Machine Learning in Trading by Ishan Shah and Rekhit Pachanekar
This repository contains the python codes as well as data and modules files which have been included in the machine learning in Trading book
You can download the data files as well as the code for the chapters and run them on your local system as well. For a seamless experience, you can set up a virtual environment which will help you download all the python libraries used in the code of the book. You can check the blog: https://blog.quantinsti.com/set-up-python-system/ if you want to know how to set up your own python environment.
Machine learning has varied applications. From agricultural forecasts, to stock price
estimations, you can use machine learning almost anywhere where data has to be
analysed. Some machine learning algorithms actually unearth information that you
didn’t even know existed.
And in today’s world, we are generating data more than ever! According to the World
Economic Forum, by 2025, we will generate almost 463 exabytes (that’s one billion GB)
of data each day!
Obviously, a human will not be able to keep up with the data overload, and conventional computer programs might not be able to handle such large amounts of data.
Thus, we need machine learning, which harnesses the adaptive learning method of a
human and combine it with the efficiency and speed of a computer program. This is
more evident in the trading domain than in others.
If we focus on algorithmic trading, we routinely execute the following tasks:
- Download and manage price, fundamental and other alternative data from multiple data vendors and in different formats.
- Pre-processing the data and cleaning
- Forming hypothesis for a trading strategy and backtesting it.
- Automating the trading strategy to make sure emotions don’t get in the way ofyour trading strategy.
We found out that we can outsource most of these tasks to the machine. And this is the reason you, the reader, have a book which talks about machine learning and its applications in trading.
The material presented here is an elementary introduction to
the world of machine learning. You can think of it as a repository telling you about the
foundations of machine learning and how it is applied in real life.
From the outset, we believe that only theory is not enough to retain knowledge. You
need to know how you can apply this knowledge in the real world. Thus, we have used real world examples, especially in the field of trading. But rest assured
that these concepts can be transferred to any other discipline which requires data analysis.
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Made on Python version 3.9.5
Courses
- Python For Trading: Basic
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