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>
78 lines
3.0 KiB
Python
78 lines
3.0 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 MultiFactorStrategy(Strategy):
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"""
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Multi-factor strategy combining momentum and value signals with a market-timing filter.
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Factors:
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- Momentum: past return from (momentum_period + skip) to skip days ago (avoids short-term reversal)
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- Value: rolling min / current price (inverted price-to-low ratio — cheaper = higher score)
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- Market timing: only invest when SPY is above its long-term moving average
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Signal generation is fully vectorized — no Python loops over time.
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"""
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def __init__(
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self,
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tickers,
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benchmark: str = "SPY",
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window: int = 200,
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momentum_period: int = 230,
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momentum_skip: int = 20,
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value_period: int = 250,
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top_n: int = 5,
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):
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self.tickers = list(tickers)
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self.benchmark = benchmark
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self.window = window
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self.momentum_period = momentum_period
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self.momentum_skip = momentum_skip
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self.value_period = value_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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stock = data[self.tickers]
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# --- Market timing filter ---
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spy_ma = data[self.benchmark].rolling(self.window).mean()
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market_up = (data[self.benchmark] > spy_ma).values # shape (T,)
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# --- Momentum factor ---
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# Return from T-(momentum_period+skip) to T-skip, avoiding the last month
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momentum = stock.shift(self.momentum_skip).pct_change(self.momentum_period)
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# --- Value factor ---
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# min_price_over_period / current_price (higher = more "undervalued" vs recent range)
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value = stock.rolling(self.value_period).min() / stock
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# --- Cross-sectional ranking (each row ranked across assets) ---
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mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom")
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val_rank = value.rank(axis=1, pct=True, na_option="bottom")
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scores = mom_rank + val_rank # combined score, higher = better
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# --- Select top_n assets per row ---
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# Only allocate rows that have enough valid scores
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n_valid = scores.notna().sum(axis=1)
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enough_data = n_valid >= self.top_n
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score_rank = scores.rank(axis=1, ascending=False, na_option="bottom")
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top_mask = (score_rank <= self.top_n) & enough_data.values.reshape(-1, 1)
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# Equal-weight allocation among selected assets
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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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# --- Apply market timing: zero out when SPY is below its MA ---
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signals[~market_up] = 0.0
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# --- Zero out warm-up period ---
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warmup = max(self.window, self.momentum_period + self.momentum_skip, self.value_period)
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signals.iloc[:warmup] = 0.0
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# Shift by 1: signal computed at close of day t trades at open of day t+1
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return signals.shift(1).fillna(0.0)
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