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30 changes: 15 additions & 15 deletions README.rst
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Mlfin.py - Advance Machine Learning application in Finance
===========================================================

**MLfin.py** is an Advance Machine Learning toolbox for financial applications. The main ideas is using
proprietary works and code snippent by Dr. Marcos López de Prado to build a morden Pythonic package
that implements newest tech stacks from various libraries such as Numpy, Pandas, Numba, and Scikit-Learn.
This work inspired by the library `MlFinLab <https://github.com/hudson-and-thames/mlfinlab>`_ by
**Hudson and Thames**. Unfortunately, the library is closed-source and I believe in the power of open
source projects, it motivates me to build this package from ground up.

Leverage best practice in packaging Python library, morden documentation style and comprehensive examples,
**MLfin.py** will be the great tool for Quant Researchers, Algorithmic Traders, and Data Scientists as well as
Finance students to reproduce the complex data transformation, labeling, sampling and feature engineering
techniques with ease.

.. image:: https://img.shields.io/pypi/v/mlfinpy.svg
:target: https://pypi.python.org/pypi/mlfinpy
:alt: PyPI Version
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:target: https://mlfinpy.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status

**MLfin.py** is an Advance Machine Learning toolbox for financial applications. The main ideas is using
proprietary works and code snippent by Dr. Marcos López de Prado to build a morden Pythonic package
that implements newest tech stacks from various libraries such as Numpy, Pandas, Numba, and Scikit-Learn.
This work inspired by the library `MlFinLab <https://github.com/hudson-and-thames/mlfinlab>`_ by
**Hudson and Thames**. Unfortunately, the library is closed-source and I believe in the power of open
source projects, it motivates me to build this package from ground up.

Leverage best practice in packaging Python library, morden documentation style and comprehensive examples,
**MLfin.py** will be the great tool for Quant Researchers, Algorithmic Traders, and Data Scientists as well as
Finance students to reproduce the complex data transformation, labeling, sampling and feature engineering
techniques with ease.

Installation
============
Installation can then be done via pip::
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Work with HFT Data
==================
In reality, testing code snippets through the first 3 chapters of the book is challenging as it relies on HFT data to
In reality, testing code snippets through the first 3 chapters of the book is challenging as it relies on HFT data to
create the new financial data structures. Sourcing the HFT data is very difficult and thus `TickData LLC`_ provides
the full history of S&P500 Emini futures tick data and available for purchase.

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----------------

TickData does offer about 20 days worth of raw tick data which can be sourced from their website `link`_.
For 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`_. You should be able to work on a few implementations of the code with this set.
For 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`_. You should be able to work on a few implementations of the code with this set.

.. _link: https://s3-us-west-2.amazonaws.com/tick-data-s3/downloads/ES_Sample.zip
.. _research repo: https://github.com/hudson-and-thames/research/tree/master/Sample-Data
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Machine Learning application in Finance Python package
######################################################

**MLfin.py** is an Advance Machine Learning toolbox for financial applications. The main ideas is using
proprietary works and code snippent by Dr. Marcos López de Prado to build a morden Pythonic package
that implements newest tech stacks from various libraries such as Numpy, Pandas, Numba, and Scikit-Learn.
This work inspired by the library `MlFinLab <https://github.com/hudson-and-thames/mlfinlab>`_ by
**Hudson and Thames**. Unfortunately, the library is closed-source and I believe in the power of open
source projects, it motivates me to build this package from ground up.

Leverage best practice in packaging Python library, morden documentation style and comprehensive examples,
**MLfin.py** will be the great tool for Quant Researchers, Algorithmic Traders, and Data Scientists as well as
Finance students to reproduce the complex data transformation, labeling, sampling and feature engineering
techniques with ease.

.. image:: https://img.shields.io/pypi/v/mlfinpy.svg
:target: https://pypi.python.org/pypi/mlfinpy
:alt: PyPI Version
Expand Down Expand Up @@ -43,6 +31,18 @@ techniques with ease.
:target: https://mlfinpy.readthedocs.io/en/latest/?version=latest
:alt: Documentation Status

**MLfin.py** is an Advance Machine Learning toolbox for financial applications. The main ideas is using
proprietary works and code snippent by Dr. Marcos López de Prado to build a morden Pythonic package
that implements newest tech stacks from various libraries such as Numpy, Pandas, Numba, and Scikit-Learn.
This work inspired by the library `MlFinLab <https://github.com/hudson-and-thames/mlfinlab>`_ by
**Hudson and Thames**. Unfortunately, the library is closed-source and I believe in the power of open
source projects, it motivates me to build this package from ground up.

Leverage best practice in packaging Python library, morden documentation style and comprehensive examples,
**MLfin.py** will be the great tool for Quant Researchers, Algorithmic Traders, and Data Scientists as well as
Finance students to reproduce the complex data transformation, labeling, sampling and feature engineering
techniques with ease.

Installation
============
Installation can then be done via pip:
Expand Down Expand Up @@ -89,7 +89,7 @@ you should try:
Work with HFT Data
==================
In reality, testing code snippets through the first 3 chapters of the book is challenging as it relies on HFT data to
In reality, testing code snippets through the first 3 chapters of the book is challenging as it relies on HFT data to
create the new financial data structures. Sourcing the HFT data is very difficult and thus `TickData LLC`_ provides
the full history of S&P500 Emini futures tick data and available for purchase.

Expand All @@ -103,19 +103,19 @@ Download Sources
----------------

TickData does offer about 20 days worth of raw tick data which can be sourced from their website `link`_.
For 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`_. You should be able to work on a few implementations of the code with this set.
For 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`_. You should be able to work on a few implementations of the code with this set.

.. _link: https://s3-us-west-2.amazonaws.com/tick-data-s3/downloads/ES_Sample.zip
.. _research repo: https://github.com/hudson-and-thames/research/tree/master/Sample-Data

.. note::
Searching for free tick data can be a challenging task. The following three sources may help:

1. `Dukascopy`_. Offers free historical tick data for some futures, though you do have to register.
2. Most crypto exchanges offer tick data but not historical (see `Binance API`_). So you'd have to run a script for a few days.
3. `Blog Post`_: How and why I got 75Gb of free foreign exchange “Tick” data.

.. _Dukascopy: https://www.dukascopy.com/swiss/english/marketwatch/historical/
.. _Binance API: https://github.com/binance-exchange/binance-official-api-docs/blob/master/rest-api.md
.. _Blog Post: https://towardsdatascience.com/how-and-why-i-got-75gb-of-free-foreign-exchange-tick-data-9ca78f5fa26c
Expand All @@ -129,14 +129,14 @@ the `research repo`_. You should be able to work on a few implementations of the
Datasets
========

To make the developing module and testing the code process more convenient, **MLfin.py** package contains various financial
To make the developing module and testing the code process more convenient, **MLfin.py** package contains various financial
datasets which can be used by a developer as sandbox data.

Tick Data Sample
----------------

**MLfin.py** provides a sample of tick data for E-Mini S&P 500 futures which can be used to test bar compression algorithms,
microstructural features, etc. Tick data sample consists of ``Timestamp``, ``Price`` and ``Volume``. The data contain
microstructural features, etc. Tick data sample consists of ``Timestamp``, ``Price`` and ``Volume``. The data contain
500,000 rows of cleaned tick data.

.. py:currentmodule:: mlfinpy.dataset.load_datasets
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.. py:currentmodule:: mlfinpy.dataset.load_datasets
.. autofunction:: load_stock_prices

The data set consists of close prices for:
* EEM, EWG, TIP, EWJ, EFA, IEF, EWQ, EWU, XLB, XLE, XLF, LQD, XLK, XLU, EPP,FXI, VGK, VPL, SPY,
TLT, BND, CSJ, DIA
* Starting from 2008 till 2016.
The data set consists of close prices for:
* EEM, EWG, TIP, EWJ, EFA, IEF, EWQ, EWU, XLB, XLE, XLF, LQD, XLK, XLU, EPP,FXI, VGK, VPL, SPY,
TLT, BND, CSJ, DIA
* Starting from 2008 till 2016.
It can be used to test and validate portfolio optimization techniques.

Example
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* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

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