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/inverse_vol.py

@@ -0,0 +1,32 @@
+import numpy as np
+import pandas as pd
+from strategies.base import Strategy
+
+
+class InverseVolatilityStrategy(Strategy):
+    """
+    Risk parity via inverse-volatility weighting.
+
+    Allocates capital inversely proportional to each asset's realized volatility
+    over a rolling window. Assets with lower recent volatility receive larger
+    allocations, equalizing the risk contribution of each position.
+
+    This is a simple but robust baseline for risk-based portfolio construction.
+    """
+
+    def __init__(self, vol_window: int = 20):
+        self.vol_window = vol_window
+
+    def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
+        returns = data.pct_change()
+        vol = returns.rolling(self.vol_window).std()
+
+        # Replace zero vol with NaN to avoid division by zero
+        vol = vol.replace(0, np.nan)
+        inv_vol = 1.0 / vol
+
+        row_sums = inv_vol.sum(axis=1).replace(0, np.nan)
+        signals = inv_vol.div(row_sums, axis=0).fillna(0.0)
+        signals.iloc[:self.vol_window] = 0.0
+
+        return signals.shift(1).fillna(0.0)