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/mean_reversion.py

@@ -0,0 +1,56 @@
+import numpy as np
+import pandas as pd
+from strategies.base import Strategy
+
+
+class MeanReversionStrategy(Strategy):
+    """
+    Monthly "buy the dip" with momentum confirmation.
+
+    Among stocks with positive 12-month momentum, overweight those
+    that dipped most in the past month. Rebalances monthly to keep
+    turnover low. Combines long-term trend (avoid losers) with
+    short-term mean reversion (buy winners on sale).
+    """
+
+    def __init__(self, mom_lookback: int = 252, dip_window: int = 21,
+                 rebal_freq: int = 21, top_n: int = 20):
+        self.mom_lookback = mom_lookback
+        self.dip_window = dip_window
+        self.rebal_freq = rebal_freq
+        self.top_n = top_n
+
+    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
+        long_mom = data.pct_change(self.mom_lookback)
+        has_momentum = long_mom > 0
+
+        short_ret = data.pct_change(self.dip_window)
+
+        # Among positive-momentum stocks, rank by biggest dip
+        scores = short_ret.where(has_momentum, np.nan)
+
+        # Rank: most negative = rank 1 (biggest dip)
+        rank = scores.rank(axis=1, ascending=True, na_option="bottom")
+        n_valid = scores.notna().sum(axis=1)
+        effective_n = n_valid.clip(upper=self.top_n)
+
+        top_mask = rank <= effective_n.values.reshape(-1, 1)
+        top_mask = top_mask & has_momentum  # ensure we only pick momentum stocks
+
+        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)
+
+        warmup = self.mom_lookback + self.dip_window
+
+        # Only keep rebalance-day signals, forward-fill between them
+        rebal_mask = pd.Series(False, index=data.index)
+        rebal_indices = range(warmup, len(data), self.rebal_freq)
+        rebal_mask.iloc[list(rebal_indices)] = True
+
+        # Zero out non-rebalance days then forward-fill
+        signals[~rebal_mask] = np.nan
+        signals = signals.ffill().fillna(0.0)
+
+        signals.iloc[:warmup] = 0.0
+        return signals.shift(1).fillna(0.0)