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TPTBusiness
1bbca062af
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
35 lines
972 B
Python
35 lines
972 B
Python
"""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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