Update app.py

app.py

@@ -1,4 +1,3 @@
-# app.py - Real-Time Auto-Refresh Trading System
 import gradio as gr
 import pandas as pd
 import numpy as np
@@ -32,20 +31,20 @@ class RealTimeMarketData:
         
         # Add new timestamp
         self.timestamps.append(current_time.strftime('%H:%M:%S'))
-        if len(self.timestamps) > 20:
+        if len(self.timestamps) > 15:
             self.timestamps.pop(0)
         
         live_data = {}
         
         for symbol in self.symbols:
             # Generate realistic price movement
-            change_pct = np.random.normal(0, 0.8)  # More volatility
+            change_pct = np.random.normal(0, 0.8)
             new_price = self.last_prices[symbol] * (1 + change_pct/100)
             self.last_prices[symbol] = new_price
             
             # Add to history
             self.data_history[symbol].append(new_price)
-            if len(self.data_history[symbol]) > 20:
+            if len(self.data_history[symbol]) > 15:
                 self.data_history[symbol].pop(0)
             
             # Calculate metrics
@@ -80,61 +79,142 @@ class AI_TradingAgents:
         data = market_data[symbol]
         current_price = data['current_price']
         change = data['change']
+        volatility = abs(change)
         
         analyses = {}
         
-        # Research Agent
+        # Research Agent - Fundamental Analysis
         if change > 2:
-            research_text = f"**Bullish Momentum** 🚀\n• Strong price action\n• **BUY** (85% confidence)"
+            research_text = f"""**Fundamental Analysis - Strong Bullish Momentum** 🚀
+
+• Revenue Growth: +18% YoY
+• Expanding profit margins
+• Market leadership position
+• Positive institutional sentiment
+• Strong balance sheet
+
+**Recommendation: BUY** (85% confidence)
+**Price Target: ${current_price * 1.15:.2f}**"""
             research_conf = 85
         elif change < -2:
-            research_text = f"**Bearish Pressure** 📉\n• Price weakness\n• **HOLD** (70% confidence)"
+            research_text = f"""**Fundamental Analysis - Bearish Pressure** 📉
+
+• Valuation concerns emerging
+• Increasing competitive pressures
+• Wait for better entry point
+• Support at ${current_price * 0.95:.2f}
+• Monitor earnings closely
+
+**Recommendation: HOLD** (70% confidence)"""
             research_conf = 70
         else:
-            research_text = f"**Consolidation** ⚖️\n• Sideways movement\n• **HOLD** (75% confidence)"
-            research_conf = 75
+            research_text = f"""**Fundamental Analysis - Consolidation Phase** ⚖️
+
+• Steady revenue growth: +8% YoY
+• Strong cash flow generation
+• Solid market position
+• Attractive risk-reward setup
+
+**Recommendation: HOLD** (78% confidence)"""
+            research_conf = 78
         
         analyses['research'] = {
             'emoji': '📊',
-            'title': 'Research',
+            'title': 'Fundamental Analysis',
             'analysis': research_text,
             'confidence': research_conf
         }
         
-        # Technical Agent
+        # Technical Agent - Technical Analysis
+        rsi_level = "Overbought" if change > 2 else "Oversold" if change < -2 else "Neutral"
         trend = "Bullish" if change > 0 else "Bearish"
+        support = current_price * 0.95
+        resistance = current_price * 1.05
+        
         analyses['technical'] = {
             'emoji': '📈',
-            'title': 'Technical',
-            'analysis': f"**{trend} Trend**\n• Price: ${current_price:.2f}\n• Change: {change:+.2f}%\n• RSI: {60 if change > 0 else 40}",
+            'title': 'Technical Analysis',
+            'analysis': f"""**{rsi_level} Conditions - {trend} Trend**
+
+• Current Price: ${current_price:.2f}
+• 24h Change: {change:+.2f}%
+• Key Levels:
+  - Support: ${support:.2f}
+  - Resistance: ${resistance:.2f}
+• RSI: {65 if change > 0 else 35} ({rsi_level})
+• Volume: {data['volume']:,}
+• Momentum: {'Positive' if change > 0 else 'Negative'}""",
             'confidence': 75
         }
         
-        # Risk Agent
-        volatility = abs(change)
-        risk_level = "HIGH" if volatility > 3 else "MEDIUM" if volatility > 1.5 else "LOW"
+        # Risk Agent - Risk Management
+        if volatility > 3:
+            risk_level = "HIGH"
+            position_size = "1-2%"
+            stop_loss = "10%"
+            risk_reward = "1:2"
+        elif volatility > 1.5:
+            risk_level = "MEDIUM"
+            position_size = "2-3%"
+            stop_loss = "8%"
+            risk_reward = "1:2.5"
+        else:
+            risk_level = "LOW"
+            position_size = "3-4%"
+            stop_loss = "6%"
+            risk_reward = "1:3"
+        
         analyses['risk'] = {
             'emoji': '🛡️',
-            'title': 'Risk',
-            'analysis': f"**{risk_level} RISK**\n• Volatility: {volatility:.1f}%\n• Position: 2-3%\n• Stop: 6-8%",
+            'title': 'Risk Assessment',
+            'analysis': f"""**{risk_level} RISK PROFILE**
+
+• Volatility: {volatility:.1f}%
+• Recommended Position: {position_size} of portfolio
+• Stop-Loss: {stop_loss} below entry
+• Risk-Reward Ratio: {risk_reward}
+• Maximum Drawdown: 12%
+• Correlation: Low with portfolio
+• Monitoring: {'Intensive' if risk_level == 'HIGH' else 'Standard'}""",
             'confidence': 80
         }
         
-        # Decision Engine
-        if change > 2:
+        # Decision Engine - Final Decision
+        if change > 2 and volatility < 4:
             decision = "BUY"
             confidence = 85
+            reason = "Strong bullish momentum with favorable risk metrics and positive fundamentals"
+            action = "Enter long position with trailing stop at 8%. Target 15-20% upside."
         elif change < -2:
             decision = "SELL"
-            confidence = 75
+            confidence = 78
+            reason = "Significant downward pressure with deteriorating technicals"
+            action = "Consider short opportunities or wait for stabilization. Set tight stop-loss."
         else:
             decision = "HOLD"
-            confidence = 70
+            confidence = 72
+            reason = "Consolidation phase with mixed signals. Awaiting clearer market direction."
+            action = "Monitor for breakout above resistance or breakdown below support."
         
         analyses['decision'] = {
             'emoji': '🎯',
-            'title': 'Decision',
-            'analysis': f"**{decision}** 🎯\n• Confidence: {confidence}%\n• Price: ${current_price:.2f}",
+            'title': 'Trading Decision',
+            'analysis': f"""**FINAL DECISION: {decision}** 🎯
+
+**Confidence Level:** {confidence}%
+**Current Price:** ${current_price:.2f}
+**Price Change:** {change:+.2f}%
+
+**Rationale:**
+{reason}
+
+**Execution Plan:**
+{action}
+
+**Key Factors:**
+• Fundamental Score: {analyses['research']['confidence']}%
+• Technical Score: {analyses['technical']['confidence']}%
+• Risk Assessment: {analyses['risk']['confidence']}%""",
             'confidence': confidence,
             'decision': decision
         }
@@ -142,186 +222,162 @@ class AI_TradingAgents:
         return analyses
     
     def _get_error_analysis(self, symbol):
+        error_msg = "Data temporarily unavailable. Please check symbol spelling or try again later."
         return {
-            'research': {'emoji': '📊', 'title': 'Research', 'analysis': 'No data', 'confidence': 0},
-            'technical': {'emoji': '📈', 'title': 'Technical', 'analysis': 'No data', 'confidence': 0},
-            'risk': {'emoji': '🛡️', 'title': 'Risk', 'analysis': 'No data', 'confidence': 0},
-            'decision': {'emoji': '🎯', 'title': 'Decision', 'analysis': 'HOLD', 'confidence': 0, 'decision': 'HOLD'}
+            'research': {'emoji': '📊', 'title': 'Research', 'analysis': error_msg, 'confidence': 0},
+            'technical': {'emoji': '📈', 'title': 'Technical', 'analysis': error_msg, 'confidence': 0},
+            'risk': {'emoji': '🛡️', 'title': 'Risk', 'analysis': error_msg, 'confidence': 0},
+            'decision': {'emoji': '🎯', 'title': 'Decision', 'analysis': error_msg, 'confidence': 0, 'decision': 'HOLD'}
         }
 
 # Initialize components
 market_data = RealTimeMarketData()
 trading_agents = AI_TradingAgents()
 
-def get_live_dataframe():
-    """Create live updating dataframe"""
+def get_ai_agents_analysis(symbol_input=""):
+    """Get detailed analysis from all AI agents"""
     live_data = market_data.generate_live_data()
     
-    data = []
-    for symbol, info in live_data.items():
-        data.append({
-            'Symbol': symbol,
-            'Price': f"${info['current_price']:.2f}",
-            'Change': f"{info['change']:+.2f}%",
-            'Volume': f"{info['volume']:,}",
-            'Last Update': info['last_updated'],
-            'Update #': info['update_count']
-        })
-    
-    df = pd.DataFrame(data)
-    return df
-
-def get_live_chart():
-    """Create live updating chart"""
-    live_data = market_data.generate_live_data()
+    if not symbol_input:
+        return "# 🤖 AI Agents Analysis\n\n**Please enter a stock symbol to see detailed analysis from all AI agents**"
     
-    fig = go.Figure()
+    symbol = symbol_input.upper()
+    if symbol not in live_data:
+        return f"# ❌ Symbol Not Found\n\n'{symbol}' is not in our tracked symbols. Try: {', '.join(market_data.symbols)}"
     
-    colors = ['#FF6B6B', '#4ECDC4', '#45B7D1', '#96CEB4']
-    for i, (symbol, data) in enumerate(live_data.items()):
-        fig.add_trace(go.Scatter(
-            x=data['timestamps'],
-            y=data['prices'],
-            mode='lines+markers',
-            name=symbol,
-            line=dict(color=colors[i], width=3),
-            marker=dict(size=6)
-        ))
+    # Get analysis from all agents
+    analysis = trading_agents.analyze_market(symbol, live_data)
     
-    fig.update_layout(
-        title=f"Live Prices - Update #{market_data.update_counter} - {datetime.now().strftime('%H:%M:%S')}",
-        template='plotly_dark',
-        height=400
-    )
-    
-    return fig
+    detailed_report = f"""
+# 🤖 AI Agents Specialized Analysis
 
-def get_performance_chart():
-    """Create performance chart"""
-    live_data = market_data.generate_live_data()
-    
-    symbols = list(live_data.keys())
-    changes = [live_data[s]['change'] for s in symbols]
-    
-    fig = go.Figure()
-    fig.add_trace(go.Bar(
-        x=symbols,
-        y=changes,
-        marker_color=['green' if c > 0 else 'red' for c in changes],
-        text=[f"{c:+.2f}%" for c in changes],
-        textposition='auto'
-    ))
-    
-    fig.update_layout(
-        title="Today's Performance",
-        template='plotly_dark',
-        height=300
-    )
-    
-    return fig
+## 📈 {symbol} - Real-time Analysis
+**Current Price:** ${live_data[symbol]['current_price']:.2f}  
+**24h Change:** {live_data[symbol]['change']:+.2f}%  
+**Last Update:** {datetime.now().strftime('%H:%M:%S')}  
+**Analysis Cycle:** #{market_data.update_counter}
+
+---
 
-def get_analysis_report(symbol_input=""):
-    """Get analysis report"""
+## {analysis['research']['emoji']} {analysis['research']['title']}
+**Confidence Level:** {analysis['research']['confidence']}%
+
+{analysis['research']['analysis']}
+
+---
+
+## {analysis['technical']['emoji']} {analysis['technical']['title']}
+**Confidence Level:** {analysis['technical']['confidence']}%
+
+{analysis['technical']['analysis']}
+
+---
+
+## {analysis['risk']['emoji']} {analysis['risk']['title']}
+**Confidence Level:** {analysis['risk']['confidence']}%
+
+{analysis['risk']['analysis']}
+
+---
+
+## {analysis['decision']['emoji']} {analysis['decision']['title']}
+**Confidence Level:** {analysis['decision']['confidence']}%
+
+{analysis['decision']['analysis']}
+
+---
+
+### 🎯 Multi-Agent Consensus
+- **Overall Confidence:** {np.mean([a['confidence'] for a in analysis.values()]):.1f}%
+- **Final Recommendation:** {analysis['decision']['decision']}
+- **Risk Level:** {'High' if analysis['risk']['confidence'] < 70 else 'Medium' if analysis['risk']['confidence'] < 80 else 'Low'}
+- **Market Outlook:** {'Bullish' if analysis['research']['confidence'] > 80 else 'Neutral' if analysis['research']['confidence'] > 65 else 'Bearish'}
+
+*Analysis generated by Multi-Agent AI Trading System*
+"""
+    return detailed_report
+
+def get_agents_performance():
+    """Get performance overview of all agents"""
     live_data = market_data.generate_live_data()
     
-    if not symbol_input:
-        # Market overview
-        report = f"# 📊 Market Overview\n\n"
-        report += f"**Last Update:** {datetime.now().strftime('%H:%M:%S')}\n"
-        report += f"**Update Count:** {market_data.update_counter}\n\n"
-        
-        for symbol in live_data.keys():
-            analysis = trading_agents.analyze_market(symbol, live_data)
-            decision = analysis['decision']
-            report += f"**{symbol}:** ${live_data[symbol]['current_price']:.2f} ({live_data[symbol]['change']:+.2f}%) - {decision['decision']}\n\n"
-        
-        return report
-    else:
-        # Specific symbol
-        symbol = symbol_input.upper()
-        if symbol in live_data:
-            analysis = trading_agents.analyze_market(symbol, live_data)
-            report = f"# 🎯 {symbol} Analysis\n\n"
-            report += f"**Price:** ${live_data[symbol]['current_price']:.2f}\n"
-            report += f"**Change:** {live_data[symbol]['change']:+.2f}%\n\n"
-            
-            for agent_type, agent_analysis in analysis.items():
-                report += f"### {agent_analysis['emoji']} {agent_analysis['title']}\n"
-                report += f"{agent_analysis['analysis']}\n\n"
-            
-            return report
-        else:
-            return f"# ❌ Symbol not found: {symbol}"
+    performance_data = []
+    for symbol in market_data.symbols:
+        analysis = trading_agents.analyze_market(symbol, live_data)
+        performance_data.append({
+            'Symbol': symbol,
+            'Price': f"${live_data[symbol]['current_price']:.2f}",
+            'Change': f"{live_data[symbol]['change']:+.2f}%",
+            'Research': f"{analysis['research']['confidence']}%",
+            'Technical': f"{analysis['technical']['confidence']}%",
+            'Risk': f"{analysis['risk']['confidence']}%",
+            'Decision': analysis['decision']['decision'],
+            'Confidence': f"{analysis['decision']['confidence']}%"
+        })
+    
+    df = pd.DataFrame(performance_data)
+    return df
 
 # Create the interface
-with gr.Blocks(theme=gr.themes.Soft(), title="Live Trading Dashboard") as demo:
+with gr.Blocks(theme=gr.themes.Soft(), title="AI Agents Analysis Dashboard") as demo:
     
     gr.Markdown("""
-    # 🤖 Real-Time Trading Dashboard
-    ## *Live Auto-Refresh Every 3 Seconds*
+    # 🤖 AI Agents Specialized Analysis
+    ## *Multi-Agent AI Trading System - Professional Analysis*
     
-    🔄 **Data updates automatically every 3 seconds**
+    **Real-time analysis from 4 specialized AI agents working in coordination**
     """)
     
     with gr.Row():
         with gr.Column(scale=1):
             symbol_input = gr.Textbox(
-                label="Stock Symbol",
-                placeholder="AAPL, TSLA... (empty for overview)",
+                label="Enter Stock Symbol",
+                placeholder="e.g., AAPL, TSLA, GOOGL...",
                 max_lines=1
             )
             gr.Markdown("""
-            **Tracked:** AAPL, GOOGL, MSFT, TSLA
-            **Refresh:** Every 3 seconds
+            **Available Symbols:** AAPL, GOOGL, MSFT, TSLA
+            
+            **🤖 AI Agents:**
+            - 📊 Research Agent: Fundamental Analysis
+            - 📈 Technical Agent: Price & Patterns  
+            - 🛡️ Risk Agent: Risk Management
+            - 🎯 Decision Engine: Final Recommendation
             """)
         
         with gr.Column(scale=2):
-            gr.Markdown("### 📈 Live Market Data")
-    
-    with gr.Row():
-        with gr.Column():
-            # Auto-updating dataframe
-            dataframe = gr.DataFrame(
-                label="Live Stock Prices",
-                every=3,  # Auto-refresh every 3 seconds
-                value=get_live_dataframe
-            )
-        
-        with gr.Column():
-            # Performance chart
-            performance_chart = gr.Plot(
-                label="Performance",
-                every=3,
-                value=get_performance_chart
+            gr.Markdown("### 📋 Agents Performance Overview")
+            agents_performance = gr.DataFrame(
+                label="AI Agents Confidence Levels",
+                every=5,
+                value=get_agents_performance
             )
     
     with gr.Row():
-        # Main price chart
-        price_chart = gr.Plot(
-            label="Live Price Movement",
-            every=3,
-            value=get_live_chart
-        )
-    
-    with gr.Row():
-        # Analysis report
-        analysis_report = gr.Markdown(
-            label="AI Analysis",
-            every=3,
-            value=lambda: get_analysis_report("")
+        # AI Agents Detailed Analysis
+        agents_analysis = gr.Markdown(
+            label="Specialized AI Agents Analysis",
+            every=5,
+            value=get_ai_agents_analysis
         )
     
     gr.Markdown(f"""
     ---
-    **🔄 Live Updates Active** • **Last Data:** {datetime.now().strftime('%H:%M:%S')}
-    *Data refreshes automatically every 3 seconds*
+    **🔄 Live AI Analysis Active** • **Last Analysis:** {datetime.now().strftime('%H:%M:%S')}
+    *Each agent provides specialized analysis updated every 5 seconds*
+    
+    **📊 Analysis Includes:**
+    - Fundamental company research and growth prospects
+    - Technical price patterns and market structure  
+    - Comprehensive risk assessment and position sizing
+    - Final trading decision with execution plan
     """)
     
-    # Update analysis when symbol input changes
+    # Update analysis when symbol changes
     symbol_input.change(
-        fn=get_analysis_report,
+        fn=get_ai_agents_analysis,
         inputs=[symbol_input],
-        outputs=[analysis_report]
+        outputs=[agents_analysis]
     )
 
 # Launch the app