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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

Exploring the MarketStack data source

MarketStack offers an extensive database of real-time, intra-day, and historical market data across major global stock exchanges. It offers free access for up to 1,000 monthly API requests.

While there is no official MarketStack Python library, the REST JSON API provides comfortable access to all its data in Python.

Let's download the adjusted close data for Apple:

import requests
params = {
  'access_key': 'XXXXX'
}
api_result = \
requests.get('http://api.marketstack.com/v1/tickers/aapl/eod', params)
api_response = api_result.json()
print(f"Symbol = {api_response['data']['symbol']}")
for eod in api_response['data']['eod']:
    print(f"{eod['date']}: {eod['adj_close']}")
Symbol = AAPL
2020-12-28T00:00:00+0000: 136.69
2020-12-24T00:00:00+0000: 131.97
2020-12-23T00:00:00+0000: 130.96
2020-12-22T00...
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