Update app.py

app.py

@@ -25,55 +25,62 @@ openai.api_key = OPENAI_API_KEY
 # Alpha Vantage API key
 ALPHA_VANTAGE_API_KEY = "JK0DVDNTEYBTBP5L"
 
+# GPT Assistant ID
+ASSISTANT_ID = "asst_Fl3rRrRijb8FJDpqjBexfUBp"
+
 # Custom CSS
 st.markdown("""
 <style>
     .reportview-container {
-        background: linear-gradient(to right, #1e3c72, #2a5298);
+        background: linear-gradient(to bottom right, #10161e, #1f2937);
     }
     .main .block-container {
-        max-width: 95%;
         padding-top: 2rem;
         padding-bottom: 2rem;
     }
     h1, h2, h3 {
-        color: #f0f2f6;
+        color: #3db892;
     }
-    .stButton>button {
-        color: #ffffff;
-        background-color: #4CAF50;
+    .stButton > button {
+        color: white;
+        background-color: #3db892;
         border-radius: 5px;
         border: none;
-        padding: 10px 24px;
+        padding: 0.5rem 1rem;
+        font-weight: bold;
         transition: all 0.3s ease 0s;
     }
-    .stButton>button:hover {
-        background-color: #45a049;
-    }
-    .stTextInput>div>div>input {
-        color: #1e3c72;
+    .stButton > button:hover {
+        background-color: #2c8d6f;
     }
-    .stDateInput>div>div>input {
-        color: #1e3c72;
+    .stTextInput > div > div > input,
+    .stDateInput > div > div > input {
+        background-color: #1f2937;
+        color: white;
+        border: 1px solid #3db892;
     }
     .stPlotlyChart {
-        background-color: rgba(255, 255, 255, 0.1);
+        background-color: #1f2937;
         border-radius: 5px;
         padding: 10px;
     }
     .css-1d391kg {
-        background-color: rgba(255, 255, 255, 0.05);
+        background-color: #1f2937;
     }
     .stDataFrame {
-        background-color: rgba(255, 255, 255, 0.1);
+        background-color: #1f2937;
+    }
+    .stTable {
+        background-color: #1f2937;
+    }
+    .css-1s0xp3b {
+        background-color: #1f2937;
+        border: 1px solid #3db892;
         border-radius: 5px;
-        padding: 10px;
     }
 </style>
-
 """, unsafe_allow_html=True)
 
-@st.cache_data
 def get_financial_data(ticker, end_date):
     base_url = "https://www.alphavantage.co/query"
     functions = ['INCOME_STATEMENT', 'BALANCE_SHEET', 'CASH_FLOW']
@@ -105,7 +112,6 @@ def get_financial_data(ticker, end_date):
 
     return data
 
-@st.cache_data
 def get_earnings_dates(ticker):
     url = f"https://www.alphavantage.co/query?function=EARNINGS&symbol={ticker}&apikey={ALPHA_VANTAGE_API_KEY}"
     response = requests.get(url)
@@ -119,7 +125,6 @@ def get_earnings_dates(ticker):
     
     return earnings_dates
 
-@st.cache_data
 def get_earnings_data(ticker):
     url = f"https://www.alphavantage.co/query?function=EARNINGS&symbol={ticker}&apikey={ALPHA_VANTAGE_API_KEY}"
     response = requests.get(url)
@@ -137,7 +142,6 @@ def get_earnings_data(ticker):
     
     return df
 
-@st.cache_data
 def process_financial_data(data, earnings_dates, earnings_data):
     quarterly_data = {}
 
@@ -161,7 +165,6 @@ def process_financial_data(data, earnings_dates, earnings_data):
 
     return df
 
-@st.cache_data
 def get_stock_data(ticker, start_date, end_date):
     df = yf.download(ticker, start=start_date, end=end_date)
     
@@ -295,7 +298,6 @@ def plot_results(dates, y, fair_values, lower_bound, upper_bound, scaler_y):
 
     return fig
 
-@st.cache_data
 def get_monthly_seasonality(ticker, start_date, end_date):
     data = yf.download(ticker, start=start_date, end=end_date)
     monthly_data = data['Adj Close'].resample('M').last()
@@ -435,77 +437,104 @@ def plot_interactive_logarithmic_stock_chart(ticker, start_date, end_date):
     return fig
 
 def analyze_stock(ticker, start_date, end_date, use_ai_assistant):
-    with st.spinner('Analyzing stock data...'):
-        try:
-            financial_data = get_financial_data(ticker, end_date)
-            earnings_dates = get_earnings_dates(ticker)
-            earnings_data = get_earnings_data(ticker)
-            quarterly_df = process_financial_data(financial_data, earnings_dates, earnings_data)
-            stock_df = get_stock_data(ticker, start_date, end_date)
+    try:
+        financial_data = get_financial_data(ticker, end_date)
+        earnings_dates = get_earnings_dates(ticker)
+        earnings_data = get_earnings_data(ticker)
+        quarterly_df = process_financial_data(financial_data, earnings_dates, earnings_data)
+        stock_df = get_stock_data(ticker, start_date, end_date)
+
+        if quarterly_df.empty:
+            st.error("No financial data available for processing.")
+            return None
 
-            if quarterly_df.empty:
-                st.error("No financial data available for processing.")
-                return
+        X_scaled, y_scaled, dates, scaler_X, scaler_y = prepare_data(quarterly_df, stock_df, end_date)
 
-            X_scaled, y_scaled, dates, scaler_X, scaler_y = prepare_data(quarterly_df, stock_df, end_date)
+        X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42)
 
-            X_train, X_test, y_train, y_test = train_test_split(X_scaled, y_scaled, test_size=0.2, random_state=42)
+        model = train_catboost_model(X_train, X_test, y_train, y_test)
+        
+        r2 = evaluate_model(model, X_test, y_test, scaler_y)
+
+        if r2 < 0.5:
+            st.warning("Model performance is poor. Results may not be reliable.")
+
+        fair_values, lower_bound, upper_bound = conformal_prediction(model, X_train, y_train, X_scaled, scaler_y)
+
+        fig = plot_results(dates, y_scaled, fair_values, lower_bound, upper_bound, scaler_y)
+
+        feature_importance = model.feature_importances_
+        feature_importance_df = pd.DataFrame({'feature': X_scaled.columns, 'importance': feature_importance})
+        feature_importance_df = feature_importance_df.sort_values('importance', ascending=False)
+
+        explainer = shap.TreeExplainer(model)
+        shap_values = explainer.shap_values(X_scaled)
+
+        shap_fig, ax = plt.subplots(figsize=(10, 6))
+        shap.summary_plot(shap_values, X_scaled, plot_type="bar", show=False)
+        plt.title("SHAP Feature Importance")
+        plt.tight_layout()
+
+        seasonality = get_monthly_seasonality(ticker, start_date, end_date)
+        seasonality_fig = plot_monthly_seasonality(seasonality, ticker, start_date, end_date)
+
+        log_chart = plot_interactive_logarithmic_stock_chart(ticker, start_date, end_date)
+
+        gpt_analysis = get_gpt_analysis(ticker, financial_data) if use_ai_assistant else "AI assistant analysis not requested."
+
+        latest_close = stock_df['Close'].iloc[-1]
+        latest_fair_value = fair_values[-1]
+        latest_lower_bound = lower_bound[-1]
+        latest_upper_bound = upper_bound[-1]
+
+        percentage_change = ((latest_fair_value - latest_close) / latest_close) * 100
+
+        fair_price_html = f"""
+        <h2 style="margin-bottom: 15px;">Fair Price Analysis</h2>
+        <p><strong>Current Price:</strong> ${latest_close:.2f}</p>
+        <p><strong>Estimated Fair Value:</strong> ${latest_fair_value:.2f}</p>
+        <p><strong>Price Prediction Range:</strong> ${latest_lower_bound:.2f} to ${latest_upper_bound:.2f}</p>
+        <p><strong>R-squared Score:</strong> {r2:.4f}</p>
+        <h3 style="margin-top: 20px;">Top 10 most important features for fair value prediction:</h3>
+        <pre>{feature_importance_df.head(10).to_string(index=False)}</pre>
+        """
+
+        current_month = datetime.now().month
+        next_month = (current_month % 12) + 1
+
+        current_month_return = seasonality.loc[current_month, 'Mean Change%'] * 100
+        next_month_return = seasonality.loc[next_month, 'Mean Change%'] * 100
+        current_month_win_rate = seasonality.loc[current_month, 'Positive Periods'] * 100
+        next_month_win_rate = seasonality.loc[next_month, 'Positive Periods'] * 100
+
+        seasonality_html = f"""
+        <h2 style="margin-bottom: 15px;">Seasonality Analysis ({start_date} to {end_date})</h2>
+        <h3>Current month ({datetime.now().strftime('%B')}):</h3>
+        <p>Average return: {current_month_return:.2f}%</p>
+        <p>Probability of positive return: {current_month_win_rate:.1f}%</p>
+        <h3>Next month ({(datetime.now() + timedelta(days=31)).strftime('%B')}):</h3>
+        <p>Average return: {next_month_return:.2f}%</p>
+        <p>Probability of positive return: {next_month_win_rate:.1f}%</p>
+        """
+
+        return {
+            'fair_price_html': fair_price_html,
+            'fig': fig,
+            'shap_fig': shap_fig,
+            'seasonality_fig': seasonality_fig,
+            'seasonality_html': seasonality_html,
+            'gpt_analysis': gpt_analysis,
+            'log_chart': log_chart,
+            'feature_importance_df': feature_importance_df.head(10),
+            'percentage_change': percentage_change
+        }
 
-            model = train_catboost_model(X_train, X_test, y_train, y_test)
-            
-            r2 = evaluate_model(model, X_test, y_test, scaler_y)
-
-            if r2 < 0.5:
-                st.warning("Model performance is poor. Results may not be reliable.")
-
-            fair_values, lower_bound, upper_bound = conformal_prediction(model, X_train, y_train, X_scaled, scaler_y)
-
-            fig = plot_results(dates, y_scaled, fair_values, lower_bound, upper_bound, scaler_y)
-
-            feature_importance = model.feature_importances_
-            feature_importance_df = pd.DataFrame({'feature': X_scaled.columns, 'importance': feature_importance})
-            feature_importance_df = feature_importance_df.sort_values('importance', ascending=False)
-
-            explainer = shap.TreeExplainer(model)
-            shap_values = explainer.shap_values(X_scaled)
-
-            shap_fig, ax = plt.subplots(figsize=(10, 6))
-            shap.summary_plot(shap_values, X_scaled, plot_type="bar", show=False)
-            plt.title("SHAP Feature Importance")
-            plt.tight_layout()
-
-            seasonality = get_monthly_seasonality(ticker, start_date, end_date)
-            seasonality_fig = plot_monthly_seasonality(seasonality, ticker, start_date, end_date)
-
-            log_chart = plot_interactive_logarithmic_stock_chart(ticker, start_date, end_date)
-
-            gpt_analysis = get_gpt_analysis(ticker, financial_data) if use_ai_assistant else "AI assistant analysis not requested."
-
-            return {
-                'ticker': ticker,
-                'fair_values': fair_values,
-                'lower_bound': lower_bound,
-                'upper_bound': upper_bound,
-                'r2': r2,
-                'fig': fig,
-                'feature_importance_df': feature_importance_df,
-                'shap_fig': shap_fig,
-                'seasonality': seasonality,
-                'seasonality_fig': seasonality_fig,
-                'gpt_analysis': gpt_analysis,
-                'log_chart': log_chart,
-                'latest_close': stock_df['Close'].iloc[-1],
-                'latest_fair_value': fair_values[-1],
-                'latest_lower_bound': lower_bound[-1],
-                'latest_upper_bound': upper_bound[-1],
-            }
-
-        except Exception as e:
-            st.error(f"An error occurred: {str(e)}")
-            return None
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+        return None
 
 def main():
-    st.title("🚀 Advanced Stock Analysis App")
+    st.title("Advanced Stock Analysis App")
     st.markdown("Enter a stock ticker and date range to perform comprehensive stock analysis.")
 
     col1, col2, col3, col4 = st.columns([2,2,2,1])
@@ -519,60 +548,34 @@ def main():
         use_ai_assistant = st.checkbox("Use AI Assistant")
 
     if st.button("Analyze Stock", key="analyze_button"):
-        results = analyze_stock(ticker, start_date, end_date, use_ai_assistant)
-        
-        if results:
-            st.header("📊 Fair Price Analysis")
-            col1, col2, col3 = st.columns(3)
-            with col1:
-                st.metric("Current Price", f"${results['latest_close']:.2f}")
-            with col2:
-                change_pct = ((results['latest_fair_value'] - results['latest_close']) / results['latest_close'] * 100)
-                st.metric("Estimated Fair Value", f"${results['latest_fair_value']:.2f}", 
-                          f"{change_pct:+.2f}%", 
-                          delta_color="normal" if change_pct >= 0 else "inverse")
-            with col3:
-                st.metric("R-squared Score", f"{results['r2']:.4f}")
-
-            st.metric("Price Prediction Range", 
-                      f"${results['latest_lower_bound']:.2f} to ${results['latest_upper_bound']:.2f}")
-
-            st.subheader("📈 Fair Price Prediction")
-            st.plotly_chart(results['fig'], use_container_width=True)
-
-            col1, col2 = st.columns(2)
-            with col1:
-                st.subheader("🔍 SHAP Feature Importance")
-                st.pyplot(results['shap_fig'])
-            with col2:
-                st.subheader("🏆 Top 10 Important Features")
-                st.dataframe(results['feature_importance_df'].head(10), height=400)
-
-            st.subheader("📅 Monthly Seasonality")
-            st.plotly_chart(results['seasonality_fig'], use_container_width=True)
-
-            current_month = datetime.now().month
-            next_month = (current_month % 12) + 1
-            col1, col2 = st.columns(2)
-            with col1:
-                current_change = results['seasonality'].loc[current_month, 'Mean Change%']*100
-                st.metric(f"Current month ({datetime.now().strftime('%B')})", 
-                          f"{current_change:.2f}%",
-                          f"{results['seasonality'].loc[current_month, 'Positive Periods']*100:.1f}% probability of positive return",
-                          delta_color="normal" if current_change >= 0 else "inverse")
-            with col2:
-                next_change = results['seasonality'].loc[next_month, 'Mean Change%']*100
-                st.metric(f"Next month ({(datetime.now() + timedelta(days=31)).strftime('%B')})",
-                          f"{next_change:.2f}%",
-                          f"{results['seasonality'].loc[next_month, 'Positive Periods']*100:.1f}% probability of positive return",
-                          delta_color="normal" if next_change >= 0 else "inverse")
-
-            if results['gpt_analysis'] != "AI assistant analysis not requested.":
-                st.subheader("🤖 AI Assistant Analysis")
-                st.text_area("Analysis", value=results['gpt_analysis'], height=300)
-
-            st.subheader("📉 Logarithmic Stock Chart")
-            st.plotly_chart(results['log_chart'], use_container_width=True)
+        with st.spinner('Analyzing stock data...'):
+            results = analyze_stock(ticker, start_date, end_date, use_ai_assistant)
+            
+            if results:
+                st.header("Fair Price Analysis")
+                st.markdown(results['fair_price_html'], unsafe_allow_html=True)
+
+                st.subheader("Fair Price Prediction")
+                st.plotly_chart(results['fig'], use_container_width=True)
+
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.subheader("SHAP Feature Importance")
+                    st.pyplot(results['shap_fig'])
+                with col2:
+                    st.subheader("Top 10 Important Features")
+                    st.dataframe(results['feature_importance_df'], height=400)
+
+                st.subheader("Monthly Seasonality")
+                st.plotly_chart(results['seasonality_fig'], use_container_width=True)
+                st.markdown(results['seasonality_html'], unsafe_allow_html=True)
+
+                if results['gpt_analysis'] != "AI assistant analysis not requested.":
+                    st.subheader("AI Assistant Analysis")
+                    st.text_area("Analysis", value=results['gpt_analysis'], height=300)
+
+                st.subheader("Logarithmic Stock Chart")
+                st.plotly_chart(results['log_chart'], use_container_width=True)
 
 if __name__ == "__main__":
     main()