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.
74 lines
3.2 KiB
Python
74 lines
3.2 KiB
Python
import numpy as np
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import pandas as pd
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from strategies.base import StockStrategy, mask_scores
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def rolling_max_drawdown(prices: pd.DataFrame, window: int) -> pd.DataFrame:
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return prices.rolling(window).apply(
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lambda x: np.min(x / np.maximum.accumulate(x) - 1), raw=True)
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class MomentumQualityStrategy(StockStrategy):
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"""
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Momentum + quality factor strategy using price-derived signals only.
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Quality proxy (from price data):
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- Return consistency: fraction of positive monthly returns over past year
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(stocks that grind up steadily are higher "quality" than volatile jumpers)
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- Low max drawdown: smaller peak-to-trough drop = more stable
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Combined with momentum, this favors stocks with strong AND stable uptrends,
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filtering out lottery-ticket stocks that spike then crash.
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"""
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def __init__(self, momentum_period: int = 252, skip: int = 21,
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quality_window: int = 252, top_n: int = 20):
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self.momentum_period = momentum_period
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self.skip = skip
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self.quality_window = quality_window
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self.top_n = top_n
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def generate_signals(self, data: pd.DataFrame, eligible: pd.DataFrame | None = None) -> pd.DataFrame:
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# --- Momentum factor ---
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momentum = data.shift(self.skip).pct_change(self.momentum_period - self.skip, fill_method=None)
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# --- Quality factor 1: return consistency ---
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# Rolling 21-day (monthly) returns, then count fraction positive over past year
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monthly_ret = data.pct_change(21, fill_method=None)
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consistency = monthly_ret.rolling(self.quality_window).apply(
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lambda x: (x > 0).sum() / len(x), raw=True
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)
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# --- Quality factor 2: inverse max drawdown ---
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# Smaller drawdown = higher quality
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quality_dd = rolling_max_drawdown(data, self.quality_window)
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# --- Cross-sectional ranking ---
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# na_option="keep" so NaN stocks stay NaN in the composite. With
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# "bottom" + default ascending=True, NaN entries receive pct=1.0 and
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# the additive composite ends up maximal for NaN rows, silently
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# selecting pre-IPO / delisted names as "top".
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mom_rank = mask_scores(momentum, eligible).rank(axis=1, pct=True, na_option="keep")
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con_rank = mask_scores(consistency, eligible).rank(axis=1, pct=True, na_option="keep")
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dd_rank = mask_scores(quality_dd, eligible).rank(axis=1, pct=True, na_option="keep")
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# Composite: momentum 50%, consistency 25%, drawdown 25%
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scores = 0.50 * mom_rank + 0.25 * con_rank + 0.25 * dd_rank
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# --- Select top_n ---
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scores = scores.where(data.notna())
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scores = mask_scores(scores, eligible)
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n_valid = scores.notna().sum(axis=1)
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enough = n_valid >= self.top_n
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score_rank = mask_scores(scores, eligible).rank(axis=1, ascending=False, na_option="bottom")
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top_mask = (score_rank <= self.top_n) & enough.values.reshape(-1, 1)
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raw = top_mask.astype(float)
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row_sums = raw.sum(axis=1).replace(0, np.nan)
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signals = raw.div(row_sums, axis=0).fillna(0.0)
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warmup = max(self.momentum_period, self.quality_window) + 21
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signals.iloc[:warmup] = 0.0
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return signals.shift(1).fillna(0.0)
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