Backtest Quantitative Trading strategies from Scratch

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

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.

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, Matplotlib.
  • Time Series.
  • Quantitative trading.
  • Moving Average Cross Over.
  • Pairs trading.
  • Stock Pipeline.

Course Content

  • Introduction –> 1 lecture • 3min.
  • Data –> 1 lecture • 4min.
  • The pipeline class –> 2 lectures • 38min.
  • Moving Average Cross Over –> 11 lectures • 4hr 8min.
  • Pairs Trading –> 13 lectures • 4hr 40min.
  • Multi Testing –> 4 lectures • 48min.
  • Take home assignment –> 1 lecture • 1min.

Backtest Quantitative Trading strategies from Scratch

Requirements

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.

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