Update ta_indi_pat.py

ta_indi_pat.py

@@ -1,21 +1,27 @@
-
 import pandas as pd
 import talib
 import numpy as np
 
 def patterns(df):
-
-    # Copy and normalize columns
+    """
+    Return a DataFrame of all CDL patterns with 0/1, keeping Date as index.
+    """
     df = df.copy()
     required_cols = ['Open','High','Low','Close']
 
-    # DataFrame to store patterns only
+    # Ensure all required columns exist
+    for col in required_cols:
+        if col not in df.columns:
+            raise ValueError(f"Missing column: {col}")
+
+    # Use Date as index if present
+    if 'Date' in df.columns:
+        df.set_index('Date', inplace=True)
+
     pattern_df = pd.DataFrame(index=df.index)
 
-    # Get all CDL pattern functions
     pattern_list = [f for f in dir(talib) if f.startswith("CDL")]
 
-    # Apply each pattern function
     for pattern in pattern_list:
         func = getattr(talib, pattern)
         result = func(
@@ -24,17 +30,22 @@ def patterns(df):
             df['Low'].values.astype(float),
             df['Close'].values.astype(float)
         )
-
-        # Convert +100/-100 → 1, 0 stays 0
         pattern_df[pattern] = (result != 0).astype(int)
 
     return pattern_df
 
-def indicators(df):
 
+def indicators(df):
+    """
+    Return a DataFrame of all numeric TA-Lib indicators, keeping Date as index.
+    """
     df_std = df.copy()
     df_std.columns = [c.lower() for c in df_std.columns]
 
+    # Use Date as index if present
+    if 'date' in df_std.columns:
+        df_std.set_index('date', inplace=True)
+
     ohlcv = {
         'open': df_std.get('open'),
         'high': df_std.get('high'),
@@ -43,38 +54,35 @@ def indicators(df):
         'volume': df_std.get('volume')
     }
 
-    #indicator_list = [f for f in dir(talib) if not f.startswith("CDL") and not f.startswith("_")]
     indicator_list = [
         f for f in dir(talib)
         if not f.startswith("CDL") and not f.startswith("_") and f not in ["wraps", "wrapped_func"]
-        ]
+    ]
 
-    df_list = []  # store all indicator columns as separate DataFrames
+    df_list = []
 
     for name in indicator_list:
         func = getattr(talib, name)
         try:
-            if 'close' in ohlcv and ohlcv['close'] is not None:
+            if ohlcv['close'] is not None:
                 result = func(ohlcv['close'].values.astype(float))
             else:
                 continue
 
-            # If function returns tuple, add each output separately
             if isinstance(result, tuple):
                 for i, arr in enumerate(result):
                     col_name = f"{name}_{i}"
-                    temp_df = pd.DataFrame(arr, index=df.index, columns=[col_name])
+                    temp_df = pd.DataFrame(arr, index=df_std.index, columns=[col_name])
                     df_list.append(temp_df)
             else:
-                temp_df = pd.DataFrame(result, index=df.index, columns=[name])
+                temp_df = pd.DataFrame(result, index=df_std.index, columns=[name])
                 df_list.append(temp_df)
         except:
             continue
 
-    # Concatenate all columns at once
     if df_list:
         indicator_df = pd.concat(df_list, axis=1)
     else:
-        indicator_df = pd.DataFrame(index=df.index)
+        indicator_df = pd.DataFrame(index=df_std.index)
 
     return indicator_df