Initial commit: quant backtesting framework with daily trading simulator

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>

strategies/multi_factor.py

@@ -0,0 +1,77 @@
+import numpy as np
+import pandas as pd
+from strategies.base import Strategy
+
+
+class MultiFactorStrategy(Strategy):
+    """
+    Multi-factor strategy combining momentum and value signals with a market-timing filter.
+
+    Factors:
+    - Momentum: past return from (momentum_period + skip) to skip days ago (avoids short-term reversal)
+    - Value: rolling min / current price (inverted price-to-low ratio — cheaper = higher score)
+    - Market timing: only invest when SPY is above its long-term moving average
+
+    Signal generation is fully vectorized — no Python loops over time.
+    """
+
+    def __init__(
+        self,
+        tickers,
+        benchmark: str = "SPY",
+        window: int = 200,
+        momentum_period: int = 230,
+        momentum_skip: int = 20,
+        value_period: int = 250,
+        top_n: int = 5,
+    ):
+        self.tickers = list(tickers)
+        self.benchmark = benchmark
+        self.window = window
+        self.momentum_period = momentum_period
+        self.momentum_skip = momentum_skip
+        self.value_period = value_period
+        self.top_n = top_n
+
+    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
+        stock = data[self.tickers]
+
+        # --- Market timing filter ---
+        spy_ma = data[self.benchmark].rolling(self.window).mean()
+        market_up = (data[self.benchmark] > spy_ma).values  # shape (T,)
+
+        # --- Momentum factor ---
+        # Return from T-(momentum_period+skip) to T-skip, avoiding the last month
+        momentum = stock.shift(self.momentum_skip).pct_change(self.momentum_period)
+
+        # --- Value factor ---
+        # min_price_over_period / current_price  (higher = more "undervalued" vs recent range)
+        value = stock.rolling(self.value_period).min() / stock
+
+        # --- Cross-sectional ranking (each row ranked across assets) ---
+        mom_rank = momentum.rank(axis=1, pct=True, na_option="bottom")
+        val_rank = value.rank(axis=1, pct=True, na_option="bottom")
+        scores = mom_rank + val_rank  # combined score, higher = better
+
+        # --- Select top_n assets per row ---
+        # Only allocate rows that have enough valid scores
+        n_valid = scores.notna().sum(axis=1)
+        enough_data = n_valid >= self.top_n
+
+        score_rank = scores.rank(axis=1, ascending=False, na_option="bottom")
+        top_mask = (score_rank <= self.top_n) & enough_data.values.reshape(-1, 1)
+
+        # Equal-weight allocation among selected assets
+        raw = top_mask.astype(float)
+        row_sums = raw.sum(axis=1).replace(0, np.nan)
+        signals = raw.div(row_sums, axis=0).fillna(0.0)
+
+        # --- Apply market timing: zero out when SPY is below its MA ---
+        signals[~market_up] = 0.0
+
+        # --- Zero out warm-up period ---
+        warmup = max(self.window, self.momentum_period + self.momentum_skip, self.value_period)
+        signals.iloc[:warmup] = 0.0
+
+        # Shift by 1: signal computed at close of day t trades at open of day t+1
+        return signals.shift(1).fillna(0.0)