Gahow Wang
722cade974
Share point-in-time signals and a cash-funded order ledger across backtests and trading. Correct eligibility, missing-price fills, quality factors, regime inputs, warmup windows, and cash-injection accounting. Add reproducible registered-strategy comparisons and paired forward candidates with a 20% resize band. Validation: 125 Python tests, 23 web tests, TypeScript/Vite build, 87 strategy causality checks, and real-data target/order/equity parity for 10 strategies.
45 lines
2.3 KiB
Python
45 lines
2.3 KiB
Python
"""One timing and point-in-time eligibility contract for every execution path."""
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import numpy as np
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import pandas as pd
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from strategies.base import BarStrategy
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def session_targets(strategy, close: pd.DataFrame, open_data: pd.DataFrame | None = None,
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bars: dict[str, pd.DataFrame] | None = None,
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eligible: pd.DataFrame | None = None) -> pd.DataFrame:
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if close.empty:
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return pd.DataFrame(0.0, index=close.index, columns=close.columns)
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member = (eligible.reindex_like(close).fillna(False).astype(bool) if eligible is not None
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else pd.DataFrame(True, index=close.index, columns=close.columns))
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if isinstance(strategy, BarStrategy):
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inputs = dict(bars if bars is not None else {"close": close})
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inputs["eligible"] = member & inputs["close"].notna()
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weights = strategy.generate_signals(inputs)
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known = member.shift(1, fill_value=False)
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else:
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# Price history remains observable before index admission. Membership
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# restricts cross-sectional selection and the eventual execution targets.
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inputs = close if open_data is None else open_data.reindex_like(close)
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native = getattr(strategy, "accepts_eligibility", False)
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kwargs = {"eligible": member} if native else {}
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inputs = inputs if native else inputs.where(member)
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if open_data is not None:
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nxt = inputs.index[-1] + pd.Timedelta(days=1)
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padded = pd.concat([inputs, inputs.iloc[[-1]].set_axis([nxt], axis=0)])
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if native:
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kwargs["eligible"] = pd.concat([member, member.iloc[[-1]].set_axis([nxt], axis=0)])
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weights = strategy.generate_signals(padded, **kwargs).shift(-1).reindex(close.index)
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known = member
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else:
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weights = strategy.generate_signals(inputs, **kwargs)
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known = member.shift(1, fill_value=False)
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weights = weights.reindex(index=close.index, columns=close.columns).fillna(0.0)
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weights = weights.where(known & member, 0.0)
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arr = weights.to_numpy()
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if (not np.isfinite(arr).all() or (arr < -1e-10).any()
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or (weights.sum(axis=1) > 1 + 1e-8).any()):
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raise ValueError("Strategy produced non-finite, short or unfunded targets")
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return weights.clip(lower=0.0)
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