fixes, lots of new ideas

.gitignore

@@ -38,4 +38,6 @@ crypto/gogo2/trading_bot.log
 crypto/gogo2/checkpoints/trading_agent_episode_*.pt
 *trading_agent_continuous_*.pt
 *trading_agent_episode_*.pt
-crypto/gogo2/models/trading_agent_continuous_150.pt
+crypto/gogo2/models/trading_agent_continuous_*.pt
+crypto/gogo2/visualizations/training_episode_*.png
+crypto/gogo2/checkpoints/trading_agent_episode_*.pt

crypto/gogo2/_model.md

@@ -0,0 +1,67 @@
+# Neural Network Architecture Analysis for Trading Bot
+
+## Overview
+
+This document provides a comprehensive analysis of the neural network architecture used in our trading bot system. The system consists of two main neural network components:
+
+1. **Price Prediction Model** - Forecasts future price movements and extrema points
+2. **DQN (Deep Q-Network)** - Makes trading decisions based on state representations
+
+## 1. Price Prediction Model
+
+### Architecture
+
+```
+PricePredictionModel(nn.Module)
+├── Input Layer: [batch_size, seq_len, 2] (price, volume)
+├── LSTM Layers: 2 stacked layers with hidden_size=128
+├── Attention Mechanism: Self-attention with linear projections
+├── Linear Layer 1: hidden_size → hidden_size
+├── ReLU Activation
+├── Linear Layer 2: hidden_size → output_size (5 future prices)
+└── Output: [batch_size, output_size]
+```
+
+### Data Flow
+
+**Inputs:**
+- `price_history`: Sequence of historical prices [batch_size, seq_len]
+- `volume_history`: Sequence of historical volumes [batch_size, seq_len]
+
+**Preprocessing:**
+- Normalization using MinMaxScaler (0-1 range)
+- Reshaping to [batch_size, seq_len, 2] (price and volume features)
+
+**Forward Pass:**
+1. Input data passes through LSTM layers
+2. Self-attention mechanism applied to LSTM outputs
+3. Linear layers process the attended features
+4. Output represents predicted prices for next 5 candles
+
+**Outputs:**
+- `predicted_prices`: Array of 5 future price predictions
+- `predicted_extrema`: Binary indicators for potential price extrema points
+
+## 2. DQN (Deep Q-Network)
+
+### Architecture
+
+```
+DQN(nn.Module)
+├── Input Layer: [batch_size, state_size]
+├── Linear Layer 1: state_size → hidden_size (384)
+├── ReLU Activation
+├── LSTM Layers: 2 stacked layers with hidden_size=384
+├── Multi-Head Attention: 4 attention heads
+├── Linear Layer 2: hidden_size → hidden_size
+├── ReLU Activation
+├── Linear Layer 3: hidden_size → action_size (4)
+└── Output: [batch_size, action_size] (Q-values for each action)
+```
+
+### Data Flow
+
+**Inputs:**
+- `state`: Current market state representation [batch_size, state_size]
+  - Price features (normalized prices, returns, volatility)
+  - Technical indicators (RSI, MACD, Stochastic,

crypto/gogo2/_notes.md

@@ -4,6 +4,12 @@ ensure we use GPU if available to train faster. during training we need to have
 
 
 
+do we call the trading api proerly when in live mode? in live mode - also validate the current ballance. also ensure trades are executed after executing the orders by checking the open orders. 
+
+our trading data chart (in tensorboard)  does not properly displayed - the candles seems displayed multiple times but shifted in time. we also do not correctly show the buy/sell evens on the time axis. we do not show the predicted price on the chart.
+
+
+
 
 2025-03-10 12:11:28,651 - INFO - Initialized environment with 500 candles
 C:\Users\popov\miniforge3\Lib\site-packages\torch\nn\modules\transformer.py:385: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)
@@ -21,4 +27,14 @@ Backend tkagg is interactive backend. Turning interactive mode on.
 
 
 
-2025-03-10 12:35:14,489 - INFO - Episode 34: Reward=232.41, Balance=$98.47, Win Rate=70.6%, Trades=17, Episode PnL=$-1.33, Total PnL=$-559.78, Max Drawdown=7.0%, Pred Accuracy=99.9%
+2025-03-10 12:35:14,489 - INFO - Episode 34: Reward=232.41, Balance=$98.47, Win Rate=70.6%, Trades=17, Episode PnL=$-1.33, Total PnL=$-559.78, Max Drawdown=7.0%, Pred Accuracy=99.9%
+
+
+----------------
+
+do we train the price prediction model by using old  known candles and masking the latest to make him guess the next and then backpropagating the next already known candle ? like a transformer (gpt2) would do? or we use RL for that as well?
+it seems the model is not learning a lot. we keep hovering about the same starting balance even after some time in training in continious mode
+
+
+ it seems we may need another NN model down the loop jut to predict the extremums of the price.  
+we may have to include  a mechanism to calculate the extremums of the price retrospectively and to use that to bootstrap pre-train the model. 

crypto/gogo2/archive.py

@@ -0,0 +1,362 @@
+class MexcTradingClient:
+    def __init__(self, api_key, secret_key, symbol="ETH/USDT", leverage=50):
+        self.client = ccxt.mexc({
+            'apiKey': api_key,
+            'secret': secret_key,
+            'enableRateLimit': True,
+        })
+        self.symbol = symbol
+        self.leverage = leverage
+        self.position = 'flat'
+        self.position_size = 0
+        self.entry_price = 0
+        self.stop_loss = 0
+        self.take_profit = 0
+        self.trades = []
+        
+        def initialize_mexc_client(self, api_key, api_secret):
+            """Initialize the MEXC API client"""
+            try:
+                from mexc_sdk import Spot
+                self.mexc_client = Spot(api_key=api_key, api_secret=api_secret)
+                # Test connection
+                self.mexc_client.ping()
+                logger.info("MEXC API client initialized successfully")
+                return True
+            except Exception as e:
+                logger.error(f"Failed to initialize MEXC API client: {e}")
+                logger.error(traceback.format_exc())
+                return False
+
+        async def fetch_account_balance(self):
+            """Fetch actual account balance from MEXC API"""
+            if self.demo or not self.mexc_client:
+                # In demo mode, use simulated balance
+                return self.balance
+            
+            try:
+                account_info = self.mexc_client.accountInfo()
+                if 'balances' in account_info:
+                    # Find USDT balance
+                    for asset in account_info['balances']:
+                        if asset['asset'] == 'USDT':
+                            return float(asset['free'])
+                
+                logger.warning("Could not find USDT balance, using current simulated balance")
+                return self.balance
+            except Exception as e:
+                logger.error(f"Error fetching account balance: {e}")
+                logger.error(traceback.format_exc())
+                # Fallback to simulated balance in case of API error
+                return self.balance
+
+        async def fetch_open_positions(self):
+            """Fetch actual open positions from MEXC API"""
+            if self.demo or not self.mexc_client:
+                # In demo mode, return current simulated position
+                return [{
+                    'symbol': 'ETH/USDT',
+                    'positionSide': 'LONG' if self.position == 'long' else 'SHORT' if self.position == 'short' else 'NONE',
+                    'positionAmt': self.position_size / self.current_price if self.position != 'flat' else 0,
+                    'entryPrice': self.entry_price,
+                    'unrealizedProfit': self.calculate_unrealized_pnl()
+                }] if self.position != 'flat' else []
+            
+            try:
+                # Fetch open positions
+                positions = self.mexc_client.openOrders('ETH/USDT')
+                return positions
+            except Exception as e:
+                logger.error(f"Error fetching open positions: {e}")
+                logger.error(traceback.format_exc())
+                # Fallback to simulated positions in case of API error
+                return []
+
+        def calculate_unrealized_pnl(self):
+            """Calculate unrealized PnL for the current position"""
+            if self.position == 'flat':
+                return 0.0
+            
+            position_value = self.position_size / self.entry_price
+            
+            if self.position == 'long':
+                pnl_percent = (self.current_price - self.entry_price) / self.entry_price * 100
+            else:  # short
+                pnl_percent = (self.entry_price - self.current_price) / self.entry_price * 100
+            
+            # Apply leverage
+            pnl_percent *= self.leverage
+            
+            return position_value * pnl_percent / 100
+
+        async def open_position(self, position_type, size, entry_price, stop_loss, take_profit):
+            """Open a new position using MEXC API in live mode, or simulate in demo mode"""
+            if self.demo or not self.mexc_client:
+                # In demo mode, simulate opening a position
+                self.position = position_type
+                self.position_size = size
+                self.entry_price = entry_price
+                self.entry_index = self.current_step
+                self.stop_loss = stop_loss
+                self.take_profit = take_profit
+                
+                logger.info(f"DEMO: Opened {position_type.upper()} position at {entry_price} | " +
+                        f"Size: ${size:.2f} | SL: {stop_loss:.2f} | TP: {take_profit:.2f}")
+                return True
+            
+            try:
+                # In live mode, place actual orders via API
+                symbol = "ETHUSDT"  # Format required by MEXC
+                side = "BUY" if position_type == 'long' else "SELL"
+                
+                # Calculate quantity based on size and price
+                quantity = size / entry_price
+                
+                # Place main order
+                order_result = self.mexc_client.newOrder(
+                    symbol=symbol,
+                    side=side,
+                    orderType="MARKET",
+                    quantity=quantity,
+                    options={
+                        "leverage": self.leverage,
+                        "newOrderRespType": "FULL"
+                    }
+                )
+                
+                # Check if order executed
+                if order_result.get('status') == 'FILLED':
+                    actual_entry_price = float(order_result.get('price', entry_price))
+                    
+                    # Place stop loss order
+                    sl_order = self.mexc_client.newOrder(
+                        symbol=symbol,
+                        side="SELL" if position_type == 'long' else "BUY",
+                        orderType="STOP_LOSS",
+                        quantity=quantity,
+                        options={
+                            "stopPrice": stop_loss,
+                            "newOrderRespType": "ACK"
+                        }
+                    )
+                    
+                    # Place take profit order
+                    tp_order = self.mexc_client.newOrder(
+                        symbol=symbol,
+                        side="SELL" if position_type == 'long' else "BUY",
+                        orderType="TAKE_PROFIT",
+                        quantity=quantity,
+                        options={
+                            "stopPrice": take_profit,
+                            "newOrderRespType": "ACK"
+                        }
+                    )
+                    
+                    # Update local state
+                    self.position = position_type
+                    self.position_size = size
+                    self.entry_price = actual_entry_price
+                    self.entry_index = self.current_step
+                    self.stop_loss = stop_loss
+                    self.take_profit = take_profit
+                    
+                    # Track orders
+                    self.open_orders.extend([sl_order, tp_order])
+                    self.order_history.append(order_result)
+                    
+                    logger.info(f"LIVE: Opened {position_type.upper()} position at {actual_entry_price} | " +
+                            f"Size: ${size:.2f} | SL: {stop_loss:.2f} | TP: {take_profit:.2f}")
+                    return True
+                    
+                else:
+                    logger.error(f"Failed to execute order: {order_result}")
+                    return False
+                    
+            except Exception as e:
+                logger.error(f"Error opening position: {e}")
+                logger.error(traceback.format_exc())
+                return False
+
+        async def close_position(self, reason="manual_close"):
+            """Close the current position using MEXC API in live mode, or simulate in demo mode"""
+            if self.position == 'flat':
+                logger.info("No position to close")
+                return False
+            
+            if self.demo or not self.mexc_client:
+                # In demo mode, simulate closing a position
+                position_type = self.position
+                entry_price = self.entry_price
+                exit_price = self.current_price
+                position_size = self.position_size
+                
+                # Calculate PnL
+                if position_type == 'long':
+                    pnl_percent = (exit_price - entry_price) / entry_price * 100
+                else:  # short
+                    pnl_percent = (entry_price - exit_price) / entry_price * 100
+                
+                # Apply leverage
+                pnl_percent *= self.leverage
+                
+                # Calculate actual PnL
+                pnl_dollar = position_size * pnl_percent / 100
+                
+                # Apply fees
+                pnl_dollar -= self.calculate_fees(position_size)
+                
+                # Update balance
+                self.balance += pnl_dollar
+                self.total_pnl += pnl_dollar
+                self.episode_pnl += pnl_dollar
+                
+                # Update max drawdown
+                if self.balance > self.peak_balance:
+                    self.peak_balance = self.balance
+                drawdown = (self.peak_balance - self.balance) / self.peak_balance
+                self.max_drawdown = max(self.max_drawdown, drawdown)
+                
+                # Record trade
+                self.trades.append({
+                    'type': position_type,
+                    'entry': entry_price,
+                    'exit': exit_price,
+                    'entry_time': self.data[self.entry_index]['timestamp'],
+                    'exit_time': self.data[self.current_step]['timestamp'],
+                    'pnl_percent': pnl_percent,
+                    'pnl_dollar': pnl_dollar,
+                    'duration': self.current_step - self.entry_index,
+                    'market_direction': self.get_market_direction(),
+                    'reason': reason,
+                    'leverage': self.leverage
+                })
+                
+                # Update win/loss count
+                if pnl_dollar > 0:
+                    self.win_count += 1
+                else:
+                    self.loss_count += 1
+                
+                logger.info(f"DEMO: Closed {position_type} at {exit_price} | PnL: {pnl_percent:.2f}% | ${pnl_dollar:.2f}")
+                
+                # Reset position
+                self.position = 'flat'
+                self.entry_price = 0
+                self.entry_index = 0
+                self.position_size = 0
+                self.stop_loss = 0
+                self.take_profit = 0
+                
+                return True
+                
+            try:
+                # In live mode, close position via API
+                symbol = "ETHUSDT"
+                position_info = await self.fetch_open_positions()
+                
+                if not position_info:
+                    logger.warning("No open positions found to close")
+                    self.position = 'flat'
+                    return False
+                
+                # First, cancel any existing stop loss/take profit orders
+                try:
+                    self.mexc_client.cancelOpenOrders(symbol)
+                except Exception as e:
+                    logger.warning(f"Error canceling open orders: {e}")
+                
+                # Close the position with a market order
+                position_type = self.position
+                side = "SELL" if position_type == 'long' else "BUY"
+                quantity = self.position_size / self.current_price
+                
+                # Execute order
+                order_result = self.mexc_client.newOrder(
+                    symbol=symbol,
+                    side=side,
+                    orderType="MARKET",
+                    quantity=quantity,
+                    options={
+                        "newOrderRespType": "FULL"
+                    }
+                )
+                
+                # Check if order executed
+                if order_result.get('status') == 'FILLED':
+                    exit_price = float(order_result.get('price', self.current_price))
+                    entry_price = self.entry_price
+                    position_size = self.position_size
+                    
+                    # Calculate PnL
+                    if position_type == 'long':
+                        pnl_percent = (exit_price - entry_price) / entry_price * 100
+                    else:  # short
+                        pnl_percent = (entry_price - exit_price) / entry_price * 100
+                    
+                    # Apply leverage
+                    pnl_percent *= self.leverage
+                    
+                    # Calculate actual PnL
+                    pnl_dollar = position_size * pnl_percent / 100
+                    
+                    # Apply fees
+                    pnl_dollar -= self.calculate_fees(position_size)
+                    
+                    # Update balance from API
+                    self.balance = await self.fetch_account_balance()
+                    self.total_pnl += pnl_dollar
+                    self.episode_pnl += pnl_dollar
+                    
+                    # Update max drawdown
+                    if self.balance > self.peak_balance:
+                        self.peak_balance = self.balance
+                    drawdown = (self.peak_balance - self.balance) / self.peak_balance
+                    self.max_drawdown = max(self.max_drawdown, drawdown)
+                    
+                    # Record trade
+                    self.trades.append({
+                        'type': position_type,
+                        'entry': entry_price,
+                        'exit': exit_price,
+                        'entry_time': self.data[self.entry_index]['timestamp'],
+                        'exit_time': self.data[self.current_step]['timestamp'],
+                        'pnl_percent': pnl_percent,
+                        'pnl_dollar': pnl_dollar,
+                        'duration': self.current_step - self.entry_index,
+                        'market_direction': self.get_market_direction(),
+                        'reason': reason,
+                        'leverage': self.leverage,
+                        'order_id': order_result.get('orderId')
+                    })
+                    
+                    # Update win/loss count
+                    if pnl_dollar > 0:
+                        self.win_count += 1
+                    else:
+                        self.loss_count += 1
+                    
+                    # Track order history
+                    self.order_history.append(order_result)
+                    
+                    logger.info(f"LIVE: Closed {position_type} at {exit_price} | PnL: {pnl_percent:.2f}% | ${pnl_dollar:.2f}")
+                    
+                    # Reset position
+                    self.position = 'flat'
+                    self.entry_price = 0
+                    self.entry_index = 0
+                    self.position_size = 0
+                    self.stop_loss = 0
+                    self.take_profit = 0
+                    
+                    return True
+                else:
+                    logger.error(f"Failed to close position: {order_result}")
+                    return False
+                    
+            except Exception as e:
+                logger.error(f"Error closing position: {e}")
+                logger.error(traceback.format_exc())
+                return False
+
+
+

crypto/gogo2/checkpoints/best_metrics.json

@@ -1 +1 @@
-{"best_reward": 202.7441047517104, "best_pnl": 9.268344827764809, "best_win_rate": 73.33333333333333, "last_episode": 30, "timestamp": "2025-03-10T17:57:19.913481"}
+{"best_reward": 202.7441047517104, "best_pnl": 9.268344827764809, "best_win_rate": 73.33333333333333, "last_episode": 0, "timestamp": "2025-03-12T00:23:19.125190"}

crypto/gogo2/count_params.py

@@ -0,0 +1,207 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import TransformerEncoder, TransformerEncoderLayer
+
+class PricePredictionModel(nn.Module):
+    def __init__(self, input_dim=2, hidden_dim=128, num_layers=2, output_dim=5):
+        super(PricePredictionModel, self).__init__()
+        self.hidden_dim = hidden_dim
+        self.num_layers = num_layers
+        
+        # LSTM layers
+        self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True)
+        
+        # Self-attention mechanism
+        self.attention = nn.MultiheadAttention(hidden_dim, num_heads=4, batch_first=True)
+        
+        # Fully connected layer for price prediction
+        self.price_fc = nn.Linear(hidden_dim, output_dim)
+        
+        # Fully connected layer for extrema prediction (high and low points)
+        self.extrema_fc = nn.Linear(hidden_dim, 10)  # 5 time steps, 2 classes (high/low) each
+        
+    def forward(self, x):
+        # x shape: (batch_size, seq_len, input_dim)
+        
+        # LSTM forward pass
+        lstm_out, _ = self.lstm(x)  # lstm_out: (batch_size, seq_len, hidden_dim)
+        
+        # Self-attention
+        attn_output, _ = self.attention(lstm_out, lstm_out, lstm_out)
+        
+        # Price prediction
+        price_pred = self.price_fc(attn_output[:, -1, :])  # Use the last time step
+        
+        # Extrema prediction
+        extrema_logits = self.extrema_fc(attn_output[:, -1, :])
+        
+        return price_pred, extrema_logits
+
+class DQN(nn.Module):
+    def __init__(self, state_dim, action_dim, hidden_dim=256):
+        super(DQN, self).__init__()
+        
+        # Feature extraction layers
+        self.feature_extraction = nn.Sequential(
+            nn.Linear(state_dim, hidden_dim),
+            nn.LeakyReLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.LeakyReLU(),
+        )
+        
+        # Advantage stream
+        self.advantage_stream = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.LeakyReLU(),
+            nn.Linear(hidden_dim, action_dim)
+        )
+        
+        # Value stream
+        self.value_stream = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.LeakyReLU(),
+            nn.Linear(hidden_dim, 1)
+        )
+        
+        # Transformer for temporal dependencies
+        encoder_layers = TransformerEncoderLayer(d_model=hidden_dim, nhead=4, dim_feedforward=hidden_dim*4, batch_first=True)
+        self.transformer = TransformerEncoder(encoder_layers, num_layers=2)
+        
+        # LSTM for sequential decision making
+        self.lstm = nn.LSTM(hidden_dim, hidden_dim, batch_first=True)
+        
+        # Final layers
+        self.final_layers = nn.Sequential(
+            nn.Linear(hidden_dim*2, hidden_dim),
+            nn.LeakyReLU(),
+            nn.Linear(hidden_dim, action_dim)
+        )
+        
+    def forward(self, state, hidden=None):
+        # Extract features
+        features = self.feature_extraction(state)
+        features = features.unsqueeze(1)  # Add sequence dimension for transformer/LSTM
+        
+        # Transformer processing
+        transformer_out = self.transformer(features)
+        
+        # LSTM processing
+        lstm_out, lstm_hidden = self.lstm(transformer_out)
+        
+        # Dueling architecture
+        advantage = self.advantage_stream(features.squeeze(1))
+        value = self.value_stream(features.squeeze(1))
+        
+        # Combine transformer, LSTM and dueling outputs
+        combined = torch.cat([transformer_out.squeeze(1), lstm_out.squeeze(1)], dim=1)
+        q_values = self.final_layers(combined)
+        
+        # Dueling Q-value computation
+        q_values = value + advantage - advantage.mean(dim=1, keepdim=True)
+        
+        return q_values, lstm_hidden
+
+def count_parameters(model):
+    total_params = 0
+    layer_params = {}
+    
+    for name, param in model.named_parameters():
+        if param.requires_grad:
+            param_count = param.numel()
+            total_params += param_count
+            layer_params[name] = (param_count, param.shape)
+    
+    return total_params, layer_params
+
+def main():
+    # Initialize the Price Prediction Model
+    price_model = PricePredictionModel()
+    price_total_params, price_layer_params = count_parameters(price_model)
+    
+    print(f"Price Prediction Model parameters: {price_total_params:,}")
+    print("\nPrice Prediction Model Layers:")
+    for name, (count, shape) in price_layer_params.items():
+        print(f"{name}: {count:,} (shape: {shape})")
+    
+    # Initialize the DQN Model with typical dimensions
+    state_dim = 50  # Typical state dimension for the trading bot
+    action_dim = 3  # Typical action dimension (buy, sell, hold)
+    dqn_model = DQN(state_dim=state_dim, action_dim=action_dim)
+    dqn_total_params, dqn_layer_params = count_parameters(dqn_model)
+    
+    # Count parameters by category
+    feature_extraction_params = sum(count for name, (count, _) in dqn_layer_params.items() if "feature_extraction" in name)
+    advantage_value_params = sum(count for name, (count, _) in dqn_layer_params.items() if "advantage_stream" in name or "value_stream" in name)
+    transformer_params = sum(count for name, (count, _) in dqn_layer_params.items() if "transformer" in name)
+    lstm_params = sum(count for name, (count, _) in dqn_layer_params.items() if "lstm" in name and "transformer" not in name)
+    final_layers_params = sum(count for name, (count, _) in dqn_layer_params.items() if "final_layers" in name)
+    
+    print(f"\nDQN Model parameters: {dqn_total_params:,}")
+    
+    # Create sets to track which parameters we've printed
+    printed_params = set()
+    
+    # Print DQN layers in groups to avoid output truncation
+    print(f"\nDQN Model Layers (Feature Extraction): {feature_extraction_params:,} parameters")
+    for name, (count, shape) in dqn_layer_params.items():
+        if "feature_extraction" in name:
+            print(f"{name}: {count:,} (shape: {shape})")
+            printed_params.add(name)
+    
+    print(f"\nDQN Model Layers (Advantage & Value Streams): {advantage_value_params:,} parameters")
+    for name, (count, shape) in dqn_layer_params.items():
+        if "advantage_stream" in name or "value_stream" in name:
+            print(f"{name}: {count:,} (shape: {shape})")
+            printed_params.add(name)
+    
+    print(f"\nDQN Model Layers (Transformer): {transformer_params:,} parameters")
+    for name, (count, shape) in dqn_layer_params.items():
+        if "transformer" in name:
+            print(f"{name}: {count:,} (shape: {shape})")
+            printed_params.add(name)
+    
+    print(f"\nDQN Model Layers (LSTM): {lstm_params:,} parameters")
+    for name, (count, shape) in dqn_layer_params.items():
+        if "lstm" in name and "transformer" not in name:
+            print(f"{name}: {count:,} (shape: {shape})")
+            printed_params.add(name)
+    
+    print(f"\nDQN Model Layers (Final Layers): {final_layers_params:,} parameters")
+    for name, (count, shape) in dqn_layer_params.items():
+        if "final_layers" in name:
+            print(f"{name}: {count:,} (shape: {shape})")
+            printed_params.add(name)
+    
+    # Print any remaining parameters that weren't caught by the categories above
+    remaining_params = set(dqn_layer_params.keys()) - printed_params
+    if remaining_params:
+        remaining_params_count = sum(dqn_layer_params[name][0] for name in remaining_params)
+        print(f"\nDQN Model Layers (Other): {remaining_params_count:,} parameters")
+        for name in remaining_params:
+            count, shape = dqn_layer_params[name]
+            print(f"{name}: {count:,} (shape: {shape})")
+    
+    # Total parameters across both models
+    print(f"\nTotal parameters (both models): {price_total_params + dqn_total_params:,}")
+    
+    # Print summary of parameter distribution
+    print("\nParameter Distribution Summary:")
+    print(f"Price Prediction Model: {price_total_params:,} parameters ({price_total_params/(price_total_params + dqn_total_params)*100:.1f}%)")
+    print(f"DQN Model: {dqn_total_params:,} parameters ({dqn_total_params/(price_total_params + dqn_total_params)*100:.1f}%)")
+    print("\nDQN Model Breakdown:")
+    print(f"- Feature Extraction: {feature_extraction_params:,} parameters ({feature_extraction_params/dqn_total_params*100:.1f}%)")
+    print(f"- Advantage & Value Streams: {advantage_value_params:,} parameters ({advantage_value_params/dqn_total_params*100:.1f}%)")
+    print(f"- Transformer: {transformer_params:,} parameters ({transformer_params/dqn_total_params*100:.1f}%)")
+    print(f"- LSTM: {lstm_params:,} parameters ({lstm_params/dqn_total_params*100:.1f}%)")
+    print(f"- Final Layers: {final_layers_params:,} parameters ({final_layers_params/dqn_total_params*100:.1f}%)")
+    
+    # Verify that all parameters are accounted for
+    total_by_category = feature_extraction_params + advantage_value_params + transformer_params + lstm_params + final_layers_params
+    if remaining_params:
+        total_by_category += remaining_params_count
+    print(f"\nSum of all categories: {total_by_category:,} parameters")
+    print(f"Difference from total: {dqn_total_params - total_by_category:,} parameters")
+
+if __name__ == "__main__":
+    main() 

crypto/gogo2/fix_indentation.py

@@ -0,0 +1,42 @@
+def fix_indentation():
+    with open('main.py', 'r') as f:
+        lines = f.readlines()
+    
+    # Fix indentation for the problematic sections
+    fixed_lines = []
+    
+    # Find the try block that starts at line 1693
+    try_start_line = 1693
+    try_block_found = False
+    in_try_block = False
+    
+    for i, line in enumerate(lines):
+        # Check if we're at the try statement
+        if i+1 == try_start_line and 'try:' in line:
+            try_block_found = True
+            in_try_block = True
+            fixed_lines.append(line)
+        # Fix the indentation of the experiences line
+        elif i+1 == 1695 and line.strip().startswith('experiences = self.memory.sample(BATCH_SIZE)'):
+            # Add proper indentation (4 spaces)
+            fixed_lines.append('            ' + line.lstrip())
+        # Check if we're at the end of the try block without an except
+        elif try_block_found and in_try_block and i+1 > try_start_line and line.strip() and not line.startswith(' '):
+            # We've reached the end of the try block without an except, add one
+            fixed_lines.append('        except Exception as e:\n')
+            fixed_lines.append('            logger.error(f"Error during learning: {e}")\n')
+            fixed_lines.append('            logger.error(f"Traceback: {traceback.format_exc()}")\n')
+            fixed_lines.append('            return None\n\n')
+            in_try_block = False
+            fixed_lines.append(line)
+        else:
+            fixed_lines.append(line)
+    
+    # Write the fixed content back to the file
+    with open('main.py', 'w') as f:
+        f.writelines(fixed_lines)
+    
+    print("Indentation fixed!")
+
+if __name__ == "__main__":
+    fix_indentation() 

crypto/gogo2/fix_try_blocks.py

@@ -0,0 +1,22 @@
+import re
+
+def fix_try_blocks():
+    with open('main.py', 'r') as f:
+        content = f.read()
+    
+    # Find all try blocks without except or finally
+    pattern = r'(\s+)try:\s*\n((?:\1\s+.*\n)+?)(?!\1\s*except|\1\s*finally)'
+    
+    # Replace with try-except blocks
+    fixed_content = re.sub(pattern, 
+                          r'\1try:\n\2\1except Exception as e:\n\1    logger.error(f"Error: {e}")\n\1    logger.error(f"Traceback: {traceback.format_exc()}")\n\1    return None\n\n', 
+                          content)
+    
+    # Write the fixed content back to the file
+    with open('main.py', 'w') as f:
+        f.write(fixed_content)
+    
+    print("Try blocks fixed!")
+
+if __name__ == "__main__":
+    fix_try_blocks() 

crypto/gogo2/main.py

@@ -1151,10 +1151,10 @@ class TradingEnvironment:
                 
                 # Reward based on PnL
                 if pnl_dollar > 0:
-                    reward = 1.0 + pnl_dollar / 10  # Positive reward for profit
+                    reward = 2.0 + pnl_dollar * 0.5  # Increased positive reward for profit
                     self.win_count += 1
                 else:
-                    reward = -1.0  # Negative reward for loss
+                    reward = -2.0 - abs(pnl_dollar) * 0.3  # Stronger negative reward for loss
                     self.loss_count += 1
                 
                 logger.info(f"CLOSED short at {self.current_price} | PnL: {pnl_percent:.2f}% | ${pnl_dollar:.2f}")
@@ -1238,10 +1238,15 @@ class TradingEnvironment:
                 
                 # Reward based on PnL
                 if pnl_dollar > 0:
-                    reward = 1.0 + pnl_dollar / 10  # Positive reward for profit
+                    reward = 2.0 + pnl_dollar * 0.5  # Increased positive reward for profit
                     self.win_count += 1
+                    
+                    # Extra reward for closing at a predicted high
+                    if hasattr(self, 'has_predicted_high') and self.has_predicted_high:
+                        reward += 1.0
+                        logger.info("Closing long at predicted high - additional reward")
                 else:
-                    reward = -1.0  # Negative reward for loss
+                    reward = -2.0 - abs(pnl_dollar) * 0.3  # Stronger negative reward for loss
                     self.loss_count += 1
                 
                 logger.info(f"CLOSED long at {self.current_price} | PnL: {pnl_percent:.2f}% | ${pnl_dollar:.2f}")
@@ -1296,15 +1301,15 @@ class TradingEnvironment:
                 
                 # Reward based on PnL
                 if pnl_dollar > 0:
-                    reward = 1.0 + pnl_dollar / 10  # Positive reward for profit
+                    reward = 2.0 + pnl_dollar * 0.5  # Increased positive reward for profit
                     self.win_count += 1
                     
                     # Extra reward for closing at a predicted high
                     if hasattr(self, 'has_predicted_high') and self.has_predicted_high:
-                        reward += 0.5
+                        reward += 1.0
                         logger.info("Closing long at predicted high - additional reward")
                 else:
-                    reward = -1.0  # Negative reward for loss
+                    reward = -2.0 - abs(pnl_dollar) * 0.3  # Stronger negative reward for loss
                     self.loss_count += 1
                 
                 logger.info(f"CLOSED long at {self.current_price} | PnL: {pnl_percent:.2f}% | ${pnl_dollar:.2f}")
@@ -1354,15 +1359,15 @@ class TradingEnvironment:
                 
                 # Reward based on PnL
                 if pnl_dollar > 0:
-                    reward = 1.0 + pnl_dollar / 10  # Positive reward for profit
+                    reward = 2.0 + pnl_dollar * 0.5  # Increased positive reward for profit
                     self.win_count += 1
                     
                     # Extra reward for closing at a predicted low
                     if hasattr(self, 'has_predicted_low') and self.has_predicted_low:
-                        reward += 0.5
+                        reward += 1.0
                         logger.info("Closing short at predicted low - additional reward")
                 else:
-                    reward = -1.0  # Negative reward for loss
+                    reward = -2.0 - abs(pnl_dollar) * 0.3  # Stronger negative reward for loss
                     self.loss_count += 1
                 
                 logger.info(f"CLOSED short at {self.current_price} | PnL: {pnl_percent:.2f}% | ${pnl_dollar:.2f}")
@@ -1378,9 +1383,9 @@ class TradingEnvironment:
         # Add reward based on direct PnL change
         balance_change = self.balance - prev_balance
         if balance_change > 0:
-            reward += balance_change * 0.5  # Positive reward for making money
+            reward += balance_change * 1.0  # Increased positive reward for making money
         else:
-            reward += balance_change * 1.0  # Stronger negative reward for losing money
+            reward += balance_change * 2.0  # Stronger negative reward for losing money
         
         # Add reward for predicted price movement alignment
         if hasattr(self, 'predicted_prices') and len(self.predicted_prices) > 0:
@@ -1611,9 +1616,15 @@ class TradingEnvironment:
     
     def initialize_price_predictor(self, device="cpu"):
         """Initialize the price prediction model"""
-        self.price_predictor = PricePredictionModel(input_size=30, hidden_size=128, output_size=5)
-        self.price_predictor.to(device)
-        self.price_predictor_optimizer = optim.Adam(self.price_predictor.parameters(), lr=1e-3)
+        # Only create a new model if one doesn't already exist
+        if not hasattr(self, 'price_predictor') or self.price_predictor is None:
+            self.price_predictor = PricePredictionModel(input_size=30, hidden_size=128, output_size=5)
+            self.price_predictor.to(device)
+            self.price_predictor_optimizer = optim.Adam(self.price_predictor.parameters(), lr=1e-3)
+        else:
+            # If model exists, just ensure it's on the right device
+            self.price_predictor.to(device)
+            
         self.predicted_prices = np.array([])
         self.predicted_extrema = np.array([])
         self.extrema_threshold = 0.7  # Threshold for extrema prediction confidence
@@ -1766,16 +1777,16 @@ class TradingEnvironment:
         return fee
 
 # Ensure GPU usage if available
-def get_device(device_preference='gpu'):
+def get_device(device_preference='auto'):
     """Get the device to use (GPU or CPU) based on preference and availability"""
-    if device_preference.lower() == 'gpu' and torch.cuda.is_available():
+    if device_preference.lower() in ['gpu', 'auto'] and torch.cuda.is_available():
         device = torch.device("cuda")
         # Set default tensor type to float32 for CUDA
         torch.set_default_tensor_type(torch.FloatTensor)
         logger.info(f"Using GPU: {torch.cuda.get_device_name(0)}")
     else:
         device = torch.device("cpu")
-        if device_preference.lower() == 'gpu':
+        if device_preference.lower() in ['gpu', 'auto']:
             logger.info("GPU requested but not available, using CPU instead")
         else:
             logger.info("Using CPU as requested")
@@ -1952,7 +1963,7 @@ class Agent:
             
             # Use mixed precision for forward/backward passes
             if self.device.type == "cuda":
-                with amp.autocast():
+                with amp.autocast(device_type='cuda'):
                     # Compute Q values
                     current_q_values = self.policy_net(states).gather(1, actions.unsqueeze(1))
                     
@@ -2943,10 +2954,12 @@ async def main():
     import traceback
     
     parser = argparse.ArgumentParser(description='Run the trading bot')
-    parser.add_argument('--mode', type=str, default='train', choices=['train', 'evaluate', 'live'], help='Mode to run the bot in')
+    parser.add_argument('--mode', type=str, default='train', choices=['train', 'evaluate', 'live', 'continuous'], help='Mode to run the bot in')
     parser.add_argument('--episodes', type=int, default=100, help='Number of episodes to train for')
     parser.add_argument('--demo', action='store_true', help='Run in demo mode (no real trades)')
     parser.add_argument('--device', type=str, default='auto', choices=['cpu', 'gpu', 'auto'], help='Device to use for training')
+    parser.add_argument('--refresh-data', '--refresh_data', dest='refresh_data', action='store_true', help='Refresh data at the start of each episode')
+    parser.add_argument('--timeframe', type=str, default='1m', help='Timeframe for data (e.g., 1s, 1m, 5m, 15m, 1h)')
     args = parser.parse_args()
     
     # Set device
@@ -2995,6 +3008,247 @@ async def main():
             results = evaluate_agent(agent, env, num_episodes=10)
             logger.info(f"Evaluation results: {results}")
             
+        elif args.mode == 'continuous':
+            # Continuous training mode - train indefinitely with data refreshing
+            logger.info("Starting continuous training mode...")
+            
+            # Set refresh_data to True for continuous mode
+            args.refresh_data = True
+            
+            # Create directories for continuous models
+            os.makedirs("models", exist_ok=True)
+            
+            # Track best PnL for model selection
+            best_pnl = float('-inf')
+            best_pnl_model_path = "models/trading_agent_best_pnl.pt"
+            
+            # Load the best PnL model if it exists
+            if os.path.exists(best_pnl_model_path):
+                logger.info(f"Loading best PnL model: {best_pnl_model_path}")
+                agent.load(best_pnl_model_path)
+                
+                # Try to load best PnL value from checkpoint file
+                checkpoint_info_path = "checkpoints/best_metrics.json"
+                if os.path.exists(checkpoint_info_path):
+                    with open(checkpoint_info_path, 'r') as f:
+                        best_metrics = json.load(f)
+                        best_pnl = best_metrics.get('best_pnl', best_pnl)
+                        logger.info(f"Loaded best PnL from checkpoint: ${best_pnl:.2f}")
+            
+            # Initialize episode counter
+            episode = 0
+            
+            # Get timeframe from args
+            timeframe = args.timeframe
+            logger.info(f"Using timeframe: {timeframe}")
+            
+            # Initialize TensorBoard writer
+            from torch.utils.tensorboard import SummaryWriter
+            tensorboard_dir = f"runs/continuous_{datetime.datetime.now().strftime('%Y%m%d_%H%M%S')}"
+            writer = SummaryWriter(tensorboard_dir)
+            logger.info(f"TensorBoard logs will be saved to {tensorboard_dir}")
+            
+            # Attach writer to agent
+            agent.writer = writer
+            
+            # Initialize stats dictionary for plotting
+            stats = {
+                'episode_rewards': [],
+                'episode_profits': [],
+                'win_rates': [],
+                'trade_counts': [],
+                'prediction_accuracies': []
+            }
+            
+            # Train continuously
+            try:
+                while True:
+                    logger.info(f"Continuous training - Episode {episode}")
+                    
+                    # Refresh data from exchange with the specified timeframe
+                    logger.info(f"Refreshing market data with timeframe {timeframe}...")
+                    await env.fetch_new_data(exchange, "ETH/USDT", timeframe, 100)
+                    
+                    # Reset environment
+                    state = env.reset()
+                    
+                    # Initialize price predictor if not already initialized
+                    if not hasattr(env, 'price_predictor') or env.price_predictor is None:
+                        logger.info("Initializing price predictor...")
+                        env.initialize_price_predictor(device=agent.device)
+                    
+                    # Initialize episode variables
+                    episode_reward = 0
+                    done = False
+                    
+                    # Train price predictor
+                    prediction_loss, extrema_loss = env.train_price_predictor()
+                    
+                    # Update price predictions
+                    env.update_price_predictions()
+                    
+                    # Training loop for this episode
+                    while not done:
+                        # Select action
+                        action = agent.select_action(state)
+                        
+                        # Take action
+                        next_state, reward, done = env.step(action)
+                        
+                        # Store experience
+                        agent.memory.push(state, action, reward, next_state, done)
+                        
+                        # Learn from experience
+                        loss = agent.learn()
+                        
+                        # Update state and reward
+                        state = next_state
+                        episode_reward += reward
+                    
+                    # Calculate win rate
+                    total_trades = env.win_count + env.loss_count
+                    win_rate = (env.win_count / total_trades * 100) if total_trades > 0 else 0
+                    
+                    # Calculate prediction accuracy
+                    if hasattr(env, 'predicted_prices') and len(env.predicted_prices) > 0:
+                        # Compare predictions with actual prices
+                        actual_prices = env.features['price'][-len(env.predicted_prices):]
+                        prediction_errors = np.abs(env.predicted_prices - actual_prices) / actual_prices
+                        prediction_accuracy = 100 * (1 - np.mean(prediction_errors))
+                    else:
+                        prediction_accuracy = 0
+                    
+                    # Update stats
+                    stats['episode_rewards'].append(episode_reward)
+                    stats['episode_profits'].append(env.episode_pnl)
+                    stats['win_rates'].append(win_rate)
+                    stats['trade_counts'].append(total_trades)
+                    stats['prediction_accuracies'].append(prediction_accuracy)
+                    
+                    # Log to TensorBoard
+                    writer.add_scalar('Reward/continuous', episode_reward, episode)
+                    writer.add_scalar('Balance/continuous', env.balance, episode)
+                    writer.add_scalar('WinRate/continuous', win_rate, episode)
+                    writer.add_scalar('PnL/episode', env.episode_pnl, episode)
+                    writer.add_scalar('PnL/cumulative', env.total_pnl, episode)
+                    writer.add_scalar('Drawdown/percent', env.max_drawdown * 100, episode)
+                    writer.add_scalar('PredictionLoss', prediction_loss, episode)
+                    writer.add_scalar('PredictionAccuracy', prediction_accuracy, episode)
+                    
+                    # Log OHLCV data to TensorBoard every 5 episodes
+                    if episode % 5 == 0:
+                        # Create a DataFrame from the environment's data
+                        df_ohlcv = pd.DataFrame([{
+                            'timestamp': candle['timestamp'],
+                            'open': candle['open'],
+                            'high': candle['high'],
+                            'low': candle['low'],
+                            'close': candle['close'],
+                            'volume': candle['volume']
+                        } for candle in env.data[-100:]])  # Use last 100 candles
+                        
+                        # Convert timestamp to datetime
+                        df_ohlcv['timestamp'] = pd.to_datetime(df_ohlcv['timestamp'], unit='ms')
+                        df_ohlcv.set_index('timestamp', inplace=True)
+                        
+                        # Extract buy/sell signals from trades
+                        buy_signals = []
+                        sell_signals = []
+                        
+                        if hasattr(env, 'trades') and env.trades:
+                            for trade in env.trades:
+                                if 'entry_time' in trade and 'entry' in trade:
+                                    if trade['type'] == 'long':
+                                        # Buy signal
+                                        entry_time = pd.to_datetime(trade['entry_time'], unit='ms')
+                                        buy_signals.append((entry_time, trade['entry']))
+                                        
+                                        # Sell signal if closed
+                                        if 'exit_time' in trade and 'exit' in trade and trade['exit'] > 0:
+                                            exit_time = pd.to_datetime(trade['exit_time'], unit='ms')
+                                            sell_signals.append((exit_time, trade['exit']))
+                                    
+                                    elif trade['type'] == 'short':
+                                        # Sell short signal
+                                        entry_time = pd.to_datetime(trade['entry_time'], unit='ms')
+                                        sell_signals.append((entry_time, trade['entry']))
+                                        
+                                        # Buy to cover signal if closed
+                                        if 'exit_time' in trade and 'exit' in trade and trade['exit'] > 0:
+                                            exit_time = pd.to_datetime(trade['exit_time'], unit='ms')
+                                            buy_signals.append((exit_time, trade['exit']))
+                        
+                        # Log to TensorBoard
+                        log_ohlcv_to_tensorboard(
+                            writer, 
+                            df_ohlcv, 
+                            buy_signals, 
+                            sell_signals, 
+                            episode, 
+                            tag_prefix=f"continuous_episode_{episode}"
+                        )
+                    
+                    logger.info(f"Episode {episode}: Reward={episode_reward:.2f}, Balance=${env.balance:.2f}, "
+                              f"Win Rate={win_rate:.1f}%, Trades={len(env.trades)}, "
+                              f"Episode PnL=${env.episode_pnl:.2f}, Total PnL=${env.total_pnl:.2f}")
+                    
+                    # Create visualization every 10 episodes
+                    if episode % 10 == 0:
+                        # Create visualization
+                        os.makedirs("visualizations", exist_ok=True)
+                        visualize_training_results(env, agent, episode)
+                        
+                        # Save model
+                        model_path = f"models/trading_agent_continuous_{episode}.pt"
+                        agent.save(model_path)
+                        logger.info(f"Saved continuous model: {model_path}")
+                        
+                        # Plot training results
+                        plot_training_results(stats)
+                    
+                    # Save best PnL model
+                    if env.episode_pnl > best_pnl:
+                        best_pnl = env.episode_pnl
+                        agent.save(best_pnl_model_path)
+                        logger.info(f"New best PnL model saved: ${env.episode_pnl:.2f}")
+                        
+                        # Save best metrics to resume training if interrupted
+                        best_metrics = {
+                            'best_pnl': float(best_pnl),
+                            'last_episode': episode,
+                            'timestamp': datetime.datetime.now().isoformat()
+                        }
+                        os.makedirs("checkpoints", exist_ok=True)
+                        with open("checkpoints/best_metrics.json", 'w') as f:
+                            json.dump(best_metrics, f)
+                    
+                    # Update target network
+                    agent.update_target_network()
+                    
+                    # Increment episode counter
+                    episode += 1
+                    
+                    # Sleep briefly to prevent overwhelming the system
+                    # Use shorter sleep for shorter timeframes
+                    if timeframe.endswith('s'):
+                        await asyncio.sleep(0.1)  # Very short sleep for second-based timeframes
+                    else:
+                        await asyncio.sleep(1)
+                    
+            except KeyboardInterrupt:
+                logger.info("Continuous training stopped by user")
+                # Save final model
+                agent.save("models/trading_agent_continuous_final.pt")
+                # Close TensorBoard writer
+                writer.close()
+            except Exception as e:
+                logger.error(f"Error in continuous training: {e}")
+                logger.error(f"Traceback: {traceback.format_exc()}")
+                # Save emergency model
+                agent.save(f"models/trading_agent_continuous_emergency_{episode}.pt")
+                # Close TensorBoard writer
+                writer.close()
+            
         elif args.mode == 'evaluate':
             # Load the best model
             agent.load("models/trading_agent_best_pnl.pt")

crypto/gogo2/mexc_trading.py

@@ -0,0 +1,304 @@
+import os
+import json
+import logging
+import traceback
+import numpy as np
+from dotenv import load_dotenv
+from mexc_api.spot import Spot
+from mexc_api.common.enums import Side, OrderType
+
+# Configure logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger('mexc_trading')
+
+# Load environment variables
+load_dotenv()
+MEXC_API_KEY = os.getenv('MEXC_API_KEY')
+MEXC_SECRET_KEY = os.getenv('MEXC_SECRET_KEY')
+
+class MexcTradingClient:
+    """Client for executing trades on MEXC exchange using the official API"""
+    
+    def __init__(self, api_key=None, api_secret=None, symbol="ETH/USDT", leverage=1):
+        """Initialize the MEXC trading client"""
+        self.api_key = api_key or MEXC_API_KEY
+        self.api_secret = api_secret or MEXC_SECRET_KEY
+        
+        # Ensure API keys are not None
+        if not self.api_key or not self.api_secret:
+            logger.warning("API keys not provided. Using empty strings for public endpoints only.")
+            self.api_key = ""
+            self.api_secret = ""
+            
+        self.symbol = symbol
+        self.formatted_symbol = symbol.replace('/', '')  # MEXC requires no slash
+        self.leverage = leverage
+        self.client = None
+        self.position = 'flat'  # 'flat', 'long', or 'short'
+        self.position_size = 0
+        self.entry_price = 0
+        self.stop_loss = 0
+        self.take_profit = 0
+        self.open_orders = []
+        self.order_history = []
+        self.trades = []
+        self.win_count = 0
+        self.loss_count = 0
+        
+        # Initialize the MEXC API client
+        self.initialize_client()
+    
+    def initialize_client(self):
+        """Initialize the MEXC API client using the API"""
+        try:
+            self.client = Spot(self.api_key, self.api_secret)
+            # Test connection
+            server_time = self.client.market.server_time()
+            logger.info(f"MEXC API client initialized successfully. Server time: {server_time}")
+            return True
+        except Exception as e:
+            logger.error(f"Failed to initialize MEXC API client: {e}")
+            logger.error(traceback.format_exc())
+            return False
+    
+    async def fetch_account_balance(self):
+        """Fetch account balance from MEXC API"""
+        try:
+            # Check if we have API keys for private endpoints
+            if not self.api_key or self.api_key == "":
+                logger.warning("No API keys provided. Cannot fetch account balance.")
+                return 0
+                
+            account_info = self.client.account.account_info()
+            if 'balances' in account_info:
+                # Find USDT balance
+                for asset in account_info['balances']:
+                    if asset['asset'] == 'USDT':
+                        return float(asset['free'])
+            
+            logger.warning("Could not find USDT balance")
+            return 0
+        except Exception as e:
+            logger.error(f"Error fetching account balance: {e}")
+            return 0
+    
+    async def fetch_open_positions(self):
+        """Fetch open positions from MEXC API"""
+        try:
+            # Check if we have API keys for private endpoints
+            if not self.api_key or self.api_key == "":
+                logger.warning("No API keys provided. Cannot fetch open positions.")
+                return []
+                
+            # Fetch open orders
+            open_orders = self.client.account.open_orders(self.formatted_symbol)
+            return open_orders
+        except Exception as e:
+            logger.error(f"Error fetching open positions: {e}")
+            return []
+    
+    async def open_position(self, position_type, size, entry_price, stop_loss, take_profit):
+        """Open a new position using MEXC API"""
+        try:
+            # Check if we have API keys for private endpoints
+            if not self.api_key or self.api_key == "":
+                logger.warning("No API keys provided. Cannot open position.")
+                return False
+                
+            # Calculate quantity based on size and price
+            quantity = size / entry_price
+            # Round quantity to appropriate precision
+            quantity = round(quantity, 4)  # Adjust precision as needed for your asset
+            
+            # Determine order side
+            side = Side.BUY if position_type == 'long' else Side.SELL
+            
+            logger.info(f"Opening {position_type} position: {quantity} {self.symbol} at market price")
+            
+            # Place market order
+            order_result = self.client.account.new_order(
+                self.formatted_symbol,
+                side,
+                OrderType.MARKET,
+                str(quantity)
+            )
+            
+            logger.info(f"Market order result: {order_result}")
+            
+            # Check if order was filled
+            if order_result.get('status') == 'FILLED' or order_result.get('status') == 'PARTIALLY_FILLED':
+                # Get actual entry price
+                actual_entry_price = float(order_result.get('price', entry_price))
+                
+                # Place stop loss order
+                sl_side = Side.SELL if position_type == 'long' else Side.BUY
+                sl_order = self.client.account.new_order(
+                    self.formatted_symbol,
+                    sl_side,
+                    OrderType.STOP_LOSS_LIMIT,
+                    str(quantity),
+                    price=str(stop_loss),
+                    stop_price=str(stop_loss),
+                    time_in_force="GTC"
+                )
+                
+                logger.info(f"Stop loss order placed: {sl_order}")
+                
+                # Place take profit order
+                tp_side = Side.SELL if position_type == 'long' else Side.BUY
+                tp_order = self.client.account.new_order(
+                    self.formatted_symbol,
+                    tp_side,
+                    OrderType.TAKE_PROFIT_LIMIT,
+                    str(quantity),
+                    price=str(take_profit),
+                    stop_price=str(take_profit),
+                    time_in_force="GTC"
+                )
+                
+                logger.info(f"Take profit order placed: {tp_order}")
+                
+                # Update local state
+                self.position = position_type
+                self.position_size = size
+                self.entry_price = actual_entry_price
+                self.stop_loss = stop_loss
+                self.take_profit = take_profit
+                
+                # Track orders
+                self.open_orders.extend([sl_order, tp_order])
+                self.order_history.append(order_result)
+                
+                logger.info(f"Successfully opened {position_type} position at {actual_entry_price}")
+                return True
+            else:
+                logger.error(f"Failed to open position: {order_result}")
+                return False
+                
+        except Exception as e:
+            logger.error(f"Error opening position: {e}")
+            logger.error(traceback.format_exc())
+            return False
+    
+    async def close_position(self, reason="manual_close"):
+        """Close an existing position"""
+        if self.position == 'flat':
+            logger.info("No position to close")
+            return True
+            
+        try:
+            # Check if we have API keys for private endpoints
+            if not self.api_key or self.api_key == "":
+                logger.warning("No API keys provided. Cannot close position.")
+                return False
+                
+            # First, cancel any existing stop loss/take profit orders
+            try:
+                self.client.account.cancel_open_orders(self.formatted_symbol)
+                logger.info("Canceled all open orders")
+            except Exception as e:
+                logger.warning(f"Error canceling open orders: {e}")
+            
+            # Determine order side (opposite of position)
+            side = Side.SELL if self.position == 'long' else Side.BUY
+            
+            # Calculate quantity
+            quantity = self.position_size / self.entry_price
+            # Round quantity to appropriate precision
+            quantity = round(quantity, 4)  # Adjust precision as needed
+            
+            logger.info(f"Closing {self.position} position: {quantity} {self.symbol} at market price")
+            
+            # Execute market order to close position
+            order_result = self.client.account.new_order(
+                self.formatted_symbol,
+                side,
+                OrderType.MARKET,
+                str(quantity)
+            )
+            
+            logger.info(f"Close order result: {order_result}")
+            
+            # Check if order was filled
+            if order_result.get('status') == 'FILLED' or order_result.get('status') == 'PARTIALLY_FILLED':
+                # Get actual exit price
+                exit_price = float(order_result.get('price', 0))
+                
+                # Calculate PnL
+                if self.position == 'long':
+                    pnl_percent = (exit_price - self.entry_price) / self.entry_price * 100
+                else:  # short
+                    pnl_percent = (self.entry_price - exit_price) / self.entry_price * 100
+                
+                pnl_dollar = pnl_percent / 100 * self.position_size
+                
+                # Record trade
+                self.trades.append({
+                    'type': self.position,
+                    'entry': self.entry_price,
+                    'exit': exit_price,
+                    'pnl_percent': pnl_percent,
+                    'pnl_dollar': pnl_dollar,
+                    'reason': reason,
+                    'order_id': order_result.get('orderId')
+                })
+                
+                # Update win/loss count
+                if pnl_dollar > 0:
+                    self.win_count += 1
+                else:
+                    self.loss_count += 1
+                
+                # Track order history
+                self.order_history.append(order_result)
+                
+                logger.info(f"Closed {self.position} position at {exit_price} | PnL: {pnl_percent:.2f}% | ${pnl_dollar:.2f}")
+                
+                # Reset position
+                self.position = 'flat'
+                self.entry_price = 0
+                self.position_size = 0
+                self.stop_loss = 0
+                self.take_profit = 0
+                
+                return True
+            else:
+                logger.error(f"Failed to close position: {order_result}")
+                return False
+                
+        except Exception as e:
+            logger.error(f"Error closing position: {e}")
+            logger.error(traceback.format_exc())
+            return False
+    
+    async def check_order_status(self, order_id):
+        """Check the status of a specific order"""
+        try:
+            # Check if we have API keys for private endpoints
+            if not self.api_key or self.api_key == "":
+                logger.warning("No API keys provided. Cannot check order status.")
+                return None
+                
+            order_status = self.client.account.query_order(
+                self.formatted_symbol,
+                order_id=order_id
+            )
+            return order_status
+        except Exception as e:
+            logger.error(f"Error checking order status: {e}")
+            return None
+    
+    async def get_market_price(self):
+        """Get current market price for the symbol"""
+        try:
+            ticker = self.client.market.ticker_price(self.formatted_symbol)
+            if isinstance(ticker, list) and len(ticker) > 0 and 'price' in ticker[0]:
+                return float(ticker[0]['price'])
+            elif isinstance(ticker, dict) and 'price' in ticker:
+                return float(ticker['price'])
+            else:
+                logger.error(f"Unexpected ticker format: {ticker}")
+                return None
+        except Exception as e:
+            logger.error(f"Error getting market price: {e}")
+            return None 

crypto/gogo2/mexcapi-readme.md

@@ -0,0 +1,456 @@
+# mexc-api-sdk
+
+ MEXC Official Market and trade api sdk, easy to connection and send request to MEXC open api !
+ 
+## Prerequisites
+  - To use our SDK you have to install nodejs LTS (https://aws.github.io/jsii/user-guides/lib-user/)
+
+## Installation
+1. 
+``` 
+git clone https://github.com/mxcdevelop/mexc-api-sdk.git
+```
+ 2. cd dist/{language} and unzip the file
+ 3. we offer five language : dotnet, go, java, js, python
+
+## Table of APIS
+  - [Init](#init)
+  - [Market](#market)
+    - [Ping](#ping)
+    - [Check Server Time](#check-server-time)
+    - [Exchange Information](#exchange-information)
+    - [Recent Trades List](#recent-trades-list)
+    - [Order Book](#order-book)
+    - [Old Trade Lookup](#old-trade-lookup)
+    - [Aggregate Trades List](#aggregate-trades-list)
+    - [kline Data](#kline-data)
+    - [Current Average Price](#current-average-price)
+    - [24hr Ticker Price Change Statistics](#24hr-ticker-price-change-statistics)
+    - [Symbol Price Ticker](#symbol-price-ticker)
+    - [Symbol Order Book Ticker](#symbol-order-book-ticker)
+  - [Trade](#trade)
+    - [Test New Order](#test-new-order)
+    - [New Order](#new-order)
+    - [cancel-order](#cancel-order)
+    - [Cancel all Open Orders on a Symbol](#cancel-all-open-orders-on-a-symbol)
+    - [Query Order](#query-order)
+    - [Current Open Orders](#current-open-orders)
+    - [All Orders](#all-orders)
+    - [Account Information](#account-information)
+    - [Account Trade List](#account-trade-list)
+## Init
+```javascript
+//Javascript
+import * as Mexc from 'mexc-sdk';
+const apiKey = 'apiKey'
+const apiSecret = 'apiSecret'
+const client = new Mexc.Spot(apiKey, apiSecret);
+```
+```go
+// Go
+package main
+import (
+	"fmt"
+	"mexc-sdk/mexcsdk"
+)
+
+func main() {
+	apiKey := "apiKey"
+	apiSecret := "apiSecret"
+	spot := mexcsdk.NewSpot(apiKey, apiSecret)
+}
+```
+```python
+# python
+from mexc_sdk import Spot
+spot = Spot(api_key='apiKey', api_secret='apiSecret')
+```
+```java
+// java
+import Mexc.Sdk.*;  
+class MyClass {
+  public static void main(String[] args) {
+    String apiKey= "apiKey";
+    String apiSecret= "apiSecret";
+    Spot mySpot = new Spot(apiKey, apiSecret);
+  }
+}
+```
+```C#
+// dotnet
+using System;
+using System.Collections.Generic;
+using Mxc.Sdk;
+
+namespace dotnet
+{
+    class Program
+    {
+        static void Main(string[] args)
+        {
+            string  apiKey = "apiKey";
+            string  apiSecret= "apiSecret";
+            var spot = new Spot(apiKey, apiSecret);
+        }
+    }
+}
+
+```
+
+## Market
+### Ping
+```javascript
+client.ping()
+```
+### Check Server Time
+```javascript
+client.time()
+```
+### Exchange Information
+```javascript
+client.exchangeInfo(options: any)
+options:{symbol, symbols}
+/**
+ * choose one parameter
+ * 
+ * symbol : 
+ *      example "BNBBTC";
+ * 
+ * symbols :
+ *      array of symbol
+ *      example ["BTCUSDT","BNBBTC"];
+ * 
+ */
+```
+
+### Recent Trades List
+```javascript
+client.trades(symbol: string, options: any = { limit: 500 })
+options:{limit}
+/**
+ * 
+ * limit : 
+ *      Number of returned data
+ *      Default 500; 
+ *      max 1000;
+ * 
+ */
+```
+### Order Book
+```javascript
+client.depth(symbol: string, options: any = { limit: 100 })
+options:{limit}
+/**
+ * limit : 
+ *      Number of returned data
+ *      Default 100;
+ *      max 5000;
+ *      Valid:[5, 10, 20, 50, 100, 500, 1000, 5000]
+ * 
+ */
+```
+
+### Old Trade Lookup
+```javascript
+client.historicalTrades(symbol: string, options: any = { limit: 500 })
+options:{limit, fromId}
+/**
+ * 
+ * limit : 
+ *      Number of returned data
+ *      Default 500;
+ *      max 1000;
+ * 
+ * fromId:
+ *      Trade id to fetch from. Default gets most recent trades
+ * 
+ */
+
+```
+
+### Aggregate Trades List
+```javascript
+client.aggTrades(symbol: string, options: any = { limit: 500 })
+options:{fromId, startTime, endTime, limit}
+/**
+ * 
+ * fromId : 
+ *      id to get aggregate trades from INCLUSIVE
+ * 
+ * startTime:
+ *      start at
+ * 
+ * endTime:
+ *      end at
+ * 
+ * limit : 
+ *      Number of returned data
+ *      Default 500;
+ *      max 1000;
+ * 
+ */
+```
+### kline Data
+```javascript
+client.klines(symbol: string, interval: string, options: any = { limit: 500 })
+options:{ startTime, endTime, limit}
+/**
+ * 
+ * interval : 
+ *      m :minute; 
+ *      h :Hour; 
+ *      d :day; 
+ *      w :week; 
+ *      M :month
+ *      example : "1m"
+ * 
+ * startTime : 
+ *      start at
+ * 
+ * endTime :
+ *      end at
+ * 
+ * limit : 
+ *      Number of returned data
+ *      Default 500;
+ *      max 1000;
+ * 
+ */
+```
+
+### Current Average Price
+```javascript
+client.avgPrice(symbol: string)
+```
+### 24hr Ticker Price Change Statistics
+```javascript
+client.ticker24hr(symbol?: string)
+```
+### Symbol Price Ticker
+```javascript
+client.tickerPrice(symbol?: string)
+```
+
+### Symbol Order Book Ticker
+```javascript
+client.bookTicker(symbol?: string)
+```
+## Trade
+### Test New Order
+```javascript
+client.newOrderTest(symbol: string, side: string, orderType: string, options: any = {})
+options:{ timeInForce, quantity, quoteOrderQty, price, newClientOrderId, stopPrice, icebergQty, newOrderRespType, recvWindow}
+/**
+ * 
+ * side:
+ *      Order side 
+ *      ENUM:
+ *        BUY
+ *        SELL
+ * 
+ * orderType:
+ *      Order type
+ *      ENUM:
+ *        LIMIT
+ *        MARKET
+ *        STOP_LOSS
+ *        STOP_LOSS_LIMIT
+ *        TAKE_PROFIT
+ *        TAKE_PROFIT_LIMIT
+ *        LIMIT_MAKER
+ * 
+ * timeInForce : 
+ *      How long an order will be active before expiration.
+ *      GTC: Active unless the order is canceled
+ *      IOC: Order will try to fill the order as much as it can before the order expires
+ *      FOK: Active unless the full order cannot be filled upon execution.
+ * 
+ * quantity :
+ *      target quantity
+ * 
+ * quoteOrderQty :
+ *      Specify the total spent or received
+ * 
+ * price :
+ *      target price
+ * 
+ * newClientOrderId :
+ *      A unique id among open orders. Automatically generated if not sent
+ * 
+ * stopPrice :
+ *      sed with STOP_LOSS, STOP_LOSS_LIMIT, TAKE_PROFIT, and TAKE_PROFIT_LIMIT orders
+ * 
+ * icebergQty :
+ *      Used with LIMIT, STOP_LOSS_LIMIT, and TAKE_PROFIT_LIMIT to create an iceberg order
+ * 
+ * newOrderRespType : 
+ *      Set the response JSON. ACK, RESULT, or FULL;
+ *      MARKET and LIMIT order types default to FULL, all other orders default to ACK
+ * 
+ * recvWindow : 
+ *      Delay accept time
+ *      The value cannot be greater than 60000
+ *      defaults: 5000
+ * 
+ */
+
+```
+
+### New Order
+```javascript
+client.newOrder(symbol: string, side: string, orderType: string, options: any = {})
+options:{ timeInForce, quantity, quoteOrderQty, price, newClientOrderId, stopPrice, icebergQty, newOrderRespType, recvWindow}
+/**
+ * 
+ * side:
+ *      Order side 
+ *      ENUM:
+ *        BUY
+ *        SELL
+ * 
+ * orderType:
+ *      Order type
+ *      ENUM:
+ *        LIMIT
+ *        MARKET
+ *        STOP_LOSS
+ *        STOP_LOSS_LIMIT
+ *        TAKE_PROFIT
+ *        TAKE_PROFIT_LIMIT
+ *        LIMIT_MAKER
+ * 
+ * timeInForce : 
+ *      How long an order will be active before expiration.
+ *      GTC: Active unless the order is canceled
+ *      IOC: Order will try to fill the order as much as it can before the order expires
+ *      FOK: Active unless the full order cannot be filled upon execution.
+ * 
+ * quantity :
+ *      target quantity
+ * 
+ * quoteOrderQty :
+ *      Specify the total spent or received
+ * 
+ * price :
+ *      target price
+ * 
+ * newClientOrderId :
+ *      A unique id among open orders. Automatically generated if not sent
+ * 
+ * stopPrice :
+ *      sed with STOP_LOSS, STOP_LOSS_LIMIT, TAKE_PROFIT, and TAKE_PROFIT_LIMIT orders
+ * 
+ * icebergQty :
+ *      Used with LIMIT, STOP_LOSS_LIMIT, and TAKE_PROFIT_LIMIT to create an iceberg order
+ * 
+ * newOrderRespType : 
+ *      Set the response JSON. ACK, RESULT, or FULL;
+ *      MARKET and LIMIT order types default to FULL, all other orders default to ACK
+ * 
+ * recvWindow : 
+ *      Delay accept time
+ *      The value cannot be greater than 60000
+ *      defaults: 5000
+ * 
+ */
+
+```
+
+### cancel-order
+```javascript
+client.cancelOrder(symbol: string, options:any = {})
+options:{ orderId, origClientOrderId, newClientOrderId}
+/**
+ * 
+ * Either orderId or origClientOrderId must be sent
+ * 
+ * orderId:
+ *      target orderId
+ * 
+ * origClientOrderId:
+ *      target origClientOrderId
+ * 
+ * newClientOrderId:
+ *      Used to uniquely identify this cancel. Automatically generated by default.
+ * 
+ */
+
+```
+
+### Cancel all Open Orders on a Symbol
+```javascript
+client.cancelOpenOrders(symbol: string)
+```
+### Query Order
+```javascript
+client.queryOrder(symbol: string, options:any = {})
+options:{ orderId, origClientOrderId}
+/**
+ * 
+ * Either orderId or origClientOrderId must be sent
+ * 
+ * orderId:
+ *      target orderId
+ * 
+ * origClientOrderId:
+ *      target origClientOrderId
+ * 
+ */
+```
+### Current Open Orders
+```javascript
+client.openOrders(symbol: string)
+```
+### All Orders
+```javascript
+client.allOrders(symbol: string, options: any = { limit: 500 })
+options:{ orderId, startTime, endTime, limit}
+
+/**
+ * 
+ * orderId:
+ *      target orderId
+ * 
+ * startTime:
+ *      start at
+ * 
+ * endTime:
+ *      end at
+ * 
+ * limit : 
+ *      Number of returned data
+ *      Default 500;
+ *      max 1000;
+ * 
+ */
+```
+### Account Information
+```javascript
+client.accountInfo()
+```
+### Account Trade List
+```javascript
+client.accountTradeList(symbol: string, options:any = { limit: 500 })
+options:{ orderId, startTime, endTime, fromId, limit}
+
+/**
+ * 
+ * orderId:
+ *      target orderId
+ * 
+ * startTime:
+ *      start at
+ * 
+ * endTime:
+ *      end at
+ * 
+ * fromId:
+ *      TradeId to fetch from. Default gets most recent trades
+ * 
+ * limit : 
+ *      Number of returned data
+ *      Default 500;
+ *      max 1000;
+ * 
+ */
+```

crypto/gogo2/readme.md

@@ -1,188 +1,91 @@
-# Crypto Trading Bot with Reinforcement Learning
+# Crypto Trading Bot with MEXC API Integration
 
-An automated cryptocurrency trading bot that uses Deep Q-Learning (DQN) to trade ETH/USDT on the MEXC exchange. The bot features a sophisticated neural network architecture with LSTM layers and attention mechanisms for better pattern recognition.
+This is an AI-powered cryptocurrency trading bot that can run in both simulation (demo) mode and live trading mode using the MEXC exchange API.
 
 ## Features
 
-- Deep Q-Learning with experience replay
-- LSTM layers for sequential data processing
-- Multi-head attention mechanism
-- Dueling DQN architecture
-- Real-time trading capabilities
-- TensorBoard integration for monitoring
-- Comprehensive technical indicators
-- Demo and live trading modes
-- Automatic model checkpointing
+- Deep Reinforcement Learning agent for trading decisions
+- Technical indicators and price prediction
+- Live trading integration with MEXC exchange via mexc-api
+- Demo mode for testing without real trades
+- Real-time data streaming via websockets
+- Performance tracking and visualization
 
-## Prerequisites
+## Setup
 
-- Python 3.8+
-- MEXC Exchange API credentials
-- GPU recommended but not required
+1. Clone the repository
+2. Install dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+3. Create a `.env` file in the root directory with your MEXC API keys:
+   ```
+   MEXC_API_KEY=your_api_key_here
+   MEXC_SECRET_KEY=your_secret_key_here
+   ```
 
-## Installation
-
-1. Clone the repository:
-
-```bash
-git clone https://github.com/yourusername/crypto-trading-bot.git
-cd crypto-trading-bot
-```
-2. Create a virtual environment:
-
-```bash
-python -m venv venv
-source venv/bin/activate # On Windows: venv\Scripts\activate
-```
-3. Install dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-
-4. Create a `.env` file in the project root with your MEXC API credentials:
-
-```bash
-MEXC_API_KEY=your_api_key
-MEXC_API_SECRET=your_api_secret
-
-
-cuda support
-
-```bash
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
-```
 ## Usage
 
-The bot can be run in three modes:
+The bot can be run in three different modes:
 
 ### Training Mode
 
-```bash
-python main.py --mode train --episodes 1000
+Train the agent on historical data:
+
+```
+python main.py --mode train --episodes 100
 ```
 
 ### Evaluation Mode
 
-```bash
-python main.py --mode eval --episodes 10
+Evaluate the trained agent on historical data:
+
+```
+python main.py --mode evaluate
 ```
 
 ### Live Trading Mode
 
-```bash
-# Demo mode (simulated trading with real market data)
-python main.py --mode live --demo
+Run the bot in live trading mode:
 
-# Real trading (actual trades on MEXC)
+```
 python main.py --mode live
 ```
 
-Demo mode simulates trading using real-time market data but does not execute actual trades. It still:
-- Logs all trading decisions and performance metrics
-- Updates the model based on market data (if in training mode)
-- Displays real-time analytics and position information
-- Calculates theoretical profits/losses
-- Saves performance data to TensorBoard
+To run in demo mode (no real trades):
 
-This makes it perfect for testing strategies without financial risk.
+```
+python main.py --mode live --demo
+```
+
+## Live Trading Implementation
+
+The bot uses the mexc-api package to execute trades on the MEXC exchange. The implementation includes:
+
+- Market order execution for opening and closing positions
+- Stop loss and take profit orders
+- Real-time balance updates
+- Trade history tracking
 
 ## Configuration
 
-Key parameters can be adjusted in `main.py`:
+You can adjust the following parameters in `main.py`:
 
-- `INITIAL_BALANCE`: Starting balance for training/demo
-- `MAX_LEVERAGE`: Maximum leverage for trades
-- `STOP_LOSS_PERCENT`: Stop loss percentage
-- `TAKE_PROFIT_PERCENT`: Take profit percentage
-- `BATCH_SIZE`: Training batch size
-- `LEARNING_RATE`: Model learning rate
-- `STATE_SIZE`: Size of the state representation
+- `INITIAL_BALANCE`: Starting balance for simulation
+- `MAX_LEVERAGE`: Leverage to use for trading
+- `STOP_LOSS_PERCENT`: Default stop loss percentage
+- `TAKE_PROFIT_PERCENT`: Default take profit percentage
 
-## Model Architecture
-
-The DQN model includes:
-- Input layer with technical indicators
-- LSTM layers for temporal pattern recognition
-- Multi-head attention mechanism
-- Dueling architecture for better Q-value estimation
-- Batch normalization for stable training
-
-## Monitoring
-
-Training progress can be monitored using TensorBoard:
-
-
-Training progress is logged to TensorBoard:
-
-```bash
-tensorboard --logdir=logs
-```
-
-This will show:
-- Training rewards
-- Account balance
-- Win rate
-- Loss metrics
-
-## Trading Strategy
-
-The bot makes decisions based on:
-- Price action
-- Technical indicators (RSI, MACD, Bollinger Bands, etc.)
-- Historical patterns through LSTM
-- Risk management with stop-loss and take-profit
-
-## Safety Features
-
-- Demo mode for safe testing
-- Automatic stop-loss
-- Position size limits
-- Error handling for API calls
-- Logging of all actions
-
-## Directory Structure
-├── main.py # Main bot implementation
-├── requirements.txt # Project dependencies
-├── .env # API credentials
-├── models/ # Saved model checkpoints
-├── runs/ # TensorBoard logs
-└── trading_bot.log # Activity logs
+## Architecture
 
+- `main.py`: Main entry point and trading logic
+- `mexc_trading.py`: MEXC API integration for live trading using mexc-api
+- `models/`: Directory for saved model weights
 
 ## Warning
 
-Cryptocurrency trading carries significant risks. This bot is for educational purposes and should not be used with real money without thorough testing and understanding of the risks involved.
+Trading cryptocurrencies involves significant risk. This bot is provided for educational purposes only. Use at your own risk.
 
 ## License
 
-[MIT License](LICENSE)
-
-The main changes I made:
-Fixed code block formatting by adding proper language identifiers
-Added missing closing code blocks
-Properly formatted directory structure
-Added complete sections that were cut off in the original
-Ensured consistent formatting throughout the document
-Added proper bash syntax highlighting for command examples
-The README.md now provides a complete guide for setting up and using the trading bot, with clear sections for installation, usage, configuration, and safety considerations.
-
-
-
-
-
-# Edits/improvements
-
-Fixes the shape mismatch by ensuring the state vector is exactly STATE_SIZE elements
-Adds robust error handling in the model's forward pass to handle mismatched inputs
-Adds a transformer encoder for more sophisticated pattern recognition
-Provides an expand_model method to increase model capacity while preserving learned weights
-Adds detailed logging about model size and shape mismatches
-The model now has:
-Configurable hidden layer sizes
-Transformer layers for complex pattern recognition
-LSTM layers for temporal patterns
-Attention mechanisms for focusing on important features
-Dueling architecture for better Q-value estimation
-With hidden_size=256, this model has about 1-2 million parameters. By increasing hidden_size to 512 or 1024, you can easily scale to 5-20 million parameters. For even larger models (billions of parameters), you would need to implement a more distributed architecture with multiple GPUs, which would require significant changes to the training loop.
+MIT

crypto/gogo2/requirements.txt

@@ -1,10 +1,11 @@
-numpy>=1.21.0
+numpy>=1.20.0
 pandas>=1.3.0
 matplotlib>=3.4.0
 torch>=1.9.0
-python-dotenv>=0.19.0
+scikit-learn>=0.24.0
 ccxt>=2.0.0
+python-dotenv>=0.19.0
 websockets>=10.0
-tensorboard>=2.6.0 
-scikit-learn
-mplfinance
+tensorboard>=2.7.0
+mexc-api>=1.0.0
+asyncio>=3.4.3

crypto/gogo2/test_mexc_api.py

@@ -0,0 +1,23 @@
+from mexc_api.spot import Spot
+
+def test_mexc_api():
+    try:
+        # Initialize client with empty API keys for public endpoints
+        client = Spot("", "")
+        
+        # Test server time endpoint
+        server_time = client.market.server_time()
+        print(f"Server time: {server_time}")
+        
+        # Test ticker price endpoint
+        ticker = client.market.ticker_price("ETHUSDT")
+        print(f"ETH/USDT price: {ticker}")
+        
+        print("MEXC API is working correctly!")
+        return True
+    except Exception as e:
+        print(f"Error testing MEXC API: {e}")
+        return False
+
+if __name__ == "__main__":
+    test_mexc_api() 

crypto/gogo2/test_trading_client.py

@@ -0,0 +1,34 @@
+import asyncio
+import os
+from dotenv import load_dotenv
+from mexc_trading import MexcTradingClient
+
+# Load environment variables
+load_dotenv()
+
+async def test_trading_client():
+    """Test the MexcTradingClient functionality"""
+    print("Initializing MexcTradingClient...")
+    client = MexcTradingClient(symbol="ETH/USDT")
+    
+    # Test getting market price
+    print("Testing get_market_price...")
+    price = await client.get_market_price()
+    print(f"Current ETH/USDT price: {price}")
+    
+    # If API keys are provided, test account balance
+    if os.getenv('MEXC_API_KEY') and os.getenv('MEXC_SECRET_KEY'):
+        print("Testing fetch_account_balance...")
+        balance = await client.fetch_account_balance()
+        print(f"Account balance: {balance} USDT")
+        
+        print("Testing fetch_open_positions...")
+        positions = await client.fetch_open_positions()
+        print(f"Open positions: {positions}")
+    else:
+        print("No API keys provided. Skipping private endpoint tests.")
+    
+    print("MexcTradingClient test completed!")
+
+if __name__ == "__main__":
+    asyncio.run(test_trading_client())