File size: 8,899 Bytes
fc115d5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 | """
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)
|