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Python Algorithmic Trading Cookbook

You're reading from  Python Algorithmic Trading Cookbook

Product type Book
Published in Aug 2020
Publisher Packt
ISBN-13 9781838989354
Pages 542 pages
Edition 1st Edition
Languages
Author (1):
Pushpak Dagade Pushpak Dagade
Profile icon Pushpak Dagade

Table of Contents (16) Chapters

Preface 1. Handling and Manipulating Date, Time, and Time Series Data 2. Stock Markets - Primer on Trading 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

The total volume traded for the day of a financial instrument

The total volume traded for a financial instrument is the sum total of all quantities that were traded (bought and sold, but counted once) in the day. For example, if trader A buys 10 quantities of stock X from trader B, while trader C sells 20 quantities of the same stock X to trader D, the total volume traded for X would be 10 + 20 = 30. It won't be 10 + 10 + 20 + 20 = 60 because the contribution of the trade to the total volume is considered only once. This data is dynamic in nature and may increase at any moment during the live trading hours.

Getting ready

Make sure the broker_connection and instrument1 objects are available in your Python namespace. Refer to the Technical requirements section of this chapter to set up broker_connection. Refer to the Attributes of a financial instruments recipe of this chapter to set up instrument1.

How to do it…

Fetch and print the total traded volume for the day of an instrument...

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