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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

The last traded quantity of a financial instrument

The last traded quantity (LTQ) of a financial instrument is the quantity that was traded the last time an order was executed for that instrument. This data is dynamic in nature as it may change continuously during the live trading hours. This recipe demonstrates how to fetch the LTQ of a financial instrument.

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 instrument recipe of this chapter to set up instrument1.

How to do it…

Fetch and print the LTQ of instrument1:

>>> ltq = broker_connection.get_ltq(instrument1)
>>> print(f'Last traded quantity: {ltq}')

We get the following output (your output may differ):

Last traded quantity: 19

How it works…

The get_ltq() method of the BrokerConnectionZerodha class fetches...

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