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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

Momentum indicators – stochastic oscillator

The stochastic oscillator is a leading momentum indicator. It is also called STOCH for short. STOCH compares the latest close with the recent trading range. Fast K is a ratio and has a value between 0 and 100. Fast K can have haphazard movement, and hence it is smoothed using a moving average, which is the slow K. Slow K is further smoothed using another moving average, which is the slow D. Values of slow K over 75 indicate an overbought condition, while values below 25 indicate an oversold condition. When slow K crosses above slow D, it is considered a buy signal. Similarly, when slow K crosses below slow D, it is considered a sell signal.

The formula for computing STOCH is as follows:

MA stands for moving average, and can be either SMA or EMA. For this recipe, we have used SMA. This formula needs three time periods: one of them is n and the other two are the time periods of the MAs. The range over which we analyze data is defined by...

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