Update indicater.py

indicater.py

@@ -3,57 +3,76 @@ import pandas as pd
 import numpy as np
 import talib
 
+# -------------------------------
+# Custom SuperTrend implementation
+# -------------------------------
+def supertrend(df, period=10, multiplier=3):
+    """
+    Compute SuperTrend
+    """
+    hl2 = (df['High'] + df['Low']) / 2
+    atr = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=period)
+    
+    upperband = hl2 + (multiplier * atr)
+    lowerband = hl2 - (multiplier * atr)
+    supertrend = pd.Series(index=df.index, dtype=float)
+    direction = pd.Series(index=df.index, dtype=int)
+    
+    for i in range(len(df)):
+        if i == 0:
+            supertrend.iloc[i] = upperband.iloc[i]
+            direction.iloc[i] = 1
+            continue
+        if df['Close'].iloc[i] > supertrend.iloc[i-1]:
+            direction.iloc[i] = 1
+            supertrend.iloc[i] = lowerband.iloc[i]
+        elif df['Close'].iloc[i] < supertrend.iloc[i-1]:
+            direction.iloc[i] = -1
+            supertrend.iloc[i] = upperband.iloc[i]
+        else:
+            direction.iloc[i] = direction.iloc[i-1]
+            supertrend.iloc[i] = supertrend.iloc[i-1]
+    return supertrend
+
+# -------------------------------
+# Main indicator calculation
+# -------------------------------
 def calculate_indicators(df):
     """
-    Calculate multiple indicators for given OHLCV df.
-    Returns dict of indicator name -> DataFrame/Series.
+    Compute major TA-Lib indicators and custom indicators.
+    Return dictionary {indicator_name: series_or_df}
     """
     indicators = {}
 
-    close = df['Close'].astype(float)
-    high = df['High'].astype(float)
-    low = df['Low'].astype(float)
-    volume = df['Volume'].astype(float)
-
-    # --- MA on main chart ---
-    indicators['SMA20'] = talib.SMA(close, timeperiod=20)
-    indicators['SMA50'] = talib.SMA(close, timeperiod=50)
+    # --- Price-based moving averages ---
+    indicators['SMA5'] = talib.SMA(df['Close'], timeperiod=5)
+    indicators['SMA20'] = talib.SMA(df['Close'], timeperiod=20)
+    indicators['SMA50'] = talib.SMA(df['Close'], timeperiod=50)
+    indicators['SMA200'] = talib.SMA(df['Close'], timeperiod=200)
+    
+    indicators['EMA5'] = talib.EMA(df['Close'], timeperiod=5)
+    indicators['EMA20'] = talib.EMA(df['Close'], timeperiod=20)
+    indicators['EMA50'] = talib.EMA(df['Close'], timeperiod=50)
+    indicators['EMA200'] = talib.EMA(df['Close'], timeperiod=200)
 
-    # --- MACD ---
-    macd, macdsignal, macdhist = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
-    indicators['MACD'] = pd.DataFrame({'MACD': macd, 'Signal': macdsignal, 'Hist': macdhist})
+    # --- Momentum indicators ---
+    indicators['RSI'] = talib.RSI(df['Close'], timeperiod=14)
+    indicators['STOCH'], indicators['STOCH_signal'] = talib.STOCH(
+        df['High'], df['Low'], df['Close'], fastk_period=14, slowk_period=3, slowk_matype=0,
+        slowd_period=3, slowd_matype=0
+    )
+    indicators['MACD'], indicators['MACD_signal'], indicators['MACD_hist'] = talib.MACD(df['Close'])
+    indicators['ADX'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14)
+    indicators['CCI'] = talib.CCI(df['High'], df['Low'], df['Close'], timeperiod=14)
+    indicators['OBV'] = talib.OBV(df['Close'], df['Volume'])
 
-    # --- RSI ---
-    indicators['RSI'] = talib.RSI(close, timeperiod=14)
+    # --- Volatility indicators ---
+    indicators['ATR'] = talib.ATR(df['High'], df['Low'], df['Close'], timeperiod=14)
+    indicators['BB_upper'], indicators['BB_middle'], indicators['BB_lower'] = talib.BBANDS(
+        df['Close'], timeperiod=20, nbdevup=2, nbdevdn=2, matype=0
+    )
 
-    # --- SuperTrend (not in TA-Lib, custom function) ---
-    indicators['SuperTrend'] = supertrend(high, low, close, period=10, multiplier=3)
+    # --- Custom indicators ---
+    indicators['SuperTrend'] = supertrend(df)
 
     return indicators
-
-def supertrend(high, low, close, period=10, multiplier=3):
-    """
-    Simple SuperTrend implementation.
-    Returns Series with trend value.
-    """
-    atr = talib.ATR(high, low, close, timeperiod=period)
-    hl2 = (high + low) / 2
-    final_upperband = hl2 + (multiplier * atr)
-    final_lowerband = hl2 - (multiplier * atr)
-    trend = pd.Series(index=close.index)
-    direction = True  # True = uptrend
-
-    for i in range(len(close)):
-        if i == 0:
-            trend.iloc[i] = final_upperband.iloc[i]
-        else:
-            if close.iloc[i] > final_upperband.iloc[i-1]:
-                direction = True
-            elif close.iloc[i] < final_lowerband.iloc[i-1]:
-                direction = False
-            if direction:
-                trend.iloc[i] = final_lowerband.iloc[i]
-            else:
-                trend.iloc[i] = final_upperband.iloc[i]
-
-    return trend