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.
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
from abc import ABC, abstractmethod
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import pandas as pd
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class Strategy(ABC):
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"""
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Abstract base class for all strategies.
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"""
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@abstractmethod
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def generate_signals(self, data):
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"""
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Generates trading signals for each stock.
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:param data: A pandas DataFrame of historical price data.
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:return: A pandas Series with the same index as the input data,
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containing the trading signals (e.g., 'buy', 'hold', 'sell').
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"""
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pass
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def mask_scores(scores: pd.DataFrame, eligible: pd.DataFrame | None) -> pd.DataFrame:
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return scores if eligible is None else scores.where(eligible.reindex_like(scores).fillna(False))
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class StockStrategy(Strategy):
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accepts_eligibility = True
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class BarStrategy(Strategy):
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"""Strategy on completed daily bars with several fields.
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``fields`` names the panels it reads among close/open/high/low/volume.
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``generate_signals(bars)`` takes ``{field: DataFrame}`` (a bare close
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DataFrame is accepted when only "close" is needed) and returns weights
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whose row t uses bars through t-1, like every Strategy. Backtests may add
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``bars["eligible"]`` (bool, point-in-time index membership): prices stay
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unmasked so signals see full histories, and only eligible names are held.
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Timing differs from close-price strategies in the live trader: those read
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the session's open as their latest price, while a BarStrategy only uses
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completed bars, so the weights of row t trade at the close of t.
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"""
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fields: tuple[str, ...] = ("close",)
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def generate_signals(self, data):
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bars = data if isinstance(data, dict) else {"close": data}
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missing = [f for f in self.fields if f not in bars]
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if missing:
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raise ValueError(f"{type(self).__name__} needs bar fields {missing}")
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return self.generate_weights(bars)
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@abstractmethod
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def generate_weights(self, bars: dict[str, pd.DataFrame]) -> pd.DataFrame:
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"""Weights (dates x assets), row t using bars through t-1."""
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