quant-drl-core 0.1.4

Deep Reinforcement Learning for Financial Portfolio Management

📈 quant-drl-core

quant-drl-core is a research-focused framework for applying Deep Reinforcement Learning (DRL) to portfolio management and financial decision-making.
It is designed to be modular, extensible, and easy to integrate into experimentation pipelines or production systems.

🎓 This project was developed as part of my Bachelor's Thesis (TFG) for the Double Degree in Mathematics and Computer Engineering at the University of Seville.

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🧠 Key Features

• ✅ Modular training pipeline for multiple DRL algorithms: PPO, SAC, DDPG, TD3
• ✅ Custom Gymnasium-compatible environments for realistic financial simulations
• ✅ Support for CNN, LSTM, Transformer and hybrid feature extractors
• ✅ Integrated technical indicators and raw market data handling
• ✅ Easy portfolio configuration (by sector and number of companies)
• ✅ Clean logging with loguru
• ✅ Compatible with interactive dashboards for visualization (e.g., Streamlit webapp)

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🛠️ Getting Started

🔧 Installation

git clone https://github.com/your-username/quant-drl-core.git
cd quant-drl-core
python -m venv venv
source venv/bin/activate         # Windows: venv\Scripts\activate
pip install -e .

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🧪 Training models

Use the train_models.py script to train models.

Basic command (default config)

python train_models.py

If you want to update the hierarchy.json automatically. Add the --update_hierarchy flag:

python train_models.py --update_hierarchy

Custom training with options:

python train_models.py \
  --algorithms PPO SAC \
  --features LSTM CNNLSTM \
  --learning_rates 0.0003 0.0001 \
  --n_companies 10 18 \
  --length_train_data 8 \
  --length_eval_data 2 \
  --end_date_year 2021 \
  --total_timesteps 1_000_000 \
  --checkpoint_freq 100_000 \
  --update_hierarchy

Models and logs will be saved in:

• 📁 models/ – trained model files
• 📁 logs/ – experiment logs
• 📄 models/metadata/hierarchy.json – metadata for evaluation

Optional (Open Tensorboard)

tensorboard --logdir=logs/

Tensorboard framework will be available at http://localhost:16006

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📊 Evaluating models

Use the evaluate_models.py script to evaluate trained models.

• Option 1: Evaluate final models

python evaluate_models.py \
  --use_final_model

• Option 2: Evaluate by specific checkpoint steps

python evaluate_models.py \
  --steps_override PPO=500000 SAC=300000

• Option 3: Full customized evaluation

python evaluate_models.py \
  --hierarchy_path models/metadata/hierarchy.json \
  --results_path results/eval_sac_vs_ppo.csv \
  --models_dir models \
  --out_dir models/metadata/out \
  --steps_override PPO=500000 SAC=300000 \
  --no_move

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📚 Notebooks

Inside notebooks/ you’ll find:

• 📈 Data exploration
• 📊 Testing models
• 📝 Exporting results to LaTeX

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🔮 Planned Features

• Risk-adjusted rewards (Sharpe, Sortino, etc.)
• Hyperparameter optimization with Optuna
• Integration with MLFlow
• Multi-agent extensions
• Integration with a live dashboard (Streamlit) (quant-drl-web)

📝 License

This project is licensed under the MIT License.
See the LICENSE file for details.

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🙋‍♂️ Author

Made with ❤️ by Pablo Diego Acosta

• 💼 LinkedIn: linkedin.com/in/pablodiegoacosta

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🤝 Contributing

Whether you're a researcher, developer, or enthusiast — feel free to contribute!
You're welcome to suggest improvements, propose new architectures, experiment with custom reward functions, or integrate new DRL algorithms.

✨ Open a pull request, start a discussion, or simply share your ideas.

Together, we can make this framework even more powerful for the DRL + Finance community 🚀

📚 References

This project is based on a wide range of research articles, theses, technical reports, and open-source implementations in the field of reinforcement learning and portfolio management.

Some of the most relevant references include:

• Filos, A. (2019). Reinforcement Learning for Portfolio Management. arXiv:1909.09571
• Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059
• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
• Haarnoja, T. et al. (2018). Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning. arXiv:1801.01290
• Raffin, A. et al. (2021). Stable-Baselines3: Reliable RL Implementations. JMLR
• Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. Yale University Press.
• Open-source: Zenlii – Deep RL Portfolio Management

A complete list of all references (BibTeX format) used in this project is available here.