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Backtest Quantitative Trading strategies from Scratch

Backtest Quantitative Trading strategies from Scratch

Genre: eLearning | Language: English + srt | Duration: 33 lectures (10h 21m) | Size: 4 GB

Building your own backtesters with a 20 year data set and large scale simulation studies

What you’ll learn
Cointegration: Engle and Granger Approach
Creating your own backtesters in python
Covering Object Orientated Programming
Python packages: Pandas, Numpy, Scikit, Joblib, MatDescriptionlib
Time Series
Quantitative trading
Moving Average Cross Over
Pairs trading
Stock Pipeline
Requirements
A curious mind will do. However, you need to be familiar with some of the elementary Python operations and OOP
Some background in mathematics or econometrics
Description
The essence of this course is a ‘from the group up movement’ type of course. You will be given a very large dataset with over 30 million rows of data of all tradable equities on the US stock markets from 2001 up until October 2021 – at daily intervals.
Having having said that, we will show how to build your own backtester step by step and apply it to two well known trading algorithms: Moving Average Cross Over strategy and Pairs trading. With a brief reader on time series is also provided in order to help understand some of the mathematical concepts behind pairs trading such as pairs unit root and cointegration.
Besides the implementation of the algorithms, we also look at large scale implementations of the algorithms using a pipeline which allows you to create a stock universe. Which is a class we will go over step by step as well. Moreover, we also look at
Finally we end this section with a take home assignment that is a real life example of an assignment at a trading firm using high frequency data, where data sort and cleaning has to be implemented, determining a cointegrated relationship and determining how you would trade these two instruments
Who this course is for
Beginner traders, students and professionals who are interested in their own trading ideas or doing research.

 

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