58bbb4520c
Enterprise clean-rebuild (no foxhunt code). Hexagonal/ports-and-adapters: pure domain (gauntlet math, strategies, backtest, models) | ports (DataProvider, repositories) | adapters (Yahoo data, SQLAlchemy operational [Postgres/SQLite], DuckDB analytical) | application (ResearchService, DI) | CLI composition root. Gauntlet-first: Deflated Sharpe (Bailey-LdP) built + falsification-tested (kills best-of-N-on-noise, keeps real premium). Full vertical slice runs end-to-end on real data: data -> strategy(trend) -> backtest(net of costs) -> IS/OOS gauntlet -> persistence. 4/4 tests green. Postgres+DuckDB split, pydantic contracts, typed, DRY via one-contract-per-port. ADR + README. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
56 lines
2.3 KiB
Python
56 lines
2.3 KiB
Python
"""End-to-end vertical slice with a FAKE data provider (no network) + temp SQLite + temp DuckDB.
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Proves data → backtest → gauntlet → persistence wires together and the domain stays infra-agnostic."""
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from __future__ import annotations
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import numpy as np
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from fxhnt.adapters.persistence import DuckDbAnalyticalStore, SqlOperationalRepository
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from fxhnt.application import ResearchService
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from fxhnt.config import GauntletSettings, Settings
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from fxhnt.domain.models import AssetClass, Market, PriceSeries, StrategySpec
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class FakeDataProvider:
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name = "fake"
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def __init__(self, prices: PriceSeries) -> None:
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self._prices = prices
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def fetch(self, market: Market, start: str | None = None, end: str | None = None) -> PriceSeries:
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return self._prices
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def _trending_prices(market: Market, n: int = 2000, seed: int = 1) -> PriceSeries:
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rng = np.random.default_rng(seed)
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rets = rng.normal(0.0004, 0.01, n) # genuine upward drift -> trend has something real to ride
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close = 100.0 * np.cumprod(1.0 + rets)
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dates = tuple(f"20{10 + i // 365:02d}-{1 + (i // 30) % 12:02d}-{1 + i % 28:02d}" for i in range(n))
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return PriceSeries(market=market, dates=dates, close=close)
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def test_vertical_slice(tmp_path) -> None:
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market = Market(symbol="TEST", asset_class=AssetClass.ETF)
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settings = Settings(
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operational_dsn=f"sqlite:///{tmp_path / 'op.db'}",
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analytical_path=str(tmp_path / "an.duckdb"),
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gauntlet=GauntletSettings(),
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)
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svc = ResearchService(
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data=FakeDataProvider(_trending_prices(market)),
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operational=SqlOperationalRepository(settings.operational_dsn),
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analytical=DuckDbAnalyticalStore(settings.analytical_path),
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settings=settings,
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)
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run = svc.evaluate_candidate(market, StrategySpec(kind="trend", params={"window": 100.0}), n_trials=1)
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# the pipeline produced a coherent run
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assert run.stats.n_obs > 1000
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assert run.verdict.n_trials == 1
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# it persisted to the operational store and round-trips
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loaded = svc._op.get_run(run.run_id) # noqa: SLF001
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assert loaded is not None and loaded.run_id == run.run_id
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assert loaded.verdict.passed == run.verdict.passed
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# the analytical store cached the prices
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assert svc._an.load_prices(market) is not None
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