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/dual_momentum.py

@@ -0,0 +1,55 @@
+import numpy as np
+import pandas as pd
+from strategies.base import Strategy
+
+
+class DualMomentumStrategy(Strategy):
+    """
+    Dual momentum with multi-timeframe confirmation.
+
+    Unlike plain momentum (single 12-1m lookback), this requires agreement
+    across THREE timeframes — short (1-3m), medium (3-6m), and long (6-12m).
+    A stock must show positive returns on ALL three to qualify.
+
+    This filters out stocks riding a single spike and favors sustained trends.
+    Position count is variable: when few stocks pass all three filters,
+    the strategy naturally goes to partial cash.
+    """
+
+    def __init__(self, top_n: int = 20):
+        self.top_n = top_n
+
+    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
+        # Three timeframe returns (each skipping most recent 5 days)
+        skip = 5
+        short_mom = data.shift(skip).pct_change(63 - skip)    # ~3 month
+        med_mom = data.shift(skip).pct_change(126 - skip)     # ~6 month
+        long_mom = data.shift(skip).pct_change(252 - skip)    # ~12 month
+
+        # All three must be positive (absolute momentum across timeframes)
+        all_positive = (short_mom > 0) & (med_mom > 0) & (long_mom > 0)
+
+        # Composite score: average percentile rank across timeframes
+        short_rank = short_mom.rank(axis=1, pct=True, na_option="bottom")
+        med_rank = med_mom.rank(axis=1, pct=True, na_option="bottom")
+        long_rank = long_mom.rank(axis=1, pct=True, na_option="bottom")
+        composite = (short_rank + med_rank + long_rank) / 3
+
+        # Only consider stocks passing absolute filter
+        composite_filtered = composite.where(all_positive, np.nan)
+
+        n_valid = composite_filtered.notna().sum(axis=1)
+        enough = n_valid >= 1
+
+        rank = composite_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)
+        top_mask = top_mask & all_positive
+
+        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)
+
+        signals.iloc[:252] = 0.0
+
+        return signals.shift(1).fillna(0.0)