2da32e8305
Phase 2 of the warehouse->TimescaleDB consolidation. Streams the DuckDB `features` table per-symbol through a server-side cursor in bounded `chunk_rows` batches (fetchmany), bulk-upserting each via the store's ON CONFLICT write path so peak RAM stays bounded for the ~5-10M-row warehouse (a prior whole-table load OOM'd a 2Gi node). Idempotent (re-run overwrites, no dupes). Verifies row counts + per-feature (count, round(sum,6)) checksums. CLI `fxhnt warehouse-migrate-pg`; exits non-zero on verification mismatch. Zero live impact — only COPIES into Timescale; FXHNT_FEATURE_STORE stays duckdb. Co-Authored-By: Claude Opus 4.8 <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.