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Honest writeups from building a multi-agent LLM trading arena — negative results included, on purpose.

I found three bugs in my own trading ledger. One of them could open a position and never close it.

2026-07-10 · postmortem · engineering · 8 min read

Paper copy-trading and live exchange mirroring shared one position slot, so a live position could be opened and never closed. Twenty-seven orders failed in a row and nobody was told. Fixing the first bug would have double-counted every subscriber's return. And the scoreboard promising to show "when we lose to buy-and-hold" didn't show buy-and-hold at all.

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My AI strategy gave back half its gains in a chop. So I rewrote its rules — in plain English.

2026-07-08 · mechanical rules · live experiment · 7 min read

A committee peaked +17% then gave back to +8% in a range-bound market. The fix wasn't code — it was a plain-English rule that trades fewer, higher-quality setups (a support/resistance pyramid). Plus 12 control arenas testing which entry rules actually have an edge.

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My AI trading committee is up 15.7% in 8 days. Here's why I still don't trust it.

2026-07-02 · live experiment · 6 min read

The arena beat its own judgment-free baseline by 33 points — and the same dashboard shows a 55% win rate, one dominant trade, and an "immature" label. The honest teardown of a green curve, plus the first time the "smart" layer leads the live A/B.

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I built self-evolving AI agent committees to trade crypto, then spent a week trying to break them. The interesting part is the one thing none of it tested.

2026-06-27 · negative results · methodology · 10 min read

Breeding overfits, equal weight beats "smart" weighting, IC and PnL can disagree in sign, and of ~94 factors exactly one survives a strict coin-and-time holdout (16 → 13 → 0). But every test ran on the bare scaffolding — the edge layer was never on the table.

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Research and paper-trading. Not investment advice.