Python Algorithmic Trading Cookbook: All the recipes you need to implement your own trading strategies in Python


Python Algorithmic Trading Cookbook: All the recipes you need to implement your own algorithmic trading strategies in Python
by 作者: Pushpak Dagade
pages 页数: 542 pages
ISBN-10 书号: 1838989358
ISBN-13 书号: 9781838989354
05 x 3.12 x 23.5 cm
Publisher Finelybook 出版社: Packt Publishing (28 Aug. 2020)
Language 语言: English


Book Description
Implement investment strategies using real market data to perform effective financial and data analysis using Python
Python can be used to build and execute algorithmic trading strategies. This book could help you increase your chances of making profits in the stock market. It can help you automate trading to find the right strategy for making effective decisions that would otherwise be impossible for human traders.
After setting up the Python environment for trading,you’ll learn the important aspects of financial markets. As you progress through the book,you’ll understand how to fetch financial instruments,and query candle and historical data. The book also demonstrates how to compute and plot technical indicators,and create algorithmic trading strategies. Next,you’ll uncover challenges faced while devising powerful algorithmic trading strategies,before focusing on how to optimize and make changes to your existing strategies based on changing customer needs. Later,you’ll learn how to use various ARIMA models based on different challenging scenarios. The concluding chapters will take you through performing backtesting on your trading strategy,performing paper trade,and finally executing a real trade using the algorithmic strategies that you’ve created from scratch.
By the end of this book,you’ll have learned how to implement various Python libraries to conduct key tasks in the algorithmic finance ecosystem using a recipe-based approach.

What you will learn
Use Python to query and understand the financial market
Fetch a list of exchanges,segments,and financial products to interact with the real market
Develop algorithmic trading strategies for financial data analysis
Compute candles,historical data,and ARIMA models to forecast time series data
Perform backtesting and paper trading on algorithmic trading strategies
Implement real trading in the live hours of stock markets
Develop and improve the performance of strategies to gain consistent returns

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