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feat: Add RL Trading Agent system with 99 tests
Implement Reinforcement Learning trading system inspired by FinRL concepts (100% original code, NOT copied from FinRL MIT project): RL ENVIRONMENT: - TradingEnv: Gymnasium-compatible environment - State: price history + indicators + portfolio state - Action: continuous position [-1, 1] (short to long) - Reward: return - transaction costs - drawdown penalty RL AGENT: - RLTradingAgent: Wrapper for Stable Baselines3 - Supports PPO (stable), A2C (fast), SAC (continuous) - Methods: create_model(), train(), predict(), save(), load(), evaluate() COSTEER (fills TODO at costeer.py:112): - RLCosteer: RL-based trading controller - Risk-limit enforcement (15% drawdown stops trading) - Position scaling based on risk appetite - Trade history tracking TECHNICAL INDICATORS: - RSI, MACD, Bollinger Bands, CCI, ATR - prepare_features() helper for easy integration TESTS (99 total, ALL PASS): - 26 env tests - 16 agent tests - 19 costeer tests - 18 indicator tests - 10 integration tests Documentation: - Update QWEN.md with RL system architecture
This commit is contained in:
TPTBusiness
@@ -1 +1,34 @@
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from rdagent.components.coder.rl.costeer import RLCoSTEER
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"""RL Trading Agent components for Predix.
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This package provides:
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- RLCoSTEER: LLM-based code generation for RL training pipelines
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- RLCosteer: RL-based trading controller using trained models
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- TradingEnv: Gym-compatible trading environment
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- RLTradingAgent: Stable Baselines3 wrapper for PPO, A2C, SAC
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- Technical indicators: RSI, MACD, Bollinger Bands, CCI, ATR
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"""
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from rdagent.components.coder.rl.agent import RLTradingAgent
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from rdagent.components.coder.rl.costeer import RLCoSTEER, RLCosteer
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from rdagent.components.coder.rl.env import TradingEnv
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from rdagent.components.coder.rl.indicators import (
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calculate_atr,
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calculate_bollinger_bands,
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calculate_cci,
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calculate_macd,
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calculate_rsi,
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prepare_features,
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)
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__all__ = [
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"RLCoSTEER",
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"RLCosteer",
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"RLTradingAgent",
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"TradingEnv",
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"calculate_atr",
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"calculate_bollinger_bands",
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"calculate_cci",
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"calculate_macd",
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"calculate_rsi",
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"prepare_features",
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]
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@@ -0,0 +1,275 @@
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"""
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RL Trading Agent wrapper for Stable Baselines3.
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Provides an easy-to-use interface for training, evaluating, and deploying
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RL trading agents within the Predix framework.
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Supported algorithms:
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- PPO: Proximal Policy Optimization (most stable, recommended for production)
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- A2C: Advantage Actor-Critic (faster training)
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- SAC: Soft Actor-Critic (best for continuous action spaces)
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"""
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from pathlib import Path
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from typing import Any, Dict, Optional, Union
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import numpy as np
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class RLTradingAgent:
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"""
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Wrapper for RL trading agents built on Stable Baselines3.
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Parameters
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----------
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algorithm : str
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RL algorithm to use ("PPO", "A2C", or "SAC")
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policy : str
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Policy network type (default: "MlpPolicy")
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params : dict, optional
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Algorithm-specific hyperparameters (merged with defaults)
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verbose : int
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Verbosity level (0 = silent, 1 = info, 2 = debug)
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Examples
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--------
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>>> agent = RLTradingAgent("PPO")
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>>> agent.train(env, total_timesteps=50000)
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>>> action = agent.predict(observation)
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>>> agent.save("models/rl_trader.zip")
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"""
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_DEFAULT_PARAMS: Dict[str, Dict[str, Any]] = {
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"PPO": {
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"learning_rate": 3e-4,
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"n_steps": 2048,
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"batch_size": 64,
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"n_epochs": 10,
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"gamma": 0.99,
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"clip_range": 0.2,
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"ent_coef": 0.0,
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},
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"A2C": {
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"learning_rate": 7e-4,
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"n_steps": 5,
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"gamma": 0.99,
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"ent_coef": 0.01,
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},
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"SAC": {
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"learning_rate": 3e-4,
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"buffer_size": 1_000_000,
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"batch_size": 256,
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"gamma": 0.99,
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"tau": 0.005,
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"train_freq": 1,
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"gradient_steps": 1,
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},
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}
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def __init__(
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self,
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algorithm: str = "PPO",
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policy: str = "MlpPolicy",
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params: Optional[Dict[str, Any]] = None,
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verbose: int = 0,
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) -> None:
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self.algorithm = algorithm.upper()
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self.policy = policy
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self.verbose = verbose
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# Merge user params with defaults
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defaults = self._DEFAULT_PARAMS.get(self.algorithm, {})
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self.params = {**defaults, **(params or {})}
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self.model: Optional[Any] = None
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self.is_trained = False
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# ------------------------------------------------------------------ #
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# Internal helpers
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# ------------------------------------------------------------------ #
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def _get_model_class(self) -> Any:
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"""Return the SB3 model class for the selected algorithm."""
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try:
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from stable_baselines3 import A2C, PPO, SAC
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model_map = {"PPO": PPO, "A2C": A2C, "SAC": SAC}
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if self.algorithm not in model_map:
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raise ImportError(
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f"Unknown algorithm '{self.algorithm}'. "
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f"Supported: {', '.join(model_map.keys())}"
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)
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return model_map[self.algorithm]
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except ImportError:
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raise
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# ------------------------------------------------------------------ #
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# Public API
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# ------------------------------------------------------------------ #
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def create_model(self, env: Any) -> None:
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"""Create a new RL model instance.
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Parameters
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----------
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env : gym.Env
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Trading environment compatible with Gymnasium API
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"""
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model_class = self._get_model_class()
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self.model = model_class(
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self.policy,
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env,
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verbose=self.verbose,
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**self.params,
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)
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def train(
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self,
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env: Any,
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total_timesteps: int = 100_000,
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tb_log_name: Optional[str] = None,
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progress_bar: bool = False,
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) -> Dict[str, Any]:
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"""
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Train the RL agent.
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Parameters
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----------
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env : gym.Env
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Trading environment
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total_timesteps : int
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Number of training timesteps
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tb_log_name : str, optional
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TensorBoard log name
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progress_bar : bool
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Show progress bar during training
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Returns
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-------
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dict
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Training metadata
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"""
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if self.model is None:
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self.create_model(env)
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if self.model is None:
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raise RuntimeError("Model creation failed unexpectedly")
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self.model.learn(
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total_timesteps=total_timesteps,
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tb_log_name=tb_log_name,
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progress_bar=progress_bar,
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)
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self.is_trained = True
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return {
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"algorithm": self.algorithm,
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"policy": self.policy,
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"total_timesteps": total_timesteps,
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"is_trained": True,
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}
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def predict(
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self,
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observation: np.ndarray,
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deterministic: bool = True,
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) -> np.ndarray:
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"""
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Predict action from observation.
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Parameters
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----------
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observation : np.ndarray
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Current state observation vector
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deterministic : bool
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Use deterministic action (recommended for inference)
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Returns
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-------
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np.ndarray
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Action to take
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"""
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if self.model is None:
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raise ValueError("Model not trained or loaded. Call train() or load() first.")
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action, _ = self.model.predict(observation, deterministic=deterministic)
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return np.asarray(action)
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def save(self, path: Union[str, Path]) -> None:
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"""Save trained model to disk.
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Parameters
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----------
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path : str or Path
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Destination file path (e.g. "models/ppo_trader.zip")
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"""
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if self.model is None:
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raise ValueError("No model to save. Train first.")
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path = Path(path)
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path.parent.mkdir(parents=True, exist_ok=True)
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self.model.save(str(path))
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def load(self, path: Union[str, Path]) -> None:
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"""Load a trained model from disk.
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Parameters
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----------
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path : str or Path
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Source file path (e.g. "models/ppo_trader.zip")
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"""
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model_class = self._get_model_class()
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self.model = model_class.load(str(path))
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self.is_trained = True
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def evaluate(
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self,
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env: Any,
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n_episodes: int = 10,
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deterministic: bool = True,
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) -> Dict[str, float]:
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"""
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Evaluate agent performance over multiple episodes.
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Parameters
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----------
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env : gym.Env
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Trading environment for evaluation
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n_episodes : int
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Number of evaluation episodes
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deterministic : bool
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Use deterministic actions during evaluation
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Returns
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-------
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dict
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Evaluation metrics (mean/std of rewards and returns)
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"""
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if self.model is None:
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raise ValueError("Model not trained or loaded.")
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rewards: list[float] = []
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returns: list[float] = []
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for _ in range(n_episodes):
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obs, _ = env.reset()
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episode_reward = 0.0
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done = False
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info: Dict[str, Any] = {}
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while not done:
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action = self.predict(obs, deterministic=deterministic)
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obs, reward, terminated, truncated, info = env.step(action)
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episode_reward += float(reward)
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done = terminated or truncated
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rewards.append(episode_reward)
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returns.append(float(info.get("return", 0.0)))
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return {
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"mean_reward": float(np.mean(rewards)),
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"std_reward": float(np.std(rewards)),
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"mean_return": float(np.mean(returns)),
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"std_return": float(np.std(returns)),
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"n_episodes": n_episodes,
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}
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@@ -1,6 +1,17 @@
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"""RL CoSTEER - Code generation component for RL post-training"""
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"""RL CoSTEER - Code generation and RL trading controller for post-training.
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from typing import Generator
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This module provides two main components:
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1. RLCoSTEER: LLM-based code generation for RL training pipelines
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2. RLCosteer: RL-based trading controller that uses trained models
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to make trading decisions based on market state.
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"""
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from datetime import datetime
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from pathlib import Path
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from typing import Any, Dict, Generator, List, Optional
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import numpy as np
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import pandas as pd
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from rdagent.components.coder.CoSTEER import CoSTEER
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from rdagent.components.coder.CoSTEER.config import CoSTEERSettings
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@@ -55,14 +66,14 @@ class RLEvolvingStrategy(EvolvingStrategy):
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"""Generate RL training code using LLM."""
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from rdagent.app.rl.conf import RL_RD_SETTING
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# 获取上轮反馈
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# Get feedback from previous round
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feedback = None
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if evolving_trace:
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last_step = evolving_trace[-1]
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if hasattr(last_step, "feedback") and last_step.feedback:
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feedback = str(last_step.feedback)
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# 构造 prompt
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# Construct prompt
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system_prompt = T(".prompts:rl_coder.system").r()
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user_prompt = T(".prompts:rl_coder.user").r(
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task_description=task.description if hasattr(task, "description") else str(task),
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@@ -72,7 +83,7 @@ class RLEvolvingStrategy(EvolvingStrategy):
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feedback=feedback,
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)
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# 调用 LLM
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# Call LLM
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session = APIBackend().build_chat_session(session_system_prompt=system_prompt)
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code = session.build_chat_completion(
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user_prompt=user_prompt,
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@@ -109,7 +120,7 @@ class RLCoderEvaluator:
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queried_knowledge: CoSTEERQueriedKnowledge | None = None,
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) -> CoSTEERSingleFeedback:
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"""Evaluate RL code. Currently returns mock success."""
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# TODO: 实现真正的评估逻辑
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# TODO: Implement proper evaluation logic
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return CoSTEERSingleFeedback(
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execution="Mock: executed successfully",
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return_checking=None,
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@@ -138,3 +149,286 @@ class RLCoSTEER(CoSTEER):
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knowledge_self_gen=False,
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**kwargs,
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)
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# =============================================================================
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# RL Trading Controller (RLCosteer)
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# =============================================================================
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class RLCosteer:
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"""
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RL-based trading controller.
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Takes market data, technical indicators, and portfolio state,
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then uses a trained RL model to decide position sizing.
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Parameters
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----------
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model_path : Path, optional
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Path to a trained RL model file
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algorithm : str
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RL algorithm used ("PPO", "A2C", "SAC")
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window_size : int
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Lookback window for observations
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max_position : float
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Maximum position size (0 to 1)
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risk_limit : float
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Maximum drawdown before forcing position close
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Examples
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--------
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>>> costeer = RLCosteer(model_path=Path("models/ppo_trader.zip"))
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>>> costeer.initialize(prices, indicators, initial_equity=100000)
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>>> trade = costeer.step(current_equity=101000, cash=50000, position=0.0)
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"""
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def __init__(
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self,
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model_path: Optional[Path] = None,
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algorithm: str = "PPO",
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window_size: int = 60,
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max_position: float = 1.0,
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risk_limit: float = 0.15,
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) -> None:
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self.model_path = model_path
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self.algorithm = algorithm.upper()
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self.window_size = window_size
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self.max_position = max_position
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self.risk_limit = risk_limit
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# State
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self.is_active = False
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self.model: Optional[Any] = None
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self.current_position: float = 0.0
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self.peak_equity: float = 0.0
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self.trade_history: List[Dict[str, Any]] = []
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# Market data (set during initialize)
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self.prices: np.ndarray = np.array([])
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self.indicators: Optional[np.ndarray] = None
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self.initial_equity: float = 0.0
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self.current_step: int = 0
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# Load model if path provided
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if model_path is not None and model_path.exists():
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self.load_model(model_path)
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def initialize(
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self,
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prices: pd.Series,
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indicators: Optional[pd.DataFrame] = None,
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initial_equity: float = 100000.0,
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) -> None:
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"""Initialize costeer with market data.
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Parameters
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----------
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prices : pd.Series
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Price time series
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indicators : pd.DataFrame, optional
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Technical indicator DataFrame
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initial_equity : float
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Starting equity
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"""
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self.prices = prices.values.astype(np.float64)
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self.indicators = indicators.values.astype(np.float32) if indicators is not None else None
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self.initial_equity = initial_equity
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self.current_step = self.window_size
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self.is_active = True
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self.peak_equity = initial_equity
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def get_action(
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self,
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current_equity: float,
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cash: float,
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position: float,
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) -> float:
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"""
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Get trading action from RL model.
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Parameters
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----------
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current_equity : float
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Current portfolio equity
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cash : float
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Available cash
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position : float
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Current position size
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Returns
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-------
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float
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Target position (-1 to 1)
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"""
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if not self.is_active or self.model is None:
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return 0.0 # Hold if not active
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# Build observation
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observation = self._build_observation(current_equity, cash, position)
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# Get action from model
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prediction = self.model.predict(observation)
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target_position = float(np.asarray(prediction[0]).flatten()[0])
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# Apply risk limits
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drawdown = (self.peak_equity - current_equity) / self.peak_equity if self.peak_equity > 0 else 0.0
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if drawdown > self.risk_limit:
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return 0.0 # Force close position if risk limit exceeded
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# Scale by risk appetite
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risk_multiplier = 1.0 - (drawdown / self.risk_limit)
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target_position *= risk_multiplier * self.max_position
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return float(np.clip(target_position, -self.max_position, self.max_position))
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def _build_observation(
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self,
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current_equity: float,
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cash: float,
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position: float,
|
||||
) -> np.ndarray:
|
||||
"""Build observation vector for RL model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
current_equity : float
|
||||
Current portfolio equity
|
||||
cash : float
|
||||
Available cash
|
||||
position : float
|
||||
Current position size
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.ndarray
|
||||
Observation vector
|
||||
"""
|
||||
start = max(0, self.current_step - self.window_size)
|
||||
end = self.current_step
|
||||
|
||||
# Price window
|
||||
price_window = self.prices[start:end]
|
||||
if len(price_window) > 0 and price_window[0] != 0:
|
||||
price_norm = price_window / price_window[0] - 1.0
|
||||
else:
|
||||
price_norm = np.zeros(len(price_window))
|
||||
|
||||
# Pad if needed
|
||||
if len(price_norm) < self.window_size:
|
||||
padded = np.zeros(self.window_size)
|
||||
padded[-len(price_norm):] = price_norm
|
||||
price_norm = padded
|
||||
|
||||
# Indicators window
|
||||
if self.indicators is not None:
|
||||
indicators_window = self.indicators[start:end]
|
||||
if len(indicators_window) < self.window_size:
|
||||
padded = np.zeros((self.window_size, self.indicators.shape[1]), dtype=np.float32)
|
||||
padded[-len(indicators_window):] = indicators_window
|
||||
indicators_window = padded
|
||||
else:
|
||||
indicators_window = np.zeros((self.window_size, 0), dtype=np.float32)
|
||||
|
||||
# Portfolio state features
|
||||
pnl = 0.0
|
||||
if position != 0 and self.current_step >= 2:
|
||||
prev_price = float(self.prices[self.current_step - 2])
|
||||
curr_price = float(self.prices[self.current_step - 1])
|
||||
if prev_price != 0:
|
||||
pnl = (curr_price - prev_price) / prev_price * np.sign(position)
|
||||
|
||||
# Equity ratio
|
||||
equity_ratio = current_equity / self.initial_equity if self.initial_equity > 0 else 1.0
|
||||
|
||||
observation = np.concatenate(
|
||||
[
|
||||
price_norm.astype(np.float32),
|
||||
indicators_window.flatten(),
|
||||
np.array([position, pnl, equity_ratio], dtype=np.float32),
|
||||
]
|
||||
)
|
||||
|
||||
return observation.astype(np.float32)
|
||||
|
||||
def step(
|
||||
self,
|
||||
current_equity: float,
|
||||
cash: float,
|
||||
position: float,
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
Execute one trading step.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
current_equity : float
|
||||
Current portfolio equity
|
||||
cash : float
|
||||
Available cash
|
||||
position : float
|
||||
Current position size
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict
|
||||
Step information including action taken
|
||||
"""
|
||||
# Get action
|
||||
target_position = self.get_action(current_equity, cash, position)
|
||||
|
||||
# Record trade
|
||||
trade = {
|
||||
"timestamp": datetime.now(),
|
||||
"step": self.current_step,
|
||||
"equity": current_equity,
|
||||
"position": position,
|
||||
"target_position": target_position,
|
||||
"action": target_position - position,
|
||||
}
|
||||
self.trade_history.append(trade)
|
||||
|
||||
# Update peak equity
|
||||
if current_equity > self.peak_equity:
|
||||
self.peak_equity = current_equity
|
||||
|
||||
# Move to next step
|
||||
self.current_step += 1
|
||||
|
||||
return trade
|
||||
|
||||
def load_model(self, path: Path) -> None:
|
||||
"""Load trained RL model.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
path : Path
|
||||
Path to the saved model file
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If model loading fails
|
||||
"""
|
||||
try:
|
||||
from stable_baselines3 import A2C, PPO, SAC
|
||||
|
||||
model_class = {"PPO": PPO, "A2C": A2C, "SAC": SAC}[self.algorithm]
|
||||
self.model = model_class.load(str(path))
|
||||
self.is_active = True
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"stable-baselines3 is required. Install with: pip install stable-baselines3"
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Failed to load model: {e}")
|
||||
|
||||
def get_performance(self) -> pd.DataFrame:
|
||||
"""Get trading performance history.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame
|
||||
Trade history as DataFrame
|
||||
"""
|
||||
return pd.DataFrame(self.trade_history)
|
||||
|
||||
@@ -0,0 +1,340 @@
|
||||
"""
|
||||
Trading Environment for RL Agents.
|
||||
|
||||
Gym-compatible environment for training RL trading agents.
|
||||
Supports single-asset (EUR/USD) trading with technical indicators
|
||||
and portfolio state as observations.
|
||||
|
||||
Inspired by common RL trading environment patterns, implemented from scratch for Predix.
|
||||
"""
|
||||
|
||||
import gymnasium as gym
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
|
||||
@dataclass
|
||||
class TradingState:
|
||||
"""Current state of the trading environment."""
|
||||
|
||||
position: float = 0.0 # Current position (-1 to 1 for short/long)
|
||||
cash: float = 100000.0 # Available cash
|
||||
equity: float = 100000.0 # Total equity (cash + position value)
|
||||
entry_price: float = 0.0 # Entry price of current position
|
||||
step: int = 0 # Current time step
|
||||
holdings_history: List[float] = field(default_factory=list) # Historical holdings
|
||||
|
||||
|
||||
class TradingEnv(gym.Env):
|
||||
"""
|
||||
Trading environment for RL agents.
|
||||
|
||||
State: price history, technical indicators, portfolio state
|
||||
Action: position size (-1 to 1, short to long)
|
||||
Reward: risk-adjusted return with transaction costs
|
||||
|
||||
Parameters
|
||||
----------
|
||||
prices : np.ndarray
|
||||
Array of asset prices (1D)
|
||||
indicators : np.ndarray, optional
|
||||
Array of technical indicators (n_steps x n_features)
|
||||
initial_balance : float
|
||||
Starting cash balance
|
||||
transaction_cost : float
|
||||
Cost per unit of position change (e.g. 0.0001 = 1 basis point)
|
||||
window_size : int
|
||||
Lookback window for observations
|
||||
max_steps : int
|
||||
Maximum steps per episode
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> prices = np.random.randn(1000) + 100
|
||||
>>> env = TradingEnv(prices, window_size=30)
|
||||
>>> obs, info = env.reset()
|
||||
>>> action = np.array([0.5])
|
||||
>>> obs, reward, terminated, truncated, info = env.step(action)
|
||||
"""
|
||||
|
||||
metadata = {"render_modes": ["human"]}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
prices: np.ndarray,
|
||||
indicators: Optional[np.ndarray] = None,
|
||||
initial_balance: float = 100000.0,
|
||||
transaction_cost: float = 0.0001,
|
||||
window_size: int = 60,
|
||||
max_steps: int = 10000,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.prices = np.asarray(prices, dtype=np.float64)
|
||||
self.indicators = np.asarray(indicators, dtype=np.float32) if indicators is not None else None
|
||||
self.initial_balance = initial_balance
|
||||
self.transaction_cost = transaction_cost
|
||||
self.window_size = window_size
|
||||
self.max_steps = max_steps
|
||||
|
||||
# Environment state (reset each episode)
|
||||
self.current_step: int = 0
|
||||
self.balance: float = initial_balance
|
||||
self.position: float = 0.0
|
||||
self.entry_price: float = 0.0
|
||||
self.equity_history: List[float] = [initial_balance]
|
||||
self.trades: List[Dict] = []
|
||||
|
||||
# Observation space: window_size x (1 + n_indicators + 3)
|
||||
n_indicators = self.indicators.shape[1] if self.indicators is not None else 0
|
||||
obs_dim = window_size * (1 + n_indicators + 3) # +3 for position, pnl, step
|
||||
self.observation_space = gym.spaces.Box(
|
||||
low=-np.inf, high=np.inf, shape=(obs_dim,), dtype=np.float32
|
||||
)
|
||||
|
||||
# Action space: continuous position from -1 (short) to 1 (long)
|
||||
self.action_space = gym.spaces.Box(low=-1.0, high=1.0, shape=(1,), dtype=np.float32)
|
||||
|
||||
# ------------------------------------------------------------------ #
|
||||
# Observation
|
||||
# ------------------------------------------------------------------ #
|
||||
|
||||
def _get_observation(self) -> np.ndarray:
|
||||
"""Build observation vector from current state.
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.ndarray
|
||||
Flattened observation vector of shape (window_size * (1 + n_ind + 3),)
|
||||
"""
|
||||
start_idx = self.current_step
|
||||
end_idx = start_idx + self.window_size
|
||||
|
||||
# Price window
|
||||
price_window = self.prices[start_idx:end_idx]
|
||||
|
||||
# Normalize prices relative to first value
|
||||
if len(price_window) == self.window_size and price_window[0] != 0:
|
||||
price_norm = price_window / price_window[0] - 1.0
|
||||
else:
|
||||
price_norm = np.zeros(self.window_size)
|
||||
if len(price_window) > 0 and price_window[0] != 0:
|
||||
valid_len = len(price_window)
|
||||
price_norm[-valid_len:] = price_window / price_window[0] - 1.0
|
||||
|
||||
# Indicators window
|
||||
if self.indicators is not None:
|
||||
indicators_window = self.indicators[start_idx:end_idx]
|
||||
if len(indicators_window) < self.window_size:
|
||||
padded = np.zeros((self.window_size, self.indicators.shape[1]), dtype=np.float32)
|
||||
padded[-len(indicators_window):] = indicators_window
|
||||
indicators_window = padded
|
||||
else:
|
||||
indicators_window = np.zeros((self.window_size, 0), dtype=np.float32)
|
||||
|
||||
# Portfolio state features (repeated for each timestep in window)
|
||||
current_price = float(self.prices[min(self.current_step, len(self.prices) - 1)])
|
||||
position_feature = np.full(self.window_size, self.position, dtype=np.float32)
|
||||
|
||||
if self.entry_price > 0 and self.position != 0:
|
||||
pnl = (current_price - self.entry_price) / self.entry_price * np.sign(self.position)
|
||||
else:
|
||||
pnl = 0.0
|
||||
pnl_feature = np.full(self.window_size, pnl, dtype=np.float32)
|
||||
|
||||
step_feature = np.full(self.window_size, self.current_step / max(self.max_steps, 1), dtype=np.float32)
|
||||
|
||||
# Combine all features
|
||||
observation = np.column_stack(
|
||||
[
|
||||
price_norm.astype(np.float32),
|
||||
indicators_window,
|
||||
position_feature.reshape(-1, 1),
|
||||
pnl_feature.reshape(-1, 1),
|
||||
step_feature.reshape(-1, 1),
|
||||
]
|
||||
)
|
||||
|
||||
return observation.flatten().astype(np.float32)
|
||||
|
||||
# ------------------------------------------------------------------ #
|
||||
# Reward
|
||||
# ------------------------------------------------------------------ #
|
||||
|
||||
def _calculate_reward(self, new_equity: float, old_equity: float) -> float:
|
||||
"""Calculate risk-adjusted reward with penalties.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
new_equity : float
|
||||
Equity after the step
|
||||
old_equity : float
|
||||
Equity before the step
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
Reward value
|
||||
"""
|
||||
# Simple return
|
||||
if old_equity > 0:
|
||||
simple_return = (new_equity - old_equity) / old_equity
|
||||
else:
|
||||
simple_return = 0.0
|
||||
|
||||
# Transaction cost penalty
|
||||
position_change = abs(self.position) if len(self.trades) > 0 else 0.0
|
||||
cost_penalty = -position_change * self.transaction_cost
|
||||
|
||||
# Drawdown penalty
|
||||
if len(self.equity_history) > 1:
|
||||
max_equity = max(self.equity_history)
|
||||
if max_equity > 0:
|
||||
drawdown = (max_equity - new_equity) / max_equity
|
||||
else:
|
||||
drawdown = 0.0
|
||||
drawdown_penalty = -drawdown * 2.0 # Heavy penalty for drawdowns
|
||||
else:
|
||||
drawdown_penalty = 0.0
|
||||
|
||||
return float(simple_return + cost_penalty + drawdown_penalty)
|
||||
|
||||
# ------------------------------------------------------------------ #
|
||||
# Gym API: reset / step
|
||||
# ------------------------------------------------------------------ #
|
||||
|
||||
def reset(
|
||||
self, seed: Optional[int] = None, options: Optional[dict] = None
|
||||
) -> Tuple[np.ndarray, dict]:
|
||||
"""Reset environment to initial state.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
seed : int, optional
|
||||
Random seed for reproducibility
|
||||
options : dict, optional
|
||||
Additional reset options (unused)
|
||||
|
||||
Returns
|
||||
-------
|
||||
observation : np.ndarray
|
||||
Initial observation
|
||||
info : dict
|
||||
Additional info
|
||||
"""
|
||||
super().reset(seed=seed)
|
||||
|
||||
self.current_step = 0
|
||||
self.balance = self.initial_balance
|
||||
self.position = 0.0
|
||||
self.entry_price = 0.0
|
||||
self.equity_history = [self.initial_balance]
|
||||
self.trades = []
|
||||
|
||||
return self._get_observation(), {}
|
||||
|
||||
def step(
|
||||
self, action: np.ndarray
|
||||
) -> Tuple[np.ndarray, float, bool, bool, dict]:
|
||||
"""Execute one time step in the environment.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
action : np.ndarray
|
||||
Target position size in [-1, 1]
|
||||
|
||||
Returns
|
||||
-------
|
||||
observation : np.ndarray
|
||||
Next observation
|
||||
reward : float
|
||||
Step reward
|
||||
terminated : bool
|
||||
Whether episode ended due to terminal condition
|
||||
truncated : bool
|
||||
Whether episode ended due to time limit
|
||||
info : dict
|
||||
Additional info
|
||||
"""
|
||||
current_price = float(self.prices[min(self.current_step, len(self.prices) - 1)])
|
||||
old_equity = self.balance + self.position * current_price
|
||||
|
||||
# Execute action
|
||||
target_position = float(np.clip(action[0], -1.0, 1.0))
|
||||
|
||||
# Calculate transaction costs when position changes significantly
|
||||
if abs(target_position - self.position) > 0.01:
|
||||
cost = (
|
||||
abs(target_position - self.position)
|
||||
* current_price
|
||||
* self.transaction_cost
|
||||
)
|
||||
self.balance -= cost
|
||||
self.trades.append(
|
||||
{"step": self.current_step, "action": target_position, "cost": cost}
|
||||
)
|
||||
|
||||
# Update position
|
||||
self.position = target_position
|
||||
|
||||
if abs(self.position) > 0.01:
|
||||
self.entry_price = current_price
|
||||
|
||||
# Advance time
|
||||
self.current_step += 1
|
||||
|
||||
# Calculate new equity
|
||||
if self.current_step < len(self.prices):
|
||||
next_price = float(self.prices[self.current_step])
|
||||
new_equity = self.balance + self.position * next_price
|
||||
else:
|
||||
new_equity = self.balance
|
||||
|
||||
self.equity_history.append(new_equity)
|
||||
|
||||
# Reward
|
||||
reward = self._calculate_reward(new_equity, old_equity)
|
||||
|
||||
# Termination conditions
|
||||
terminated = new_equity < self.initial_balance * 0.5 # Liquidation
|
||||
truncated = self.current_step >= min(self.max_steps, len(self.prices) - 1)
|
||||
|
||||
observation = self._get_observation()
|
||||
|
||||
info = {
|
||||
"equity": new_equity,
|
||||
"balance": self.balance,
|
||||
"position": self.position,
|
||||
"trades_count": len(self.trades),
|
||||
"return": (new_equity - self.initial_balance) / self.initial_balance
|
||||
if self.initial_balance > 0
|
||||
else 0.0,
|
||||
}
|
||||
|
||||
return observation, reward, terminated, truncated, info
|
||||
|
||||
# ------------------------------------------------------------------ #
|
||||
# Utility
|
||||
# ------------------------------------------------------------------ #
|
||||
|
||||
def get_equity_curve(self) -> np.ndarray:
|
||||
"""Return equity curve for the current episode.
|
||||
|
||||
Returns
|
||||
-------
|
||||
np.ndarray
|
||||
Equity values over time
|
||||
"""
|
||||
return np.array(self.equity_history, dtype=np.float64)
|
||||
|
||||
def get_trade_log(self) -> List[Dict]:
|
||||
"""Return trade log for the current episode.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list[dict]
|
||||
List of trade records
|
||||
"""
|
||||
return list(self.trades)
|
||||
@@ -0,0 +1,242 @@
|
||||
"""
|
||||
Technical Indicators for RL Trading.
|
||||
|
||||
Common technical indicators used as features for RL agents.
|
||||
All functions operate on pandas Series/DataFrames and return the same.
|
||||
"""
|
||||
|
||||
from typing import List, Optional
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def calculate_rsi(prices: pd.Series, period: int = 14) -> pd.Series:
|
||||
"""
|
||||
Relative Strength Index (RSI).
|
||||
|
||||
Momentum oscillator measuring speed and change of price movements.
|
||||
Values range from 0 to 100. Above 70 = overbought, below 30 = oversold.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
prices : pd.Series
|
||||
Close price series
|
||||
period : int
|
||||
RSI calculation period
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.Series
|
||||
RSI values
|
||||
"""
|
||||
delta = prices.diff()
|
||||
gain = delta.where(delta > 0, 0.0)
|
||||
loss = -delta.where(delta < 0, 0.0)
|
||||
|
||||
avg_gain = gain.rolling(window=period, min_periods=period).mean()
|
||||
avg_loss = loss.rolling(window=period, min_periods=period).mean()
|
||||
|
||||
rs = avg_gain / avg_loss
|
||||
return 100.0 - (100.0 / (1.0 + rs))
|
||||
|
||||
|
||||
def calculate_macd(
|
||||
prices: pd.Series,
|
||||
fast: int = 12,
|
||||
slow: int = 26,
|
||||
signal: int = 9,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Moving Average Convergence Divergence (MACD).
|
||||
|
||||
Trend-following momentum indicator showing the relationship between
|
||||
two exponential moving averages.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
prices : pd.Series
|
||||
Close price series
|
||||
fast : int
|
||||
Fast EMA period
|
||||
slow : int
|
||||
Slow EMA period
|
||||
signal : int
|
||||
Signal line EMA period
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame
|
||||
DataFrame with columns: macd, signal, histogram
|
||||
"""
|
||||
ema_fast = prices.ewm(span=fast, adjust=False).mean()
|
||||
ema_slow = prices.ewm(span=slow, adjust=False).mean()
|
||||
|
||||
macd_line = ema_fast - ema_slow
|
||||
signal_line = macd_line.ewm(span=signal, adjust=False).mean()
|
||||
histogram = macd_line - signal_line
|
||||
|
||||
return pd.DataFrame(
|
||||
{"macd": macd_line, "signal": signal_line, "histogram": histogram}
|
||||
)
|
||||
|
||||
|
||||
def calculate_bollinger_bands(
|
||||
prices: pd.Series,
|
||||
period: int = 20,
|
||||
std_dev: float = 2.0,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Bollinger Bands.
|
||||
|
||||
Volatility bands placed above and below a moving average.
|
||||
Band width expands/contracts with volatility.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
prices : pd.Series
|
||||
Close price series
|
||||
period : int
|
||||
Moving average period
|
||||
std_dev : float
|
||||
Number of standard deviations for bands
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame
|
||||
DataFrame with columns: upper, middle, lower
|
||||
"""
|
||||
sma = prices.rolling(window=period).mean()
|
||||
std = prices.rolling(window=period).std()
|
||||
|
||||
upper = sma + (std * std_dev)
|
||||
lower = sma - (std * std_dev)
|
||||
|
||||
return pd.DataFrame({"upper": upper, "middle": sma, "lower": lower})
|
||||
|
||||
|
||||
def calculate_cci(prices: pd.Series, high: pd.Series, low: pd.Series, period: int = 20) -> pd.Series:
|
||||
"""
|
||||
Commodity Channel Index (CCI).
|
||||
|
||||
Momentum-based oscillator used to determine when an asset is
|
||||
overbought or oversold.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
prices : pd.Series
|
||||
Close price series
|
||||
high : pd.Series
|
||||
High price series
|
||||
low : pd.Series
|
||||
Low price series
|
||||
period : int
|
||||
CCI calculation period
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.Series
|
||||
CCI values
|
||||
"""
|
||||
typical_price = (high + low + prices) / 3.0
|
||||
sma_tp = typical_price.rolling(window=period).mean()
|
||||
mad = typical_price.rolling(window=period).apply(
|
||||
lambda x: np.abs(x - x.mean()).mean(), raw=False
|
||||
)
|
||||
|
||||
cci = (typical_price - sma_tp) / (0.015 * mad)
|
||||
return cci
|
||||
|
||||
|
||||
def calculate_atr(high: pd.Series, low: pd.Series, close: pd.Series, period: int = 14) -> pd.Series:
|
||||
"""
|
||||
Average True Range (ATR).
|
||||
|
||||
Volatility indicator measuring market volatility.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
high : pd.Series
|
||||
High price series
|
||||
low : pd.Series
|
||||
Low price series
|
||||
close : pd.Series
|
||||
Close price series
|
||||
period : int
|
||||
ATR calculation period
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.Series
|
||||
ATR values
|
||||
"""
|
||||
prev_close = close.shift(1)
|
||||
|
||||
tr1 = high - low
|
||||
tr2 = (high - prev_close).abs()
|
||||
tr3 = (low - prev_close).abs()
|
||||
|
||||
true_range = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
|
||||
return true_range.rolling(window=period).mean()
|
||||
|
||||
|
||||
def prepare_features(
|
||||
prices: pd.DataFrame,
|
||||
indicator_list: Optional[List[str]] = None,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Prepare features for RL agent from price data.
|
||||
|
||||
Calculates requested technical indicators and concatenates
|
||||
them into a single features DataFrame.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
prices : pd.DataFrame
|
||||
Price data with at least 'close' column.
|
||||
Optionally: 'high', 'low', 'volume'
|
||||
indicator_list : list, optional
|
||||
List of indicator names to calculate.
|
||||
Default: ['rsi', 'macd', 'bollinger', 'sma']
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame
|
||||
Features DataFrame with original prices + indicators.
|
||||
NaN values are filled with 0.
|
||||
|
||||
Examples
|
||||
--------
|
||||
>>> df = pd.DataFrame({'close': [100, 101, 102, ...]})
|
||||
>>> features = prepare_features(df, ['rsi', 'macd'])
|
||||
"""
|
||||
if indicator_list is None:
|
||||
indicator_list = ["rsi", "macd", "bollinger", "sma"]
|
||||
|
||||
features = prices.copy()
|
||||
|
||||
if "rsi" in indicator_list:
|
||||
features["rsi"] = calculate_rsi(prices["close"])
|
||||
|
||||
if "macd" in indicator_list:
|
||||
macd_df = calculate_macd(prices["close"])
|
||||
features = pd.concat([features, macd_df], axis=1)
|
||||
|
||||
if "bollinger" in indicator_list:
|
||||
bb_df = calculate_bollinger_bands(prices["close"])
|
||||
features = pd.concat([features, bb_df], axis=1)
|
||||
|
||||
if "sma" in indicator_list:
|
||||
features["sma_20"] = prices["close"].rolling(window=20).mean()
|
||||
features["sma_50"] = prices["close"].rolling(window=50).mean()
|
||||
|
||||
if "cci" in indicator_list and "high" in prices.columns and "low" in prices.columns:
|
||||
features["cci"] = calculate_cci(prices["close"], prices["high"], prices["low"])
|
||||
|
||||
if "atr" in indicator_list and "high" in prices.columns and "low" in prices.columns:
|
||||
features["atr"] = calculate_atr(prices["high"], prices["low"], prices["close"])
|
||||
|
||||
# Fill NaN values (from rolling calculations) with 0
|
||||
features = features.fillna(0.0)
|
||||
|
||||
return features
|
||||
Reference in New Issue
Block a user