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f27e3efbb8
This CI runs the mirrored suite against the PyPI wheel without a licence. Twelve tests exercised paths Community gates: walk-forward optimization raises LicenseError before any geometry is read, and the look-ahead detector both needs Pro and counts sub-daily bars that the Community daily floor reduces to four. Same module-level guard the sweep tests already carry, with the reason named.
96 lines
4.0 KiB
Python
96 lines
4.0 KiB
Python
"""Le walk-forward doit accepter les series EXOGENES, comme tous les autres
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chemins du moteur.
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Il chargeait ses colonnes exo pour les deux runs qui tracent les courbes
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d'equite, mais appelait le moteur avec des tables VIDES pour la selection du
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reglage. Une branche sur trois etait oubliee, et c'etait celle qui decide :
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toute strategie lisant `exo.<nom>.<colonne>` echouait sur "unknown input
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column" alors que la meme strategie tourne dans `run`, `run_sweep_lite` et
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`run_batch_lite`.
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Le second test verifie le point qui rend la correction sure : la selection lit
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desormais des colonnes DECOUPEES une fois pour toutes, la ou les courbes les
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rechargent par fenetre. Les deux chemins doivent rendre la meme metrique
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d'in-sample pour le reglage retenu, sinon le decoupage est faux.
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"""
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import os
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import pytest
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import manifoldbt as bt
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# Walk-forward optimization is a Pro feature: on a Community wheel the
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# gate raises before the exogenous columns are ever loaded.
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pytestmark = pytest.mark.skipif(
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bt.license_info()[0] != "Pro",
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reason="requires Pro: skipped on a Community wheel",
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)
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pd = pytest.importorskip("pandas")
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np = pytest.importorskip("numpy")
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def _monte(tmp_path):
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"""Une serie horaire regulière, plus une moyenne posee en serie exogene."""
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n = 2400
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idx = pd.date_range("2021-01-01", periods=n, freq="1h", tz="UTC", name="timestamp")
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pas = np.sin(np.arange(n) / 37.0) * 2.0 + np.cos(np.arange(n) / 11.0)
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px = 100.0 + np.cumsum(pas) * 0.05
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df = pd.DataFrame({"open": px, "high": px * 1.004, "low": px * 0.996,
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"close": px, "volume": np.full(n, 1000.0)}, index=idx)
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racine = str(tmp_path / "data")
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meta = str(tmp_path / "meta.sqlite")
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os.makedirs(racine, exist_ok=True)
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store = bt.import_dataframe(df.reset_index(), symbol="ZEXO", symbol_id=1,
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interval="1h", asset_class="equity",
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exchange="TEST", data_root=racine, metadata_db=meta)
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moy = pd.Series(px).rolling(24).mean().to_numpy()
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bt.register_exo("moyenne", pd.DataFrame({"timestamp": idx, "sma": moy}),
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store=store, data_root=racine, timeframe="1h")
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# la plage doit coller aux donnees : 2400 heures = 100 jours
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tr0, tr1 = bt.time_range("2021-01-01", "2021-04-11")
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cfg = bt.BacktestConfig(universe=[1], time_range_start=tr0, time_range_end=tr1,
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initial_capital=1000.0, provider="TEST",
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bar_interval=bt.Interval.hours(1), symbol_names={"ZEXO": 1})
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cfg.warmup_bars = 0
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cfg.exo_data = ["moyenne"]
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return store, cfg
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def _strategie():
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from manifoldbt.indicators import close, col
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m = col("exo.moyenne.sma")
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bande = m * (bt.lit(1.0) - bt.param("dev"))
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return (bt.Strategy.create("s")
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.signal("aux", bande)
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.size(bt.when(close < bande, 1.0, bt.when(close > m, 0.0, bt.hold()))))
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WF = {"method": "Anchored", "n_splits": 3, "train_ratio": 0.5,
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"optimize_metric": "sharpe",
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"param_grid": {"dev": [0.002, 0.005, 0.01]}}
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def test_walk_forward_accepte_une_serie_exogene(tmp_path):
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store, cfg = _monte(tmp_path)
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r = bt.run_walk_forward(_strategie(), WF, cfg, store)
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assert len(r["folds"]) == 3
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# chaque pli doit avoir EVALUE la grille, pas l'avoir sautee
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for f in r["folds"]:
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assert len(f["all_is_results"]) == 3, "la grille n'a pas ete evaluee"
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def test_selection_et_courbes_voient_les_memes_colonnes(tmp_path):
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"""La selection tranche les colonnes une fois, les courbes les rechargent
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par fenetre : le meme reglage doit donner le meme in-sample des deux cotes."""
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store, cfg = _monte(tmp_path)
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r = bt.run_walk_forward(_strategie(), WF, cfg, store)
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for f in r["folds"]:
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meilleur = max(f["all_is_results"],
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key=lambda x: x["metrics"].get("sharpe", float("-inf")))
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a = meilleur["metrics"]["sharpe"]
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b = f["is_metrics"]["sharpe"]
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assert a == b, "selection {} contre courbe {} au pli {}".format(
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a, b, f["fold_index"])
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