c39d78fd7b
Reusable infra from the diversifier research (the strategies themselves found no deployable edge on our 22-market panel, but the construction is validated): - dbn_local.load_curve: front + 2nd-nearby CONTINUOUS series + annualized carry (log(F1/F2)/gap_years) from parent-symbology outright expiries. Unlocks carry + basis-momentum without re-reading raw .dbn. - cta_trend.py / cta_runner.py: literature-grounded managed-futures trend (vol-normalized risk-adj momentum -> tanh(x/0.89) response -> 1/3/12mo blend -> inverse-vol -> equal-risk-per-sector budget -> portfolio vol-target -> long/short). Construction validated (reproduces SG Trend +29% in 2022); premium absent for us (Sharpe ~0 over 2010-26 on 22 markets, ends mid-historic-drawdown). - book_allocator: OPTIONAL edge-quality gate (min_sleeve_sharpe, default None=OFF) to bench dead/decaying sleeves so they can't be levered up by the vol-target. Off by default because a naive Sharpe floor also benches legitimate anticorrelated hedges; a correct quality/decay layer is future work. Suite green, mypy clean. 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.