Remove deprecated modules and files related to the backtesting framework, including backtest.py, cli.py, config.py, data.py, intrabar.py, logging_utils.py, market_costs.py, metrics.py, trade.py, and supertrend indicators. Introduce a new structure for the backtesting engine with improved organization and functionality, including a CLI handler, data manager, and reporting capabilities. Update dependencies in pyproject.toml to support the new architecture.

strategies/supertrend_pkg/indicators.py

@@ -0,0 +1,128 @@
+"""
+Supertrend indicators and helper functions.
+"""
+import numpy as np
+import vectorbt as vbt
+from numba import njit
+
+# --- Numba Compiled Helper Functions ---
+
+@njit(cache=False)  # Disable cache to avoid stale compilation issues
+def get_tr_nb(high, low, close):
+    """Calculate True Range (Numba compiled)."""
+    # Ensure 1D arrays
+    high = high.ravel()
+    low = low.ravel()
+    close = close.ravel()
+    
+    tr = np.empty_like(close)
+    tr[0] = high[0] - low[0]
+    for i in range(1, len(close)):
+        tr[i] = max(high[i] - low[i], abs(high[i] - close[i-1]), abs(low[i] - close[i-1]))
+    return tr
+
+@njit(cache=False)
+def get_atr_nb(high, low, close, period):
+    """Calculate ATR using Wilder's Smoothing (Numba compiled)."""
+    # Ensure 1D arrays
+    high = high.ravel()
+    low = low.ravel()
+    close = close.ravel()
+    
+    # Ensure period is native Python int (critical for Numba array indexing)
+    n = len(close)
+    p = int(period)
+    
+    tr = get_tr_nb(high, low, close)
+    atr = np.full(n, np.nan, dtype=np.float64)
+    
+    if n < p:
+        return atr
+        
+    # Initial ATR is simple average of TR
+    sum_tr = 0.0
+    for i in range(p):
+        sum_tr += tr[i]
+    atr[p - 1] = sum_tr / p
+    
+    # Subsequent ATR is Wilder's smoothed
+    for i in range(p, n):
+        atr[i] = (atr[i - 1] * (p - 1) + tr[i]) / p
+        
+    return atr
+
+@njit(cache=False)
+def get_supertrend_nb(high, low, close, period, multiplier):
+    """Calculate SuperTrend completely in Numba."""
+    # Ensure 1D arrays
+    high = high.ravel()
+    low = low.ravel()
+    close = close.ravel()
+    
+    # Ensure params are native Python types (critical for Numba)
+    n = len(close)
+    p = int(period)
+    m = float(multiplier)
+    
+    atr = get_atr_nb(high, low, close, p)
+    
+    final_upper = np.full(n, np.nan, dtype=np.float64)
+    final_lower = np.full(n, np.nan, dtype=np.float64)
+    trend = np.ones(n, dtype=np.int8)  # 1 Bull, -1 Bear
+    
+    # Skip until we have valid ATR
+    start_idx = p
+    if start_idx >= n:
+        return trend
+        
+    # Init first valid point
+    hl2 = (high[start_idx] + low[start_idx]) / 2
+    final_upper[start_idx] = hl2 + m * atr[start_idx]
+    final_lower[start_idx] = hl2 - m * atr[start_idx]
+    
+    # Loop
+    for i in range(start_idx + 1, n):
+        cur_hl2 = (high[i] + low[i]) / 2
+        cur_atr = atr[i]
+        basic_upper = cur_hl2 + m * cur_atr
+        basic_lower = cur_hl2 - m * cur_atr
+        
+        # Upper Band Logic
+        if basic_upper < final_upper[i-1] or close[i-1] > final_upper[i-1]:
+            final_upper[i] = basic_upper
+        else:
+            final_upper[i] = final_upper[i-1]
+            
+        # Lower Band Logic
+        if basic_lower > final_lower[i-1] or close[i-1] < final_lower[i-1]:
+            final_lower[i] = basic_lower
+        else:
+            final_lower[i] = final_lower[i-1]
+            
+        # Trend Logic
+        if trend[i-1] == 1:
+            if close[i] < final_lower[i-1]:
+                trend[i] = -1
+            else:
+                trend[i] = 1
+        else:
+            if close[i] > final_upper[i-1]:
+                trend[i] = 1
+            else:
+                trend[i] = -1
+                
+    return trend
+
+# --- VectorBT Indicator Factory ---
+
+SuperTrendIndicator = vbt.IndicatorFactory(
+    class_name='SuperTrend',
+    short_name='st',
+    input_names=['high', 'low', 'close'],
+    param_names=['period', 'multiplier'],
+    output_names=['trend']
+).from_apply_func(
+    get_supertrend_nb,
+    keep_pd=False,     # Disable automatic Pandas wrapping of inputs
+    param_product=True # Enable Cartesian product for list params
+)