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.
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.
The bug wasn't in the code, it was in the math
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:
- Correlation: SPY, QQQ, TSLA, and NVDA move together. A "diversified" watchlist of four tech-heavy symbols is actually just one big trade split four ways.
- Risk Imbalance: A $20 position in SPY (low volatility) has a completely different risk profile than a $20 position in NVDA (high volatility). Treating them as equal "slots" was mathematically lazy.
- The T+1 Trap: In a real-money environment, selling a stock today doesn't give you the cash back instantly. By maxing out the budget every day, I was ensuring the bot would be unable to trade the following day while waiting for settlement.
The Small-Budget Reality Check
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:
- ETFs (SPY/QQQ): a smaller per-trade slice
- Individual stocks (TSLA/NVDA): an even smaller per-trade slice, since they carry more volatility per dollar
- A total cap with a real buffer set aside for fees and settlement gaps
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.
Automating the "Check," not the "Act"
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.
Lessons from the logs
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.
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Infrastructure matters more than "Alpha": Most of my time is spent fixing
NoneTypeerrors in reports and working out currency-conversion buffers, not tuning RSI parameters. - The Budget is a Constraint, not a Suggestion: If you ignore your budget in your logic, the exchange will enforce it for you by killing your best trades.
- MCP is a distraction: I looked into connecting Claude to financial data via MCP (Model Context Protocol), but realized it was useless for a headless bot. The bot needs a REST API in Python, not a chat tool. We skipped it.
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
