Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Learning to trade under the reinforcement learning framework
| Date | Stars |
|---|---|
| 2026-07-31 | 516 |
| 2026-08-02 | 516 |
| 2026-08-03 | 516 |
| 2026-08-04 | 516 |
| 2026-08-06 | 516 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
Trading Using Q-Learning ================== In this project, I will present an adaptive learning model to trade a single stock under the reinforcement learning framework. This area of machine learning consists in training an agent by reward and punishment without needing to specify the expected action. The agent learns from its experience and develops a strategy that maximizes its profits. This is my capstone project for the [Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009), from Udacity. You can check my report <a href="https://www.dropbox.com/s/1zjk2f3cph5ve5c/learning-trade-q.pdf?dl=0" target="_blank">here</a> and the notebook with the tests of the codes used in this project <a href="https://nbviewer.jupyter.org/github/ucaiado/QLearning_Trading/blob/master/learning_trade.ipynb" target="_blank">here</a>. The TEX file was produced with help of [Overleaf](https://www.overleaf.com/read/mmzwqfbrkdvf). ### Install This project requires **Python 2.7** and the following Python libraries installed: - [Matplotlib](http://matplotlib.org/) - [NumPy](http://www.numpy.org/) - [Pandas](http://pandas.pydata.org) - [Seaborn](https://web.stanford.edu/~mwaskom/software/seaborn/) - [Bintrees](https://pypi.python.org/pypi/bintrees/2.0.2) ### Run In a terminal or command window, navigate to the top-level project directory `QLearning_Trading/` (that contains this README) and run one of the following commands: ```python qtrader/agent.py <OPTION>``` ```python -m qtrader.agent <OPTION>``` Where *OPTION* could be *train_learner*, *test_learner*, *test_random*, *optimize_k* or *optimize_gamma*. The simulation will generate log files to be analyzed later on. Be aware that any of those commands take several minutes to finish. ### Reference 1. T.M. Mitchell. *Machine Learning*. McGraw-Hill International Editions, 1997. [*link*](http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/mlbook.html) 2. M. Mohri, A. Rostamizadeh, A. Talwalkar. *Foundations of Machine Learning*. 2012. [*link*](https://mitpress.mit.edu/books/foundations-machine-learning) 3. N.T. Chan, C.R. Shelton. *An Electronic Market-Maker*. 2001 [*link*](ftp://publications.ai.mit.edu/ai-publications/2001/AIM-2001-005.pdf) 4. N.T. Chan. *Artificial Markets and Intelligent Agents*. 2001 [*link*](http://cbcl.mit.edu/cbcl/publications/theses/thesis-chan.pdf) 5. R. Cont, k. Arseniy, and S. Sasha. *The price impact of order book events*. Journal of financial econometrics, 2014 [*link*](https://pdfs.semanticscholar.org/d064/5eb3d744f9e962ff09b8a5e9156f2147e983.pdf) 6. Du, Xin, Jinjian Zhai, and Koupin Lv. *Algorithm Trading using Q-Learning and Recurrent Reinforcement Learning*. [*link*](http://cs229.stanford.edu/proj2009/LvDuZhai.pdf) ### License The contents of this repository are covered under the [Apache 2.0 License](LICENSE.md).
Excerpt of 2,898 characters
Read on GitHub
98
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:13da5c7cc580d086, topic:reinforcement-learning, desc:reinforcement learning, readme:reinforcement learning