Update app.py

app.py

@@ -22,20 +22,10 @@ os.makedirs('src/visualizers', exist_ok=True)
 os.makedirs('src/utils', exist_ok=True)
 
 # Create __init__.py files
-with open('src/__init__.py', 'w') as f:
-    f.write('')
-
-with open('src/environments/__init__.py', 'w') as f:
-    f.write('')
-
-with open('src/agents/__init__.py', 'w') as f:
-    f.write('')
-
-with open('src/visualizers/__init__.py', 'w') as f:
-    f.write('')
-
-with open('src/utils/__init__.py', 'w') as f:
-    f.write('')
+for dir_path in ['src', 'src/environments', 'src/agents', 'src/visualizers', 'src/utils']:
+    init_file = os.path.join(dir_path, '__init__.py')
+    with open(init_file, 'w') as f:
+        f.write('')
 
 # Now import our custom modules
 sys.path.append('src')
@@ -43,117 +33,16 @@ sys.path.append('src')
 # Import our custom modules
 from src.environments.visual_trading_env import VisualTradingEnvironment
 from src.agents.visual_agent import VisualTradingAgent
-
-class ChartRenderer:
-    """Simple chart renderer for visualization"""
-    def __init__(self):
-        pass
-    
-    def render_price_chart(self, prices, actions=None, current_step=0):
-        """Render price chart with actions"""
-        fig = go.Figure()
-        
-        if not prices:
-            # Return empty figure if no data
-            fig.update_layout(
-                title="Price Chart - No Data",
-                xaxis_title="Time Step",
-                yaxis_title="Price",
-                height=300
-            )
-            return fig
-        
-        # Add price line
-        fig.add_trace(go.Scatter(
-            x=list(range(len(prices))),
-            y=prices,
-            mode='lines',
-            name='Price',
-            line=dict(color='blue', width=2)
-        ))
-        
-        # Add action markers if provided
-        if actions and len(actions) == len(prices):
-            buy_indices = [i for i, action in enumerate(actions) if action == 1]
-            sell_indices = [i for i, action in enumerate(actions) if action == 2]
-            close_indices = [i for i, action in enumerate(actions) if action == 3]
-            
-            if buy_indices:
-                fig.add_trace(go.Scatter(
-                    x=buy_indices,
-                    y=[prices[i] for i in buy_indices],
-                    mode='markers',
-                    name='Buy',
-                    marker=dict(color='green', size=10, symbol='triangle-up', line=dict(width=2, color='darkgreen'))
-                ))
-            
-            if sell_indices:
-                fig.add_trace(go.Scatter(
-                    x=sell_indices,
-                    y=[prices[i] for i in sell_indices],
-                    mode='markers',
-                    name='Sell',
-                    marker=dict(color='red', size=10, symbol='triangle-down', line=dict(width=2, color='darkred'))
-                ))
-            
-            if close_indices:
-                fig.add_trace(go.Scatter(
-                    x=close_indices,
-                    y=[prices[i] for i in close_indices],
-                    mode='markers',
-                    name='Close',
-                    marker=dict(color='orange', size=8, symbol='x', line=dict(width=2, color='darkorange'))
-                ))
-        
-        fig.update_layout(
-            title=f"Price Chart (Step: {current_step})",
-            xaxis_title="Time Step",
-            yaxis_title="Price",
-            height=300,
-            showlegend=True
-        )
-        
-        return fig
-
-class DataLoader:
-    """Data loader for synthetic market data"""
-    def __init__(self):
-        pass
-    
-    def generate_synthetic_data(self, num_points=1000, trend=0.0005, volatility=0.02):
-        """Generate realistic synthetic market data"""
-        np.random.seed(42)
-        
-        prices = [100.0]
-        for i in range(1, num_points):
-            # Random walk with trend and some mean reversion
-            change = np.random.normal(trend, volatility)
-            # Add some mean reversion
-            mean_reversion = (100 - prices[-1]) * 0.001
-            price = max(1.0, prices[-1] * (1 + change) + mean_reversion)
-            prices.append(price)
-        
-        return np.array(prices)
-
-class TradingConfig:
-    """Configuration class for trading parameters"""
-    def __init__(self):
-        self.initial_balance = 10000
-        self.max_steps = 1000
-        self.transaction_cost = 0.001
-        self.learning_rate = 0.001
-        self.gamma = 0.99
+from src.visualizers.chart_renderer import ChartRenderer
 
 class TradingAIDemo:
     def __init__(self):
-        self.config = TradingConfig()
         self.env = None
         self.agent = None
         self.current_state = None
         self.is_training = False
         self.episode_history = []
         self.chart_renderer = ChartRenderer()
-        self.data_loader = DataLoader()
         self.initialized = False
         
     def initialize_environment(self, initial_balance, risk_level, asset_type):
@@ -169,7 +58,7 @@ class TradingAIDemo:
             
             # Initialize agent with correct dimensions
             self.agent = VisualTradingAgent(
-                state_dim=(84, 84, 4),  # Fixed dimensions
+                state_dim=(84, 84, 4),
                 action_dim=4
             )
             
@@ -290,7 +179,9 @@ class TradingAIDemo:
             # Calculate performance metrics
             initial_balance = self.env.initial_balance
             final_net_worth = info['net_worth']
-            total_return = (final_net_worth - initial_balance) / initial_balance * 100
+            total_return = 0.0
+            if initial_balance > 0:
+                total_return = (final_net_worth - initial_balance) / initial_balance * 100
             
             summary = (
                 f"🎯 Episode Completed!\n"
@@ -318,13 +209,14 @@ class TradingAIDemo:
         training_history = []
         
         try:
-            for episode in range(int(num_episodes)):
+            num_episodes = int(num_episodes)
+            for episode in range(num_episodes):
                 state = self.env.reset()
-                episode_reward = 0
+                episode_reward = 0.0
                 done = False
                 steps = 0
                 
-                while not done and steps < 100:  # Limit steps per episode
+                while not done and steps < 100:
                     action = self.agent.select_action(state)
                     next_state, reward, done, info = self.env.step(action)
                     self.agent.store_transition(state, action, reward, next_state, done)
@@ -339,506 +231,40 @@ class TradingAIDemo:
                     'episode': episode,
                     'reward': episode_reward,
                     'net_worth': info['net_worth'],
-                    'loss': loss if loss else 0,
+                    'loss': loss,
                     'steps': steps
                 })
                 
                 # Yield progress every 5 episodes or at the end
                 if episode % 5 == 0 or episode == num_episodes - 1:
                     progress_chart = self.create_training_progress(training_history)
+                    
                     status = (
                         f"🔄 Training Progress: {episode+1}/{num_episodes}\n"
                         f"• Episode Reward: {episode_reward:.2f}\n"
                         f"• Final Net Worth: ${info['net_worth']:.2f}\n"
-                        f"• Loss: {loss:.4f if loss else 0:.4f}\n"
+                        f"• Loss: {loss:.4f}\n"
                         f"• Epsilon: {self.agent.epsilon:.3f}"
                     )
                     yield progress_chart, status
                 
-                # Small delay to make training visible
                 time.sleep(0.01)
             
             self.is_training = False
+            
+            # Calculate average reward
+            rewards = [h['reward'] for h in training_history]
+            avg_reward = np.mean(rewards) if rewards else 0.0
+            
             final_status = (
                 f"✅ Training Completed!\n"
                 f"• Total Episodes: {num_episodes}\n"
                 f"• Final Epsilon: {self.agent.epsilon:.3f}\n"
-                f"• Average Reward: {np.mean([h['reward'] for h in training_history]):.2f}"
+                f"• Average Reward: {avg_reward:.2f}"
             )
             yield self.create_training_progress(training_history), final_status
             
         except Exception as e:
             self.is_training = False
             error_msg = f"❌ Training error: {str(e)}"
-            print(error_msg)
-            yield None, error_msg
-    
-    def create_price_chart(self, info):
-        """Create price chart with actions"""
-        if not self.episode_history:
-            # Return empty chart with message
-            fig = go.Figure()
-            fig.update_layout(
-                title="Price Chart - No Data Available",
-                xaxis_title="Time Step",
-                yaxis_title="Price",
-                height=300
-            )
-            return fig
-        
-        prices = [h['price'] for h in self.episode_history]
-        actions = [h['action'] for h in self.episode_history]
-        
-        fig = go.Figure()
-        
-        # Price line
-        fig.add_trace(go.Scatter(
-            x=list(range(len(prices))),
-            y=prices,
-            mode='lines',
-            name='Price',
-            line=dict(color='blue', width=3)
-        ))
-        
-        # Action markers
-        buy_indices = [i for i, action in enumerate(actions) if action == 1]
-        sell_indices = [i for i, action in enumerate(actions) if action == 2]
-        close_indices = [i for i, action in enumerate(actions) if action == 3]
-        
-        if buy_indices:
-            fig.add_trace(go.Scatter(
-                x=buy_indices,
-                y=[prices[i] for i in buy_indices],
-                mode='markers',
-                name='Buy',
-                marker=dict(color='green', size=12, symbol='triangle-up', 
-                          line=dict(width=2, color='darkgreen'))
-            ))
-        
-        if sell_indices:
-            fig.add_trace(go.Scatter(
-                x=sell_indices,
-                y=[prices[i] for i in sell_indices],
-                mode='markers',
-                name='Sell',
-                marker=dict(color='red', size=12, symbol='triangle-down', 
-                          line=dict(width=2, color='darkred'))
-            ))
-        
-        if close_indices:
-            fig.add_trace(go.Scatter(
-                x=close_indices,
-                y=[prices[i] for i in close_indices],
-                mode='markers',
-                name='Close',
-                marker=dict(color='orange', size=10, symbol='x', 
-                          line=dict(width=2, color='darkorange'))
-            ))
-        
-        fig.update_layout(
-            title="Price Chart with Trading Actions",
-            xaxis_title="Step",
-            yaxis_title="Price",
-            height=350,
-            showlegend=True,
-            template="plotly_white"
-        )
-        
-        return fig
-    
-    def create_performance_chart(self):
-        """Create portfolio performance chart"""
-        if not self.episode_history:
-            fig = go.Figure()
-            fig.update_layout(
-                title="Portfolio Performance - No Data Available",
-                height=400
-            )
-            return fig
-        
-        net_worth = [h['net_worth'] for h in self.episode_history]
-        rewards = [h['reward'] for h in self.episode_history]
-        
-        fig = make_subplots(
-            rows=2, cols=1,
-            subplot_titles=['Portfolio Value Over Time', 'Step Rewards'],
-            vertical_spacing=0.15
-        )
-        
-        # Portfolio value
-        fig.add_trace(go.Scatter(
-            x=list(range(len(net_worth))),
-            y=net_worth,
-            mode='lines+markers',
-            name='Net Worth',
-            line=dict(color='green', width=3),
-            marker=dict(size=4)
-        ), row=1, col=1)
-        
-        # Add initial balance reference line
-        if self.env:
-            fig.add_hline(y=self.env.initial_balance, line_dash="dash", 
-                         line_color="red", annotation_text="Initial Balance",
-                         row=1, col=1)
-        
-        # Rewards as bar chart
-        fig.add_trace(go.Bar(
-            x=list(range(len(rewards))),
-            y=rewards,
-            name='Reward',
-            marker_color=['green' if r >= 0 else 'red' for r in rewards],
-            opacity=0.7
-        ), row=2, col=1)
-        
-        fig.update_layout(height=500, showlegend=False, template="plotly_white")
-        fig.update_yaxes(title_text="Value ($)", row=1, col=1)
-        fig.update_yaxes(title_text="Reward", row=2, col=1)
-        fig.update_xaxes(title_text="Step", row=2, col=1)
-        
-        return fig
-    
-    def create_action_chart(self):
-        """Create action distribution chart"""
-        if not self.episode_history:
-            fig = go.Figure()
-            fig.update_layout(
-                title="Action Distribution - No Data Available",
-                height=300
-            )
-            return fig
-        
-        actions = [h['action'] for h in self.episode_history]
-        action_names = ['Hold', 'Buy', 'Sell', 'Close']
-        action_counts = [actions.count(i) for i in range(4)]
-        
-        colors = ['blue', 'green', 'red', 'orange']
-        
-        fig = go.Figure(data=[go.Pie(
-            labels=action_names,
-            values=action_counts,
-            hole=.4,
-            marker_colors=colors,
-            textinfo='label+percent+value',
-            hoverinfo='label+percent+value'
-        )])
-        
-        fig.update_layout(
-            title="Action Distribution",
-            height=350,
-            annotations=[dict(text='Actions', x=0.5, y=0.5, font_size=16, showarrow=False)]
-        )
-        
-        return fig
-    
-    def create_training_progress(self, training_history):
-        """Create training progress visualization"""
-        if not training_history:
-            fig = go.Figure()
-            fig.update_layout(
-                title="Training Progress - No Data Available",
-                height=500
-            )
-            return fig
-        
-        df = pd.DataFrame(training_history)
-        
-        fig = make_subplots(
-            rows=2, cols=2,
-            subplot_titles=['Episode Rewards', 'Portfolio Value',
-                          'Training Loss', 'Moving Average Reward (5)'],
-            specs=[[{}, {}], [{}, {}]]
-        )
-        
-        # Rewards
-        fig.add_trace(go.Scatter(
-            x=df['episode'], y=df['reward'], mode='lines+markers',
-            name='Reward', line=dict(color='blue', width=2),
-            marker=dict(size=4)
-        ), row=1, col=1)
-        
-        # Portfolio value
-        fig.add_trace(go.Scatter(
-            x=df['episode'], y=df['net_worth'], mode='lines+markers',
-            name='Net Worth', line=dict(color='green', width=2),
-            marker=dict(size=4)
-        ), row=1, col=2)
-        
-        # Add initial balance reference
-        if self.env:
-            fig.add_hline(y=self.env.initial_balance, line_dash="dash", 
-                         line_color="red", annotation_text="Initial Balance",
-                         row=1, col=2)
-        
-        # Loss
-        if 'loss' in df.columns and df['loss'].notna().any() and df['loss'].sum() > 0:
-            fig.add_trace(go.Scatter(
-                x=df['episode'], y=df['loss'], mode='lines+markers',
-                name='Loss', line=dict(color='red', width=2),
-                marker=dict(size=4)
-            ), row=2, col=1)
-        
-        # Moving average reward
-        if len(df) > 5:
-            df['ma_reward'] = df['reward'].rolling(window=5).mean()
-            fig.add_trace(go.Scatter(
-                x=df['episode'], y=df['ma_reward'], mode='lines',
-                name='MA Reward (5)', line=dict(color='orange', width=3, dash='dash')
-            ), row=2, col=2)
-        
-        fig.update_layout(
-            height=600, 
-            showlegend=True, 
-            title_text="Training Progress Over Episodes",
-            template="plotly_white"
-        )
-        
-        return fig
-
-# Initialize the demo
-demo = TradingAIDemo()
-
-# Create Gradio interface
-def create_interface():
-    with gr.Blocks(theme=gr.themes.Soft(), title="Visual Trading AI") as interface:
-        gr.Markdown("""
-        # 🚀 Visual Trading AI
-        **هوش مصنوعی معامله‌گر بصری - تحلیل چارت‌های قیمت با یادگیری تقویتی عمیق**
-        
-        *این پروژه از شبکه‌های عصبی کانولوشن برای تحلیل بصری نمودارهای قیمت و یادگیری تقویتی برای تصمیم‌گیری معاملاتی استفاده می‌کند.*
-        """)
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                # Configuration section
-                gr.Markdown("## ⚙️ پیکربندی محیط")
-                
-                with gr.Row():
-                    initial_balance = gr.Slider(
-                        minimum=1000, maximum=50000, value=10000, step=1000,
-                        label="موجودی اولیه ($)", info="میزان سرمایه اولیه برای معامله"
-                    )
-                
-                with gr.Row():
-                    risk_level = gr.Radio(
-                        ["Low", "Medium", "High"], 
-                        value="Medium",
-                        label="سطح ریسک",
-                        info="سطح ریسک پذیری در معاملات"
-                    )
-                
-                with gr.Row():
-                    asset_type = gr.Radio(
-                        ["Stock", "Crypto", "Forex"], 
-                        value="Stock",
-                        label="نوع دارایی",
-                        info="نوع بازار مالی برای شبیه‌سازی"
-                    )
-                
-                with gr.Row():
-                    init_btn = gr.Button(
-                        "🚀 راه‌اندازی محیط معاملاتی", 
-                        variant="primary",
-                        size="lg"
-                    )
-                
-                with gr.Row():
-                    init_status = gr.Textbox(
-                        label="وضعیت راه‌اندازی",
-                        interactive=False,
-                        placeholder="برای شروع، محیط را راه‌اندازی کنید...",
-                        lines=2
-                    )
-            
-            with gr.Column(scale=2):
-                # Status output
-                gr.Markdown("## 📊 وضعیت معاملات")
-                status_output = gr.Textbox(
-                    label="وضعیت اجرا",
-                    interactive=False,
-                    placeholder="وضعیت معاملات اینجا نمایش داده می‌شود...",
-                    lines=4
-                )
-        
-        with gr.Row():
-            gr.Markdown("## 🎮 کنترل معاملات")
-            
-        with gr.Row():
-            # Action controls
-            action_choice = gr.Radio(
-                ["AI Decision", "Buy", "Sell", "Hold", "Close"],
-                value="AI Decision",
-                label="انتخاب اقدام",
-                info="AI Decision: تصمیم خودکار هوش مصنوعی"
-            )
-            
-        with gr.Row():
-            with gr.Column(scale=1):
-                step_btn = gr.Button(
-                    "▶️ اجرای یک قدم", 
-                    variant="secondary",
-                    size="lg"
-                )
-            
-            with gr.Column(scale=1):
-                episode_btn = gr.Button(
-                    "🎯 اجرای یک اپیزود (20 قدم)", 
-                    variant="secondary",
-                    size="lg"
-                )
-        
-        with gr.Row():
-            # Visualization outputs
-            with gr.Column(scale=1):
-                price_chart = gr.Plot(
-                    label="📈 نمودار قیمت و اقدامات"
-                )
-            
-            with gr.Column(scale=1):
-                performance_chart = gr.Plot(
-                    label="💰 عملکرد پرتفولیو"
-                )
-        
-        with gr.Row():
-            with gr.Column(scale=1):
-                action_chart = gr.Plot(
-                    label="🎯 توزیع اقدامات"
-                )
-        
-        with gr.Row():
-            gr.Markdown("## 🎓 آموزش هوش مصنوعی")
-            
-        with gr.Row():
-            with gr.Column(scale=1):
-                num_episodes = gr.Slider(
-                    minimum=10, maximum=200, value=50, step=10,
-                    label="تعداد اپیزودهای آموزش",
-                    info="تعداد دوره‌های آموزشی"
-                )
-                
-                learning_rate = gr.Slider(
-                    minimum=0.0001, maximum=0.01, value=0.001, step=0.0001,
-                    label="نرخ یادگیری",
-                    info="سرعت یادگیری الگوریتم"
-                )
-                
-                train_btn = gr.Button(
-                    "🤖 شروع آموزش", 
-                    variant="primary",
-                    size="lg"
-                )
-            
-            with gr.Column(scale=2):
-                training_plot = gr.Plot(
-                    label="📊 پیشرفت آموزش"
-                )
-                
-                training_status = gr.Textbox(
-                    label="وضعیت آموزش",
-                    interactive=False,
-                    placeholder="وضعیت آموزش اینجا نمایش داده می‌شود...",
-                    lines=3
-                )
-        
-        with gr.Row():
-            gr.Markdown("## ℹ️ راهنمای استفاده")
-            
-        with gr.Row():
-            with gr.Column(scale=1):
-                gr.Markdown("""
-                **🎯 اقدامات ممکن:**
-                - **Hold (0)**: حفظ وضعیت فعلی
-                - **Buy (1)**: باز کردن پوزیشن خرید
-                - **Sell (2)**: افزایش سایز پوزیشن
-                - **Close (3)**: بستن پوزیشن فعلی
-                
-                **📈 معیارهای عملکرد:**
-                - **Reward**: امتیاز دریافتی از محیط
-                - **Net Worth**: ارزش کل پرتفولیو
-                - **Balance**: موجودی نقدی
-                - **Position**: سایز پوزیشن فعلی
-                """)
-            
-            with gr.Column(scale=1):
-                gr.Markdown("""
-                **🔧 نحوه استفاده:**
-                1. محیط را راه‌اندازی کنید
-                2. اقدامات تکی یا اپیزودها را اجرا کنید
-                3. عملکرد را در نمودارها مشاهده کنید
-                4. هوش مصنوعی را آموزش دهید
-                5. نتایج را تحلیل کنید
-                
-                **⚠️ توجه:**
-                این یک شبیه‌ساز آموزشی است و برای معاملات واقعی طراحی نشده است.
-                """)
-        
-        # Event handlers
-        init_btn.click(
-            demo.initialize_environment,
-            inputs=[initial_balance, risk_level, asset_type],
-            outputs=[init_status]
-        )
-        
-        step_btn.click(
-            demo.run_single_step,
-            inputs=[action_choice],
-            outputs=[price_chart, performance_chart, action_chart, status_output]
-        )
-        
-        episode_btn.click(
-            demo.run_episode,
-            inputs=[],
-            outputs=[price_chart, performance_chart, action_chart, status_output]
-        )
-        
-        train_btn.click(
-            demo.train_agent,
-            inputs=[num_episodes, learning_rate],
-            outputs=[training_plot, training_status]
-        )
-        
-        gr.Markdown("""
-        ## 🏗 معماری فنی
-        
-        **🎯 هسته هوش مصنوعی:**
-        - **پردازش بصری**: شبکه عصبی کانولوشن (CNN) برای تحلیل نمودارهای قیمت
-        - **یادگیری تقویتی**: الگوریتم Deep Q-Network (DQN) برای تصمیم‌گیری
-        - **تجربه replay**: ذخیره و بازیابی تجربیات برای یادگیری پایدار
-        
-        **🛠 فناوری‌ها:**
-        - **یادگیری عمیق**: PyTorch
-        - **محیط شبیه‌سازی**: محیط اختصاصی معاملاتی
-        - **رابط کاربری**: Gradio
-        - **ویژوالیزیشن**: Plotly, Matplotlib
-        - **پردازش داده**: NumPy, Pandas
-        
-        **📊 ویژگی‌های کلیدی:**
-        - تحلیل بصری نمودارهای قیمت
-        - یادگیری خودکار استراتژی‌های معاملاتی
-        - نمایش زنده عملکرد و تصمیم‌ها
-        - کنترل دستی و خودکار
-        - آنالیز جامع عملکرد
-        
-        *توسعه داده شده توسط Omid Sakaki - 2024*
-        """)
-    
-    return interface
-
-# Create and launch interface
-if __name__ == "__main__":
-    print("🚀 Starting Visual Trading AI Application...")
-    print("📊 Initializing components...")
-    
-    interface = create_interface()
-    
-    print("✅ Application initialized successfully!")
-    print("🌐 Starting server on http://0.0.0.0:7860")
-    print("📱 You can now access the application in your browser")
-    
-    # Launch with better configuration
-    interface.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_error=True,
-        debug=True
-    )
+            print(f"Training error