Gahow Wang
42218741d4
Backtesting engine supporting 11 strategies across US (S&P 500) and CN (CSI 300) markets with open-to-close execution, proportional + fixed per-trade fees. Daily trader (trader.py) with auto/morning/evening/simulate/status commands and cron-friendly `auto` mode for unattended daily runs on a server. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
import numpy as np
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import pandas as pd
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from strategies.base import Strategy
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class TrendFollowingStrategy(Strategy):
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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) -> 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)
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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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n_valid = momentum_filtered.notna().sum(axis=1)
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enough = n_valid >= 1
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rank = momentum_filtered.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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