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.
53 lines
2.0 KiB
Python
53 lines
2.0 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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class TrendFollowingStrategy(StockStrategy):
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"""
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Per-stock trend following with momentum ranking.
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Two filters applied:
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1. Trend filter: only hold stocks trading above their own moving average
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(individual uptrend, not market-level timing)
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2. Momentum rank: among trending stocks, pick the top_n by 6-month return
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This avoids the Multi-Factor problem of all-or-nothing market timing
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while still providing downside protection at the individual stock level.
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"""
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def __init__(self, ma_window: int = 150, momentum_period: int = 126, top_n: int = 20):
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self.ma_window = ma_window
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self.momentum_period = momentum_period
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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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# Per-stock trend filter: price > MA
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ma = data.rolling(self.ma_window).mean()
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in_uptrend = data > ma
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# Momentum score among trending stocks
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momentum = data.pct_change(self.momentum_period, fill_method=None)
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# Mask out stocks not in uptrend
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momentum_filtered = momentum.where(in_uptrend, np.nan)
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# Rank and select top_n
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momentum_filtered = mask_scores(momentum_filtered, eligible)
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n_valid = momentum_filtered.notna().sum(axis=1)
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enough = n_valid >= 1
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rank = mask_scores(momentum_filtered, eligible).rank(axis=1, ascending=False, na_option="bottom")
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effective_n = n_valid.clip(upper=self.top_n)
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top_mask = (rank <= effective_n.values.reshape(-1, 1)) & enough.values.reshape(-1, 1)
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# Ensure we only pick stocks that are actually in uptrend
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top_mask = top_mask & in_uptrend
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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.ma_window, self.momentum_period)
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signals.iloc[:warmup] = 0.0
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return signals.shift(1).fillna(0.0)
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