adds relevant indicators from structured product creation

README.md

@@ -13,7 +13,7 @@ short_description: Use Amazon Chronos To Predict Stock Prices
 
 # Stock Analysis and Prediction Demo
 
-A comprehensive stock analysis and prediction tool built with Gradio, featuring multiple prediction strategies and technical analysis indicators.
+A comprehensive stock analysis and prediction tool built with Gradio, featuring multiple prediction strategies and technical analysis indicators. The application is particularly suited for structured financial product creation and analysis.
 
 ## Features
 
@@ -25,7 +25,7 @@ A comprehensive stock analysis and prediction tool built with Gradio, featuring
   - RSI (Relative Strength Index)
   - MACD (Moving Average Convergence Divergence)
   - Bollinger Bands
-  - Simple Moving Averages (20 and 50-day)
+  - Simple Moving Averages (20, 50, and 200-day)
 
 - **Trading Signals**:
   - Buy/Sell recommendations based on multiple indicators
@@ -38,6 +38,41 @@ A comprehensive stock analysis and prediction tool built with Gradio, featuring
   - Volume analysis
   - Historical price trends
 
+- **Structured Product Analysis**:
+  - Extended prediction horizons (up to 1 year)
+  - Historical analysis up to 10 years
+  - Comprehensive risk metrics
+  - Sector and industry analysis
+  - Liquidity assessment
+
+## Structured Product Features
+
+### Extended Time Horizons
+- Prediction window up to 365 days
+- Historical data analysis up to 10 years
+- Long-term trend analysis
+- Extended technical indicators
+
+### Risk Analysis
+- Annualized volatility
+- Maximum drawdown analysis
+- Current drawdown tracking
+- Sharpe and Sortino ratios
+- Risk-adjusted return metrics
+
+### Product Metrics
+- Market capitalization
+- Sector and industry classification
+- Dividend yield analysis
+- Volume metrics
+- Liquidity scoring
+
+### Sector Analysis
+- Market cap ranking (Large/Mid/Small)
+- Sector exposure
+- Industry classification
+- Liquidity assessment
+
 ## Installation
 
 1. Clone the repository:
@@ -68,10 +103,37 @@ python app.py
 
 3. Enter a stock symbol (e.g., AAPL, GOOGL, MSFT) and select your desired parameters:
    - Timeframe (1d, 1h, 15m)
-   - Number of days to predict
+   - Number of days to predict (up to 365 days)
+   - Historical lookback period (up to 10 years)
    - Prediction strategy (Chronos or Technical)
 
-4. Click "Analyze Stock" to get predictions and trading signals
+4. Click "Analyze Stock" to get:
+   - Price predictions and trading signals
+   - Structured product metrics
+   - Risk analysis
+   - Sector analysis
+
+## Using for Structured Products
+
+### Initial Screening
+1. Use extended lookback period (up to 10 years) for long-term performance analysis
+2. Look for stocks with stable volatility and good risk-adjusted returns
+3. Check liquidity scores for trading feasibility
+
+### Risk Assessment
+1. Review risk metrics to match client risk profile
+2. Analyze maximum drawdowns for worst-case scenarios
+3. Compare risk-adjusted returns using Sharpe and Sortino ratios
+
+### Product Structuring
+1. Use prediction horizon (up to 1 year) for product maturity design
+2. Consider dividend yields for income-generating products
+3. Use sector analysis for proper diversification
+
+### Portfolio Construction
+1. Analyze multiple stocks for diversified bundles
+2. Use sector metrics to avoid overexposure
+3. Consider market cap rankings for appropriate sizing
 
 ## Prediction Strategies
 

app.py

@@ -60,9 +60,17 @@ def get_historical_data(symbol: str, timeframe: str = "1d", lookback_days: int =
         ticker = yf.Ticker(symbol)
         df = ticker.history(start=start_date, end=end_date, interval=interval)
         
+        # Get additional info for structured products
+        info = ticker.info
+        df['Market_Cap'] = info.get('marketCap', None)
+        df['Sector'] = info.get('sector', None)
+        df['Industry'] = info.get('industry', None)
+        df['Dividend_Yield'] = info.get('dividendYield', None)
+        
         # Calculate technical indicators
         df['SMA_20'] = df['Close'].rolling(window=20).mean()
         df['SMA_50'] = df['Close'].rolling(window=50).mean()
+        df['SMA_200'] = df['Close'].rolling(window=200).mean()
         df['RSI'] = calculate_rsi(df['Close'])
         df['MACD'], df['MACD_Signal'] = calculate_macd(df['Close'])
         df['BB_Upper'], df['BB_Middle'], df['BB_Lower'] = calculate_bollinger_bands(df['Close'])
@@ -70,6 +78,16 @@ def get_historical_data(symbol: str, timeframe: str = "1d", lookback_days: int =
         # Calculate returns and volatility
         df['Returns'] = df['Close'].pct_change()
         df['Volatility'] = df['Returns'].rolling(window=20).std()
+        df['Annualized_Vol'] = df['Volatility'] * np.sqrt(252)  # Annualized volatility
+        
+        # Calculate drawdown metrics
+        df['Rolling_Max'] = df['Close'].rolling(window=252, min_periods=1).max()
+        df['Drawdown'] = (df['Close'] - df['Rolling_Max']) / df['Rolling_Max']
+        df['Max_Drawdown'] = df['Drawdown'].rolling(window=252, min_periods=1).min()
+        
+        # Calculate liquidity metrics
+        df['Avg_Daily_Volume'] = df['Volume'].rolling(window=20).mean()
+        df['Volume_Volatility'] = df['Volume'].rolling(window=20).std()
         
         # Drop NaN values
         df = df.dropna()
@@ -265,9 +283,9 @@ def calculate_trading_signals(df: pd.DataFrame) -> Dict:
 
 def create_interface():
     """Create the Gradio interface"""
-    with gr.Blocks(title="Stock Analysis and Prediction") as demo:
-        gr.Markdown("# Stock Analysis and Prediction")
-        gr.Markdown("Enter a stock symbol and select parameters to get price forecasts and trading signals.")
+    with gr.Blocks(title="Structured Product Analysis") as demo:
+        gr.Markdown("# Structured Product Analysis")
+        gr.Markdown("Analyze stocks for inclusion in structured financial products with extended time horizons.")
         
         with gr.Row():
             with gr.Column():
@@ -279,11 +297,18 @@ def create_interface():
                 )
                 prediction_days = gr.Slider(
                     minimum=1,
-                    maximum=30,
-                    value=5,
+                    maximum=365,  # Extended to 1 year
+                    value=30,
                     step=1,
                     label="Days to Predict"
                 )
+                lookback_days = gr.Slider(
+                    minimum=365,
+                    maximum=3650,  # 10 years of history
+                    value=365,
+                    step=30,
+                    label="Historical Lookback (Days)"
+                )
                 strategy = gr.Dropdown(
                     choices=["chronos", "technical"],
                     label="Prediction Strategy",
@@ -295,10 +320,56 @@ def create_interface():
                 plot = gr.Plot(label="Analysis and Prediction")
                 signals = gr.JSON(label="Trading Signals")
         
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### Structured Product Metrics")
+                metrics = gr.JSON(label="Product Metrics")
+                
+                gr.Markdown("### Risk Analysis")
+                risk_metrics = gr.JSON(label="Risk Metrics")
+                
+                gr.Markdown("### Sector Analysis")
+                sector_metrics = gr.JSON(label="Sector Metrics")
+        
+        def analyze_stock(symbol, timeframe, prediction_days, lookback_days, strategy):
+            signals, fig = make_prediction(symbol, timeframe, prediction_days, strategy)
+            
+            # Get historical data for additional metrics
+            df = get_historical_data(symbol, timeframe, lookback_days)
+            
+            # Calculate structured product metrics
+            product_metrics = {
+                "Market_Cap": df['Market_Cap'].iloc[-1],
+                "Sector": df['Sector'].iloc[-1],
+                "Industry": df['Industry'].iloc[-1],
+                "Dividend_Yield": df['Dividend_Yield'].iloc[-1],
+                "Avg_Daily_Volume": df['Avg_Daily_Volume'].iloc[-1],
+                "Volume_Volatility": df['Volume_Volatility'].iloc[-1]
+            }
+            
+            # Calculate risk metrics
+            risk_metrics = {
+                "Annualized_Volatility": df['Annualized_Vol'].iloc[-1],
+                "Max_Drawdown": df['Max_Drawdown'].iloc[-1],
+                "Current_Drawdown": df['Drawdown'].iloc[-1],
+                "Sharpe_Ratio": (df['Returns'].mean() * 252) / (df['Returns'].std() * np.sqrt(252)),
+                "Sortino_Ratio": (df['Returns'].mean() * 252) / (df['Returns'][df['Returns'] < 0].std() * np.sqrt(252))
+            }
+            
+            # Calculate sector metrics
+            sector_metrics = {
+                "Sector": df['Sector'].iloc[-1],
+                "Industry": df['Industry'].iloc[-1],
+                "Market_Cap_Rank": "Large" if df['Market_Cap'].iloc[-1] > 1e10 else "Mid" if df['Market_Cap'].iloc[-1] > 1e9 else "Small",
+                "Liquidity_Score": "High" if df['Avg_Daily_Volume'].iloc[-1] > 1e6 else "Medium" if df['Avg_Daily_Volume'].iloc[-1] > 1e5 else "Low"
+            }
+            
+            return signals, fig, product_metrics, risk_metrics, sector_metrics
+        
         predict_btn.click(
-            fn=make_prediction,
-            inputs=[symbol, timeframe, prediction_days, strategy],
-            outputs=[signals, plot]
+            fn=analyze_stock,
+            inputs=[symbol, timeframe, prediction_days, lookback_days, strategy],
+            outputs=[signals, plot, metrics, risk_metrics, sector_metrics]
         )
     
     return demo