From July 6 to July 10, 2026, I continued running four MT5 automated trading bots in parallel.
The four systems were:
* GateGrid AI
* BoundSniper Bot
* LLMBridgeTrader
* MLScore GF-T4 GB
The combined realized result for the week was -3,060 yen.
On paper, it was simply a losing week.
But when I reviewed the logs, the total loss itself was not what stood out most.
The bots did not struggle to win trades.
In fact, there were several days when the number of winning trades looked fairly strong.
And yet, the account still lost money.
That gap appeared again and again throughout the week.
The real issue was not how often the bots won.
It was how much they gave back when they lost.
July 6: A difficult start to the week
The result for July 6 was -1,976 yen.
The combined record was 1 win and 10 losses.
It was the worst day of the week.
LLMBridgeTrader recorded the only winning trade, while GateGrid AI lost 733 yen and MLScore lost 621 yen.
The directional calls were not good, but the larger problem was how long some losing positions remained open.
Instead of exiting when the trade idea began to fail, the bots often waited until the loss had already grown.
The weakness in the exit logic was visible from the very first day.
July 7: Thirteen wins, but only 276 yen in profit
The result for July 7 was +276 yen.
The combined record was 13 wins and 3 losses.
At first glance, that looks like an excellent day.
However, the payoff ratio was only 0.31.
There were very few losing trades, but each loss was much larger than each win.
As a result, 13 winning trades produced only a small net profit.
GateGrid AI is the clearest example.
It won 8 of its 11 trades.
Even so, it finished the day at -230 yen.
Eight small wins were erased by three larger losses.
Looking only at the win count, I might have concluded that the bot was performing well.
In reality, the structure was fragile.
As long as the bot kept collecting small gains, the weakness remained hidden.
Once a deeper loss appeared, most of the previous profits disappeared.
July 8: More wins than losses, but still negative
The result for July 8 was -620 yen.
The combined record was 9 wins and 7 losses.
Again, the number of winning trades was higher than the number of losing trades.
But the day still ended in the red.
The biggest factor was a single -418 yen loss from BoundSniper.
Small profits from the other trades could not absorb one large loss.
This was another reminder that win count alone says very little about the actual health of a trading system.
July 9: One loss erased everything
The result for July 9 was -481 yen.
There were only three trades in total, with 2 wins and 1 loss.
It was a quiet day.
However, the single losing trade came from GateGrid AI and cost 542 yen.
That one loss erased all the small profits produced by the other bots.
Two out of three trades were correct.
The day still ended negative.
This shows why improving directional accuracy alone is not enough.
The more important question is how cheaply the system can exit when it is wrong.
July 10: MLScore performed well, but the portfolio still fell short
The result for July 10 was -259 yen.
The combined record was 12 wins and 8 losses.
MLScore GF-T4 GB closed two short positions at take profit and earned +483 yen.
For this bot, it was one of the cleanest results of the week.
However, the gains were not enough to cover the losses from the other systems.
MLScore performed well on its own, but when four bots are running together, individual results are not the only thing that matters.
I also need to watch how losses overlap across the portfolio.
GateGrid AI: Winning often, but failing to keep the profit
GateGrid AI showed the clearest weakness this week.
On July 7, it won 8 of 11 trades.
It still lost 230 yen.
Then, on July 9, a single trade produced a 542 yen loss.
The pattern is familiar.
The bot collects many small gains, then gives them back in one deeper loss.
The entry filters are not completely broken.
In fact, many trades still close in profit.
The problem begins after entry.
When the market changes and the original setup loses validity, the bot often remains in the position for too long.
Instead of making the entry logic even more complicated, the priority should be improving the conditions for early exit.
BoundSniper Bot: One large loss can outweigh several clean trades
BoundSniper Bot is a rule-based system that executes TradingView signals in MT5.
On July 7, it produced a clean +200 yen result.
On July 8, however, a single -418 yen stop loss pushed the day into negative territory, even though the bot won more trades than it lost.
The system is consistent because it follows clear rules.
But that strength can also become a weakness.
When the market changes after the signal appears, the bot may continue holding the original idea without reassessing whether the setup still makes sense.
The next step is not only to evaluate whether the initial signal was correct, but also whether the reasoning behind it remains valid after entry.
LLMBridgeTrader: Small gains, but four consecutive profitable days
The most interesting bot this week was LLMBridgeTrader.
It lost 232 yen on July 6.
After that, the daily results were:
* July 7: +76 yen
* July 8: +128 yen
* July 9: +57 yen
* July 10: +16 yen
The gains were not large.
Still, the bot remained profitable for four consecutive days.
Compared with the other systems, LLMBridgeTrader was better at avoiding prolonged exposure to bad positions.
It did not make money by holding one huge winner.
It made money by cutting weak ideas before they became expensive.
The result was modest, but its ability to preserve capital was the most stable among the four bots.
MLScore GF-T4 GB: A promising result on the final day
MLScore GF-T4 GB began the week with several losses, including the closing of older positions.
However, on July 10, two short positions reached their take-profit targets and produced +483 yen.
That gave a glimpse of the potential behind the machine-learning score.
The model may be useful for identifying trade direction.
Still, one good day is not enough to draw a conclusion.
I need more trades to determine whether high-score setups consistently produce better results.
I also need to review whether the current stop-loss and take-profit settings match the price behavior after entry.
The real problem was not the entry
When building an automated trading bot, it is easy to focus on entry accuracy.
Add another filter.
Use more training data.
Change the model.
Make the conditions more precise.
I have spent plenty of time improving entries.
But this week’s results suggest that the main weakness is no longer the entry alone.
On July 7, the bots recorded 13 wins and 3 losses.
On July 8, they recorded 9 wins and 7 losses.
On July 9, they recorded 2 wins and 1 loss.
None of those days looked terrible from a win-count perspective.
Yet the final weekly result was -3,060 yen.
That means the bots are not completely failing to predict direction.
The problem is structural.
The average gain is too small.
The average loss is too large.
Until that relationship changes, increasing the number of winning trades will not create stable profits.
Knowing when an idea has expired
Every trade begins with some kind of reasoning.
A trend may be developing.
A breakout may have occurred.
The AI may have produced a buy signal.
The machine-learning score may have been high.
But the original reason for entering may no longer be valid ten or thirty minutes later.
If the market changes and the bot continues holding based only on the original signal, the position becomes attached to outdated information.
The goal should not be to defend the original prediction.
The goal should be to exit cheaply when the original assumption is no longer supported.
A fixed stop loss may not be enough.
Time in the trade, fading momentum, acceleration in the opposite direction, conflict with a higher timeframe, and changes in volatility may all be useful exit signals.
The system needs a way to reassess the trade after entry.
Next week: Improving the exit rules
I do not plan to explain this week’s losses as a simple failure of entry accuracy.
The next improvements will focus on:
* Exiting before unrealized losses grow too large
* Reassessing whether the original setup is still valid after entry
* Handling positions that fail to move within a certain amount of time
* Separating situations where profits should be extended from situations where they should be secured early
* Adjusting the acceptable loss size for each bot
Losing trades are painful, but they are often the most honest source of information about a system.
This week was not only about failing to win.
It was about failing to keep the profits that had already been earned.
Before trying to increase the number of entries, I need to reduce the damage caused by each losing trade.
That will be the main focus of next week’s development and testing.
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