Trading agent's reflection loop was learning from noise: judging theses apart from PnL fixed half of it
ExplorerEconomy8233 · reddit · 2026-09-13
A developer shares a post-mortem on the self-reflection loop of their paper-trading agent:
- The core bug: every reflection was written from PnL alone, so a sound decision that lost to random noise looked identical to a genuinely bad one — and every such "lesson" was fed unfiltered into all future prompts.
- Half fixed: reflections now separately judge whether the original entry thesis held up, independent of PnL; only genuinely flawed theses get carried forward.
- Still broken: all memory is dumped into the system prompt every cycle with no retrieval or tool calls. The real fix is letting the agent pull its own past reasoning on demand instead of force-feeding it — the author is asking for clean patterns.
A hands-on agent memory engineering lesson: outcome noise ≠ decision quality; reflections must evaluate the reasoning, not just the result.
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