Update app.py

app.py

@@ -321,8 +321,8 @@ def calculate_cci(df, window=20):
     cci = (typical_price - sma) / (0.015 * mean_deviation)
     return cci
 
+
 import numpy as np
-import pandas as pd
 
 def generate_trading_signals(df):
     # Calculate various indicators
@@ -336,44 +336,42 @@ def generate_trading_signals(df):
     df['CMF'] = calculate_cmf(df)
     df['CCI'] = calculate_cci(df)
 
-    # Ultra-strict SMA Signal - Require at least 3% difference
+    # Less strict SMA Signal - Require only 1% difference (was 3%)
     df['SMA_Signal'] = np.where((df['SMA_30'] > df['SMA_100']) & 
-                                 ((df['SMA_30'] - df['SMA_100']) / df['SMA_100'] > 0.03), 1, 0)
-    
+                                 ((df['SMA_30'] - df['SMA_100']) / df['SMA_100'] > 0.01), 1, 0)
+
     macd, signal = calculate_macd(df)
-    
 
-    # Inverted: Bullish = Sell, Bearish = Buy
+    # Less strict MACD Signal - Reduce required MACD-signal gap from 1.0 to 0.3
     df['MACD_Signal'] = np.select([
-        (macd > signal) & (macd.shift(1) <= signal.shift(1)) & ((macd - signal) > 1.0),   # Was buy, now sell
-        (macd < signal) & (macd.shift(1) >= signal.shift(1)) & ((signal - macd) > 1.0)    # Was sell, now buy
+        (macd > signal) & (macd.shift(1) <= signal.shift(1)) & ((macd - signal) > 0.3),   # Bullish crossover → Sell (inverted logic)
+        (macd < signal) & (macd.shift(1) >= signal.shift(1)) & ((signal - macd) > 0.3)    # Bearish crossover → Buy
     ], [-1, 1], default=0)
-    
-    # Ultra-strict RSI Signal - Extreme thresholds
-    df['RSI_Signal'] = np.where(df['RSI'] < 15, 1, 0)
-    df['RSI_Signal'] = np.where(df['RSI'] > 90, -1, df['RSI_Signal'])
-    
-    # Ultra-strict Bollinger Bands Signal - Require extreme deviations
-    df['BB_Signal'] = np.where(df['Close'] < df['LowerBB'] * 0.97, 1, 0)
-    df['BB_Signal'] = np.where(df['Close'] > df['UpperBB'] * 1.03, -1, df['BB_Signal'])
-    
-    # Ultra-strict Stochastic Signal - Extreme overbought/oversold conditions
-    df['Stochastic_Signal'] = np.where((df['SlowK'] < 10) & (df['SlowD'] < 10), 1, 0)
-    df['Stochastic_Signal'] = np.where((df['SlowK'] > 95) & (df['SlowD'] > 95), -1, df['Stochastic_Signal'])
-    
-    # Ultra-strict CMF Signal - Require stronger money flow confirmation
-    df['CMF_Signal'] = np.where(df['CMF'] > 0.4, -1, np.where(df['CMF'] < -0.4, 1, 0))
-    
-    # Ultra-strict CCI Signal - Require extreme deviations
-    df['CCI_Signal'] = np.where(df['CCI'] < -220, 1, 0)
-    df['CCI_Signal'] = np.where(df['CCI'] > 220, -1, df['CCI_Signal'])
-    
-    # Combined signal for ultra-strict confirmations
-    df['Combined_Signal'] = df[['MACD_Signal','RSI_Signal', 'BB_Signal', 
+
+    # Less strict RSI Signal - Use common thresholds (30/70 instead of 15/90)
+    df['RSI_Signal'] = np.where(df['RSI'] < 30, 1, 0)
+    df['RSI_Signal'] = np.where(df['RSI'] > 70, -1, df['RSI_Signal'])
+
+    # Less strict Bollinger Bands Signal - Touch or slight breach (no 3% buffer)
+    df['BB_Signal'] = np.where(df['Close'] <= df['LowerBB'], 1, 0)
+    df['BB_Signal'] = np.where(df['Close'] >= df['UpperBB'], -1, df['BB_Signal'])
+
+    # Less strict Stochastic Signal - Standard overbought/oversold (20/80 instead of 10/95)
+    df['Stochastic_Signal'] = np.where((df['SlowK'] < 20) & (df['SlowD'] < 20), 1, 0)
+    df['Stochastic_Signal'] = np.where((df['SlowK'] > 80) & (df['SlowD'] > 80), -1, df['Stochastic_Signal'])
+
+    # Less strict CMF Signal - Use ±0.1 instead of ±0.4
+    df['CMF_Signal'] = np.where(df['CMF'] > 0.1, -1, np.where(df['CMF'] < -0.1, 1, 0))
+
+    # Less strict CCI Signal - Standard thresholds (±100 instead of ±220)
+    df['CCI_Signal'] = np.where(df['CCI'] < -100, 1, 0)
+    df['CCI_Signal'] = np.where(df['CCI'] > 100, -1, df['CCI_Signal'])
+
+    # Combined signal (still sums all component signals)
+    df['Combined_Signal'] = df[['MACD_Signal', 'RSI_Signal', 'BB_Signal',
                                 'Stochastic_Signal', 'CMF_Signal', 'CCI_Signal']].sum(axis=1)
-    
-    return df
 
+    return df
 
 def plot_combined_signals(df, ticker):
     # Create a figure
@@ -563,18 +561,6 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
     # Create tabs for different analysis types
     with gr.Tabs() as tabs:
 
-        with gr.TabItem("Technical Analysis"):
-            analysis_button = gr.Button("Generate Technical Analysis")
-        
-            individual_signals = gr.Plot(label="Individual Trading Signals")
-            combined_signals = gr.Plot(label="Combined Trading Signals")
-            
-            # Connect button to function
-            analysis_button.click(
-                technical_analysis,
-                inputs=[ticker_input, start_date_input, end_date_input],
-                outputs=[combined_signals, individual_signals]
-            )
 
         with gr.TabItem("TimesFM Forecast"):
             timesfm_button = gr.Button("Generate TimesFM Forecast")
@@ -592,6 +578,19 @@ with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
             prophet_recent_plot = gr.Plot(label="Recent Stock Prices")
             prophet_forecast_plot = gr.Plot(label="Prophet 30-Day Forecast")
             prophet_components = gr.Plot(label="Forecast Components")  # Changed from gr.Image to gr.Plot
+
+        with gr.TabItem("Technical Analysis"):
+            analysis_button = gr.Button("Generate Technical Analysis")
+        
+            individual_signals = gr.Plot(label="Individual Trading Signals")
+            combined_signals = gr.Plot(label="Combined Trading Signals")
+            
+            # Connect button to function
+            analysis_button.click(
+                technical_analysis,
+                inputs=[ticker_input, start_date_input, end_date_input],
+                outputs=[combined_signals, individual_signals]
+            )
             
             # Connect button to function
             prophet_button.click(