""" PPO-LSTM Trading Agent Implements the deep reinforcement learning brain for trading decisions. """ import os import numpy as np from typing import Optional, Dict, Any, Tuple from pathlib import Path from stable_baselines3 import PPO from stable_baselines3.common.callbacks import BaseCallback, EvalCallback from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize import gymnasium as gym class ConfidenceCallback(BaseCallback): """ Callback to track action confidence during training. """ def __init__(self, verbose=0): super().__init__(verbose) self.confidences = [] def _on_step(self) -> bool: return True def get_average_confidence(self) -> float: if not self.confidences: return 0.0 return np.mean(self.confidences) class TradingAgent: """ PPO-LSTM agent for cryptocurrency trading. Uses Stable-Baselines3's RecurrentPPO with LSTM policy to capture temporal dependencies in market data. """ def __init__( self, env: gym.Env, config: Optional[Dict] = None, model_path: Optional[str] = None, ): """ Initialize the trading agent. Args: env: Gymnasium trading environment config: Model hyperparameters model_path: Path to load pretrained model from """ self.config = config or self._default_config() self.env = env # Wrap in DummyVecEnv for SB3 compatibility self.vec_env = DummyVecEnv([lambda: env]) # Optional: Add observation normalization if self.config.get('normalize_observations', True): self.vec_env = VecNormalize( self.vec_env, norm_obs=True, norm_reward=True, clip_obs=10.0, clip_reward=10.0, ) # Initialize or load model if model_path and os.path.exists(model_path): self.model = self._load_model(model_path) else: self.model = self._create_model() # Tracking self.training_steps = 0 self.last_action_probs = None def _default_config(self) -> Dict: """Default hyperparameters for PPO.""" return { 'policy': 'MlpPolicy', # Use standard MlpPolicy (MlpLstmPolicy requires sb3-contrib) 'learning_rate': 3e-4, 'n_steps': 2048, 'batch_size': 64, 'n_epochs': 10, 'gamma': 0.99, 'gae_lambda': 0.95, 'clip_range': 0.2, 'ent_coef': 0.01, 'vf_coef': 0.5, 'max_grad_norm': 0.5, 'normalize_observations': True, 'verbose': 1, } def _create_model(self) -> PPO: """Create a new PPO model with LSTM policy.""" return PPO( policy=self.config.get('policy', 'MlpPolicy'), env=self.vec_env, learning_rate=self.config.get('learning_rate', 3e-4), n_steps=self.config.get('n_steps', 2048), batch_size=self.config.get('batch_size', 64), n_epochs=self.config.get('n_epochs', 10), gamma=self.config.get('gamma', 0.99), gae_lambda=self.config.get('gae_lambda', 0.95), clip_range=self.config.get('clip_range', 0.2), ent_coef=self.config.get('ent_coef', 0.01), vf_coef=self.config.get('vf_coef', 0.5), max_grad_norm=self.config.get('max_grad_norm', 0.5), verbose=self.config.get('verbose', 1), tensorboard_log="./logs/tensorboard/", ) def _load_model(self, path: str) -> PPO: """Load a pretrained model.""" print(f"Loading model from {path}") model = PPO.load(path, env=self.vec_env) return model def train( self, total_timesteps: int = 100000, eval_env: Optional[gym.Env] = None, eval_freq: int = 10000, save_path: Optional[str] = None, callbacks: Optional[list] = None, ) -> Dict[str, Any]: """ Train the agent. Args: total_timesteps: Total training steps eval_env: Optional evaluation environment eval_freq: Evaluation frequency save_path: Path to save best model callbacks: Additional callbacks Returns: Training metrics dictionary """ callback_list = callbacks or [] # Add evaluation callback if eval_env provided if eval_env: eval_vec_env = DummyVecEnv([lambda: eval_env]) eval_callback = EvalCallback( eval_vec_env, best_model_save_path=save_path or "./data/models/", log_path="./logs/eval/", eval_freq=eval_freq, deterministic=True, render=False, ) callback_list.append(eval_callback) # Train self.model.learn( total_timesteps=total_timesteps, callback=callback_list, progress_bar=True, ) self.training_steps += total_timesteps return { 'total_timesteps': self.training_steps, } def predict( self, observation: np.ndarray, state: Optional[np.ndarray] = None, deterministic: bool = True, ) -> Tuple[int, Optional[np.ndarray], float]: """ Predict action for given observation. Args: observation: Current observation state: LSTM hidden state (for recurrent policy) deterministic: Whether to use deterministic action Returns: Tuple of (action, new_state, confidence) """ # Get action and state action, state = self.model.predict( observation, state=state, deterministic=deterministic, ) # Calculate confidence from action probabilities confidence = self._get_action_confidence(observation) return int(action), state, confidence def _get_action_confidence(self, observation: np.ndarray) -> float: """ Calculate confidence score for the predicted action. Higher confidence = agent is more certain about its decision. """ import torch obs = observation.reshape(1, -1) # Get action distribution with torch.no_grad(): obs_tensor = self.model.policy.obs_to_tensor(obs)[0] dist = self.model.policy.get_distribution(obs_tensor) # Get probabilities probs = dist.distribution.probs.detach().cpu().numpy()[0] self.last_action_probs = probs # Confidence is max probability confidence = float(np.max(probs)) return confidence def get_action_probabilities(self) -> Optional[np.ndarray]: """Get the last computed action probabilities.""" return self.last_action_probs def save(self, path: str): """Save the model to disk.""" Path(path).parent.mkdir(parents=True, exist_ok=True) self.model.save(path) # Also save VecNormalize statistics if applicable if isinstance(self.vec_env, VecNormalize): vec_norm_path = path.replace('.zip', '_vecnorm.pkl') self.vec_env.save(vec_norm_path) print(f"Model saved to {path}") def load(self, path: str): """Load model from disk.""" self.model = PPO.load(path, env=self.vec_env) # Load VecNormalize if exists vec_norm_path = path.replace('.zip', '_vecnorm.pkl') if os.path.exists(vec_norm_path) and isinstance(self.vec_env, VecNormalize): self.vec_env = VecNormalize.load(vec_norm_path, self.vec_env.venv) print(f"Model loaded from {path}") def create_agent( env: gym.Env, config_path: Optional[str] = None, model_path: Optional[str] = None, ) -> TradingAgent: """ Factory function to create a trading agent. Args: env: Trading environment config_path: Path to config YAML model_path: Path to pretrained model Returns: TradingAgent instance """ config = None if config_path: import yaml with open(config_path, 'r') as f: full_config = yaml.safe_load(f) config = full_config.get('model', {}) return TradingAgent(env=env, config=config, model_path=model_path)