fix model mappings,dash updates, trading
This commit is contained in:
Dobromir Popov
204
reset_models_and_fix_mapping.py
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204
reset_models_and_fix_mapping.py
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#!/usr/bin/env python3
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"""
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Reset Models and Fix Action Mapping
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This script:
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1. Deletes existing model files
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2. Creates new model files with consistent action mapping
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3. Updates action mapping in key files
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"""
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import os
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import shutil
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import logging
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import sys
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import torch
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import numpy as np
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from datetime import datetime
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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def ensure_directory(directory):
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"""Ensure directory exists"""
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if not os.path.exists(directory):
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os.makedirs(directory)
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logger.info(f"Created directory: {directory}")
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def delete_directory_contents(directory):
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"""Delete all files in a directory"""
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if os.path.exists(directory):
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for filename in os.listdir(directory):
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file_path = os.path.join(directory, filename)
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try:
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if os.path.isfile(file_path) or os.path.islink(file_path):
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os.unlink(file_path)
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elif os.path.isdir(file_path):
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shutil.rmtree(file_path)
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logger.info(f"Deleted: {file_path}")
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except Exception as e:
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logger.error(f"Failed to delete {file_path}. Reason: {e}")
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def create_backup_directory():
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"""Create a backup directory with timestamp"""
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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backup_dir = f"models/backup_{timestamp}"
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ensure_directory(backup_dir)
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return backup_dir
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def backup_models():
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"""Backup existing models"""
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backup_dir = create_backup_directory()
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# List of model directories to backup
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model_dirs = [
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"models/enhanced_rl",
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"models/enhanced_cnn",
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"models/realtime_rl_cob",
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"models/rl",
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"models/cnn"
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]
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for model_dir in model_dirs:
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if os.path.exists(model_dir):
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dest_dir = os.path.join(backup_dir, os.path.basename(model_dir))
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ensure_directory(dest_dir)
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# Copy files
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for filename in os.listdir(model_dir):
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file_path = os.path.join(model_dir, filename)
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if os.path.isfile(file_path):
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shutil.copy2(file_path, dest_dir)
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logger.info(f"Backed up: {file_path} to {dest_dir}")
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return backup_dir
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def initialize_dqn_model():
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"""Initialize a new DQN model with consistent action mapping"""
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try:
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# Import necessary modules
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from NN.models.dqn_agent import DQNAgent
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# Define state shape for BTC and ETH
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state_shape = (100,) # Default feature dimension
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# Create models directory
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ensure_directory("models/enhanced_rl")
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# Initialize DQN with 3 actions (BUY=0, SELL=1, HOLD=2)
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dqn_btc = DQNAgent(
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state_shape=state_shape,
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n_actions=3, # BUY=0, SELL=1, HOLD=2
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learning_rate=0.001,
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epsilon=0.5, # Start with moderate exploration
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epsilon_min=0.01,
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epsilon_decay=0.995,
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model_name="BTC_USDT_dqn"
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)
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dqn_eth = DQNAgent(
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state_shape=state_shape,
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n_actions=3, # BUY=0, SELL=1, HOLD=2
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learning_rate=0.001,
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epsilon=0.5, # Start with moderate exploration
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epsilon_min=0.01,
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epsilon_decay=0.995,
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model_name="ETH_USDT_dqn"
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)
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# Save initial models
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torch.save(dqn_btc.policy_net.state_dict(), "models/enhanced_rl/BTC_USDT_dqn_policy.pth")
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torch.save(dqn_btc.target_net.state_dict(), "models/enhanced_rl/BTC_USDT_dqn_target.pth")
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torch.save(dqn_eth.policy_net.state_dict(), "models/enhanced_rl/ETH_USDT_dqn_policy.pth")
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torch.save(dqn_eth.target_net.state_dict(), "models/enhanced_rl/ETH_USDT_dqn_target.pth")
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logger.info("Initialized new DQN models with consistent action mapping (BUY=0, SELL=1, HOLD=2)")
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return True
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except Exception as e:
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logger.error(f"Failed to initialize DQN models: {e}")
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return False
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def initialize_cnn_model():
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"""Initialize a new CNN model with consistent action mapping"""
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try:
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# Import necessary modules
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from NN.models.enhanced_cnn import EnhancedCNN
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# Define input dimension and number of actions
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input_dim = 100 # Default feature dimension
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n_actions = 3 # BUY=0, SELL=1, HOLD=2
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# Create models directory
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ensure_directory("models/enhanced_cnn")
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# Initialize CNN models for BTC and ETH
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cnn_btc = EnhancedCNN(input_dim, n_actions)
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cnn_eth = EnhancedCNN(input_dim, n_actions)
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# Save initial models
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torch.save(cnn_btc.state_dict(), "models/enhanced_cnn/BTC_USDT_cnn.pth")
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torch.save(cnn_eth.state_dict(), "models/enhanced_cnn/ETH_USDT_cnn.pth")
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logger.info("Initialized new CNN models with consistent action mapping (BUY=0, SELL=1, HOLD=2)")
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return True
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except Exception as e:
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logger.error(f"Failed to initialize CNN models: {e}")
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return False
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def initialize_realtime_rl_model():
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"""Initialize a new realtime RL model with consistent action mapping"""
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try:
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# Create models directory
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ensure_directory("models/realtime_rl_cob")
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# Create empty model files to ensure directory is not empty
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with open("models/realtime_rl_cob/README.txt", "w") as f:
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f.write("Realtime RL COB models will be saved here.\n")
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f.write("Action mapping: BUY=0, SELL=1, HOLD=2\n")
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logger.info("Initialized realtime RL model directory")
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return True
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except Exception as e:
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logger.error(f"Failed to initialize realtime RL models: {e}")
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return False
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def main():
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"""Main function to reset models and fix action mapping"""
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logger.info("Starting model reset and action mapping fix")
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# Backup existing models
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backup_dir = backup_models()
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logger.info(f"Backed up existing models to {backup_dir}")
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# Delete existing model files
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model_dirs = [
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"models/enhanced_rl",
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"models/enhanced_cnn",
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"models/realtime_rl_cob"
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]
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for model_dir in model_dirs:
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delete_directory_contents(model_dir)
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logger.info(f"Deleted contents of {model_dir}")
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# Initialize new models with consistent action mapping
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dqn_success = initialize_dqn_model()
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cnn_success = initialize_cnn_model()
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rl_success = initialize_realtime_rl_model()
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if dqn_success and cnn_success and rl_success:
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logger.info("Successfully reset models and fixed action mapping")
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logger.info("New action mapping: BUY=0, SELL=1, HOLD=2")
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else:
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logger.error("Failed to reset models and fix action mapping")
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logger.info("Model reset complete")
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if __name__ == "__main__":
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main()
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