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