{ "info": { "author": "Hudson and Thames Quantitative Research", "author_email": "research@hudsonthames.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Developers", "Intended Audience :: Education", "Intended Audience :: Financial and Insurance Industry", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Office/Business :: Financial :: Investment", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence" ], "description": "
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\n\n-----------------\n# Machine Learning Financial Laboratory (mlfinlab)\n[![Build Status](https://travis-ci.com/hudson-and-thames/mlfinlab.svg?branch=master)](https://travis-ci.com/hudson-and-thames/mlfinlab)\n[![codecov](https://codecov.io/gh/hudson-and-thames/mlfinlab/branch/master/graph/badge.svg)](https://codecov.io/gh/hudson-and-thames/mlfinlab)\n![pylint Score](https://mperlet.github.io/pybadge/badges/10.svg)\n[![License: BSD3](https://img.shields.io/github/license/hudson-and-thames/mlfinlab.svg)](https://opensource.org/licenses/BSD-3-Clause)\n\n[![Documentation Status](https://readthedocs.org/projects/mlfinlab/badge/?version=latest)](https://mlfinlab.readthedocs.io/en/latest/?badge=latest)\n[![PyPi](https://img.shields.io/pypi/v/mlfinlab.svg)]((https://pypi.org/project/mlfinlab/))\n[![Downloads](https://img.shields.io/pypi/dm/mlfinlab.svg)]((https://pypi.org/project/mlfinlab/))\n[![Python](https://img.shields.io/pypi/pyversions/mlfinlab.svg)]((https://pypi.org/project/mlfinlab/))\n\nMLFinLab is an open source package based on the research of Dr Marcos Lopez de Prado in his new book\nAdvances in Financial Machine Learning. This implementation started out as a spring board for a research project in the [Masters in Financial Engineering programme at WorldQuant University](https://wqu.org/) and has grown into a mini research group called [Hudson and Thames Quantitative Research](https://hudsonthames.org/) (not affiliated with the university).\n\nThe following is the online documentation for the package: [read-the-docs](https://mlfinlab.readthedocs.io/en/latest/#).\n\n## Barriers to Entry\nAs most of you know, getting through the first 3 chapters of the book is challenging as it relies on HFT data to \ncreate the new financial data structures. Sourcing the HFT data is very difficult and thus we have resorted to purchasing the full history of S&P500 Emini futures tick data from [TickData LLC](https://www.tickdata.com/).\n\nWe are not affiliated with TickData in any way but would like to recommend others to make use of their service. The full history cost us about $750 and is worth every penny. They have really done a great job at cleaning the data and providing it in a user friendly manner. \n\n### Sample Data\nTickData does offer about 20 days worth of raw tick data which can be sourced from their website [link](https://s3-us-west-2.amazonaws.com/tick-data-s3/downloads/ES_Sample.zip).\n\nFor those of you interested in working with a two years of sample tick, volume, and dollar bars, it is provided for in the [research repo.](https://github.com/hudson-and-thames/research/tree/master/Sample-Data).\n\nYou should be able to work on a few implementations of the code with this set. \n\n\n
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\n\n---\n\n## Progress\n\n**Part 4: Useful Financial Features**\n* Working on Chapter 19: Microstructural Features (Maksim)\n\n**Part 3: Backtesting**\n* Working on Chapter 16: Asset Allocation\n* Working on Chapter 10: Bet Sizing\n\n**Part 2: Modelling**\n* Working on Chapter 8: Feature Importance\n* Done on Chapter 7: Cross-Validation\n* Done Chapter 6: Ensemble Methods (Sequential Bootstrap Ensemble)\n\n**Part 1: Data Analysis**\n* Done Chapter 5: Fractional Differentiation\n* Done Chapter 4: Sample Weights\n* Done Chapter 3: Labeling\n* Done Chapter 2: Data Structures\n* Purchased high quality raw tick data.\n* Email us if you would like a sample of the standard bars.\n\n---\n\n## Getting Started\n\nRecommended versions:\n* Anaconda 3\n* Python 3.6\n\n### Installation for users\nThe package can be installed from the PyPi index via the console:\n 1. Launch the terminal and run: ```pip install mlfinlab```\n\n### Installation for developers\nClone the [package repo](https://github.com/hudson-and-thames/mlfinlab) to your local machine then follow the steps below.\n\n#### Installation on Mac OS X and Ubuntu Linux\n1. Make sure you install the latest version of the Anaconda 3 distribution. To do this you can follow the install and update instructions found on this link: https://www.anaconda.com/download/#mac\n2. Launch a terminal\n3. Create a New Conda Environment. From terminal: ```conda create -n python=3.6 anaconda``` accept all the requests to install.\n4. Now activate the environment with ```source activate ```.\n5. From Terminal: go to the directory where you have saved the file, example: cd Desktop/mlfinlab/.\n6. Install Python requirements, by running the command: ```pip install -r requirements.txt```\n\n#### Installation on Windows\n1. Download and install the latest version of [Anaconda 3](https://www.anaconda.com/distribution/#download-section)\n2. Launch Anaconda Navigator\n3. Click Environments, choose an environment name, select Python 3.6, and click Create\n4. Click Home, browse to your new environment, and click Install under Jupyter Notebook\n5. Launch Anaconda Prompt and activate the environment: ```conda activate ```\n6. From Anaconda Prompt: go to the directory where you have saved the file, example: cd Desktop/mlfinlab/.\n7. Install Python requirements, by running the command: ```pip install -r requirements.txt```\n\n### How To Run Checks Locally\nOn your local machine open the terminal and cd into the working dir. \n1. Code style checks: ```./pylint```\n2. Unit tests: ```python -m unittest discover```\n3. Code coverage: ```bash coverage```\n\n## Built With\n* [Github](https://github.com/hudson-and-thames/mlfinlab) - Development platform and repo\n* [Travis-CI](https://www.travis-ci.com) - Continuous integration, test and deploy\n\n## Authors\n\n* **Ashutosh Singh** - [LinkedIn](https://www.linkedin.com/in/ashusinghpenn/)\n* **Jacques Joubert** - [LinkedIn](https://www.linkedin.com/in/jacquesjoubert/)\n* **Oleksandr Proskurin** - [LinkedIn](https://www.linkedin.com/in/proskurinolexandr/)\n\n\n## Additional Research Repo\nBlackArbsCEO has a great repo based on de Prado's research. It covers many of the questions at the back of every chapter and was the first source on Github to do so. It has also been a good source of inspiration for our research. \n\n* [Adv Fin ML Exercises](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises)\n\n## Contact us\nAt the moment the project is still rather small and thus I would recommend getting in touch with us over email so that we can further discuss the areas of contribution that interest you the most. We have a slack channel where we all communicate.\n\nFor now you can get hold us at: research@hudsonthames.org\n\nLooking forward to hearing from you!\n\n## License\n\nThis project is licensed under the 3-Clause BSD License - see the [LICENSE.txt](https://github.com/hudson-and-thames/mlfinlab/blob/master/LICENSE.txt) file for details.\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://www.hudsonthames.org/", "keywords": "machinelearning finance investment education", "license": "", "maintainer": "", "maintainer_email": "", "name": "mlfinlab", "package_url": "https://pypi.org/project/mlfinlab/", "platform": "any", "project_url": "https://pypi.org/project/mlfinlab/", "project_urls": { "Blog": "https://hudsonthames.org/blog/", "Bug Reports": "https://github.com/hudson-and-thames/mlfinlab/issues", "Documentation": "https://mlfinlab.readthedocs.io/en/latest/", 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