u/hikewithcaramel

Been running a multi-strategy trading system on IG Markets for about 6 weeks — sharing the real numbers, not a highlight reel.

The fleet:

V3 — regime-classifying bot (ADX/Hurst), switches between trend-following and mean-reversion

AGENT — pure deterministic rules engine, no AI in the trading decisions

SCALP+TREND — two strategies sharing infrastructure

HAIKU_GB — the interesting one: Claude Haiku makes genuinely discretionary LONG/SHORT/FLAT calls on Gold and Brent, full reasoning, Python only enforces sizing/risk. No overlay telling it what to do.

What I actually found, not just what worked:

Only ~53% of my sandbox data turned out to be genuinely clean once I dug in — a mid-price logging bug had been quietly turning a real £220 loss into a fake £237 "profit" for eight weeks before I caught it

A rules overlay was silently vetoing ~half of Haiku's directional calls, which meant I was accidentally measuring "Haiku's judgment filtered through a rulebook" instead of Haiku's actual judgment

Found and fixed a fleet-crash bug that had been intermittently taking down the whole system for weeks — root cause was a Go binary (GitHub CLI) segfaulting under Android's sandboxed ptrace emulation

The most interesting pattern so far: my best entry-quality bot (61% win rate) is my worst performer overall, and my worst entry-quality bots (26-28% win rate) are flat-to-positive — exit management seems to matter more than entries, testing that hypothesis now with counterfactual logging

Current phase: spent the last month making the existing system honest before adding anything new — real broker reconciliation, fixed data integrity bugs, no new strategies. Targeting a clean data run through the end of the year, live money decision in January based on what the data actually shows, not vibes.

Happy to go deeper on any piece of this — the Haiku experiment, the bug-hunting, the architecture, whatever's interesting to people.

Been running a multi-strategy trading system on IG Markets for about 6 weeks — sharing the real numbers, not a highlight reel.

The fleet:

V3 — regime-classifying bot (ADX/Hurst), switches between trend-following and mean-reversion

AGENT — pure deterministic rules engine, no AI in the trading decisions

SCALP+TREND — two strategies sharing infrastructure

HAIKU_GB — the interesting one: Claude Haiku makes genuinely discretionary LONG/SHORT/FLAT calls on Gold and Brent, full reasoning, Python only enforces sizing/risk. No overlay telling it what to do.

What I actually found, not just what worked:

Only ~53% of my sandbox data turned out to be genuinely clean once I dug in — a mid-price logging bug had been quietly turning a real £220 loss into a fake £237 "profit" for eight weeks before I caught it

A rules overlay was silently vetoing ~half of Haiku's directional calls, which meant I was accidentally measuring "Haiku's judgment filtered through a rulebook" instead of Haiku's actual judgment

Found and fixed a fleet-crash bug that had been intermittently taking down the whole system for weeks — root cause was a Go binary (GitHub CLI) segfaulting under Android's sandboxed ptrace emulation

The most interesting pattern so far: my best entry-quality bot (61% win rate) is my worst performer overall, and my worst entry-quality bots (26-28% win rate) are flat-to-positive — exit management seems to matter more than entries, testing that hypothesis now with counterfactual logging

Current phase: spent the last month making the existing system honest before adding anything new — real broker reconciliation, fixed data integrity bugs, no new strategies. Targeting a clean data run through the end of the year, live money decision in January based on what the data actually shows, not vibes.

Happy to go deeper on any piece of this — the Haiku experiment, the bug-hunting, the architecture, whatever's interesting to people.