| library_name: stable-baselines3 | |
| tags: | |
| - reinforcement-learning | |
| - finrl | |
| - ppo | |
| - stock-trading | |
| # FinRL PPO Agent (Quick Demo, 2000 steps) | |
| Trained on DOW 30 stocks (2014-2025) using FinRL + Stable-Baselines3 PPO. | |
| ⚠️ **Toy model — only 2000 timesteps**, used to validate training pipeline. | |
| Not for real trading. | |
| ## Usage | |
| ```python | |
| from huggingface_hub import hf_hub_download | |
| from stable_baselines3 import PPO | |
| path = hf_hub_download( | |
| repo_id="2045max/finrl-ppo-dow30-quick", | |
| filename="agent_ppo.zip" | |
| ) | |
| model = PPO.load(path) | |
| ``` | |
| ## Training Setup | |
| - Algorithm: PPO (Proximal Policy Optimization) | |
| - Total timesteps: 2,000 | |
| - State space: 301 (cash + 30 prices + 30 holdings + 30×8 indicators) | |
| - Action space: 30 (continuous, [-1, 1] per stock) | |
| - Reward: portfolio value change × 1e-4 | |
| ## Source | |
| https://github.com/AI4Finance-Foundation/FinRL | |