Python Algorithmic Trading Cookbook: All the recipes you need to implement your own trading strategies
Implement investment strategies using real market data to perform effective financial and data analysis using Python
Key Features
Use core Python libraries to devise investment strategies to carry out financial analysis tasks
Implement necessary changes to your algorithmic strategies without human intervention
Uncover common and not-so-common challenges faced in the algorithmic trading domain using varied financial data
Book Description
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
Who This Book Is For
If you are a financial analyst, financial trader, data analyst, or algorithmic trader who wants to learn algorithmic trading techniques to address challenges faced in the finance domain, then this book is for you. Basic knowledge of financial terminologies and working knowledge of the Python programming language are expected.
English | 2020 | ISBN-13: 978-1800207455 | 542 Pages | True (EPUB, MOBI) + Code | 134 MB
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Implement investment strategies using real market data to perform effective financial and data analysis using Python
Key Features
Use core Python libraries to devise investment strategies to carry out financial analysis tasks
Implement necessary changes to your algorithmic strategies without human intervention
Uncover common and not-so-common challenges faced in the algorithmic trading domain using varied financial data
Book Description
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
Who This Book Is For
If you are a financial analyst, financial trader, data analyst, or algorithmic trader who wants to learn algorithmic trading techniques to address challenges faced in the finance domain, then this book is for you. Basic knowledge of financial terminologies and working knowledge of the Python programming language are expected.
English | 2020 | ISBN-13: 978-1800207455 | 542 Pages | True (EPUB, MOBI) + Code | 134 MB
*
or
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