Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,123 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import plotly.graph_objects as go
+from stockbot import StockAnalyzer
+import time
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
+st.set_page_config(page_title="Stock Analysis Dashboard", layout="wide")
 
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
+# Initialize session state
+if 'analyzer' not in st.session_state:
+    st.session_state.analyzer = StockAnalyzer()
+    st.session_state.last_update = None
 
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
+def create_candlestick_chart(symbol, price_data):
+    """Create a candlestick chart using plotly"""
+    fig = go.Figure(data=[go.Candlestick(x=price_data.index,
+                                        open=price_data['Open'],
+                                        high=price_data['High'],
+                                        low=price_data['Low'],
+                                        close=price_data['Close'])])
+    fig.update_layout(title=f'{symbol} Price History',
+                     xaxis_title='Date',
+                     yaxis_title='Price ($)',
+                     template='plotly_dark')
+    return fig
 
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
+def main():
+    st.title("Stock Analysis Dashboard")
+    
+    # Add refresh button
+    col1, col2 = st.columns([1, 5])
+    with col1:
+        if st.button("🔄 Refresh Data"):
+            st.session_state.analyzer.update_data()
+            st.session_state.analyzer.analyze_stocks()
+            st.session_state.last_update = time.strftime("%Y-%m-%d %H:%M:%S")
+            st.success("Data refreshed!")
+    
+    with col2:
+        if st.session_state.last_update:
+            st.text(f"Last Update: {st.session_state.last_update}")
 
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
+    # Display stock analysis results
+    results = st.session_state.analyzer.analysis_results
+    if results:
+        # Convert results to DataFrame for easier display
+        data = []
+        for symbol, info in results.items():
+            data.append({
+                'Symbol': symbol,
+                'Price': f"${info['current_price']}",
+                '7d Change': f"{info['change_7d']}%",
+                '30d Change': f"{info['change_30d']}%",
+                '90d Change': f"{info['change_90d']}%",
+                'YTD Change': f"{info['change_365d']}%",
+                'RSI': round(info['rsi'], 2),
+                'Trend': info['trend'],
+                'Support': f"${info['support']}",
+                'Resistance': f"${info['resistance']}",
+                'Recommendation': info['recommendation'],
+                'LLM Sentiment': info['llm_analysis']['sentiment'],
+                'LLM Confidence': f"{info['llm_analysis']['confidence']:.1%}",
+                '7d Prediction': f"{info['predictions']['prediction_7d']}%",
+                '21d Prediction': f"{info['predictions']['prediction_21d']}%"
+            })
+        
+        df = pd.DataFrame(data)
+        
+        # Style the dataframe
+        def color_negative_red(val):
+            try:
+                value = float(val.replace('%', '').replace('$', ''))
+                color = 'red' if value < 0 else 'green' if value > 0 else 'white'
+                return f'color: {color}'
+            except:
+                return ''
+        
+        styled_df = df.style.applymap(color_negative_red, subset=['7d Change', '30d Change', '90d Change', 'YTD Change'])
+        
+        # Display the main table
+        st.dataframe(styled_df, height=400)
+        
+        # Add detailed view for selected stock
+        st.subheader("Detailed Stock Analysis")
+        selected_symbol = st.selectbox("Select a stock for detailed analysis", list(results.keys()))
+        
+        if selected_symbol:
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                # Display candlestick chart
+                if selected_symbol in st.session_state.analyzer.price_data:
+                    price_data = st.session_state.analyzer.price_data[selected_symbol]
+                    fig = create_candlestick_chart(selected_symbol, price_data)
+                    st.plotly_chart(fig, use_container_width=True)
+            
+            with col2:
+                # Display detailed analysis
+                stock_info = results[selected_symbol]
+                st.write("### Technical Analysis")
+                st.write(f"**Current Price:** ${stock_info['current_price']}")
+                st.write(f"**RSI:** {stock_info['rsi']:.2f}")
+                st.write(f"**Trend:** {stock_info['trend']}")
+                st.write(f"**Support Levels:** ${stock_info['support']}")
+                st.write(f"**Resistance Levels:** ${stock_info['resistance']}")
+                
+                st.write("### LLM Analysis")
+                st.write(f"**Sentiment:** {stock_info['llm_analysis']['sentiment']}")
+                st.write(f"**Confidence:** {stock_info['llm_analysis']['confidence']:.1%}")
+                
+                st.write("### Predictions")
+                st.write(f"**7-Day Forecast:** {stock_info['predictions']['prediction_7d']}%")
+                st.write(f"**21-Day Forecast:** {stock_info['predictions']['prediction_21d']}%")
+                st.write(f"**Confidence:** {stock_info['predictions']['confidence']}%")
+                
+                # Display support/resistance analysis from LLM
+                if 'support_resistance' in stock_info['llm_analysis']:
+                    st.write("### Support & Resistance Analysis")
+                    sr_analysis = stock_info['llm_analysis']['support_resistance']
+                    st.write(sr_analysis['analysis'])
 
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+if __name__ == "__main__":
+    main()