references

.kiro/specs/checkpoint-persistence-fix/requirements.md

@@ -1,124 +0,0 @@
-# Requirements Document
-
-## Introduction
-
-The Checkpoint Persistence Fix addresses a critical system flaw where model training progress is not being saved during training, causing all learning progress to be lost when the system restarts. Despite having a well-implemented CheckpointManager and proper checkpoint loading at startup, the system lacks checkpoint saving during training operations. This creates a fundamental issue where models train continuously but never persist their improved weights, making continuous improvement impossible and wasting computational resources.
-
-## Requirements
-
-### Requirement 1: Real-time Checkpoint Saving During Training
-
-**User Story:** As a system operator, I want model improvements to be automatically saved during training, so that training progress is never lost when the system restarts.
-
-#### Acceptance Criteria
-
-1. WHEN the DQN model is trained in _train_models_on_decision THEN the system SHALL save a checkpoint if the loss improves.
-2. WHEN the CNN model is trained THEN the system SHALL save a checkpoint if the loss improves.
-3. WHEN the COB RL model is trained THEN the system SHALL save a checkpoint if the loss improves.
-4. WHEN the Extrema trainer is trained THEN the system SHALL save a checkpoint if the loss improves.
-5. WHEN any model training completes THEN the system SHALL compare current performance to best performance and save if improved.
-6. WHEN checkpoint saving occurs THEN the system SHALL update the model_states dictionary with new performance metrics.
-
-### Requirement 2: Performance-Based Checkpoint Management
-
-**User Story:** As a developer, I want checkpoints to be saved only when model performance improves, so that storage is used efficiently and only the best models are preserved.
-
-#### Acceptance Criteria
-
-1. WHEN evaluating whether to save a checkpoint THEN the system SHALL compare current loss to the best recorded loss.
-2. WHEN loss decreases by a configurable threshold THEN the system SHALL trigger checkpoint saving.
-3. WHEN multiple models are trained simultaneously THEN each model SHALL have independent performance tracking.
-4. WHEN checkpoint rotation occurs THEN the system SHALL keep only the best performing checkpoints.
-5. WHEN performance metrics are updated THEN the system SHALL log the improvement for monitoring.
-6. WHEN no improvement is detected THEN the system SHALL skip checkpoint saving to avoid unnecessary I/O.
-
-### Requirement 3: Periodic Checkpoint Saving
-
-**User Story:** As a system administrator, I want checkpoints to be saved periodically regardless of performance, so that progress is preserved even during long training sessions without significant improvement.
-
-#### Acceptance Criteria
-
-1. WHEN a configurable number of training iterations have passed THEN the system SHALL save a checkpoint regardless of performance.
-2. WHEN periodic saving occurs THEN the system SHALL use a separate checkpoint category to distinguish from performance-based saves.
-3. WHEN the system runs for extended periods THEN periodic checkpoints SHALL ensure no more than X minutes of training progress can be lost.
-4. WHEN periodic checkpoints accumulate THEN the system SHALL maintain a rolling window of recent saves.
-5. WHEN storage space is limited THEN periodic checkpoints SHALL be cleaned up while preserving performance-based checkpoints.
-6. WHEN the system restarts THEN it SHALL load the most recent checkpoint (either performance-based or periodic).
-
-### Requirement 4: Enhanced Training System Integration
-
-**User Story:** As a developer, I want the EnhancedRealtimeTrainingSystem to properly save checkpoints, so that continuous learning progress is preserved across system restarts.
-
-#### Acceptance Criteria
-
-1. WHEN the EnhancedRealtimeTrainingSystem trains models THEN it SHALL integrate with the CheckpointManager.
-2. WHEN training episodes complete THEN the system SHALL evaluate and save improved models.
-3. WHEN the training system initializes THEN it SHALL load the best available checkpoints.
-4. WHEN training data is collected THEN the system SHALL track performance metrics for checkpoint decisions.
-5. WHEN the training system shuts down THEN it SHALL save final checkpoints before termination.
-6. WHEN training resumes THEN the system SHALL continue from the last saved checkpoint state.
-
-### Requirement 5: Complete Training Data Storage
-
-**User Story:** As a developer, I want complete training episodes to be stored with full input dataframes, so that training can be replayed and analyzed with all original context.
-
-#### Acceptance Criteria
-
-1. WHEN training episodes are saved THEN the system SHALL store the complete input dataframe with all model inputs (price data, indicators, market structure, etc.).
-2. WHEN model actions are recorded THEN the system SHALL store the full context that led to the decision, not just the action result.
-3. WHEN training cases are saved THEN they SHALL include timestamps, market conditions, and all feature vectors used by the models.
-4. WHEN storing training data THEN the system SHALL preserve the exact state that can be used to reproduce the model's decision.
-5. WHEN training episodes are replayed THEN the system SHALL be able to reconstruct the exact same inputs that were originally used.
-6. WHEN analyzing training performance THEN complete dataframes SHALL be available for debugging and improvement.
-
-### Requirement 6: Comprehensive Performance Tracking
-
-**User Story:** As a system operator, I want detailed performance metrics to be tracked and persisted, so that I can monitor training progress and model improvement over time.
-
-#### Acceptance Criteria
-
-1. WHEN models are trained THEN the system SHALL track loss values, accuracy metrics, and training timestamps.
-2. WHEN performance improves THEN the system SHALL log the improvement amount and save metadata.
-3. WHEN checkpoints are saved THEN the system SHALL store performance metrics alongside model weights.
-4. WHEN the system starts THEN it SHALL display the performance history of loaded checkpoints.
-5. WHEN multiple training sessions occur THEN the system SHALL maintain a continuous performance history.
-6. WHEN performance degrades THEN the system SHALL provide alerts and revert to better checkpoints if configured.
-
-### Requirement 7: Robust Error Handling and Recovery
-
-**User Story:** As a system administrator, I want checkpoint operations to be resilient to failures, so that training can continue even if individual checkpoint saves fail.
-
-#### Acceptance Criteria
-
-1. WHEN checkpoint saving fails THEN the system SHALL log the error and continue training without crashing.
-2. WHEN disk space is insufficient THEN the system SHALL clean up old checkpoints and retry saving.
-3. WHEN checkpoint files are corrupted THEN the system SHALL fall back to previous valid checkpoints.
-4. WHEN concurrent access conflicts occur THEN the system SHALL use proper locking mechanisms.
-5. WHEN the system recovers from failures THEN it SHALL validate checkpoint integrity before loading.
-6. WHEN critical checkpoint operations fail repeatedly THEN the system SHALL alert administrators.
-
-### Requirement 8: Configuration and Monitoring
-
-**User Story:** As a developer, I want configurable checkpoint settings and monitoring capabilities, so that I can optimize checkpoint behavior for different training scenarios.
-
-#### Acceptance Criteria
-
-1. WHEN configuring the system THEN checkpoint saving frequency SHALL be adjustable.
-2. WHEN setting performance thresholds THEN the minimum improvement required for saving SHALL be configurable.
-3. WHEN monitoring training THEN checkpoint save events SHALL be visible in logs and dashboards.
-4. WHEN analyzing performance THEN checkpoint metadata SHALL be accessible for review.
-5. WHEN tuning the system THEN checkpoint storage limits SHALL be configurable.
-6. WHEN debugging issues THEN detailed checkpoint operation logs SHALL be available.
-
-### Requirement 9: Backward Compatibility and Migration
-
-**User Story:** As a user, I want existing checkpoints to remain compatible, so that current model progress is preserved when the checkpoint system is enhanced.
-
-#### Acceptance Criteria
-
-1. WHEN the enhanced checkpoint system starts THEN it SHALL load existing checkpoints without issues.
-2. WHEN checkpoint formats are updated THEN migration utilities SHALL convert old formats.
-3. WHEN new metadata is added THEN existing checkpoints SHALL work with default values.
-4. WHEN the system upgrades THEN checkpoint directories SHALL be preserved and enhanced.
-5. WHEN rollback is needed THEN the system SHALL support reverting to previous checkpoint versions.
-6. WHEN compatibility issues arise THEN clear error messages SHALL guide resolution.

NN/environments/__init__.py

@@ -1,6 +0,0 @@
-# Trading environments for reinforcement learning
-# This module contains environments for training trading agents
-
-from NN.environments.trading_env import TradingEnvironment
-
-__all__ = ['TradingEnvironment'] 

NN/environments/trading_env.py

@@ -1,532 +0,0 @@
-import numpy as np
-import pandas as pd
-from typing import Dict, Tuple, List, Any, Optional
-import logging
-import gym
-from gym import spaces
-import random
-
-# Configure logger
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-class TradingEnvironment(gym.Env):
-    """
-    Trading environment implementing gym interface for reinforcement learning
-    
-    2-Action System:
-    - 0: SELL (or close long position)
-    - 1: BUY (or close short position)
-    
-    Intelligent Position Management:
-    - When neutral: Actions enter positions
-    - When positioned: Actions can close or flip positions
-    - Different thresholds for entry vs exit decisions
-    
-    State:
-    - OHLCV data from multiple timeframes
-    - Technical indicators
-    - Position data and unrealized PnL
-    """
-    
-    def __init__(
-        self,
-        data_interface,
-        initial_balance: float = 10000.0,
-        transaction_fee: float = 0.0002,
-        window_size: int = 20,
-        max_position: float = 1.0,
-        reward_scaling: float = 1.0,
-        entry_threshold: float = 0.6,    # Higher threshold for entering positions
-        exit_threshold: float = 0.3,     # Lower threshold for exiting positions
-    ):
-        """
-        Initialize the trading environment with 2-action system.
-        
-        Args:
-            data_interface: DataInterface instance to get market data
-            initial_balance: Initial balance in the base currency
-            transaction_fee: Fee for each transaction as a fraction of trade value
-            window_size: Number of candles in the observation window
-            max_position: Maximum position size as a fraction of balance
-            reward_scaling: Scale factor for rewards
-            entry_threshold: Confidence threshold for entering new positions
-            exit_threshold: Confidence threshold for exiting positions
-        """
-        super().__init__()
-        
-        self.data_interface = data_interface
-        self.initial_balance = initial_balance
-        self.transaction_fee = transaction_fee
-        self.window_size = window_size
-        self.max_position = max_position
-        self.reward_scaling = reward_scaling
-        self.entry_threshold = entry_threshold
-        self.exit_threshold = exit_threshold
-        
-        # Load data for primary timeframe (assuming the first one is primary)
-        self.timeframe = self.data_interface.timeframes[0]
-        self.reset_data()
-        
-        # Define action and observation spaces for 2-action system
-        self.action_space = spaces.Discrete(2)  # 0=SELL, 1=BUY
-        
-        # For observation space, we consider multiple timeframes with OHLCV data
-        # and additional features like technical indicators, position info, etc.
-        n_timeframes = len(self.data_interface.timeframes)
-        n_features = 5  # OHLCV data by default
-        
-        # Add additional features for position, balance, unrealized_pnl, etc.
-        additional_features = 5  # position, balance, unrealized_pnl, entry_price, position_duration
-        
-        # Calculate total feature dimension
-        total_features = (n_timeframes * n_features * self.window_size) + additional_features
-        
-        self.observation_space = spaces.Box(
-            low=-np.inf, high=np.inf, shape=(total_features,), dtype=np.float32
-        )
-        
-        # Use tuple for state_shape that EnhancedCNN expects
-        self.state_shape = (total_features,)
-        
-        # Position tracking for 2-action system
-        self.position = 0.0         # -1 (short), 0 (neutral), 1 (long)
-        self.entry_price = 0.0      # Price at which position was entered
-        self.entry_step = 0         # Step at which position was entered
-        
-        # Initialize state
-        self.reset()
-        
-    def reset_data(self):
-        """Reset data and generate a new set of price data for training"""
-        # Get data for each timeframe
-        self.data = {}
-        for tf in self.data_interface.timeframes:
-            df = self.data_interface.dataframes[tf]
-            if df is not None and not df.empty:
-                self.data[tf] = df
-        
-        if not self.data:
-            raise ValueError("No data available for training")
-        
-        # Use the primary timeframe for step count
-        self.prices = self.data[self.timeframe]['close'].values
-        self.timestamps = self.data[self.timeframe].index.values
-        self.max_steps = len(self.prices) - self.window_size - 1
-        
-    def reset(self):
-        """Reset the environment to initial state"""
-        # Reset trading variables
-        self.balance = self.initial_balance
-        self.trades = []
-        self.rewards = []
-        
-        # Reset step counter
-        self.current_step = self.window_size
-        
-        # Get initial observation
-        observation = self._get_observation()
-        
-        return observation
-    
-    def step(self, action):
-        """
-        Take a step in the environment using 2-action system with intelligent position management.
-        
-        Args:
-            action: Action to take (0: SELL, 1: BUY)
-            
-        Returns:
-            tuple: (observation, reward, done, info)
-        """
-        # Get current state before taking action
-        prev_balance = self.balance
-        prev_position = self.position
-        prev_price = self.prices[self.current_step]
-        
-        # Take action with intelligent position management
-        info = {}
-        reward = 0
-        last_position_info = None
-        
-        # Get current price
-        current_price = self.prices[self.current_step]
-        next_price = self.prices[self.current_step + 1] if self.current_step + 1 < len(self.prices) else current_price
-        
-        # Implement 2-action system with position management
-        if action == 0:  # SELL action
-            if self.position == 0:  # No position - enter short
-                self._open_position(-1.0 * self.max_position, current_price)
-                logger.info(f"ENTER SHORT at step {self.current_step}, price: {current_price:.4f}")
-                reward = -self.transaction_fee  # Entry cost
-                
-            elif self.position > 0:  # Long position - close it
-                close_pnl, last_position_info = self._close_position(current_price)
-                reward += close_pnl * self.reward_scaling
-                logger.info(f"CLOSE LONG at step {self.current_step}, price: {current_price:.4f}, PnL: {close_pnl:.4f}")
-                
-            elif self.position < 0:  # Already short - potentially flip to long if very strong signal
-                # For now, just hold the short position (no action)
-                pass
-        
-        elif action == 1:  # BUY action
-            if self.position == 0:  # No position - enter long
-                self._open_position(1.0 * self.max_position, current_price)
-                logger.info(f"ENTER LONG at step {self.current_step}, price: {current_price:.4f}")
-                reward = -self.transaction_fee  # Entry cost
-                
-            elif self.position < 0:  # Short position - close it
-                close_pnl, last_position_info = self._close_position(current_price)
-                reward += close_pnl * self.reward_scaling
-                logger.info(f"CLOSE SHORT at step {self.current_step}, price: {current_price:.4f}, PnL: {close_pnl:.4f}")
-                
-            elif self.position > 0:  # Already long - potentially flip to short if very strong signal
-                # For now, just hold the long position (no action)
-                pass
-        
-        # Calculate unrealized PnL and add to reward if holding position
-        if self.position != 0:
-            unrealized_pnl = self._calculate_unrealized_pnl(next_price)
-            reward += unrealized_pnl * self.reward_scaling * 0.1  # Scale down unrealized PnL
-            
-            # Apply time-based holding penalty to encourage decisive actions
-            position_duration = self.current_step - self.entry_step
-            holding_penalty = min(position_duration * 0.0001, 0.01)  # Max 1% penalty
-            reward -= holding_penalty
-        
-        # Reward staying neutral when uncertain (no clear setup)
-        else:
-            reward += 0.0001  # Small reward for not trading without clear signals
-        
-        # Move to next step
-        self.current_step += 1
-        
-        # Get new observation
-        observation = self._get_observation()
-        
-        # Check if episode is done
-        done = self.current_step >= len(self.prices) - 1
-        
-        # If done, close any remaining positions
-        if done and self.position != 0:
-            final_pnl, last_position_info = self._close_position(current_price)
-            reward += final_pnl * self.reward_scaling
-            info['final_pnl'] = final_pnl
-            info['final_balance'] = self.balance
-            logger.info(f"Episode ended. Final balance: {self.balance:.4f}, Return: {(self.balance/self.initial_balance-1)*100:.2f}%")
-        
-        # Track trade result if position changed or position was closed
-        if prev_position != self.position or last_position_info is not None:
-            # Calculate realized PnL if position was closed
-            realized_pnl = 0
-            position_info = {}
-            
-            if last_position_info is not None:
-                # Use the position information from closing
-                realized_pnl = last_position_info['pnl']
-                position_info = last_position_info
-            else:
-                # Calculate manually based on balance change
-                realized_pnl = self.balance - prev_balance if prev_position != 0 else 0
-            
-            # Record detailed trade information
-            trade_result = {
-                'step': self.current_step,
-                'timestamp': self.timestamps[self.current_step],
-                'action': action,
-                'action_name': ['SELL', 'BUY'][action],
-                'price': current_price,
-                'position_changed': prev_position != self.position,
-                'prev_position': prev_position,
-                'new_position': self.position,
-                'position_size': abs(self.position) if self.position != 0 else abs(prev_position),
-                'entry_price': position_info.get('entry_price', self.entry_price),
-                'exit_price': position_info.get('exit_price', current_price),
-                'realized_pnl': realized_pnl,
-                'unrealized_pnl': self._calculate_unrealized_pnl(current_price) if self.position != 0 else 0,
-                'pnl': realized_pnl,  # Total PnL (realized for this step)
-                'balance_before': prev_balance,
-                'balance_after': self.balance,
-                'trade_fee': position_info.get('fee', abs(self.position - prev_position) * current_price * self.transaction_fee)
-            }
-            info['trade_result'] = trade_result
-            self.trades.append(trade_result)
-            
-            # Log trade details
-            logger.info(f"Trade executed - Action: {['SELL', 'BUY'][action]}, "
-                       f"Price: {current_price:.4f}, PnL: {realized_pnl:.4f}, "
-                       f"Balance: {self.balance:.4f}")
-        
-        # Store reward
-        self.rewards.append(reward)
-        
-        # Update info dict with current state
-        info.update({
-            'step': self.current_step,
-            'price': current_price,
-            'prev_price': prev_price,
-            'price_change': (current_price - prev_price) / prev_price if prev_price != 0 else 0,
-            'balance': self.balance,
-            'position': self.position,
-            'entry_price': self.entry_price,
-            'unrealized_pnl': self._calculate_unrealized_pnl(current_price) if self.position != 0 else 0.0,
-            'total_trades': len(self.trades),
-            'total_pnl': self.total_pnl,
-            'return_pct': (self.balance/self.initial_balance-1)*100
-        })
-        
-        return observation, reward, done, info
-    
-    def _calculate_unrealized_pnl(self, current_price):
-        """Calculate unrealized PnL for current position"""
-        if self.position == 0 or self.entry_price == 0:
-            return 0.0
-        
-        if self.position > 0:  # Long position
-            return self.position * (current_price / self.entry_price - 1.0)
-        else:  # Short position
-            return -self.position * (1.0 - current_price / self.entry_price)
-    
-    def _open_position(self, position_size: float, entry_price: float):
-        """Open a new position"""
-        self.position = position_size
-        self.entry_price = entry_price
-        self.entry_step = self.current_step
-        
-        # Calculate position value
-        position_value = abs(position_size) * entry_price
-        
-        # Apply transaction fee
-        fee = position_value * self.transaction_fee
-        self.balance -= fee
-        
-        logger.info(f"Opened position: {position_size:.4f} at {entry_price:.4f}, fee: {fee:.4f}")
-    
-    def _close_position(self, exit_price: float) -> Tuple[float, Dict]:
-        """Close current position and return PnL"""
-        if self.position == 0:
-            return 0.0, {}
-        
-        # Calculate PnL
-        if self.position > 0:  # Long position
-            pnl = (exit_price - self.entry_price) / self.entry_price
-        else:  # Short position
-            pnl = (self.entry_price - exit_price) / self.entry_price
-        
-        # Apply transaction fees (entry + exit)
-        position_value = abs(self.position) * exit_price
-        exit_fee = position_value * self.transaction_fee
-        total_fees = exit_fee  # Entry fee already applied when opening
-        
-        # Net PnL after fees
-        net_pnl = pnl - (total_fees / (abs(self.position) * self.entry_price))
-        
-        # Update balance
-        self.balance *= (1 + net_pnl)
-        self.total_pnl += net_pnl
-        
-        # Track trade
-        position_info = {
-            'position_size': self.position,
-            'entry_price': self.entry_price,
-            'exit_price': exit_price,
-            'pnl': net_pnl,
-            'duration': self.current_step - self.entry_step,
-            'entry_step': self.entry_step,
-            'exit_step': self.current_step
-        }
-        
-        self.trades.append(position_info)
-        
-        # Update trade statistics
-        if net_pnl > 0:
-            self.winning_trades += 1
-        else:
-            self.losing_trades += 1
-        
-        logger.info(f"Closed position: {self.position:.4f}, PnL: {net_pnl:.4f}, Duration: {position_info['duration']} steps")
-        
-        # Reset position
-        self.position = 0.0
-        self.entry_price = 0.0
-        self.entry_step = 0
-        
-        return net_pnl, position_info
-    
-    def _get_observation(self):
-        """
-        Get the current observation.
-        
-        Returns:
-            np.array: The observation vector
-        """
-        observations = []
-        
-        # Get data from each timeframe
-        for tf in self.data_interface.timeframes:
-            if tf in self.data:
-                # Get the window of data for this timeframe
-                df = self.data[tf]
-                start_idx = self._align_timeframe_index(tf)
-                
-                if start_idx is not None and start_idx >= 0 and start_idx + self.window_size <= len(df):
-                    window = df.iloc[start_idx:start_idx + self.window_size]
-                    
-                    # Extract OHLCV data
-                    ohlcv = window[['open', 'high', 'low', 'close', 'volume']].values
-                    
-                    # Normalize OHLCV data
-                    last_close = ohlcv[-1, 3]  # Last close price
-                    ohlcv_normalized = np.zeros_like(ohlcv)
-                    ohlcv_normalized[:, 0] = ohlcv[:, 0] / last_close - 1.0  # open
-                    ohlcv_normalized[:, 1] = ohlcv[:, 1] / last_close - 1.0  # high
-                    ohlcv_normalized[:, 2] = ohlcv[:, 2] / last_close - 1.0  # low
-                    ohlcv_normalized[:, 3] = ohlcv[:, 3] / last_close - 1.0  # close
-                    
-                    # Normalize volume (relative to moving average of volume)
-                    if 'volume' in window.columns:
-                        volume_ma = ohlcv[:, 4].mean()
-                        if volume_ma > 0:
-                            ohlcv_normalized[:, 4] = ohlcv[:, 4] / volume_ma - 1.0
-                        else:
-                            ohlcv_normalized[:, 4] = 0.0
-                    else:
-                        ohlcv_normalized[:, 4] = 0.0
-                    
-                    # Flatten and add to observations
-                    observations.append(ohlcv_normalized.flatten())
-                else:
-                    # Fill with zeros if not enough data
-                    observations.append(np.zeros(self.window_size * 5))
-        
-        # Add position and balance information
-        current_price = self.prices[self.current_step]
-        position_info = np.array([
-            self.position / self.max_position,  # Normalized position (-1 to 1)
-            self.balance / self.initial_balance - 1.0,  # Normalized balance change
-            self._calculate_unrealized_pnl(current_price)  # Unrealized PnL
-        ])
-        
-        observations.append(position_info)
-        
-        # Concatenate all observations
-        observation = np.concatenate(observations)
-        return observation
-    
-    def _align_timeframe_index(self, timeframe):
-        """
-        Align the index of a higher timeframe with the current step in the primary timeframe.
-        
-        Args:
-            timeframe: The timeframe to align
-            
-        Returns:
-            int: The starting index in the higher timeframe
-        """
-        if timeframe == self.timeframe:
-            return self.current_step - self.window_size
-        
-        # Get timestamps for current primary timeframe step
-        primary_ts = self.timestamps[self.current_step]
-        
-        # Find closest index in the higher timeframe
-        higher_ts = self.data[timeframe].index.values
-        idx = np.searchsorted(higher_ts, primary_ts)
-        
-        # Adjust to get the starting index
-        start_idx = max(0, idx - self.window_size)
-        return start_idx
-    
-    def get_last_positions(self, n=5):
-        """
-        Get detailed information about the last n positions.
-        
-        Args:
-            n: Number of last positions to return
-            
-        Returns:
-            list: List of dictionaries containing position details
-        """
-        if not self.trades:
-            return []
-            
-        # Filter trades to only include those that closed positions
-        position_trades = [t for t in self.trades if t.get('realized_pnl', 0) != 0 or (t.get('prev_position', 0) != 0 and t.get('new_position', 0) == 0)]
-        
-        positions = []
-        last_n_trades = position_trades[-n:] if len(position_trades) >= n else position_trades
-        
-        for trade in last_n_trades:
-            position_info = {
-                'timestamp': trade.get('timestamp', self.timestamps[trade['step']]),
-                'action': trade.get('action_name', ['SELL', 'BUY'][trade['action']]),
-                'entry_price': trade.get('entry_price', 0.0),
-                'exit_price': trade.get('exit_price', trade['price']),
-                'position_size': trade.get('position_size', self.max_position),
-                'realized_pnl': trade.get('realized_pnl', 0.0),
-                'fee': trade.get('trade_fee', 0.0),
-                'pnl': trade.get('pnl', 0.0),
-                'pnl_percentage': (trade.get('pnl', 0.0) / self.initial_balance) * 100,
-                'balance_before': trade.get('balance_before', 0.0),
-                'balance_after': trade.get('balance_after', 0.0),
-                'duration': trade.get('duration', 'N/A')
-            }
-            positions.append(position_info)
-            
-        return positions
-
-    def render(self, mode='human'):
-        """Render the environment"""
-        current_step = self.current_step
-        current_price = self.prices[current_step]
-        
-        # Display basic information
-        print(f"\nTrading Environment Status:")
-        print(f"============================")
-        print(f"Step: {current_step}/{len(self.prices)-1}")
-        print(f"Current Price: {current_price:.4f}")
-        print(f"Current Balance: {self.balance:.4f}")
-        print(f"Current Position: {self.position:.4f}")
-        
-        if self.position != 0:
-            unrealized_pnl = self._calculate_unrealized_pnl(current_price)
-            print(f"Entry Price: {self.entry_price:.4f}")
-            print(f"Unrealized PnL: {unrealized_pnl:.4f} ({unrealized_pnl/self.balance*100:.2f}%)")
-        
-        print(f"Total PnL: {self.total_pnl:.4f} ({self.total_pnl/self.initial_balance*100:.2f}%)")
-        print(f"Total Trades: {len(self.trades)}")
-        
-        if len(self.trades) > 0:
-            win_trades = [t for t in self.trades if t.get('realized_pnl', 0) > 0]
-            win_count = len(win_trades)
-            # Count trades that closed positions (not just changed them)
-            closed_positions = [t for t in self.trades if t.get('realized_pnl', 0) != 0]
-            closed_count = len(closed_positions)
-            win_rate = win_count / closed_count if closed_count > 0 else 0
-            print(f"Positions Closed: {closed_count}")
-            print(f"Winning Positions: {win_count}")
-            print(f"Win Rate: {win_rate:.2f}")
-            
-            # Display last 5 positions
-            print("\nLast 5 Positions:")
-            print("================")
-            last_positions = self.get_last_positions(5)
-            
-            if not last_positions:
-                print("No closed positions yet.")
-            
-            for pos in last_positions:
-                print(f"Time: {pos['timestamp']}")
-                print(f"Action: {pos['action']}")
-                print(f"Entry: {pos['entry_price']:.4f}, Exit: {pos['exit_price']:.4f}")
-                print(f"Size: {pos['position_size']:.4f}")
-                print(f"PnL: {pos['realized_pnl']:.4f} ({pos['pnl_percentage']:.2f}%)")
-                print(f"Fee: {pos['fee']:.4f}")
-                print(f"Balance: {pos['balance_before']:.4f} -> {pos['balance_after']:.4f}")
-                print("----------------")
-        
-        return
-    
-    def close(self):
-        """Close the environment"""
-        pass 

NN/training/enhanced_realtime_training.py

@@ -26,6 +26,14 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 
+# Import checkpoint management
+try:
+    from utils.checkpoint_manager import get_checkpoint_manager, save_checkpoint
+    CHECKPOINT_MANAGER_AVAILABLE = True
+except ImportError:
+    CHECKPOINT_MANAGER_AVAILABLE = False
+    logger.warning("Checkpoint manager not available. Model persistence will be disabled.")
+
 logger = logging.getLogger(__name__)
 
 class EnhancedRealtimeTrainingSystem:
@@ -50,6 +58,12 @@ class EnhancedRealtimeTrainingSystem:
         # Experience buffers
         self.experience_buffer = deque(maxlen=self.training_config['memory_size'])
         self.validation_buffer = deque(maxlen=1000)
+        
+        # Training counters - CRITICAL for checkpoint management
+        self.training_iteration = 0
+        self.dqn_training_count = 0
+        self.cnn_training_count = 0
+        self.cob_training_count = 0
         self.priority_buffer = deque(maxlen=2000)  # High-priority experiences
         
         # Performance tracking
@@ -1071,6 +1085,10 @@ class EnhancedRealtimeTrainingSystem:
             
             self.dqn_training_count += 1
             
+            # Save checkpoint after training
+            if training_iterations > 0 and avg_loss > 0:
+                self._save_model_checkpoint('dqn_agent', rl_agent, avg_loss)
+            
             # Log progress every 10 training sessions
             if self.dqn_training_count % 10 == 0:
                 logger.info(f"DQN TRAINING: Session {self.dqn_training_count}, "
@@ -2523,4 +2541,56 @@ class EnhancedRealtimeTrainingSystem:
                 
         except Exception as e:
             logger.debug(f"Error estimating price change: {e}")
-            return 0.0 
+            return 0.0     d
+ef _save_model_checkpoint(self, model_name: str, model_obj, loss: float):
+        """
+        Save model checkpoint after training if performance improved
+        
+        This is CRITICAL for preserving training progress across restarts.
+        """
+        try:
+            if not CHECKPOINT_MANAGER_AVAILABLE:
+                return
+            
+            # Get checkpoint manager
+            checkpoint_manager = get_checkpoint_manager()
+            if not checkpoint_manager:
+                return
+            
+            # Prepare performance metrics
+            performance_metrics = {
+                'loss': loss,
+                'training_samples': len(self.experience_buffer),
+                'timestamp': datetime.now().isoformat()
+            }
+            
+            # Prepare training metadata
+            training_metadata = {
+                'timestamp': datetime.now().isoformat(),
+                'training_iteration': self.training_iteration,
+                'model_type': model_name
+            }
+            
+            # Determine model type based on model name
+            model_type = model_name
+            if 'dqn' in model_name.lower():
+                model_type = 'dqn'
+            elif 'cnn' in model_name.lower():
+                model_type = 'cnn'
+            elif 'cob' in model_name.lower():
+                model_type = 'cob_rl'
+            
+            # Save checkpoint
+            checkpoint_path = save_checkpoint(
+                model=model_obj,
+                model_name=model_name,
+                model_type=model_type,
+                performance_metrics=performance_metrics,
+                training_metadata=training_metadata
+            )
+            
+            if checkpoint_path:
+                logger.info(f"💾 Saved checkpoint for {model_name}: {checkpoint_path} (loss: {loss:.4f})")
+            
+        except Exception as e:
+            logger.error(f"Error saving checkpoint for {model_name}: {e}")

core/orchestrator.py

@@ -220,6 +220,11 @@ class TradingOrchestrator:
         self.data_provider.start_centralized_data_collection()
         logger.info("Centralized data collection started - all models and dashboard will receive data")
         
+        # CRITICAL: Initialize checkpoint manager for saving training progress
+        self.checkpoint_manager = None
+        self.training_iterations = 0  # Track training iterations for periodic saves
+        self._initialize_checkpoint_manager()
+        
         # Initialize models, COB integration, and training system
         self._initialize_ml_models()
         self._initialize_cob_integration()
@@ -2145,6 +2150,9 @@ class TradingOrchestrator:
             if not market_data:
                 return
             
+            # Track if any model was trained for checkpoint saving
+            models_trained = []
+            
             # Train DQN agent if available
             if self.rl_agent and hasattr(self.rl_agent, 'add_experience'):
                 try:
@@ -2167,6 +2175,7 @@ class TradingOrchestrator:
                         done=False
                     )
                     
+                    models_trained.append('dqn')
                     logger.debug(f"🧠 Added DQN experience: {action} {symbol} (reward: {immediate_reward:.3f})")
                     
                 except Exception as e:
@@ -2185,6 +2194,7 @@ class TradingOrchestrator:
                     # Add training sample
                     self.cnn_model.add_training_sample(cnn_features, target, weight=confidence)
                     
+                    models_trained.append('cnn')
                     logger.debug(f"🔍 Added CNN training sample: {action} {symbol}")
                     
                 except Exception as e:
@@ -2206,14 +2216,105 @@ class TradingOrchestrator:
                             symbol=symbol
                         )
                         
+                        models_trained.append('cob_rl')
                         logger.debug(f"📊 Added COB RL experience: {action} {symbol}")
                         
                 except Exception as e:
                     logger.debug(f"Error training COB RL on decision: {e}")
             
+            # CRITICAL FIX: Save checkpoints after training
+            if models_trained:
+                self._save_training_checkpoints(models_trained, confidence)
+            
         except Exception as e:
             logger.error(f"Error training models on decision: {e}")
     
+    def _save_training_checkpoints(self, models_trained: List[str], performance_score: float):
+        """Save checkpoints for trained models if performance improved
+        
+        This is CRITICAL for preserving training progress across restarts.
+        """
+        try:
+            if not self.checkpoint_manager:
+                return
+            
+            # Increment training counter
+            self.training_iterations += 1
+            
+            # Save checkpoints for each trained model
+            for model_name in models_trained:
+                try:
+                    model_obj = None
+                    current_loss = None
+                    
+                    # Get model object and calculate current performance
+                    if model_name == 'dqn' and self.rl_agent:
+                        model_obj = self.rl_agent
+                        # Use negative performance score as loss (higher confidence = lower loss)
+                        current_loss = 1.0 - performance_score
+                        
+                    elif model_name == 'cnn' and self.cnn_model:
+                        model_obj = self.cnn_model
+                        current_loss = 1.0 - performance_score
+                        
+                    elif model_name == 'cob_rl' and self.cob_rl_agent:
+                        model_obj = self.cob_rl_agent
+                        current_loss = 1.0 - performance_score
+                    
+                    if model_obj and current_loss is not None:
+                        # Check if this is the best performance so far
+                        model_state = self.model_states.get(model_name, {})
+                        best_loss = model_state.get('best_loss', float('inf'))
+                        
+                        # Update current loss
+                        model_state['current_loss'] = current_loss
+                        model_state['last_training'] = datetime.now()
+                        
+                        # Save checkpoint if performance improved or periodic save
+                        should_save = (
+                            current_loss < best_loss or  # Performance improved
+                            self.training_iterations % 100 == 0  # Periodic save every 100 iterations
+                        )
+                        
+                        if should_save:
+                            # Prepare metadata
+                            metadata = {
+                                'loss': current_loss,
+                                'performance_score': performance_score,
+                                'training_iterations': self.training_iterations,
+                                'timestamp': datetime.now().isoformat(),
+                                'model_type': model_name
+                            }
+                            
+                            # Save checkpoint
+                            checkpoint_path = self.checkpoint_manager.save_checkpoint(
+                                model=model_obj,
+                                model_name=model_name,
+                                performance=current_loss,
+                                metadata=metadata
+                            )
+                            
+                            if checkpoint_path:
+                                # Update best performance
+                                if current_loss < best_loss:
+                                    model_state['best_loss'] = current_loss
+                                    model_state['best_checkpoint'] = checkpoint_path
+                                    logger.info(f"💾 Saved BEST checkpoint for {model_name}: {checkpoint_path} (loss: {current_loss:.4f})")
+                                else:
+                                    logger.debug(f"💾 Saved periodic checkpoint for {model_name}: {checkpoint_path}")
+                                
+                                model_state['last_checkpoint'] = checkpoint_path
+                                model_state['checkpoints_saved'] = model_state.get('checkpoints_saved', 0) + 1
+                        
+                        # Update model state
+                        self.model_states[model_name] = model_state
+                        
+                except Exception as e:
+                    logger.error(f"Error saving checkpoint for {model_name}: {e}")
+            
+        except Exception as e:
+            logger.error(f"Error saving training checkpoints: {e}")
+    
     def _get_current_market_data(self, symbol: str) -> Optional[Dict]:
         """Get current market data for training context"""
         try:

core/trading_executor.py

@@ -22,8 +22,8 @@ import sys
 # Add NN directory to path for exchange interfaces
 sys.path.append(os.path.join(os.path.dirname(__file__), '..', 'NN'))
 
-from NN.exchanges.exchange_factory import ExchangeFactory
-from NN.exchanges.exchange_interface import ExchangeInterface
+from core.exchanges.exchange_factory import ExchangeFactory
+from core.exchanges.exchange_interface import ExchangeInterface
 from .config import get_config
 from .config_sync import ConfigSynchronizer
 

diagnose_training_issues.py

@@ -1 +0,0 @@
- 

fix_dashboard_metrics.py

@@ -1,49 +0,0 @@
-#!/usr/bin/env python3
-"""
-Fix Dashboard Metrics Script
-
-This script fixes the incomplete code in the update_metrics function
-of the web/clean_dashboard.py file.
-"""
-
-import re
-import os
-
-def fix_dashboard_metrics():
-    """Fix the incomplete code in the update_metrics function"""
-    file_path = 'web/clean_dashboard.py'
-    
-    # Read the file content
-    with open(file_path, 'r', encoding='utf-8') as file:
-        content = file.read()
-    
-    # Find and replace the incomplete code
-    pattern = r"# Add unrealized P&L from current position \(adjustable leverage\)\s+if self\.curr"
-    replacement = """# Add unrealized P&L from current position (adjustable leverage)
-                if self.current_position and current_price:
-                    side = self.current_position.get('side', 'UNKNOWN')
-                    size = self.current_position.get('size', 0)
-                    entry_price = self.current_position.get('price', 0)
-                    
-                    if entry_price and size > 0:
-                        # Calculate unrealized P&L with current leverage
-                        if side.upper() == 'LONG' or side.upper() == 'BUY':
-                            raw_pnl_per_unit = current_price - entry_price
-                        else:  # SHORT or SELL
-                            raw_pnl_per_unit = entry_price - current_price
-                        
-                        # Apply current leverage to unrealized P&L
-                        leveraged_unrealized_pnl = raw_pnl_per_unit * size * self.current_leverage
-                        total_session_pnl += leveraged_unrealized_pnl"""
-    
-    # Replace the pattern
-    fixed_content = re.sub(pattern, replacement, content)
-    
-    # Write the fixed content back to the file
-    with open(file_path, 'w', encoding='utf-8') as file:
-        file.write(fixed_content)
-    
-    print(f"Fixed dashboard metrics in {file_path}")
-
-if __name__ == "__main__":
-    fix_dashboard_metrics()

fix_rl_training_issues.py

@@ -1,283 +0,0 @@
-#!/usr/bin/env python3
-"""
-Fix RL Training Issues - Comprehensive Solution
-
-This script addresses the critical RL training audit issues:
-1. MASSIVE INPUT DATA GAP (99.25% Missing) - Implements full 13,400 feature state
-2. Disconnected Training Pipeline - Fixes data flow between components
-3. Missing Enhanced State Builder - Connects orchestrator to dashboard
-4. Reward Calculation Issues - Ensures enhanced pivot-based rewards
-5. Williams Market Structure Integration - Proper feature extraction
-6. Real-time Data Integration - Live market data to RL
-
-Usage:
-    python fix_rl_training_issues.py
-"""
-
-import os
-import sys
-import logging
-from pathlib import Path
-
-# Add project root to path
-project_root = Path(__file__).parent
-sys.path.insert(0, str(project_root))
-
-logger = logging.getLogger(__name__)
-
-def fix_orchestrator_missing_methods():
-    """Fix missing methods in enhanced orchestrator"""
-    try:
-        logger.info("Checking enhanced orchestrator...")
-        
-        from core.enhanced_orchestrator import EnhancedTradingOrchestrator
-        
-        # Test if methods exist
-        test_orchestrator = EnhancedTradingOrchestrator()
-        
-        methods_to_check = [
-            '_get_symbol_correlation',
-            'build_comprehensive_rl_state', 
-            'calculate_enhanced_pivot_reward'
-        ]
-        
-        missing_methods = []
-        for method in methods_to_check:
-            if not hasattr(test_orchestrator, method):
-                missing_methods.append(method)
-        
-        if missing_methods:
-            logger.error(f"Missing methods in enhanced orchestrator: {missing_methods}")
-            return False
-        else:
-            logger.info("✅ All required methods present in enhanced orchestrator")
-            return True
-            
-    except Exception as e:
-        logger.error(f"Error checking orchestrator: {e}")
-        return False
-
-def test_comprehensive_state_building():
-    """Test comprehensive RL state building"""
-    try:
-        logger.info("Testing comprehensive state building...")
-        
-        from core.enhanced_orchestrator import EnhancedTradingOrchestrator
-        from core.data_provider import DataProvider
-        
-        # Create test instances
-        data_provider = DataProvider()
-        orchestrator = EnhancedTradingOrchestrator(data_provider=data_provider)
-        
-        # Test comprehensive state building
-        state = orchestrator.build_comprehensive_rl_state('ETH/USDT')
-        
-        if state is not None:
-            logger.info(f"✅ Comprehensive state built: {len(state)} features")
-            
-            if len(state) == 13400:
-                logger.info("✅ PERFECT: Exactly 13,400 features as required!")
-            else:
-                logger.warning(f"⚠️ Expected 13,400 features, got {len(state)}")
-            
-            # Check feature distribution
-            import numpy as np
-            non_zero = np.count_nonzero(state)
-            logger.info(f"Non-zero features: {non_zero} ({non_zero/len(state)*100:.1f}%)")
-            
-            return True
-        else:
-            logger.error("❌ Comprehensive state building failed")
-            return False
-            
-    except Exception as e:
-        logger.error(f"Error testing state building: {e}")
-        return False
-
-def test_enhanced_reward_calculation():
-    """Test enhanced reward calculation"""
-    try:
-        logger.info("Testing enhanced reward calculation...")
-        
-        from core.enhanced_orchestrator import EnhancedTradingOrchestrator
-        from datetime import datetime, timedelta
-        
-        orchestrator = EnhancedTradingOrchestrator()
-        
-        # Test data
-        trade_decision = {
-            'action': 'BUY',
-            'confidence': 0.75,
-            'price': 2500.0,
-            'timestamp': datetime.now()
-        }
-        
-        trade_outcome = {
-            'net_pnl': 50.0,
-            'exit_price': 2550.0,
-            'duration': timedelta(minutes=15)
-        }
-        
-        market_data = {
-            'volatility': 0.03,
-            'order_flow_direction': 'bullish',
-            'order_flow_strength': 0.8
-        }
-        
-        # Test enhanced reward
-        enhanced_reward = orchestrator.calculate_enhanced_pivot_reward(
-            trade_decision, market_data, trade_outcome
-        )
-        
-        logger.info(f"✅ Enhanced reward calculated: {enhanced_reward:.3f}")
-        return True
-        
-    except Exception as e:
-        logger.error(f"Error testing reward calculation: {e}")
-        return False
-
-def test_williams_integration():
-    """Test Williams market structure integration"""
-    try:
-        logger.info("Testing Williams market structure integration...")
-        
-        from training.williams_market_structure import extract_pivot_features, analyze_pivot_context
-        from core.data_provider import DataProvider
-        import pandas as pd
-        import numpy as np
-        
-        # Create test data
-        test_data = {
-            'open': np.random.uniform(2400, 2600, 100),
-            'high': np.random.uniform(2500, 2700, 100),
-            'low': np.random.uniform(2300, 2500, 100),
-            'close': np.random.uniform(2400, 2600, 100),
-            'volume': np.random.uniform(1000, 5000, 100)
-        }
-        df = pd.DataFrame(test_data)
-        
-        # Test pivot features
-        pivot_features = extract_pivot_features(df)
-        
-        if pivot_features is not None:
-            logger.info(f"✅ Williams pivot features extracted: {len(pivot_features)} features")
-            
-            # Test pivot context analysis
-            market_data = {'ohlcv_data': df}
-            context = analyze_pivot_context(market_data, datetime.now(), 'BUY')
-            
-            if context is not None:
-                logger.info("✅ Williams pivot context analysis working")
-                return True
-            else:
-                logger.warning("⚠️ Pivot context analysis returned None")
-                return False
-        else:
-            logger.error("❌ Williams pivot feature extraction failed")
-            return False
-            
-    except Exception as e:
-        logger.error(f"Error testing Williams integration: {e}")
-        return False
-
-def test_dashboard_integration():
-    """Test dashboard integration with enhanced features"""
-    try:
-        logger.info("Testing dashboard integration...")
-        
-        from web.clean_dashboard import CleanTradingDashboard as TradingDashboard
-        from core.enhanced_orchestrator import EnhancedTradingOrchestrator
-        from core.data_provider import DataProvider
-        from core.trading_executor import TradingExecutor
-        
-        # Create components
-        data_provider = DataProvider()
-        orchestrator = EnhancedTradingOrchestrator(data_provider=data_provider)
-        executor = TradingExecutor()
-        
-        # Create dashboard
-        dashboard = TradingDashboard(
-            data_provider=data_provider,
-            orchestrator=orchestrator,
-            trading_executor=executor
-        )
-        
-        # Check if dashboard has access to enhanced features
-        has_comprehensive_builder = hasattr(dashboard, '_build_comprehensive_rl_state')
-        has_enhanced_orchestrator = hasattr(dashboard.orchestrator, 'build_comprehensive_rl_state')
-        
-        if has_comprehensive_builder and has_enhanced_orchestrator:
-            logger.info("✅ Dashboard properly integrated with enhanced features")
-            return True
-        else:
-            logger.warning("⚠️ Dashboard missing some enhanced features")
-            logger.info(f"Comprehensive builder: {has_comprehensive_builder}")
-            logger.info(f"Enhanced orchestrator: {has_enhanced_orchestrator}")
-            return False
-            
-    except Exception as e:
-        logger.error(f"Error testing dashboard integration: {e}")
-        return False
-
-def main():
-    """Main function to run all fixes and tests"""
-    # Setup logging
-    logging.basicConfig(
-        level=logging.INFO,
-        format='%(asctime)s - %(levelname)s - %(message)s'
-    )
-    
-    logger.info("=" * 70)
-    logger.info("COMPREHENSIVE RL TRAINING FIX - AUDIT ISSUE RESOLUTION")
-    logger.info("=" * 70)
-    
-    # Track results
-    test_results = {}
-    
-    # Run all tests
-    tests = [
-        ("Enhanced Orchestrator Methods", fix_orchestrator_missing_methods),
-        ("Comprehensive State Building", test_comprehensive_state_building),
-        ("Enhanced Reward Calculation", test_enhanced_reward_calculation),
-        ("Williams Market Structure", test_williams_integration),
-        ("Dashboard Integration", test_dashboard_integration)
-    ]
-    
-    for test_name, test_func in tests:
-        logger.info(f"\n🔧 {test_name}...")
-        try:
-            result = test_func()
-            test_results[test_name] = result
-        except Exception as e:
-            logger.error(f"❌ {test_name} failed: {e}")
-            test_results[test_name] = False
-    
-    # Summary
-    logger.info("\n" + "=" * 70)
-    logger.info("COMPREHENSIVE RL TRAINING FIX RESULTS")
-    logger.info("=" * 70)
-    
-    passed = sum(test_results.values())
-    total = len(test_results)
-    
-    for test_name, result in test_results.items():
-        status = "✅ PASS" if result else "❌ FAIL"
-        logger.info(f"{test_name}: {status}")
-    
-    logger.info(f"\nOverall: {passed}/{total} tests passed")
-    
-    if passed == total:
-        logger.info("🎉 ALL RL TRAINING ISSUES FIXED!")
-        logger.info("The system now supports:")
-        logger.info("  - 13,400 comprehensive RL features")
-        logger.info("  - Enhanced pivot-based rewards")
-        logger.info("  - Williams market structure integration")
-        logger.info("  - Proper data flow between components")
-        logger.info("  - Real-time data integration")
-    else:
-        logger.warning("⚠️ Some issues remain - check logs above")
-    
-    return 0 if passed == total else 1
-
-if __name__ == "__main__":
-    sys.exit(main()) 

test_mexc_order_fix.py

@@ -29,7 +29,7 @@ def test_mexc_order_fix():
     
     # Import after path setup
     try:
-        from NN.exchanges.mexc_interface import MEXCInterface
+        from core.exchanges.mexc_interface import MEXCInterface
     except ImportError as e:
         print(f"❌ Import error: {e}")
         return False