Update ta_indi_pat.py

ta_indi_pat.py

@@ -2,66 +2,64 @@ import pandas as pd
 import talib
 import numpy as np
 
-def patterns(df):
+def talib_df(df):
     """
-    Return a DataFrame of all CDL patterns (0/1),
-    with original Date + OHLC as the first columns.
+    Return a single DataFrame containing:
+    - Original Date + OHLCV columns
+    - All numeric TA-Lib indicators
+    - All CDL patterns (0/1)
     """
     df = df.copy()
-    for col in ['Open','High','Low','Close']:
+    
+    # Ensure OHLCV columns exist
+    for col in ['Open','High','Low','Close','Volume']:
         if col not in df.columns:
             raise ValueError(f"Missing column: {col}")
 
-    pattern_df = pd.DataFrame({
-        p: (getattr(talib, p)(
-            df['Open'].values.astype(float),
-            df['High'].values.astype(float),
-            df['Low'].values.astype(float),
-            df['Close'].values.astype(float)
-        ) != 0).astype(int)
-        for p in dir(talib) if p.startswith("CDL")
-    }, index=df.index)
+    # Base DF with Date + OHLCV
+    result_df = df[['Date','Close','High','Low','Open','Volume']].copy()
 
-    # Prepend Date + OHLC
-    for col in ['Date', 'Open', 'High', 'Low', 'Close'][::-1]:
-        pattern_df.insert(0, col, df[col].values)
-
-    return pattern_df
-
-
-def indicators(df):
-    """
-    Return a DataFrame of numeric TA-Lib indicators,
-    with original Date + OHLC as the first columns.
-    """
+    # --- Indicators ---
     df_std = df.copy()
     df_std.columns = [c.lower() for c in df_std.columns]
-
     ohlcv = {k: df_std.get(k) for k in ['open','high','low','close','volume']}
-    indicator_list = [f for f in dir(talib)
-                      if not f.startswith("CDL") and not f.startswith("_")
-                      and f not in ["wraps", "wrapped_func"]]
-
-    dfs = []
+    
+    indicator_list = [
+        f for f in dir(talib)
+        if not f.startswith("CDL") and not f.startswith("_")
+        and f not in ["wraps", "wrapped_func"]
+    ]
+    
+    indicator_dfs = []
     for name in indicator_list:
         func = getattr(talib, name)
         try:
             if ohlcv['close'] is None:
                 continue
-            result = func(ohlcv['close'].values.astype(float))
-            # handle tuple outputs
-            if isinstance(result, tuple):
-                for i, arr in enumerate(result):
-                    dfs.append(pd.DataFrame(arr, index=df.index, columns=[f"{name}_{i}"]))
+            res = func(ohlcv['close'].values.astype(float))
+            if isinstance(res, tuple):
+                for i, arr in enumerate(res):
+                    indicator_dfs.append(pd.DataFrame(arr, index=df.index, columns=[f"{name}_{i}"]))
             else:
-                dfs.append(pd.DataFrame(result, index=df.index, columns=[name]))
+                indicator_dfs.append(pd.DataFrame(res, index=df.index, columns=[name]))
         except:
             continue
+    if indicator_dfs:
+        result_df = pd.concat([result_df] + indicator_dfs, axis=1)
 
-    indicator_df = pd.concat(dfs, axis=1) if dfs else pd.DataFrame(index=df.index)
-
-    # Prepend Date + OHLC
-    for col in ['Date', 'Open', 'High', 'Low', 'Close'][::-1]:
-        indicator_df.insert(0, col, df[col].values)
+    # --- CDL Patterns ---
+    pattern_list = [f for f in dir(talib) if f.startswith("CDL")]
+    for p in pattern_list:
+        func = getattr(talib, p)
+        try:
+            res = func(
+                df['Open'].values.astype(float),
+                df['High'].values.astype(float),
+                df['Low'].values.astype(float),
+                df['Close'].values.astype(float)
+            )
+            result_df[p] = (res != 0).astype(int)
+        except:
+            continue
 
-    return indicator_df
+    return result_df