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Hands-On Financial Trading with Python

You're reading from   Hands-On Financial Trading with Python A practical guide to using Zipline and other Python libraries for backtesting trading strategies

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781838982881
Length 360 pages
Edition 1st Edition
Languages
Tools
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Authors (2):
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Sourav Ghosh Sourav Ghosh
Author Profile Icon Sourav Ghosh
Sourav Ghosh
Jiri Pik Jiri Pik
Author Profile Icon Jiri Pik
Jiri Pik
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1: Introduction to Algorithmic Trading
2. Chapter 1: Introduction to Algorithmic Trading FREE CHAPTER 3. Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets
4. Chapter 2: Exploratory Data Analysis in Python 5. Chapter 3: High-Speed Scientific Computing Using NumPy 6. Chapter 4: Data Manipulation and Analysis with pandas 7. Chapter 5: Data Visualization Using Matplotlib 8. Chapter 6: Statistical Estimation, Inference, and Prediction 9. Section 3: Algorithmic Trading in Python
10. Chapter 7: Financial Market Data Access in Python 11. Chapter 8: Introduction to Zipline and PyFolio 12. Chapter 9: Fundamental Algorithmic Trading Strategies 13. Other Books You May Enjoy Appendix A: How to Setup a Python Environment

Learning mathematical model-based strategies

We will now look at the various mathematical model-based strategies in the following sections.

Minimization of the portfolio volatility strategy with monthly trading

The objective of this strategy is to minimize portfolio volatility. It has been inspired by https://github.com/letianzj/QuantResearch/tree/master/backtest.

In the following example, the portfolio consists of all stocks in the Dow Jones Industrial Average index.

The key success factors of the strategy are the following:

  • The stock universe – perhaps a portfolio of global index ETFs would fare better.
  • The rolling window – we go back 200 days.
  • The frequency of trades – the following algorithm uses monthly trading – notice the construct.

The code is as follows:

%matplotlib inline
from zipline import run_algorithm 
from zipline.api import order_target_percent, symbol, set_commission, schedule_function, date_rules, time_rules...
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