Update app.py

app.py

@@ -15,7 +15,6 @@ def calculate_sma(df, window):
 def calculate_ema(df, window):
     return df['Close'].ewm(span=window, adjust=False).mean()
 
-
 def calculate_macd(df):
     short_ema = df['Close'].ewm(span=12, adjust=False).mean()
     long_ema = df['Close'].ewm(span=26, adjust=False).mean()
@@ -23,7 +22,6 @@ def calculate_macd(df):
     signal = macd.ewm(span=9, adjust=False).mean()
     return macd, signal
 
-
 def calculate_rsi(df):
     delta = df['Close'].diff()
     gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
@@ -68,8 +66,6 @@ def calculate_cci(df, window=20):
 
 
 
-
-
 def generate_trading_signals(df):
     # Calculate various indicators
     df['SMA_30'] = calculate_sma(df, 30)
@@ -85,35 +81,34 @@ def generate_trading_signals(df):
 
     
 
+
     # Generate trading signals
-    df['SMA_Signal'] = np.where(df['SMA_30'] > df['SMA_100'], 1, 0)
+    df['SMA_Signal'] = np.where(df['SMA_30'] > df['SMA_100']+3, 1, 0)
     
     macd, signal = calculate_macd(df)
-    df['MACD_Signal'] = np.select([(macd > signal) & (macd.shift(1) <= signal.shift(1)),
-                                    (macd < signal) & (macd.shift(1) >= signal.shift(1))],[1, -1], default=0)
-
-
+    df['MACD_Signal'] = np.where((macd > signal.shift(1)) & (macd.shift(1) < signal), 1, 0)
     
-    df['RSI_Signal'] = np.where(df['RSI'] < 20, 1, 0)
-    df['RSI_Signal'] = np.where(df['RSI'] > 90, -1, df['RSI_Signal'])
+    df['RSI_Signal'] = np.where(df['RSI'] < 30, 1, 0)
+    df['RSI_Signal'] = np.where(df['RSI'] > 70, -1, df['RSI_Signal'])
     
-    df['BB_Signal'] = np.where(df['Close'] < df['LowerBB'], 0, 0)
+    df['BB_Signal'] = np.where(df['Close'] < df['LowerBB'], 1, 0)
     df['BB_Signal'] = np.where(df['Close'] > df['UpperBB'], -1, df['BB_Signal'])
     
-    df['Stochastic_Signal'] = np.where((df['SlowK'] < 10) & (df['SlowD'] < 15), 1, 0)
-    df['Stochastic_Signal'] = np.where((df['SlowK'] > 90) & (df['SlowD'] > 85), -1, df['Stochastic_Signal'])
+    df['Stochastic_Signal'] = np.where((df['SlowK'] < 15) & (df['SlowD'] < 20), 1, 0)
+    df['Stochastic_Signal'] = np.where((df['SlowK'] > 80) & (df['SlowD'] > 80), -1, df['Stochastic_Signal'])
     
-    df['CMF_Signal'] = np.where(df['CMF'] > 0.3, -1, np.where(df['CMF'] < -0.3, 1, 0))
+    df['ATR_Signal'] = np.where(df['ATR'] > df['ATR'].rolling(window=20).mean(), 1, 0)
+    df['CMF_Signal'] = np.where(df['CMF'] > 0.05, 1, np.where(df['CMF'] < -0.05, -1, 0))
 
     
-    df['CCI_Signal'] = np.where(df['CCI'] < -180, 1, 0)
+    df['CCI_Signal'] = np.where(df['CCI'] < -150, 1, 0)
     df['CCI_Signal'] = np.where(df['CCI'] > 150, -1, df['CCI_Signal'])
     
 
 
     # Combined signal for stronger buy/sell points
-    df['Combined_Signal'] = df[['RSI_Signal', 'BB_Signal', 
-                                  'Stochastic_Signal', 'CMF_Signal', 
+    df['Combined_Signal'] = df[['SMA_Signal', 'MACD_Signal', 'RSI_Signal', 'BB_Signal', 
+                                  'Stochastic_Signal', 'ATR_Signal', 'CMF_Signal', 
                                   'CCI_Signal']].sum(axis=1)
 
     return df
@@ -192,94 +187,7 @@ def stock_analysis(ticker, start_date, end_date):
 
 
 # %%
-def plot_individual_signals(df, ticker):
-    # Create a figure
-    fig = go.Figure()
-    fig.add_trace(go.Scatter(
-        x=df.index, y=df['Close'], 
-        mode='lines', 
-        name='Closing Price', 
-        line=dict(color='lightcoral', width=2)
-    ))
-
-    # Add buy/sell signals for each indicator
-    signal_names = ['RSI_Signal', 'BB_Signal', 
-                    'Stochastic_Signal', 'CMF_Signal', 
-                    'CCI_Signal']
-
-    for signal in signal_names:
-        buy_signals = df[df[signal] == 1]
-        sell_signals = df[df[signal] == -1]
-        
-        fig.add_trace(go.Scatter(
-            x=buy_signals.index, y=buy_signals['Close'], 
-            mode='markers', 
-            marker=dict(symbol='triangle-up', size=10, color='lightgreen'), 
-            name=f'{signal} Buy Signal'
-        ))
-
-        fig.add_trace(go.Scatter(
-            x=sell_signals.index, y=sell_signals['Close'], 
-            mode='markers', 
-            marker=dict(symbol='triangle-down', size=10, color='lightsalmon'), 
-            name=f'{signal} Sell Signal'
-        ))
-
-    fig.update_layout(
-        title=f'{ticker}: Individual Trading Signals',
-        xaxis=dict(title='Date'),
-        yaxis=dict(title='Price', side='left'),
-        plot_bgcolor='black',
-        paper_bgcolor='black',
-        font=dict(color='white')
-    )
 
-    return fig
-
-
-def display_signals(df):
-    # Create a signals DataFrame
-    signals_df = df[['Close', 'SMA_Signal', 'MACD_Signal', 'RSI_Signal', 
-                      'BB_Signal', 'Stochastic_Signal', 'ATR_Signal', 
-                      'CMF_Signal', 'CCI_Signal']].copy()
-
-    # The Date is the index, so we don't need to add it as a column
-    signals_df.index.name = 'Date'  # Name the index for better readability
-
-    # Replace signal values with 'Buy', 'Sell', or 'Hold'
-    for column in signals_df.columns:
-        signals_df[column] = signals_df[column].replace(
-            {1: 'Buy', -1: 'Sell', 0: 'Hold'}
-        )
-
-    return signals_df
-
-def stock_analysis(ticker, start_date, end_date):
-    # Download stock data from Yahoo Finance
-    df = yf.download(ticker, start=start_date, end=end_date)
-
-    # Generate signals
-    df = generate_trading_signals(df)
-    
-    # Last 60 days for plotting
-    df_last_60 = df.tail(60)
-
-    # Plot combined signals
-    fig_signals = plot_combined_signals(df_last_60, ticker)
-
-    # Plot individual signals
-    fig_individual_signals = plot_individual_signals(df_last_60, ticker)
-
-    # Display signals DataFrame
-    signals_df = df_last_60[['Close', 'SMA_Signal', 'MACD_Signal', 'RSI_Signal', 'BB_Signal', 
-                              'Stochastic_Signal', 'ATR_Signal', 'CMF_Signal', 
-                              'CCI_Signal']]
-
-    return fig_signals, fig_individual_signals
-
-
-
-# %%
 # Define Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("## Stock Market Analysis App")
@@ -292,16 +200,14 @@ with gr.Blocks() as demo:
     button = gr.Button("Analyze Stock")
     
     # Outputs: Display results, charts
-    combined_signals_output = gr.Plot(label="Combined Trading Signals")
-    individual_signals_output = gr.Plot(label="Individual Trading Signals")
-    #signals_df_output = gr.Dataframe(label="Buy/Sell Signals")
+    signals_output = gr.Plot(label="Trading Signals")
 
     # Link button to function
-    button.click(stock_analysis, inputs=[ticker_input, start_date_input, end_date_input], 
-                  outputs=[combined_signals_output, individual_signals_output])
+    button.click(stock_analysis, inputs=[ticker_input, start_date_input, end_date_input], outputs=[signals_output])
 
 # Launch the interface
 demo.launch()
 
 
 
+