mirror of
https://github.com/manifoldbt/manifoldbt.git
synced 2026-11-02 07:56:11 +00:00
107 lines
4.3 KiB
Python
107 lines
4.3 KiB
Python
"""Walk-forward sur un signal plus grossier que le store.
|
|
|
|
Un signal 12h sur des barres 1h (mode hybride) rendait, en sortie
|
|
automatique, une courbe OOS sous-echantillonnee au pas du signal. Sa longueur
|
|
ne valait plus celle des barres natives, et le walk-forward retombait sur les
|
|
metriques du run ENTIER, chauffe plate comprise : Sharpe hors echantillon
|
|
dilue, WFE faux, sans un avertissement. Avec `output_resolution` a 1h, le meme
|
|
walk-forward donnait le bon chiffre.
|
|
|
|
Le contrat : les metriques hors echantillon ne dependent pas de la resolution
|
|
de sortie, et la courbe rendue suit celle-ci, avec autant d'horodatages que de
|
|
points.
|
|
"""
|
|
import os
|
|
|
|
import pytest
|
|
|
|
import manifoldbt as bt
|
|
|
|
pytestmark = pytest.mark.skipif(
|
|
bt.license_info()[0] != "Pro",
|
|
reason="requires Pro: skipped on a Community wheel",
|
|
)
|
|
|
|
pd = pytest.importorskip("pandas")
|
|
np = pytest.importorskip("numpy")
|
|
|
|
HEURE_NS = 3_600 * 1_000_000_000
|
|
|
|
|
|
def _magasin_horaire(tmp_path):
|
|
"""100 jours de barres horaires, prix cyclique pour garantir des trades."""
|
|
n = 2400
|
|
idx = pd.date_range("2021-01-01", periods=n, freq="1h", tz="UTC", name="timestamp")
|
|
pas = np.sin(np.arange(n) / 37.0) * 2.0 + np.cos(np.arange(n) / 11.0)
|
|
px = 100.0 + np.cumsum(pas) * 0.05
|
|
df = pd.DataFrame({"open": px, "high": px * 1.004, "low": px * 0.996,
|
|
"close": px, "volume": np.full(n, 1000.0)}, index=idx)
|
|
racine = str(tmp_path / "data")
|
|
meta = str(tmp_path / "meta.sqlite")
|
|
os.makedirs(racine, exist_ok=True)
|
|
store = bt.import_dataframe(df.reset_index(), symbol="ZWFR", symbol_id=1,
|
|
interval="1h", asset_class="equity",
|
|
exchange="TEST", data_root=racine, metadata_db=meta)
|
|
return store
|
|
|
|
|
|
def _config(signal):
|
|
tr0, tr1 = bt.time_range("2021-01-01", "2021-04-11")
|
|
cfg = bt.BacktestConfig(universe=[1], time_range_start=tr0, time_range_end=tr1,
|
|
initial_capital=1000.0, provider="TEST",
|
|
bar_interval=signal, symbol_names={"ZWFR": 1})
|
|
cfg.warmup_bars = 0
|
|
return cfg
|
|
|
|
|
|
def _strategie():
|
|
from manifoldbt.indicators import close, sma
|
|
m = sma(close, bt.param("n"))
|
|
return (bt.Strategy.create("s")
|
|
.signal("m", m)
|
|
.size(bt.when(close > m, 1.0, 0.0)))
|
|
|
|
|
|
WF = {"geometry": "anchored", "n_splits": 2, "train_ratio": 0.6,
|
|
"optimize_metric": "total_return", "param_grid": {"n": [4, 8]}}
|
|
|
|
|
|
def test_les_metriques_oos_ne_dependent_pas_de_la_sortie(tmp_path):
|
|
store = _magasin_horaire(tmp_path)
|
|
auto = bt.run_walk_forward(_strategie(), WF, _config(bt.Interval.hours(12)), store)
|
|
cfg_native = _config(bt.Interval.hours(12))
|
|
cfg_native.output_resolution = bt.Interval.hours(1)
|
|
native = bt.run_walk_forward(_strategie(), WF, cfg_native, store)
|
|
|
|
assert auto["n_folds"] == native["n_folds"] == 2
|
|
for fa, fn in zip(auto["folds"], native["folds"]):
|
|
assert fa["best_params"] == fn["best_params"]
|
|
for k in ("sharpe", "cagr", "total_return", "volatility", "max_drawdown"):
|
|
assert fa["oos_metrics"][k] == pytest.approx(fn["oos_metrics"][k], rel=1e-12), k
|
|
assert fa["oos_metrics"]["sharpe"] != 0.0, "le pli doit trader"
|
|
if fn["wfe"] is None:
|
|
assert fa["wfe"] is None
|
|
else:
|
|
assert fa["wfe"] == pytest.approx(fn["wfe"], rel=1e-12)
|
|
|
|
|
|
def test_la_courbe_oos_suit_la_resolution_de_sortie(tmp_path):
|
|
store = _magasin_horaire(tmp_path)
|
|
auto = bt.run_walk_forward(_strategie(), WF, _config(bt.Interval.hours(12)), store)
|
|
cfg_native = _config(bt.Interval.hours(12))
|
|
cfg_native.output_resolution = bt.Interval.hours(1)
|
|
native = bt.run_walk_forward(_strategie(), WF, cfg_native, store)
|
|
for fa, fn in zip(auto["folds"], native["folds"]):
|
|
eq, ts = fa["oos_equity"], fa["oos_timestamps"]
|
|
assert len(eq) == len(ts) > 0
|
|
assert ts[0] >= fa["test_range"]["start"]
|
|
assert ts[-1] < fa["test_range"]["end"]
|
|
# Un point par seau de sortie, au plus 12h d'ecart...
|
|
pas = np.diff(np.asarray(ts))
|
|
assert pas.max() == 12 * HEURE_NS and pas.min() > 0
|
|
# ... et chaque point est celui de la courbe native a cet instant.
|
|
natif = dict(zip(fn["oos_timestamps"], fn["oos_equity"]))
|
|
assert len(natif) == len(fn["oos_timestamps"]) > len(ts)
|
|
for t, e in zip(ts, eq):
|
|
assert natif[t] == e
|