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---
library_name: stable-baselines3
tags:
- reinforcement-learning
- finance
- stock-trading
- deep-reinforcement-learning
- dqn
- ppo
- a2c
model-index:
- name: RL-Trading-Agents
results:
- task:
type: reinforcement-learning
name: Stock Trading
metrics:
- type: sharpe_ratio
value: Variable
- type: total_return
value: Variable
---
# 🤖 Multi-Agent Reinforcement Learning Trading System
This repository contains trained Deep Reinforcement Learning agents for automated stock trading. The agents were trained using `stable-baselines3` on a custom OpenAI Gym environment simulating the US Stock Market (AAPL, MSFT, GOOGL).
## 🧠 Models
The following algorithms were used:
1. **DQN (Deep Q-Network)**: Off-policy RL algorithm suitable for discrete action spaces.
2. **PPO (Proximal Policy Optimization)**: On-policy gradient method known for stability.
3. **A2C (Advantage Actor-Critic)**: Synchronous deterministic policy gradient method.
4. **Ensemble**: A meta-voter that takes the majority decision from the above three.
## 🏋️ Training Data
The models were trained on technical indicators derived from historical daily price data (2018-2024):
* **Returns**: Daily percentage change.
* **RSI (14)**: Relative Strength Index.
* **MACD**: Moving Average Convergence Divergence.
* **Bollinger Bands**: Volatility measure.
* **Volume Ratio**: Relative volume intensity.
* **Market Regime**: Bull/Bear trend classification.
## 🔗 Related Data
* **Dataset Repository**: [AdityaaXD/Multi-Agent_Reinforcement_Learning_Trading_System_Data](https://huggingface.co/AdityaaXD/Multi-Agent_Reinforcement_Learning_Trading_System_Data)
* **GitHub Repository**: [ADITYA-tp01/Multi-Agent-Reinforcement-Learning-Trading-System-Data](https://github.com/ADITYA-tp01/Multi-Agent-Reinforcement-Learning-Trading-System-Data)
## 🎮 Environment (`TradingEnv`)
* **Action Space**: Discrete(3) - `0: HOLD`, `1: BUY`, `2: SELL`.
* **Observation Space**: Box(10,) - Normalized technical features + portfolio state.
* **Reward**: Profit & Loss (PnL) minus transaction costs and drawdown penalties.
## 🚀 Usage
```python
import gymnasium as gym
from stable_baselines3 import PPO
# Load the environment (custom wrapper required)
# env = TradingEnv(df)
# Load model
model = PPO.load("ppo_AAPL.zip")
# Predict
action, _ = model.predict(obs, deterministic=True)
```
## 📈 Performance
Performance varies by ticker and market condition. See the generated `results/` CSVs for detailed Sharpe Ratios and Max Drawdown stats per agent.
## 🛠️ Credits
Developed by **Adityaraj Suman** as part of the Multi-Agent RL Trading System project.
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