FinRock
Reinforcement Learning package for Finance
Environment Structure:
Install requirements:
pip install -r requirements.txt
pip install pygame
pip install .
Create sinusoid data:
python bin/create_sinusoid_data.py
Train RL (PPO) agent on discrete actions:
experiments/training_ppo_sinusoid.py
Test trained agent (Change path to the saved model):
experiments/testing_ppo_sinusoid.py
Environment Render:
Links to YouTube videos:
TODO:
- [ ] Train model on
continuous
actions (control allocation percentage)
- [ ] Add more indicators
- [ ] Add more metrics
- [ ] Add more reward functions
- [ ] Add more scalers
- [ ] Train RL agent on real data
- [ ] Add more RL algorithms
- [ ] Refactor rendering, maybe move to browser?