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"""
FinRL Agent for Algorithmic Trading
This module provides a FinRL-based reinforcement learning agent that can be integrated
with the existing algorithmic trading system. It supports various RL algorithms
including PPO, A2C, DDPG, and TD3.
"""
import numpy as np
import pandas as pd
import gymnasium as gym
from gymnasium import spaces
from stable_baselines3 import PPO, A2C, DDPG, TD3
from stable_baselines3.common.vec_env import DummyVecEnv
from stable_baselines3.common.callbacks import EvalCallback
import torch
import logging
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass
import yaml
logger = logging.getLogger(__name__)
@dataclass
class FinRLConfig:
"""Configuration for FinRL agent"""
algorithm: str = "PPO" # PPO, A2C, DDPG, TD3
learning_rate: float = 0.0003
batch_size: int = 64
buffer_size: int = 1000000
learning_starts: int = 100
gamma: float = 0.99
tau: float = 0.005
train_freq: int = 1
gradient_steps: int = 1
target_update_interval: int = 1
exploration_fraction: float = 0.1
exploration_initial_eps: float = 1.0
exploration_final_eps: float = 0.05
max_grad_norm: float = 10.0
verbose: int = 1
tensorboard_log: str = "logs/finrl_tensorboard"
class TradingEnvironment(gym.Env):
"""
Custom trading environment for FinRL
This environment simulates a trading scenario where the agent can:
- Buy, sell, or hold positions
- Use technical indicators for decision making
- Manage portfolio value and risk
"""
def __init__(self, data: pd.DataFrame, initial_balance: float = 100000,
transaction_fee: float = 0.001, max_position: int = 100):
super().__init__()
self.data = data
self.initial_balance = initial_balance
self.transaction_fee = transaction_fee
self.max_position = max_position
# Reset state
self.reset()
# Define action space: [-1, 0, 1] for sell, hold, buy
self.action_space = spaces.Discrete(3)
# Define observation space
# Features: OHLCV + technical indicators + portfolio state
n_features = len(self._get_features(self.data.iloc[0]))
self.observation_space = spaces.Box(
low=-np.inf, high=np.inf, shape=(n_features,), dtype=np.float32
)
def _get_features(self, row: pd.Series) -> np.ndarray:
"""Extract features from market data row"""
features = []
# Price features
features.extend([
row['open'], row['high'], row['low'], row['close'], row['volume']
])
# Technical indicators (if available)
for indicator in ['sma_20', 'sma_50', 'rsi', 'bb_upper', 'bb_lower', 'macd']:
if indicator in row.index:
features.append(row[indicator])
else:
features.append(0.0)
# Portfolio state
features.extend([
self.balance,
self.position,
self.portfolio_value,
self.total_return
])
return np.array(features, dtype=np.float32)
def _calculate_portfolio_value(self) -> float:
"""Calculate current portfolio value"""
current_price = self.data.iloc[self.current_step]['close']
return self.balance + (self.position * current_price)
def _calculate_reward(self) -> float:
"""Calculate reward based on portfolio performance"""
current_value = self._calculate_portfolio_value()
previous_value = self.previous_portfolio_value
# Calculate return
if previous_value > 0:
return (current_value - previous_value) / previous_value
else:
return 0.0
def step(self, action: int) -> Tuple[np.ndarray, float, bool, bool, Dict]:
"""Execute one step in the environment"""
# Get current market data
current_data = self.data.iloc[self.current_step]
current_price = current_data['close']
# Execute action
if action == 0: # Sell
if self.position > 0:
shares_to_sell = min(self.position, self.max_position)
sell_value = shares_to_sell * current_price * (1 - self.transaction_fee)
self.balance += sell_value
self.position -= shares_to_sell
elif action == 2: # Buy
if self.balance > 0:
max_shares = min(
int(self.balance / current_price),
self.max_position - self.position
)
if max_shares > 0:
buy_value = max_shares * current_price * (1 + self.transaction_fee)
self.balance -= buy_value
self.position += max_shares
# Update portfolio value
self.previous_portfolio_value = self.portfolio_value
self.portfolio_value = self._calculate_portfolio_value()
self.total_return = (self.portfolio_value - self.initial_balance) / self.initial_balance
# Calculate reward
reward = self._calculate_reward()
# Move to next step
self.current_step += 1
# Check if episode is done
done = self.current_step >= len(self.data) - 1
# Get observation
if not done:
observation = self._get_features(self.data.iloc[self.current_step])
else:
# Use last available data for final observation
observation = self._get_features(self.data.iloc[-1])
info = {
'balance': self.balance,
'position': self.position,
'portfolio_value': self.portfolio_value,
'total_return': self.total_return,
'current_price': current_price
}
return observation, reward, done, False, info
def reset(self, seed: Optional[int] = None) -> Tuple[np.ndarray, Dict]:
"""Reset the environment"""
super().reset(seed=seed)
self.current_step = 0
self.balance = self.initial_balance
self.position = 0
self.portfolio_value = self.initial_balance
self.previous_portfolio_value = self.initial_balance
self.total_return = 0.0
observation = self._get_features(self.data.iloc[self.current_step])
info = {
'balance': self.balance,
'position': self.position,
'portfolio_value': self.portfolio_value,
'total_return': self.total_return
}
return observation, info
class FinRLAgent:
"""
FinRL-based reinforcement learning agent for algorithmic trading
"""
def __init__(self, config: FinRLConfig):
self.config = config
self.model = None
self.env = None
self.eval_env = None
self.callback = None
logger.info(f"Initializing FinRL agent with algorithm: {config.algorithm}")
def create_environment(self, data: pd.DataFrame, initial_balance: float = 100000) -> TradingEnvironment:
"""Create trading environment from market data"""
return TradingEnvironment(
data=data,
initial_balance=initial_balance,
transaction_fee=0.001,
max_position=100
)
def prepare_data(self, data: pd.DataFrame) -> pd.DataFrame:
"""Prepare data with technical indicators for FinRL"""
df = data.copy()
# Add technical indicators if not present
if 'sma_20' not in df.columns:
df['sma_20'] = df['close'].rolling(window=20).mean()
if 'sma_50' not in df.columns:
df['sma_50'] = df['close'].rolling(window=50).mean()
if 'rsi' not in df.columns:
df['rsi'] = self._calculate_rsi(df['close'])
if 'bb_upper' not in df.columns or 'bb_lower' not in df.columns:
bb_upper, bb_lower = self._calculate_bollinger_bands(df['close'])
df['bb_upper'] = bb_upper
df['bb_lower'] = bb_lower
if 'macd' not in df.columns:
df['macd'] = self._calculate_macd(df['close'])
# Fill NaN values
df = df.fillna(method='bfill').fillna(0)
return df
def _calculate_rsi(self, prices: pd.Series, period: int = 14) -> pd.Series:
"""Calculate RSI indicator"""
delta = prices.diff()
gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
rs = gain / loss
rsi = 100 - (100 / (1 + rs))
return rsi
def _calculate_bollinger_bands(self, prices: pd.Series, period: int = 20, std_dev: int = 2) -> Tuple[pd.Series, pd.Series]:
"""Calculate Bollinger Bands"""
sma = prices.rolling(window=period).mean()
std = prices.rolling(window=period).std()
upper_band = sma + (std * std_dev)
lower_band = sma - (std * std_dev)
return upper_band, lower_band
def _calculate_macd(self, prices: pd.Series, fast: int = 12, slow: int = 26, signal: int = 9) -> pd.Series:
"""Calculate MACD indicator"""
ema_fast = prices.ewm(span=fast).mean()
ema_slow = prices.ewm(span=slow).mean()
macd_line = ema_fast - ema_slow
return macd_line
def train(self, data: pd.DataFrame, total_timesteps: int = 100000,
eval_freq: int = 10000, eval_data: Optional[pd.DataFrame] = None) -> Dict[str, Any]:
"""Train the FinRL agent"""
logger.info("Starting FinRL agent training")
# Prepare data
train_data = self.prepare_data(data)
# Create training environment
self.env = DummyVecEnv([lambda: self.create_environment(train_data)])
# Create evaluation environment if provided
if eval_data is not None:
eval_data = self.prepare_data(eval_data)
self.eval_env = DummyVecEnv([lambda: self.create_environment(eval_data)])
self.callback = EvalCallback(
self.eval_env,
best_model_save_path="models/finrl_best/",
log_path="logs/finrl_eval/",
eval_freq=eval_freq,
deterministic=True,
render=False
)
# Initialize model based on algorithm
if self.config.algorithm == "PPO":
self.model = PPO(
"MlpPolicy",
self.env,
learning_rate=self.config.learning_rate,
batch_size=self.config.batch_size,
gamma=self.config.gamma,
verbose=self.config.verbose,
tensorboard_log=self.config.tensorboard_log
)
elif self.config.algorithm == "A2C":
self.model = A2C(
"MlpPolicy",
self.env,
learning_rate=self.config.learning_rate,
gamma=self.config.gamma,
verbose=self.config.verbose,
tensorboard_log=self.config.tensorboard_log
)
elif self.config.algorithm == "DDPG":
self.model = DDPG(
"MlpPolicy",
self.env,
learning_rate=self.config.learning_rate,
buffer_size=self.config.buffer_size,
learning_starts=self.config.learning_starts,
gamma=self.config.gamma,
tau=self.config.tau,
train_freq=self.config.train_freq,
gradient_steps=self.config.gradient_steps,
verbose=self.config.verbose,
tensorboard_log=self.config.tensorboard_log
)
elif self.config.algorithm == "TD3":
self.model = TD3(
"MlpPolicy",
self.env,
learning_rate=self.config.learning_rate,
buffer_size=self.config.buffer_size,
learning_starts=self.config.learning_starts,
gamma=self.config.gamma,
tau=self.config.tau,
train_freq=self.config.train_freq,
gradient_steps=self.config.gradient_steps,
target_update_interval=self.config.target_update_interval,
verbose=self.config.verbose,
tensorboard_log=self.config.tensorboard_log
)
else:
raise ValueError(f"Unsupported algorithm: {self.config.algorithm}")
# Train the model
callbacks = [self.callback] if self.callback else None
self.model.learn(
total_timesteps=total_timesteps,
callback=callbacks
)
logger.info("FinRL agent training completed")
return {
'algorithm': self.config.algorithm,
'total_timesteps': total_timesteps,
'model_path': f"models/finrl_{self.config.algorithm.lower()}"
}
def predict(self, data: pd.DataFrame) -> List[int]:
"""Generate trading predictions using the trained model"""
if self.model is None:
raise ValueError("Model not trained. Call train() first.")
# Prepare data
test_data = self.prepare_data(data)
# Create test environment
test_env = self.create_environment(test_data)
predictions = []
obs, _ = test_env.reset()
done = False
while not done:
action, _ = self.model.predict(obs, deterministic=True)
predictions.append(action)
obs, _, done, _, _ = test_env.step(action)
return predictions
def evaluate(self, data: pd.DataFrame) -> Dict[str, float]:
"""Evaluate the trained model on test data"""
if self.model is None:
raise ValueError("Model not trained. Call train() first.")
# Prepare data
test_data = self.prepare_data(data)
# Create test environment
test_env = self.create_environment(test_data)
obs, _ = test_env.reset()
done = False
total_reward = 0
steps = 0
while not done:
action, _ = self.model.predict(obs, deterministic=True)
obs, reward, done, _, info = test_env.step(action)
total_reward += reward
steps += 1
# Calculate metrics
final_portfolio_value = info['portfolio_value']
initial_balance = test_env.initial_balance
total_return = (final_portfolio_value - initial_balance) / initial_balance
return {
'total_reward': total_reward,
'total_return': total_return,
'final_portfolio_value': final_portfolio_value,
'steps': steps,
'sharpe_ratio': total_reward / steps if steps > 0 else 0
}
def save_model(self, path: str):
"""Save the trained model"""
if self.model is None:
raise ValueError("No model to save. Train the model first.")
self.model.save(path)
logger.info(f"Model saved to {path}")
def load_model(self, path: str):
"""Load a trained model"""
if self.config.algorithm == "PPO":
self.model = PPO.load(path)
elif self.config.algorithm == "A2C":
self.model = A2C.load(path)
elif self.config.algorithm == "DDPG":
self.model = DDPG.load(path)
elif self.config.algorithm == "TD3":
self.model = TD3.load(path)
else:
raise ValueError(f"Unsupported algorithm: {self.config.algorithm}")
logger.info(f"Model loaded from {path}")
def create_finrl_agent_from_config(config_path: str) -> FinRLAgent:
"""Create FinRL agent from configuration file"""
with open(config_path, 'r') as file:
config_data = yaml.safe_load(file)
finrl_config = FinRLConfig(**config_data.get('finrl', {}))
return FinRLAgent(finrl_config) |