TPTBusiness 1bbca062af 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

2026-04-03 13:26:10 +02:00

..

__init__.py

feat(rl): add AutoRL-Bench framework and benchmark integrations (#1348)

2026-03-17 15:49:30 +08:00

test_costeer.py

feat: Add RL Trading Agent system with 99 tests

2026-04-03 13:26:10 +02:00

test_indicators.py

feat: Add RL Trading Agent system with 99 tests

2026-04-03 13:26:10 +02:00

test_rl_agent.py

feat: Add RL Trading Agent system with 99 tests

2026-04-03 13:26:10 +02:00

test_rl_env.py

feat: Add RL Trading Agent system with 99 tests

2026-04-03 13:26:10 +02:00