The five bots closed 13 trades on August 4 and won 10 of them. That sounds like a strong session, but the realized result was negative 16 yen after swap. The problem was not a lack of winning trades. It was the size and shape of the losses. The average winner was 62.6 yen, while the average loser was 208 yen, leaving the combined payoff ratio at only 0.30. BoundSniper Bot won four of five closed trades, yet one 263-yen loss and 18 yen of negative swap erased all four small wins. ML_ScoreAnalyst produced a 303-yen winner and a 304-yen loser, almost a perfect cancellation. I had to look at those two numbers twice. The MT5 reports also showed two positions still open at the end of the day. ML_ScoreAnalyst carried a 90-yen floating loss, and MAribbonTrader carried a 111-yen floating loss. The realized result was close to flat, but the risk had not disappeared. Bot-by-Bot Results ■ GateGrid AI +99 yenRecord: 2W / 0LWin rate: 100.0%Gross profit: +99 yenGross loss: 0 yenPayoff ratio: N/AMax loss: 0 yenOpen P/L: 0 yen ■ BoundSniper Bot -225 yenRecord: 4W / 1LWin rate: 80.0%Gross profit: +56 yenGross loss: -263 yenSwap: -18 yenPayoff ratio: 0.05Max loss: -263 yenOpen P/L: 0 yen ■ LLMBridgeTrader +61 yenRecord: 2W / 1LWin rate: 66.7%Gross profit: +118 yenGross loss: -57 yenPayoff ratio: 1.04Max loss: -57 yenOpen P/L: 0 yen ■ ML_ScoreAnalyst -1 yenRecord: 1W / 1LWin rate: 50.0%Gross profit: +303 yenGross loss: -304 yenPayoff ratio: 1.00Max loss: -304 yenOpen P/L: -90 yen ■ MAribbonTrader +50 yenRecord: 1W / 0LWin rate: 100.0%Gross profit: +50 yenGross loss: 0 yenPayoff ratio: N/AMax loss: 0 yenOpen P/L: -111 yen ■ Total -16 yenRecord: 10W / 3LWin rate: 76.9%Gross profit: +626 yenGross loss: -624 yenSwap: -18 yenPayoff ratio: 0.30Max loss: -304 yenOpen P/L: -201 yen The gross trading result before swap was positive by only 2 yen. Once the 18-yen swap was included, the realized result became negative 16 yen. Today’s Theme: A High Win Rate Can Hide Weak Exits The day was a clean example of why I do not want to rank these bots by win rate alone. Ten winners looked reassuring, but most of them were too small to absorb the three losing trades. This matters even more for bots that let an LLM decide whether to HOLD, CLOSE, or REVERSE. An entry can be reasonable and still become a poor trade if the model keeps defending the position for too long. The useful question is not only whether the model predicted the direction correctly, but whether it stopped believing its own thesis at the right moment. The MT5 report tells me when and where a trade was closed. It does not contain the full AI response, confidence score, chart interpretation, or exit reason. To evaluate the LLM layer properly, each deal now needs to be joined with the model log that produced OPEN, HOLD, CLOSE, REVERSE, WAIT, or EXIT. GateGrid AI: Clean Realized Result, but the Exit Reason Is Missing GateGrid AI finished with two winners worth 99 yen in total and no open position. Six earlier pending orders were canceled before two buy-stop orders were eventually filled, so the order-management layer did not simply leave old entries sitting in the market. The first position was opened at 14:44 and the second at 17:08. Both were closed by market orders around 17:12, producing 91 yen and 8 yen. Closing the two grid legs together left the account flat, which is the result I want to see from a strategy that manages positions as a group. Still, the report does not show whether the close came from trailing logic, a local Ollama decision, a grid-level target, or another rule. A profitable exit is welcome, but one two-trade sample does not tell me whether the exit manager is improving. The next step is to match the close timestamp with the CatBoost score, ATR state, session gate, Ollama response, and recorded exit trigger. BoundSniper Bot: Four Wins Could Not Repair One Old Loss BoundSniper Bot posted four winners after its first closing trade, but those wins were only 31, 8, 10, and 7 yen. Together they earned 56 yen. The earlier loss was 263 yen, with another 18 yen charged as swap. That single exit made the whole day negative 225 yen. Seeing a payoff ratio of 0.05 made me pause; the bot could repeat this exact 80% win rate and still lose money. BoundSniper itself does not predict the market. It receives TradingView signals through the webhook pipeline and executes them in MT5. The issue therefore appears less like an MT5 execution problem and more like an exit problem in the upstream TradingView strategy, or in the management of a position carried from the previous session. The losing position was closed at 01:02, while no corresponding same-day entry appears in the report. That suggests it was already open before August 4. I cannot determine from this report whether the exit was late, but the negative swap and oversized loss make the inherited-position logic worth reviewing. LLMBridgeTrader: The Most Balanced Exit Profile of the Day LLMBridgeTrader closed three EURUSD trades for a net gain of 61 yen. Its two winners totaled 118 yen, while its single loss was 57 yen, producing a payoff ratio of 1.04. The first short was closed by a stop order for a 93-yen profit. After that, two long positions were opened and closed within about 15 minutes, one for a 57-yen loss and one for a 25-yen gain. The loss was contained rather than allowed to grow into the largest loss of the session. This was the best balance between winning and losing size among the bots that recorded both outcomes. The numbers do not prove that the LLM made good discretionary decisions, though. The MT5 report does not reveal whether those exits were CLOSE responses, fixed safety rules, stop movement, or scheduled reevaluations. Because LLMBridgeTrader can choose OPEN, HOLD, CLOSE, and REVERSE, its real experiment is the change of mind. The valuable log is the moment when confidence weakens enough to replace HOLD with CLOSE. Today’s trade sizes look reasonable, but I still need the decision trace before giving the model credit. ML_ScoreAnalyst: One Winner, One Loser, and No Edge Left Over ML_ScoreAnalyst earned 303 yen on its first GBPJPY trade and lost 304 yen on the next. The realized result was negative 1 yen, almost a numerical draw, while a third long position remained open with a 90-yen floating loss. The 304-yen stop was the largest closed loss across the five bots. It canceled a trade that had been held for several hours and reached a similarly sized profit. Again, almost the same amount in both directions — not disastrous, but not much of an edge either. This bot uses CatBoost to score entries rather than an LLM to interpret chart context. Its entry model may still have selected valid breakout candidates, but the day shows that entry scoring alone cannot create positive expectancy when the exit distribution is symmetrical. The open third trade also means the day cannot be evaluated only from the negative 1-yen realized result. The next useful comparison is the entry score of the 303-yen winner, the 304-yen loser, and the still-open trade. If the scores were similar, the model may not be separating strong and weak setups yet. MAribbonTrader: A Good Protective Exit Followed by a Difficult Re-entry MAribbonTrader closed one GBPCAD long for a 50-yen gain. The position was exited through a stop above the entry price, which looks like a protective stop that had already locked in profit. About ten minutes later, the bot opened another long. That second position ended the reporting period with a 111-yen floating loss. The realized exit was good, but the immediate return to the same direction is the part I want to inspect. MAribbonTrader sends chart images, moving-average ribbons, higher-timeframe context, support and resistance information, ranges, and channel structure to Qwen. The model is supposed to distinguish a fresh setup from a chart that only resembles the previous one. The open question in the log is whether the second BUY came from genuinely renewed evidence or from a bullish interpretation that never really changed. The first trade shows that EXIT can protect profit. The second entry will test whether WAIT is strong enough after that exit. Closing The five bots did not have a bad day in the usual sense. Three finished with positive realized results, one was almost flat, and only BoundSniper Bot recorded a clear realized loss. Yet the fleet still finished negative because the losing trades were allowed to carry much more weight than most of the winners. A 76.9% win rate did not rescue a 0.30 payoff ratio. For the next review, I care less about adding another entry filter. I want the decision logs around the three losing exits, the BoundSniper position carried into the day, and MAribbonTrader’s quick re-entry. The bots are finding winners. I am not convinced they all know when their idea has expired. ② Substack Note Five MT5 bots closed 13 trades on August 4. The record was 10 wins and 3 losses, but the realized result was still -16 yen. BoundSniper won four times, yet one larger loss erased them. ML_ScoreAnalyst made +303 yen and then lost 304 yen. The fleet’s win rate was 76.9%. Its payoff ratio was only 0.30. The entries were not the main problem today. The exits were. 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
