---
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