Update app.py

app.py

@@ -4,7 +4,7 @@ import yfinance as yf
 import plotly.graph_objects as go
 import numpy as np
 
-# Functions for calculating indicators (keeping these unchanged)
+# Functions for calculating indicators
 def calculate_sma(df, window):
     return df['Close'].rolling(window=window).mean()
 
@@ -51,7 +51,51 @@ def calculate_cci(df, window=20):
     cci = (typical_price - sma) / (0.015 * mean_deviation)
     return cci
 
-def generate_trading_signals(df):
+# Function to adjust thresholds based on sensitivity
+def adjust_thresholds_by_sensitivity(sensitivity):
+    """
+    Convert a single sensitivity value (1-10) to appropriate thresholds
+    1 = Most sensitive (more signals)
+    10 = Least sensitive (fewer, stronger signals)
+    """
+    # Map sensitivity to thresholds
+    if sensitivity == 1:  # Most sensitive
+        return {
+            'SMA': 5,
+            'RSI_lower': 30,
+            'RSI_upper': 70,
+            'BB': 0.5,
+            'Stochastic_lower': 20,
+            'Stochastic_upper': 80,
+            'CMF': 0.1,
+            'CCI': 100
+        }
+    elif sensitivity == 10:  # Least sensitive
+        return {
+            'SMA': 50,
+            'RSI_lower': 5,
+            'RSI_upper': 95,
+            'BB': 5,
+            'Stochastic_lower': 5,
+            'Stochastic_upper': 95,
+            'CMF': 0.6,
+            'CCI': 300
+        }
+    else:
+        # Linear interpolation between extremes
+        factor = (sensitivity - 1) / 9  # 0 to 1
+        return {
+            'SMA': int(5 + (50 - 5) * factor),
+            'RSI_lower': int(30 - (30 - 5) * factor),
+            'RSI_upper': int(70 + (95 - 70) * factor),
+            'BB': 0.5 + (5 - 0.5) * factor,
+            'Stochastic_lower': int(20 - (20 - 5) * factor),
+            'Stochastic_upper': int(80 + (95 - 80) * factor),
+            'CMF': 0.1 + (0.6 - 0.1) * factor,
+            'CCI': int(100 + (300 - 100) * factor)
+        }
+
+def generate_trading_signals(df, thresholds, enabled_signals):
     # Calculate various indicators
     df['SMA_30'] = calculate_sma(df, 30)
     df['SMA_100'] = calculate_sma(df, 100)
@@ -63,54 +107,71 @@ def generate_trading_signals(df):
     df['CMF'] = calculate_cmf(df)
     df['CCI'] = calculate_cci(df)
 
-    # Generate trading signals with stricter SMA threshold
-    # Making SMA threshold stricter - require 3% difference between SMAs
-    df['SMA_Diff_Pct'] = (df['SMA_30'] - df['SMA_100']) / df['SMA_100'] * 100
-    df['SMA_Signal'] = np.where(df['SMA_Diff_Pct'] > 30, 1, 0)  # Buy when SMA_30 is 30% above SMA_100
-    df['SMA_Signal'] = np.where(df['SMA_Diff_Pct'] < -30, -1, df['SMA_Signal'])  # Sell when SMA_30 is 30% below SMA_100
+    # Initialize all signals as 0 (no signal)
+    signal_columns = ['SMA_Signal', 'MACD_Signal', 'RSI_Signal', 'BB_Signal', 
+                      'Stochastic_Signal', 'CMF_Signal', 'CCI_Signal']
+    for col in signal_columns:
+        df[col] = 0
+    
+    # Only generate signals for enabled indicators
+    
+    # SMA Signal
+    if 'SMA' in enabled_signals:
+        sma_threshold = thresholds['SMA']
+        df['SMA_Diff_Pct'] = (df['SMA_30'] - df['SMA_100']) / df['SMA_100'] * 100
+        df['SMA_Signal'] = np.where(df['SMA_Diff_Pct'] > sma_threshold, 1, 0)
+        df['SMA_Signal'] = np.where(df['SMA_Diff_Pct'] < -sma_threshold, -1, df['SMA_Signal'])
+    
+    # MACD Signal
+    if 'MACD' in enabled_signals:
+        macd, signal = calculate_macd(df)
+        df['MACD'] = macd
+        df['MACD_Signal_Line'] = signal
+        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)
     
-    macd, signal = calculate_macd(df)
-    df['MACD'] = macd
-    df['MACD_Signal_Line'] = signal
-    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)
-
     # RSI Signals
-    df['RSI_Signal'] = np.where(df['RSI'] < 12, 1, 0)
-    df['RSI_Signal'] = np.where(df['RSI'] > 95, -1, df['RSI_Signal'])
+    if 'RSI' in enabled_signals:
+        rsi_lower = thresholds['RSI_lower']
+        rsi_upper = thresholds['RSI_upper']
+        df['RSI_Signal'] = np.where(df['RSI'] < rsi_lower, 1, 0)
+        df['RSI_Signal'] = np.where(df['RSI'] > rsi_upper, -1, df['RSI_Signal'])
     
-    # Bollinger Bands with buffer
-    buffer_percentage = 0.01  # 1% buffer
-    df['BB_Signal'] = np.where(
-        (df['Close'] < df['LowerBB'] * (1 - buffer_percentage)) & 
-        (df['Close'].shift(1) < df['LowerBB'].shift(1) * (1 - buffer_percentage)) & 
-        (df['Close'].shift(2) < df['LowerBB'].shift(2) * (1 - buffer_percentage)), 1, 0
-    )
-    df['BB_Signal'] = np.where(
-        (df['Close'] > df['UpperBB'] * (1 + buffer_percentage)) & 
-        (df['Close'].shift(1) > df['UpperBB'].shift(1) * (1 + buffer_percentage)) & 
-        (df['Close'].shift(2) > df['UpperBB'].shift(2) * (1 + buffer_percentage)), -1, df['BB_Signal']
-    )
+    # Bollinger Bands
+    if 'BB' in enabled_signals:
+        bb_buffer = thresholds['BB'] / 100  # Convert percentage to decimal
+        df['BB_Signal'] = np.where(
+            (df['Close'] < df['LowerBB'] * (1 - bb_buffer)) & 
+            (df['Close'].shift(1) < df['LowerBB'].shift(1) * (1 - bb_buffer)) & 
+            (df['Close'].shift(2) < df['LowerBB'].shift(2) * (1 - bb_buffer)), 1, 0
+        )
+        df['BB_Signal'] = np.where(
+            (df['Close'] > df['UpperBB'] * (1 + bb_buffer)) & 
+            (df['Close'].shift(1) > df['UpperBB'].shift(1) * (1 + bb_buffer)) & 
+            (df['Close'].shift(2) > df['UpperBB'].shift(2) * (1 + bb_buffer)), -1, df['BB_Signal']
+        )
     
     # Stochastic signals
-    df['Stochastic_Signal'] = np.where((df['SlowK'] < 5) & (df['SlowD'] < 5), 1, 0)
-    df['Stochastic_Signal'] = np.where((df['SlowK'] > 99) & (df['SlowD'] > 95), -1, df['Stochastic_Signal'])
-
+    if 'Stochastic' in enabled_signals:
+        stoch_lower = thresholds['Stochastic_lower']
+        stoch_upper = thresholds['Stochastic_upper']
+        df['Stochastic_Signal'] = np.where((df['SlowK'] < stoch_lower) & (df['SlowD'] < stoch_lower), 1, 0)
+        df['Stochastic_Signal'] = np.where((df['SlowK'] > stoch_upper) & (df['SlowD'] > stoch_upper), -1, df['Stochastic_Signal'])
+    
     # CMF Signals
-    df['CMF_Signal'] = np.where(df['CMF'] > 0.4, -1, np.where(df['CMF'] < -0.4, 1, 0))
-
+    if 'CMF' in enabled_signals:
+        cmf_threshold = thresholds['CMF']
+        df['CMF_Signal'] = np.where(df['CMF'] > cmf_threshold, -1, np.where(df['CMF'] < -cmf_threshold, 1, 0))
+    
     # CCI Signals
-    df['CCI_Signal'] = np.where(df['CCI'] < -195, 1, 0)
-    df['CCI_Signal'] = np.where(df['CCI'] > 195, -1, df['CCI_Signal'])
-
-    # Combined signal (keeping for reference but not used in the output)
-    df['Combined_Signal'] = df[['RSI_Signal', 'BB_Signal', 
-                               'Stochastic_Signal', 'CMF_Signal', 
-                               'CCI_Signal', 'MACD_Signal']].sum(axis=1)
+    if 'CCI' in enabled_signals:
+        cci_threshold = thresholds['CCI']
+        df['CCI_Signal'] = np.where(df['CCI'] < -cci_threshold, 1, 0)
+        df['CCI_Signal'] = np.where(df['CCI'] > cci_threshold, -1, df['CCI_Signal'])
 
     return df
 
-def plot_simplified_signals(df, ticker):
+def plot_simplified_signals(df, ticker, enabled_signals):
     # Create a figure with improved styling
     fig = go.Figure()
     
@@ -140,30 +201,30 @@ def plot_simplified_signals(df, ticker):
     ))
 
     # Add bollinger bands with lighter appearance
-    fig.add_trace(go.Scatter(
-        x=df.index, y=df['UpperBB'],
-        mode='lines',
-        name='Upper BB',
-        line=dict(color='rgba(250, 250, 250, 0.3)', width=1),
-        showlegend=True
-    ))
-    
-    fig.add_trace(go.Scatter(
-        x=df.index, y=df['LowerBB'],
-        mode='lines',
-        name='Lower BB',
-        line=dict(color='rgba(250, 250, 250, 0.3)', width=1),
-        fill='tonexty',
-        fillcolor='rgba(173, 216, 230, 0.1)',
-        showlegend=True
-    ))
+    if 'BB' in enabled_signals:
+        fig.add_trace(go.Scatter(
+            x=df.index, y=df['UpperBB'],
+            mode='lines',
+            name='Upper BB',
+            line=dict(color='rgba(250, 250, 250, 0.3)', width=1),
+            showlegend=True
+        ))
+        
+        fig.add_trace(go.Scatter(
+            x=df.index, y=df['LowerBB'],
+            mode='lines',
+            name='Lower BB',
+            line=dict(color='rgba(250, 250, 250, 0.3)', width=1),
+            fill='tonexty',
+            fillcolor='rgba(173, 216, 230, 0.1)',
+            showlegend=True
+        ))
 
     # Group signals by type to reduce legend clutter
     buy_signals_df = pd.DataFrame(index=df.index)
     sell_signals_df = pd.DataFrame(index=df.index)
     
-    signal_names = ['RSI_Signal', 'BB_Signal', 'Stochastic_Signal', 
-                   'CMF_Signal', 'CCI_Signal', 'MACD_Signal', 'SMA_Signal']
+    signal_names = [f"{signal}_Signal" for signal in enabled_signals]
     
     # Collect all buy and sell signals
     for signal in signal_names:
@@ -213,7 +274,7 @@ def plot_simplified_signals(df, ticker):
             hovertext=sell_texts
         ))
 
-    # Improve the layout
+    # Improve the layout with larger dimensions
     fig.update_layout(
         title=dict(
             text=f'{ticker}: Technical Analysis & Trading Signals',
@@ -247,7 +308,9 @@ def plot_simplified_signals(df, ticker):
             xanchor='center',
             x=0.5
         ),
-        margin=dict(l=50, r=50, b=100, t=100, pad=4)
+        margin=dict(l=50, r=50, b=100, t=100, pad=4),
+        height=800,  # Increased height
+        width=1200   # Increased width
     )
     
     # Add range selector for better time navigation
@@ -270,7 +333,10 @@ def plot_simplified_signals(df, ticker):
     
     return fig
 
-def stock_analysis(ticker, start_date, end_date):
+def stock_analysis(ticker, start_date, end_date, 
+                  sensitivity,  # New simplified parameter
+                  use_sma, use_macd, use_rsi, use_bb, 
+                  use_stoch, use_cmf, use_cci):
     try:
         # Download stock data from Yahoo Finance
         df = yf.download(ticker, start=start_date, end=end_date)
@@ -287,7 +353,9 @@ def stock_analysis(ticker, start_date, end_date):
             )
             fig.update_layout(
                 plot_bgcolor='#1e1e1e',
-                paper_bgcolor='#1e1e1e'
+                paper_bgcolor='#1e1e1e',
+                height=800,
+                width=1200
             )
             return fig
 
@@ -295,16 +363,33 @@ def stock_analysis(ticker, start_date, end_date):
         if isinstance(df.columns, pd.MultiIndex):
             df.columns = df.columns.droplevel(1) if len(df.columns.levels) > 1 else df.columns
         
+        # Create list of enabled signals
+        enabled_signals = []
+        if use_sma: enabled_signals.append('SMA')
+        if use_macd: enabled_signals.append('MACD')
+        if use_rsi: enabled_signals.append('RSI')
+        if use_bb: enabled_signals.append('BB')
+        if use_stoch: enabled_signals.append('Stochastic')
+        if use_cmf: enabled_signals.append('CMF')
+        if use_cci: enabled_signals.append('CCI')
+        
+        # If no signals are enabled, enable all by default
+        if not enabled_signals:
+            enabled_signals = ['SMA', 'MACD', 'RSI', 'BB', 'Stochastic', 'CMF', 'CCI']
+        
+        # Get thresholds from sensitivity
+        thresholds = adjust_thresholds_by_sensitivity(sensitivity)
+        
         # Generate signals
-        df = generate_trading_signals(df)
+        df = generate_trading_signals(df, thresholds, enabled_signals)
         
         # Last 360 days for plotting (or all data if less than 360 days)
         df_last_360 = df.tail(min(360, len(df)))
 
         # Plot simplified signals
-        fig_individual = plot_simplified_signals(df_last_360, ticker)
+        fig = plot_simplified_signals(df_last_360, ticker, enabled_signals)
         
-        return fig_individual
+        return fig
         
     except Exception as e:
         # Create error figure
@@ -319,7 +404,9 @@ def stock_analysis(ticker, start_date, end_date):
         fig.update_layout(
             plot_bgcolor='#1e1e1e',
             paper_bgcolor='#1e1e1e',
-            font=dict(color='white')
+            font=dict(color='white'),
+            height=800,
+            width=1200
         )
         return fig
 
@@ -333,8 +420,8 @@ custom_theme = gr.themes.Monochrome(
 )
 
 with gr.Blocks(theme=custom_theme) as demo:
-    gr.Markdown("# Signal Analysis")
-    gr.Markdown("This app analyzes stock data and visualizes trading signals based on multiple technical indicators with a clean, simplified display.")
+    gr.Markdown("# Stock Market Signal Analysis")
+    gr.Markdown("This app helps you analyze stocks with technical indicators and generates trading signals.")
 
     with gr.Row():
         with gr.Column(scale=1):
@@ -351,19 +438,45 @@ with gr.Blocks(theme=custom_theme) as demo:
             end_date_input = gr.Textbox(
                 label="End Date",
                 placeholder="YYYY-MM-DD",
-                value="2026-01-01"
+                value="2025-03-10"  # Updated to current date
             )
             
+            gr.Markdown("### Choose Indicators")
+            with gr.Row():
+                use_sma = gr.Checkbox(label="SMA", value=True)
+                use_macd = gr.Checkbox(label="MACD", value=True)
+                use_rsi = gr.Checkbox(label="RSI", value=True)
+                use_bb = gr.Checkbox(label="Bollinger", value=True)
+                use_stoch = gr.Checkbox(label="Stochastic", value=True)
+                use_cmf = gr.Checkbox(label="CMF", value=True)
+                use_cci = gr.Checkbox(label="CCI", value=True)
+            
+            gr.Markdown("### Signal Sensitivity")
+            with gr.Row():
+                sensitivity = gr.Slider(
+                    label="Signal Sensitivity", 
+                    minimum=1, 
+                    maximum=10, 
+                    step=1, 
+                    value=5, 
+                    info="1 = (sensitive), 10 = (strict)"
+                )
+            
             # Create a submit button with styling
             button = gr.Button("Analyze Stock", variant="primary")
     
-    # Output: Signals plot
+    # Output: Signals plot with increased height
     signals_output = gr.Plot(label="Technical Analysis & Trading Signals")
 
-    # Link button to function
+    # Link button to function with updated parameters
     button.click(
         stock_analysis, 
-        inputs=[ticker_input, start_date_input, end_date_input], 
+        inputs=[
+            ticker_input, start_date_input, end_date_input,
+            sensitivity,  # Single threshold parameter
+            use_sma, use_macd, use_rsi, use_bb, 
+            use_stoch, use_cmf, use_cci
+        ], 
         outputs=[signals_output]
     )
 
@@ -373,21 +486,16 @@ with gr.Blocks(theme=custom_theme) as demo:
     - **Red Triangle Down (▼)** indicates Sell signals
     - Hover over signals to see which indicators triggered them
     
-    ## 🔍 Indicators & Thresholds
-    - **SMA**: Simple Moving Average (30 & 100 days) - 30% threshold
-    - **MACD**: Moving Average Convergence Divergence (12, 26, 9)
-    - **RSI**: Relative Strength Index (Buy < 12, Sell > 95)
-    - **BB**: Bollinger Bands (with 1% buffer)
-    - **Stochastic**: Stochastic Oscillator (Buy < 5, Sell > 99)
-    - **CMF**: Chaikin Money Flow (Buy < -0.4, Sell > 0.4)
-    - **CCI**: Commodity Channel Index (Buy < -195, Sell > 195)
+    ## 🔍 Signal Sensitivity Explained
+    - **Lower values (1-3)**: More frequent signals, good for short-term trading
+    - **Medium values (4-6)**: Balanced approach, moderate number of signals
+    - **Higher values (7-10)**: Fewer but potentially stronger signals, good for long-term investors
     
-    ## 💡 Visualization Improvements
-    - Simple line chart for price
-    - Consolidated buy/sell signals
-    - Reduced visual clutter
-    - Enhanced hover information
-    - Interactive time range selection
+    ## 🛠️ Trading Strategy Tips
+    - **Day Trading**: Use lower sensitivity with multiple indicators
+    - **Swing Trading**: Use medium sensitivity with 3-4 indicators
+    - **Long-term Investing**: Use higher sensitivity focusing on trend indicators
+    - **Combine**: Using multiple indicators helps confirm signals and reduce false positives
     """)
 
 # Launch the interface