Podcast
AI FX Bot Lab: Real Trading Experiments
Hosted by Kimi | Japan FX Bot Lab · EN
Can AI really trade forex?
AI FX Bot Lab is a real-time experiment from Japan, where I build and test AI-assisted FX trading bots using MT5, Python, machine learning, and local LLM tools. I share live results, failures, risk lessons, and bot improvements from rule-based, AI-driven, and ML + LLM hybrid systems. Not financial advice.
fxaibotlab.substack.com
fxaibotlab.substack.com
20episodes
Episodes
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Turning Red Days into Training Data: The June 11th AI Bot Trade
Yesterday00:01:45Tap to summarizeTurning Red Days into Training Data: The June 11th AI Bot TestIn today’s episode, we break down the June 11th live test of our four MT5 automated trading bots. The portfolio ended the session with a total realized loss of -1,295 JPY, representing a daily return of about -0.75%. But as we discuss in today’s podcast, in the world of machine learning, a losing day isn’t just a failure—it’s highly valuable, labeled training data.We dive into the completely different behaviors of each bot to uncover what went wrong and what went right:* GateGrid AI (GBPUSD): The only profitable bot of the day, securing a clean +279 JPY. It perfectly executed two short trades with zero losses, proving that its strict, multi-layered entry filtering works effectively to capture controlled profits.* BoundSniper (USDJPY): Finished at -282 JPY. With one win and one loss, it highlighted that while the MT5 execution layer is working, the upstream TradingView signal logic needs a better risk-to-reward balance.* LLMBridgeTrader (EURUSD): Ended at -283 JPY. Despite maintaining a 50% win rate across six trades, the size of the losses simply outweighed the wins. It clearly showed that while the AI can make winning decisions, its overall expectancy and risk-reward structure still require adjustment.* MLScore GF-T4 (GBPJPY): Took the hardest hit of the day at -1,009 JPY from two stopped-out trades. However, this provided the clearest and most valuable learning sample for our machine learning model. These clean losing patterns are exactly the feedback the model needs to analyze what market structures failed and improve its future predictions.The ultimate goal of this project isn’t to perfectly avoid losing days—that is impossible. The real goal is to build automated systems that record, analyze, and learn from them. Join us as we explore how we use a “data-rich” red day to build smarter trading bots!#FX #MT5 #AITrading #MachineLearning #AlgorithmicTrading #SystemTrading This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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Diversification Saved the Day: The Power of a Multi-Bot Portfolio [June 10th Trade]
2d ago00:01:59Tap to summarizeIn today’s episode, we break down the June 10th parallel test of our four MT5 automated trading bots. The total realized profit for the day was a modest +333 JPY, representing a +0.19% return on the portfolio. While it wasn’t a massive windfall, this session perfectly demonstrated a vital concept: the power of diversification.We dive into the performance of each bot to see how portfolio structure mattered more than individual bot performance:* GateGrid AI (GBPUSD): The only losing bot today, finishing at -153 JPY. It opened positions on both the buy and sell sides but was caught in a difficult zone without enough follow-through, signaling a need to review its dual-position exit logic.* BoundSniper (USDJPY): The most stable performer of the day. Acting as a pure execution bot, it closed three clean, winning trades for +172 JPY. It proved that sometimes simple, rule-based execution beats complex AI planning.* LLMBridgeTrader (EURUSD): Delivered the highest absolute profit of +182 JPY. It caught several great trades, but a late -193 JPY loss reduced its earlier gains, highlighting the need for stronger “daily profit protection” rules once a target is reached.* GBPJPY Bot: Added a +132 JPY profit to the portfolio. While it was only a single closing transaction, it perfectly executed its role by helping offset the losses from GateGrid AI.The biggest lesson from today’s session? One bot lost, but the portfolio still won. By running completely different logics—rule-based execution, AI-driven trade planning, and machine-learning grid filters—across multiple currency pairs, we absorbed individual weaknesses and maintained a positive balance.Join us as we discuss why a controlled, diversified green day is the ultimate goal for a live automated trading system!#FX #MT5 #AITrading #AlgorithmicTrading #Diversification #RiskManagement This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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Live Data Beats Backtests: Measuring Real EV After a Red Day [June 9th ]
3d ago00:01:39Tap to summarizeIn today’s episode, we break down the June 9th parallel test of our four MT5 automated trading bots. The portfolio ended the session with a combined realized loss of -1,432 JPY. At first glance, it looks like a simple losing day, but the detailed results revealed exactly what we need to do next to improve our systems.We dive into the distinct performances of each bot to uncover why live data is far more valuable than historical optimization:* GateGrid AI (GBPUSD): The main source of today’s deficit, suffering a -1,602 JPY loss on a single trade. This taught us a critical lesson: actual execution quality—such as spreads, volatility, and LLM judgment delays—can drastically alter real-world outcomes. Moving forward, we are running this bot at 0.01 lot to measure its true Expected Value (EV) in live conditions for the next few weeks.* BoundSniper (USDJPY): Finished at -278 JPY. Despite maintaining a good win rate with two winners and one loser, the single losing trade wiped out the gains. It serves as a textbook example of why win rate alone isn’t enough, highlighting the urgent need to rebalance its payoff ratio and exit rules.* LLMBridgeTrader (EURUSD): Ended almost perfectly flat at -6 JPY when factoring in unrealized profits. It effectively avoided large losses, suggesting that its AI-driven position management for holding and closing trades is functioning as a solid defensive mechanism.* MLScore GF-T4 GB (GBPJPY): The undisputed MVP of the day, securing a solid +496 JPY realized profit and reaching +615 JPY with open positions included. It successfully capitalized on the high volatility of GBPJPY, effectively carrying the weight of the entire portfolio today.The ultimate takeaway from today’s session is that while past optimization shows what worked historically, live trading shows what is working right now. Today’s loss was not just a loss—it was highly valuable data.Join us as we discuss our strategic pivot toward live EV measurement and how we use red days to build smarter bots!#FX #MT5 #AITrading #AlgorithmicTrading #MachineLearning #SystemTrading This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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The Day the Gate Worked: Why Doing Nothing is a Winning Strategy [June 8th Trade]
4d ago00:01:40Tap to summarizeIn today’s episode, we break down the June 8th parallel test of our four MT5 automated trading bots. The portfolio ended the day with a solid realized profit of +1,189 JPY. But the real success story wasn’t just about the money we made—it was about the money we didn’t lose, thanks to our newly implemented safety layers.We dive into the performance of each bot to see how they executed their distinct roles perfectly:* GateGrid AI (GBPUSD): The top earner of the day, securing +643 JPY. It executed two highly efficient, short-term trades and closed them quickly in profit, leaving no open exposure.* BoundSniper (USDJPY): Finished at +294 JPY. It perfectly demonstrated the resilience of rule-based execution, absorbing two tiny initial losses (-4 JPY each) before catching three solid profitable exits.* MLScore GF-T4 (GBPJPY): Secured a +252 JPY realized profit on a short trade while holding only a microscopic -7 JPY open drawdown.* LLMBridgeTrader (EURUSD): The most important bot of the day—because it didn’t trade at all. While the LLM generated three “BUY” signals, our newly built Machine Learning (ML) safety gate blocked every single one of them due to candidate and direction mismatches.The ultimate lesson from today’s session is simple: in automated trading, a blocked trade can be just as valuable as a winning trade. Our portfolio won today not by being aggressive, but by letting each bot do its job and allowing the ML gate to say “no” when confirmation was missing.Join us as we discuss how giving our AI bots the power to hit the brakes is taking our system stability to the next level!#FX #MT5 #AITrading #MachineLearning #AlgorithmicTrading #RiskManagement This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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Why AI Trading Bots Need Brakes: The June 1–5 Weekly Review
6d ago00:01:43Tap to summarizeIn today’s episode, we review the weekly performance of our four MT5 automated trading bots from June 1 to June 5. The portfolio ended the week with a combined realized loss of -3,307 JPY. While it wasn’t a profitable week financially, it was arguably our most valuable week for system development.We break down each bot’s behavior to understand why giving AI complete autonomy is a risky game, and why the ultimate feature of a trading bot is a reliable “brake”:* GateGrid AI: The clear winner of the week, finishing at +707 JPY. Its multi-layered filtering system proved that a bot’s true power lies not only in finding entries, but in its ability to say “do nothing” and avoid bad trades.* BoundSniper: Finished at -868 JPY. As a pure execution bot, its losses confirmed that the MT5 execution layer is doing its job, but the upstream TradingView signal logic needs serious refinement and better filtering.* LLMBridgeTrader: Took a hard hit at -1,399 JPY. It clearly demonstrated that giving an AI full autonomy over position management (OPEN, HOLD, CLOSE, REVERSE) is dangerous without a strict “ML gate” to reject weak trading plans before they reach the market.* MLScore GF-T4: Ended at -1,747 JPY. It exposed a critical structural flaw: re-entering the market under the same unfavorable conditions immediately after a stop-loss. It highlighted the urgent need for re-entry logic, cooldown rules, and daily risk limits.The biggest takeaway from this week? AI can create brilliant trading plans, but the system still needs the final authority to hit the brakes. Join us as we discuss how we are using this week’s “valuable losses” as direct training and debugging data to build smarter, safer trading systems!#FX #MT5 #AITrading #AlgorithmicTrading #MachineLearning #SystemTrading This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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Turning Losses Into Training Data: The Evolution of AI Bots [June 5th Trade]
1w ago00:01:57Tap to summarizeIn today’s episode, we break down the June 5th parallel test of our four MT5 automated trading bots. The portfolio finished the session with an overall loss of -1,068 JPY. At first glance, it looks like a tough day. But for an AI-driven project, a red day with a clear diagnosis is far more valuable than a lucky green day.We dive into the distinct behaviors of each system and the major structural upgrades they inspired:* GateGrid AI (GBPUSD): The only profitable bot today, securing +199 JPY. Its conservative, multi-layered decision structure (combining CatBoost and Ollama) proved its worth by taking small profits and effectively staying out of trouble.* BoundSniper (USDJPY): Finished with a minor -100 JPY loss. As a pure execution bridge, its loss simply tells us that the upstream TradingView signal logic needs better exit controls, rather than indicating an execution failure.* LLMBridgeTrader (EURUSD): Took the hardest hit at -675 JPY. The AI’s immense freedom became a liability. In response, we discuss our massive upgrade: implementing a Machine Learning (ML) Gate powered by CatBoost to strictly filter the LLM’s “OPEN” and “REVERSE” trade plans before they reach MT5.* MLScore GF-T4 (GBPJPY): Ended at -492 JPY, but received the biggest structural overhaul. We’ve upgraded this bot to differentiate between “Breakout” (trend-following) and “Range” (mean-reversion) setups. With new historical backfill data, strategy-specific TP/SL settings, and strict daily safety limits, it’s evolving from a bot that simply guesses into a bot that learns from its logs.The ultimate lesson from today’s session is that our systems are shifting toward a new phase of development. Join us as we discuss how we are literally turning today’s financial losses into tomorrow’s training data!#FX #MT5 #AITrading #MachineLearning #AlgorithmicTrading #SystemTrading This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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Small Wins, Heavy Homework: Why Autonomous AI Needs Brakes [June 4th Trade]
1w ago00:01:47Tap to summarizeIn today’s episode, we break down the June 4th parallel test of our four MT5 automated trading bots. The portfolio ended the session with a total realized loss of -550 JPY, or -916 JPY when factoring in open positions. While the result wasn’t dramatic, the day provided a crystal-clear split between our systems: the strictly filtered bots won, while the autonomous AI bots struggled.We dive into the completely different behaviors of each bot to uncover why AI needs strict boundaries:* GateGrid AI (GBPUSD): Delivered the cleanest performance of the day. It secured two wins for +197 JPY and ended the session completely flat with no open exposure. It perfectly executed what a grid-style bot should do: get in, get out, and avoid unnecessary risks.* BoundSniper Bot (USDJPY): Finished in the green at +38 JPY. Acting as a simple executor for TradingView signals, it took an early hit but successfully recovered through a 75% win rate across four trades.* LLMBridgeTrader (EURUSD): Ended with a -281 JPY realized loss. As our most autonomous bot—capable of deciding whether to open, hold, close, or reverse—its flexibility became its downfall today. The AI’s decisions failed to produce a stable expectancy, proving that it desperately needs stricter filtering around confidence and stop-loss distances.* MLScore GF-T4 GB (GBPJPY): Took the heaviest hit, suffering a combined realized and floating loss of -828 JPY. The biggest issue wasn’t just the stop-loss; it was the fact that the bot immediately re-entered the market under the same difficult conditions. It highlighted the urgent need for a “cooldown rule” to prevent immediate re-entries after large losses.The ultimate takeaway from today’s session is simple but profound: Automation should not only decide when to enter. It must also know when not to continue. Giving AI freedom is powerful, but without structured risk filters and “brakes,” that freedom can quickly destroy your edge.Join us as we discuss the “heavy homework” ahead and how we plan to build these crucial safety nets for our autonomous bots!#FX #MT5 #AITrading #AlgorithmicTrading #RiskManagement #MachineLearning This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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Learning Starts With Losses: Turning a Red Day into Training Data [June 3rd Test]
1w ago00:01:59Tap to summarizeIn today’s episode, we break down the June 3rd parallel test of our four MT5 automated trading bots. The portfolio ended the session with a combined result of -999 JPY. At first glance, it looks like a simple losing day, but for our AI and machine-learning-based systems, these results provide invaluable training material.We dive into the completely different profiles of each bot to see what we learned:* GateGrid AI (GBPUSD): The cleanest and strongest performer of the day, securing +133 JPY. It perfectly demonstrated its selective design philosophy by taking exactly one trade, winning it, and leaving no open exposure. It proved that a bot’s real value often lies in deciding when not to enter.* BoundSniper (USDJPY): Finished at -584 JPY. Despite having a high win rate with 5 winning exits and 2 losing exits, the losses were simply too large. It serves as a stark reminder that a good win rate means nothing if your average loss isn’t strictly controlled.* LLMBridgeTrader (EURUSD): Ended slightly negative at -147 JPY. Because this bot relies on high AI autonomy (deciding to OPEN, HOLD, CLOSE, or REVERSE), today’s results showed that it still needs stricter guardrails and better confidence filtering around its stop-loss placement.* MLScore (GBPJPY): Closed at -401 JPY. It had two winning exits, but one oversized loss dominated the day. However, because MLScore accumulates learning data, this specific loss is crucial feedback that will help the bot avoid similar bad setups in the future.The biggest takeaway from today’s session is that for bots like GateGrid and MLScore, every trade is feedback. A losing day might be painful, but if the logs are used to refine the models, today’s losses will literally become tomorrow’s filters.Join us as we discuss how we turn a red day into smarter trading logic!#FX #MT5 #AITrading #AlgorithmicTrading #MachineLearning #SystemTrading This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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Valuable Losses: What a Red Day Teaches Us About AI Trading [June 2nd Trade]
1w ago00:05:11Tap to summarizeIn today’s episode, we break down the June 2nd parallel test of our four MT5 automated trading bots. It was a tough session across the board, with the total realized loss hitting -1,429 JPY, and the overall equity impact reaching -1,567 JPY when including floating losses. However, from a system-evaluation perspective, it was an incredibly useful day. Today, the main question wasn’t about who won, but rather: “Which bot lost in the most controlled way?”.We dive deep into the completely different loss profiles of each bot to understand their structural weaknesses and strengths:* LLMBridgeTrader (EURUSD): The winner among the losing bots. It ended with the smallest realized loss of -185 JPY. It successfully demonstrated that its risk management can contain the damage when AI judgments or market conditions turn unfavorable.* GateGrid AI (GBPUSD): Finished at -206 JPY. While it showed resilience by securing small wins (+81 JPY and +19 JPY) earlier in the day, a single larger loss of -306 JPY pushed it into negative territory, highlighting the importance of preventing one bad trade from overpowering multiple wins.* BoundSniper Bot (USDJPY): Ended at -438 JPY. Since its job is purely to execute TradingView signals, today’s drawdown was not an execution failure, but a signal-quality issue. It serves as a reminder that upstream logic needs robust filters for choppy or reversing sessions.* MLScore GF-T4 GB (GBPJPY): The main source of today’s drawdown, closing with a -600 JPY realized loss and carrying a -138 JPY floating loss for a total impact of -738 JPY. We discuss why its risk-reward structure requires an urgent review, especially since the reward target is relatively tight compared to the stop range.Join us as we discuss why we aren’t stopping the test, but instead tightening our review loop. Because in automated trading, controlled losses are often far more valuable for improving systems than easy profits.#FX #MT5 #AITrading #AlgorithmicTrading #RiskManagement #TradingStrategy This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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The Power of Diversification: Why You Need Multiple Bots [June 1st Test]
2w ago00:04:00Tap to summarizeIn today’s episode, we break down the June 1st parallel test of our MT5 automated trading bots. The portfolio ended the day with a solid realized profit of +739 JPY (or +757 JPY including floating profit), winning an impressive 12 out of 13 closed trades. But the real story isn’t just about an easy winning day—it’s about how portfolio diversification protected our profits.We dive into the performance of each bot to see how their distinct architectures worked together:* GateGrid AI (GBPUSD): The strongest performer of the day. It secured +384 JPY across 4 flawless wins. Its complex design—combining model-based filtering, local AI judgment, and volatility checks—proved that its greatest strength is effectively avoiding low-quality entries.* MLScore GF-T4 (GBPJPY): Delivered the cleanest execution. It took one single trade and successfully hit its take-profit for +250 JPY, leaving no open positions or floating risks behind.* BoundSniper (USDJPY): Quiet and consistent. Acting as a disciplined rule-based executor, it closed 5 winning trades for +216 JPY. It proved once again that this bot’s true value lies in its strict discipline rather than complex intelligence.* LLMBridgeTrader (EURUSD): The only bot to struggle, ending with a -111 JPY realized loss. Despite winning two out of three trades, a single large stop-loss outweighed its combined gains, highlighting the ongoing challenge of risk asymmetry when an AI acts as a trading planner.The biggest lesson from today’s session is clear: a single AI bot can be fragile, but a diversified group of bots is resilient. Because we ran rule-based execution, AI planning, machine-learning scoring, and grid-style filtering simultaneously, the overall portfolio easily absorbed LLMBridgeTrader’s weak performance and remained comfortably positive.Join us as we discuss why a multi-bot structure makes individual weaknesses easier to see and manage, and why an imperfect day can still be a highly useful win.#FX #MT5 #AITrading #AlgorithmicTrading #Diversification #RiskManagement This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit fxaibotlab.substack.com
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