My Trading Bot Skipped 28 Trades Because I Ignored Volatility on a Tiny Real-Money Budget

A developer built a custom monitoring script, daily_check.py, to audit their AI trading bots and found the equities bot skipped 28 trades in a single session due to budget constraints, filling only 8. The bot attempted to enter four highly correlated symbols (SPY, QQQ, TSLA, NVDA) at once, exhausting its $40 total notional budget before reaching the high-volatility movers. The developer rewrote the position-sizing logic to use volatility-proportional sizing and added a manual security gate after deciding against letting the AI agent auto-deploy code fixes.

I built a custom monitoring script, daily check.py , to watch my bots while I slept. I wanted to see if they were actually following the rules I set or if they were just hallucinating success in the logs. The script didn't find a catastrophic market crash. Instead, it found that my equities bot was paralyzed. In a single session, it had skipped 28 trades due to "budget constraints" while only successfully filling 8. It was sitting on its hands for 77% of its opportunities. Full code for the monitoring setup and the bots is here: github.com/tatsuyawwp/ai-trading-bot-experiment https://github.com/tatsuyawwp/ai-trading-bot-experiment . This post is about why a "perfectly working" bot can fail because of a tiny real-money budget and a lack of respect for volatility. On paper, the logic was fine. The bot monitored four symbols: SPY, QQQ, TSLA, and NVDA. I had set a PER TRADE NOTIONAL of $20 and a MAX TOTAL NOTIONAL of $40. In my head, this meant the bot would always have two positions open. The daily check.py report showed the reality: the bot was trying to enter all four symbols at once because they are highly correlated. When SPY and QQQ signaled a buy, the $40 budget was gone. TSLA and NVDA - the high-volatility movers where the actual "alpha" usually lives - were getting skipped 28 times over because they were third or fourth in the execution queue. I asked Gemini to review the design. It flagged three structural flaws I'd ignored: The most embarrassing part of the audit came when I checked my actual liquidity. I had been building the bots assuming a $100 test budget on paper. When I sat down to actually think about funding a real account for this specific experiment, I realized I only wanted to commit a much smaller amount to it than that - nowhere near what the paper-trading logic had assumed. That gap between the number the code was designed around and the number I actually wanted to risk was the real bug. Every position-sizing assumption downstream of it was wrong by the same proportion. I had to rewrite the entire sizing logic to fit into a much smaller shoebox. We moved from flat per-symbol amounts to volatility-proportional sizing: This wasn't about being "clever" with the AI; it was about the AI forcing me to be honest about the numbers before I lost real money to a settlement error. While fixing the budget, the daily check.py script caught another real bug. The crypto momentum bot's liquidation logic wasn't passing the unrealized plpc unrealized profit/loss percentage value to the logger. This caused report.py to attempt a None + float calculation, crashing the entire reporting suite. It was a simple fix, but it reinforced a hard rule I've adopted: The "Check" phase is automated, but the "Act" phase is not. I had briefly considered letting the AI agent automatically deploy code fixes when it found bugs like the logger crash. I decided against it. An earlier session taught me that unattended code edits while a 5-minute scheduler is running is a recipe for broken state. Now, I have a "security gate" in security gate.py and a manual review step. If I want to change the code, I manually disable the Windows Task Scheduler, run the fix, verify it with python-reviewer , and then re-enable the task. It's slower, but it's the only way to ensure a small real-money account doesn't become a $0 account because of a typo in a docstring. The AI agent is excellent at finding these "silent" failures - the skipped trades, the correlated risks, and the math errors that humans ignore because the "Live" light is green. NoneType errors in reports and working out currency-conversion buffers, not tuning RSI parameters. The bots are back online with the smaller, real-money-sized constraints and the volatility-adjusted sizing. They are currently watching the markets, and more importantly, the daily check.py script is watching them. Full logs, the budget recalculations, and the updated run.py logic are available here: github.com/tatsuyawwp/ai-trading-bot-experiment https://github.com/tatsuyawwp/ai-trading-bot-experiment