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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
Fetching Financial Data

Having financial data handy is essential for carrying out algorithmic trading. Financial data can be both static and dynamic in nature. Static financial data is data that doesn't change during trading hours. Static data consists of lists of financial instruments, the attributes of financial instruments, the circuit limits of financial instruments, and the recorded close price of the last trading day. Dynamic financial data is data that may change continuously during trading hours. Dynamic data consists of market depth, the last traded prices, the time and quantity of financial instruments, and the recorded high and low prices of the day. This chapter includes recipes on fetching various types of financial data.

The following is a list of the recipes in this chapter:

  • Fetching the list of financial instruments
  • Attributes of a financial instrument
  • Expiry...
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