But the outcome tells me what to inspect next: not only why the AI entered USDCHF, but what information would have justified abandoning the setup earlier. The Entry model can wait. I want to understand the Exit behavior first.
BoundSniper Bot2: Wrong Twice, but Cheaply Wrong
BoundSniper Bot2 lost both of its USDJPY trades and finished at -¥23. A 0% win rate looks ugly on paper, but the maximum loss was only -¥19.
That is a useful reminder of why I do not rank these Bots by win rate alone. Two losing signals with tightly contained damage are easier to live with than a high-win-rate system that occasionally gives back several winners at once.
Bot2 uses the same bridge concept as BoundSniper with a different TradingView indicator. With only two trades, I would look upstream at signal quality before changing the MT5 execution layer.
Summary
The combined win rate was only 43.5%, but that number does not explain the day very well. The other four accounts were collectively positive; GateGrid’s repeated losing baskets and MAribbon’s asymmetric loss size changed the sign of the whole portfolio.
For the LLM experiments, I want more than better BUY and SELL decisions. I want the model and the surrounding rules to recognize when the original story has expired. August 13 looked less like an entry-model problem and more like a reminder that an intelligent Bot also needs to know when to stop defending its own idea.
This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com/subscribe
