Update app.py

app.py

@@ -45,13 +45,7 @@ def calculate_stochastic_oscillator(df):
     slowd = slowk.rolling(window=3).mean()
     return slowk, slowd
 
-def calculate_atr(df, window=14):
-    high_low = df['High'] - df['Low']
-    high_close = np.abs(df['High'] - df['Close'].shift())
-    low_close = np.abs(df['Low'] - df['Close'].shift())
-    tr = high_low.combine(high_close, max).combine(low_close, max)
-    atr = tr.rolling(window=window).mean()
-    return atr
+
 
 def calculate_cmf(df, window=20):
     mfv = ((df['Close'] - df['Low']) - (df['High'] - df['Close'])) / (df['High'] - df['Low']) * df['Volume']
@@ -66,29 +60,6 @@ def calculate_cci(df, window=20):
     cci = (typical_price - sma) / (0.015 * mean_deviation)
     return cci
 
-def calculate_atr_signal(df, atr_column='ATR', close_column='Close', atr_window=20, ma_window=50, volatility_days=3, atr_threshold=1.2):
-
-    # Calculate the 20-day ATR rolling mean and high volatility threshold
-    df['ATR_20'] = df[atr_column].rolling(window=atr_window).mean()
-    df['High_Volatility'] = np.where(df[atr_column] > atr_threshold * df['ATR_20'], 1, 0)
-
-    # Calculate the 50-day moving average of the close prices for trend direction
-    df['MA_50'] = df[close_column].rolling(window=ma_window).mean()
-
-    # Generate the ATR signal based on high volatility and trend direction
-    df['ATR_Signal'] = np.where(
-        (df['High_Volatility'].rolling(window=volatility_days).sum() >= volatility_days) & (df[close_column] > df['MA_50']),
-        1,  # Buy Signal
-        np.where(
-            (df['High_Volatility'].rolling(window=volatility_days).sum() >= volatility_days) & (df[close_column] < df['MA_50']),
-            -1,  # Sell Signal
-            0   # No Signal
-        )
-    )
-    
-    # Return only the ATR_Signal column as output
-    return df['ATR_Signal']
-
 
 
 def generate_trading_signals(df):