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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
Tools
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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
Computing Candlesticks and Historical Data

The historical data of a financial instrument is data about all the past prices at which a financial instrument was brought or sold. An algorithmic trading strategy is always vpot_candlestickirtually executed on historical data to evaluate its past performance before it's deployed with real money. This process is called backtesting. Historical data is quintessential for backtesting (covered in detail in Chapter 8, Backtesting Strategies). Also, historical data is needed for computing technical indicators (covered in detail in Chapter 5, Computing and Plotting Technical Indicators), which help in making buy-or-sell decisions in real-time. Candlestick patterns are widely used tools for stock analysis. Various types of candlestick patterns are commonly used by analysts. This chapter provides recipes that show you how to fetch historical...

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