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Python Algorithmic Trading Cookbook

You're reading from   Python Algorithmic Trading Cookbook All the recipes you need to implement your own algorithmic trading strategies in Python

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781838989354
Length 542 pages
Edition 1st Edition
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Author (1):
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Pushpak Dagade Pushpak Dagade
Author Profile Icon Pushpak Dagade
Pushpak Dagade
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Toc

Table of Contents (16) Chapters Close

Preface 1. Handling and Manipulating Date, Time, and Time Series Data 2. Stock Markets - Primer on Trading FREE CHAPTER 3. Fetching Financial Data 4. Computing Candlesticks and Historical Data 5. Computing and Plotting Technical Indicators 6. Placing Regular Orders on the Exchange 7. Placing Bracket and Cover Orders on the Exchange 8. Algorithmic Trading Strategies - Coding Step by Step 9. Algorithmic Trading - Backtesting 10. Algorithmic Trading - Paper Trading 11. Algorithmic Trading - Real Trading 12. Other Books You May Enjoy Appendix I
1. Appendix II
2. Appendix III

Volatility indicators – average true range

Average true range (ATR) is a lagging volatility indicator. ATR is a measure of volatility. High ATR values indicate high volatility, and low values indicate low volatility.

The formula for computing ATR is not straightforward and is hence not mentioned here. If you are interested in the underlying math, please refer to the official documentation of TA-Lib on ATR at http://www.tadoc.org/indicator/ATR.htm. Although it is a good idea to know the mathematics of how this works, this recipe does not require you to understand or remember the given formula. We use a third-party Python package, talib, which provides a ready function for calculating ATR.

Getting started

Make sure your Python namespace has the following objects:

  1. talib (package)
  2. pd (module)
  3. plot_candlesticks_chart (function)
  4. PlotType (enum)
  5. historical_data (a pandas DataFrame)

Refer to the Technical requirements section of this chapter to set up these objects.

How to do it…...

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