Add check_symbols.py for ETH perpetuals filtering and enhance backtester with size handling

- Introduced `check_symbols.py` to load and filter ETH perpetual markets from the OKX exchange using CCXT.
- Updated the backtester to normalize signals to a 5-tuple format, incorporating size management for trades.
- Enhanced portfolio functions to support variable size and leverage adjustments based on initial capital.
- Added a new method in `CryptoQuantClient` for chunked historical data fetching to avoid API limits.
- Improved market symbol normalization in `market.py` to handle different formats.
- Updated regime strategy parameters based on recent research findings for optimal performance.

engine/portfolio.py

@@ -74,75 +74,35 @@ def run_long_short_portfolio(
     sl_stop: float | None,
     tp_stop: float | None,
     sl_trail: bool,
-    leverage: int
+    leverage: int,
+    size: pd.Series | float = 1.0,
+    size_type: str = 'value'  # Changed to 'value' to support reversals/sizing
 ) -> vbt.Portfolio:
     """
     Run a portfolio supporting both long and short positions.
     
-    Runs two separate portfolios (long and short) and combines results.
-    Each gets half the capital.
-    
-    Args:
-        close: Close price series
-        long_entries: Long entry signals
-        long_exits: Long exit signals
-        short_entries: Short entry signals
-        short_exits: Short exit signals
-        init_cash: Initial capital
-        fees: Transaction fee percentage
-        slippage: Slippage percentage
-        freq: Data frequency string
-        sl_stop: Stop loss percentage
-        tp_stop: Take profit percentage
-        sl_trail: Enable trailing stop loss
-        leverage: Leverage multiplier
-        
-    Returns:
-        VectorBT Portfolio object (long portfolio, short stats logged)
+    Uses VectorBT's native support for short_entries/short_exits
+    to simulate a single unified portfolio.
     """
     effective_cash = init_cash * leverage
-    half_cash = effective_cash / 2
     
-    # Run long-only portfolio
-    long_pf = vbt.Portfolio.from_signals(
+    # If size is passed as value (USD), we don't scale it by leverage here
+    # The backtester has already scaled it by init_cash.
+    # If using 'value', vbt treats it as "Amount of CASH to use for the trade"
+    
+    return vbt.Portfolio.from_signals(
         close=close,
         entries=long_entries,
         exits=long_exits,
-        direction='longonly',
-        init_cash=half_cash,
+        short_entries=short_entries,
+        short_exits=short_exits,
+        init_cash=effective_cash,
         fees=fees,
         slippage=slippage,
         freq=freq,
         sl_stop=sl_stop,
         tp_stop=tp_stop,
         sl_trail=sl_trail,
-        size=1.0,
-        size_type='percent',
+        size=size,
+        size_type=size_type,
     )
-    
-    # Run short-only portfolio
-    short_pf = vbt.Portfolio.from_signals(
-        close=close,
-        entries=short_entries,
-        exits=short_exits,
-        direction='shortonly',
-        init_cash=half_cash,
-        fees=fees,
-        slippage=slippage,
-        freq=freq,
-        sl_stop=sl_stop,
-        tp_stop=tp_stop,
-        sl_trail=sl_trail,
-        size=1.0,
-        size_type='percent',
-    )
-    
-    # Log both portfolio stats
-    # TODO: Implement proper portfolio combination
-    logger.info(
-        "Long portfolio: %.2f%% return, Short portfolio: %.2f%% return",
-        long_pf.total_return().mean() * 100,
-        short_pf.total_return().mean() * 100
-    )
-    
-    return long_pf