86eadae42f
Fix 1: wire record_cycle in factory-cycle --execute so the funnel table is populated. LoopResult gains deployed_before/deployed_after/forward_count; cli.py calls fs.record_cycle(proposed=forward_count, ...) and record_allocation after the loop. Fix 2+4: replace equal-weight book proxy with HRP-weighted blend of deployed sleeves; rewrite admission as sequential — each candidate is re-gated against the running mean of already-admitted-this-cycle candidates, so two candidates uncorrelated to the book but correlated to each other cannot both be admitted in one cycle. Fix 3: HRP inclusion now requires >= min_forward_days (was >= 2). Folded into the final rets comprehension in allocate_and_promote. Fix 5: StrategyStoreLoopAdapter.set_status now asserts status == "DEPLOYED" so a future demotion path cannot silently promote via the loop adapter. Fix 6: persisted kill-switch — FactoryFlagRow ORM model added to cockpit_models.py; FactoryStore.set_kill(on)/is_killed() read/write a key="kill" row; factory-cycle folds db kill with env kill; factory-flatten renamed to factory-halt; factory-resume and factory-status commands added. Fix 7: sleeve returns fetched once per sleeve in the final HRP comprehension via a shared dict, avoiding double store I/O. Fix 8: three new integration tests covering min_forward_days filter, empty-book founding-admit-then-gate, and mutual-correlation blocking within a cycle. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
fxhnt
Agentic strategy-research & multi-strategy execution platform. It systematically discovers, backtests and out-of-sample-validates trading strategies across many markets, keeps only what survives a rigorous statistical gauntlet, and runs the survivors live (multiple strategies at once).
The bet is not a secret edge — it's breadth + discipline + automation. The hard part (and the moat) is refusing to fool yourself at scale; the validation gauntlet is the core, built and proven first.
Architecture (hexagonal / ports-and-adapters)
src/fxhnt/
domain/ pure logic: gauntlet (Deflated Sharpe), strategies, backtest, models ← no I/O
ports/ contracts: DataProvider, repositories (the only seams)
adapters/ infra: yahoo data, SQLAlchemy (Postgres/SQLite) + DuckDB stores
application/ use-case services (ResearchService) — orchestrate via ports
cli.py composition root (wires concrete adapters)
See docs/architecture/0001-architecture.md.
Quickstart
pip install -e ".[dev]"
pytest # unit (gauntlet falsification) + integration (vertical slice)
fxhnt strategies # list strategy kinds
fxhnt research SPY --kind trend --window 200 # data → backtest → gauntlet → persist
fxhnt list --passed-only # the survivor library
Config via FXHNT_* env vars (e.g. FXHNT_OPERATIONAL_DSN=postgresql+psycopg://...). Defaults to
SQLite + a local DuckDB file under ~/.fxhnt/.
Status
Vertical slice working: data (Yahoo) → strategy (trend) → backtest (net of costs) → IS/OOS gauntlet → persistence (operational + analytical). Next: the multi-strategy execution layer, more strategy templates + data adapters, and the agentic discovery search on top of the proven gauntlet.