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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 – simple moving average

SMA is a lagging trend indicator. It is used to smooth the price data by eliminating noise and thus identifying trends.

SMA is the simplest form of a moving average. Each output value is the average of the previous n values of the historical data. You can define the value of n, which is also called the time period. In SMA, each value in the time period carries the same weight, and values outside the time period are not included. This makes it less responsive to recent changes compared to previous changes in the data, and is thus useful for smoothing out the prices' data. A consecutive rise in SMA indicates a clear bullish trend, while a consecutive fall indicates a bearish trend. Thus, it is a trend indicator. Also, since it indicates the trend after it has started, it is a lagging indicator.

SMA is widely used in technical analysis. It is also used for calculating other technical indicators, either in combination with itself or other...

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