Create app.py

app.py

@@ -0,0 +1,147 @@
+import yfinance as yf
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from prophet import Prophet
+import gradio as gr
+from ta.trend import SMAIndicator, EMAIndicator, MACD
+from ta.momentum import RSIIndicator
+
+# Function for trading strategy signals
+def trading_signals(df, strategy="day trading"):
+    signals = ""
+    df = df.copy()
+    
+    try:
+        if strategy.lower() == "day trading":
+            # Use Bollinger Bands and RSI for intraday trades
+            df['RSI'] = RSIIndicator(df['Close'], window=14).rsi()
+            df['EMA_9'] = EMAIndicator(df['Close'], window=9).ema_indicator()
+            df['EMA_21'] = EMAIndicator(df['Close'], window=21).ema_indicator()
+            df['Signal'] = (df['EMA_9'] > df['EMA_21']).astype(int)
+            
+            signals = f"Day Trading Signals: {'Buy' if df['Signal'].iloc[-1] else 'Sell'}"
+        
+        elif strategy.lower() == "swing trading":
+            # Swing Trading: SMA crossover
+            df['SMA_50'] = SMAIndicator(df['Close'], window=50).sma_indicator()
+            df['SMA_200'] = SMAIndicator(df['Close'], window=200).sma_indicator()
+            df['Signal'] = (df['SMA_50'] > df['SMA_200']).astype(int)
+            
+            signals = f"Swing Trading Signals: {'Bullish' if df['Signal'].iloc[-1] else 'Bearish'}"
+        
+        elif strategy.lower() == "momentum trading":
+            # Momentum Trading: RSI + MACD
+            df['RSI'] = RSIIndicator(df['Close'], window=14).rsi()
+            macd = MACD(df['Close'])
+            df['MACD'] = macd.macd()
+            df['MACD_signal'] = macd.macd_signal()
+            
+            # Simplified momentum condition
+            df['Momentum'] = ((df['RSI'] > 50) & (df['MACD'] > df['MACD_signal'])).astype(int)
+            
+            signals = f"Momentum Trading Signal: {'Strong Momentum' if df['Momentum'].iloc[-1] else 'Weak Momentum'}"
+        
+        elif strategy.lower() == "scalping":
+            # Scalping: EMA crossover on 1-min data
+            df['EMA_3'] = EMAIndicator(df['Close'], window=3).ema_indicator()
+            df['EMA_9'] = EMAIndicator(df['Close'], window=9).ema_indicator()
+            df['Signal'] = (df['EMA_3'] > df['EMA_9']).astype(int)
+            
+            signals = f"Scalping Signal: {'Buy' if df['Signal'].iloc[-1] else 'Sell'}"
+        
+        else:
+            signals = "Invalid Strategy"
+        
+    except Exception as e:
+        signals = f"Error in trading signals: {e}"
+    
+    return signals
+
+# Main function to fetch data, generate trends, and trading signals
+def stock_analysis_with_trading(ticker, period="6mo", interval="1d", analysis_type="closing price trend", strategy=None):
+    try:
+        # Fetch historical stock data with enhanced error handling
+        stock = yf.Ticker(ticker)
+        
+        # Try different methods to fetch data
+        try:
+            df = stock.history(period=period, interval=interval)
+        except Exception as initial_error:
+            # Fallback to different periods or intervals
+            try:
+                df = stock.history(period="1y", interval="1d")
+            except Exception as fallback_error:
+                return f"Error fetching data: {initial_error} | Fallback Error: {fallback_error}", None
+        
+        if df is None or df.empty:
+            return f"No data found for ticker: {ticker}. Check ticker symbol and network connection.", None
+        
+        if analysis_type.lower() == "closing price trend":
+            # Plot Closing Price Trend
+            plt.figure(figsize=(10, 6))
+            plt.plot(df.index, df['Close'], label='Closing Price')
+            plt.title(f"Closing Price Trend for {ticker}")
+            plt.xlabel("Date")
+            plt.ylabel("Closing Price (USD)")
+            plt.grid()
+            plt.legend()
+            plot_path = f"{ticker}_closing_price_trend.png"
+            plt.savefig(plot_path)
+            plt.close()
+            return f"Closing Price Analysis for {ticker}", plot_path
+        
+        elif analysis_type.lower() == "forecast":
+            # Forecast with Prophet
+            forecast_df = df.reset_index()[['Date', 'Close']]
+            forecast_df.columns = ['ds', 'y']
+            model = Prophet()
+            model.fit(forecast_df)
+            future = model.make_future_dataframe(periods=30)
+            forecast = model.predict(future)
+            
+            plt.figure(figsize=(10, 6))
+            model.plot(forecast)
+            plt.title(f"Forecasted Closing Price for {ticker}")
+            plt.xlabel("Date")
+            plt.ylabel("Price (USD)")
+            plot_path = f"{ticker}_forecast_trend.png"
+            plt.savefig(plot_path)
+            plt.close()
+            return "Forecast Generated Successfully", plot_path
+        
+        elif strategy:
+            signals = trading_signals(df, strategy)
+            return signals, None
+        
+        else:
+            return "Invalid analysis type or strategy provided.", None
+    
+    except Exception as e:
+        return f"Comprehensive Error: {e}", None
+
+# Gradio Interface
+def chat_with_agent(ticker, period, interval, query, strategy):
+    response, plot_path = stock_analysis_with_trading(ticker, period, interval, query, strategy)
+    return response, plot_path if plot_path else None
+
+# Define Gradio Interface
+iface = gr.Interface(
+    fn=chat_with_agent,
+    inputs=[
+        gr.Textbox(label="Stock Ticker (e.g., AAPL, MSFT)"),
+        gr.Textbox(label="Time Period (e.g., 6mo, 1d)", value="6mo"),
+        gr.Textbox(label="Data Interval (e.g., 1d, 1h)", value="1d"),
+        gr.Textbox(label="Query (e.g., closing price trend, forecast)", value="closing price trend"),
+        gr.Textbox(label="Trading Strategy (optional)", value=""),
+    ],
+    outputs=[
+        gr.Textbox(label="Analysis Result or Trading Signal"),
+        gr.Image(label="Trend or Forecast Visualization")
+    ],
+    title="Stock Analysis & Trading Strategy Agent",
+    description="Analyze stock trends, forecast prices, and generate trading signals using technical indicators."
+)
+
+# Launch the interface
+iface.launch(share=True)