Hands-On Algorithmic Trading with Python

By Sourav Ghosh
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About this book

Algorithmic trading, also known as automated trading, helps you stay ahead of the market by devising strategies in quantitative analysis to gain profits and cut losses. This book will help you to understand financial theories and execute a range of algorithmic trading strategies confidently.

The book starts by introducing you to algorithmic trading, the pyfinance ecosystem, and Quantopian. You’ll then cover algorithmic trading and quantitative analysis using Python, and learn how to build algorithmic trading strategies on Quantopian. As you advance, you’ll gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and also explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. Moving on, you’ll explore useful financial concepts and theories such as financial statistics, leveraging and hedging, and short selling that will help you understand how financial markets operate. Finally, you will discover mathematical models and approaches for analyzing and understanding financial time series data.

By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization on the Quantopian platform.

Publication date:
April 2021
Publisher
Packt
Pages
449
ISBN
9781838982881

About the Author

  • Sourav Ghosh

    Sourav Ghosh has worked in several proprietary high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making, and pairs trading strategies for the most liquid global futures contracts. He works as a Senior Quantitative Developer at a trading firm in Chicago. He holds a Masters in Computer Science from the University of Southern California. His areas of interest include Computer Architecture, FinTech, Probability Theory and Stochastic Processes, Statistical Learning and Inference Methods, and Natural Language Processing.

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