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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

Trend indicators – exponential moving average

EMA is a lagging trend indicator. It is used to smooth the price data by eliminating noise and thus identifying trends, with more weightage to recent values.

The EMA technical indicator calculation is cumulative and includes all the data with decreasing weights. Past values have a lower contribution to the average, while recent values have a greater contribution. The further away the value, the smaller the contribution. Thus, EMA is a moving average that is more responsive to recent changes in the data.

The EMA technical indicator is not like the SMA technical indicator, where each value in the time period carries equal weight and values outside of the time period are not included in the calculation.

EMA is widely used in technical analysis. It is also used for calculating other technical indicators, either in combination with itself or other indicators, with the same or different time periods.

A recursive formula for calculating EMA...

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