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/trend_following.py

@@ -0,0 +1,51 @@
+import numpy as np
+import pandas as pd
+from strategies.base import Strategy
+
+
+class TrendFollowingStrategy(Strategy):
+    """
+    Per-stock trend following with momentum ranking.
+
+    Two filters applied:
+    1. Trend filter: only hold stocks trading above their own moving average
+       (individual uptrend, not market-level timing)
+    2. Momentum rank: among trending stocks, pick the top_n by 6-month return
+
+    This avoids the Multi-Factor problem of all-or-nothing market timing
+    while still providing downside protection at the individual stock level.
+    """
+
+    def __init__(self, ma_window: int = 150, momentum_period: int = 126, top_n: int = 20):
+        self.ma_window = ma_window
+        self.momentum_period = momentum_period
+        self.top_n = top_n
+
+    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
+        # Per-stock trend filter: price > MA
+        ma = data.rolling(self.ma_window).mean()
+        in_uptrend = data > ma
+
+        # Momentum score among trending stocks
+        momentum = data.pct_change(self.momentum_period)
+        # Mask out stocks not in uptrend
+        momentum_filtered = momentum.where(in_uptrend, np.nan)
+
+        # Rank and select top_n
+        n_valid = momentum_filtered.notna().sum(axis=1)
+        enough = n_valid >= 1
+
+        rank = momentum_filtered.rank(axis=1, ascending=False, na_option="bottom")
+        effective_n = n_valid.clip(upper=self.top_n)
+        top_mask = (rank <= effective_n.values.reshape(-1, 1)) & enough.values.reshape(-1, 1)
+        # Ensure we only pick stocks that are actually in uptrend
+        top_mask = top_mask & in_uptrend
+
+        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 = max(self.ma_window, self.momentum_period)
+        signals.iloc[:warmup] = 0.0
+
+        return signals.shift(1).fillna(0.0)