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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
Languages
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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

Trend indicators – parabolic stop and reverse

Parabolic stop and reverse (SAR) is a leading trend indicator.

The parabolic SAR computes a trailing stop loss for every data point. As the data points are stop-loss points, they are away from the prices when there is a trend and cross the price line during a trend reversal. The parabolic SAR takes two parameters as input: the acceleration factor and the maximum point.

The formula for computing the parabolic SAR 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 parabolic SAR at http://www.tadoc.org/indicator/SAR.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 the parabolic SAR.

Getting started

Make sure your Python namespace has the...

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