Jeroen Grusewski
3a26b87f15
An exhaustive sweep of every caller in the positioning-cap dependency chain found two more live paths still uncapped: the Bybit paper-book BACKFILL (bybit_paper_backfill.py, sizes/persists live paper positions across history) and the honest-reference allocation inputs (allocation_honest_inputs.py, feeds record_allocation, the LIVE strategy allocation engine's ex-ante capital sizing). Threads positioning_coin_gross_cap (opt-in, default None) through both, matching the existing pattern: - bybit_paper_backfill.py: _extract_per_sleeve + backfill_bybit_paper_book now accept and forward the cap; cli.py's bybit-paper-backfill command passes POSITIONING_COIN_GROSS_CAP. - allocation_honest_inputs.py: _sleeve_inputs + _book_curve + _deploy_curve + honest_allocation_inputs now accept and forward the cap; allocation_ingest.py's record_allocation (called from the cockpit_forward Dagster asset) passes POSITIONING_COIN_GROSS_CAP into honest_allocation_inputs. Adds tests/unit/test_positioning_cap_coverage_audit.py — a source-level regression guard asserting every LIVE-book/position/allocation callsite passes positioning_coin_gross_cap. Fixed a boundary bug in the brief's own _func_body helper regex (bare ^\S with re.MULTILINE always matches position 0 of the sliced remainder, truncating every function body to its signature line) so the audit actually inspects each function's body instead of false-failing on already-capped callsites. Research/eval callers (verify_positioning_edge, walk_forward_positioning, look_ahead_audit, _variant_weights, _drop_top_n, _liquidity_sweep, positioning_metalabel, bybit_overlay_ab, vrp_eval, onchain_fundamental_eval, spread_overlay, honest_report) are untouched — they stay uncapped by design. 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.