Available only $219, Python For Algorithmic Trading by Yves Hilpisch – The Python Quants Course

Original price was: $997.00.Current price is: $219.00.

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Available only $219, Python For Algorithmic Trading by Yves Hilpisch – The Python Quants Course

Original price was: $997.00.Current price is: $219.00.

Status: AVAILABLEDownload immediately

Salepage: Click HEREArchive: https://archive.is/wip/ZDO2w

Combined file size: 94 GB

Included files: ()

Languages: English

PURCHASE THIS COURSE, YOU ACCUMLATE: 219 POINTs!


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Algorithmic trading, once the exclusive domain of institutional players, is now open to small organizations and individual traders using online platforms. The tool of choice for many traders today is Python and its ecosystem of powerful packages. In this practical book, author Yves Hilpisch shows students, academics, and practitioners how to use Python in the fascinating field of algorithmic trading.

You’ll learn several ways to apply Python to different aspects of algorithmic trading, such as backtesting trading strategies and interacting with online trading platforms. Some of the biggest buy- and sell-side institutions make heavy use of Python. By exploring options for systematically building and deploying automated algorithmic trading strategies, this book will help you level the playing field.

  • Set up a proper Python environment for algorithmic trading
  • Learn how to retrieve financial data from public and proprietary data sources
  • Explore vectorization for financial analytics with NumPy and pandas
  • Master vectorized backtesting of different algorithmic trading strategies
  • Generate market predictions by using machine learning and deep learning
  • Tackle real-time processing of streaming data with socket programming tools
  • Implement automated algorithmic trading strategies with the OANDA and FXCM trading platforms

ABOUT THE AUTHOR

Dr. Yves J. Hilpisch is founder and CEO of The Python Quants and The AI Machine. The group focuses on Open Source technologies for Financial Data Science, Artificial Intelligence, Algorithmic Trading, Computational Finance, and Asset Management. It also provides data, financial and derivatives analytics software (see Quant Platform and DX Analytics) as well as consulting services and Python for Finance online and corporate training programs.

Yves is author of the books Financial Theory with Python (O’Reilly, 2021), Artificial Intelligence in Finance (O’Reilly, 2020), Python for Algorithmic Trading (O’Reilly, 2020), Python for Finance (2nd ed., O’Reilly, 2018),  Derivatives Analytics with Python (Wiley, 2015) and Listed Volatility and Variance Derivatives (Wiley, 2017). As a graduate in Business Administration with a Dr.rer.pol. in Mathematical Finance, he lectures on Computational Finance at the CQF Program and is Adjunct Professor of Computational Finance. Yves is also the director of the Certificate Program in Python for Finance.

Furthermore, Yves organizes Python for Finance, AI, and Algorithmic Trading Meetup group events in Berlin, Frankfurt, Paris, London (see Python for Quant Finance) and New York (see AI & Algo Trading).

QUANT PLATFORM

All Jupyter Notebooks and all Python code files are available for immediate execution and usage on the Quant Platform. No installation necessary, just an easy and quick registration under

GITHUB REPOSITORY

All Jupyter Notebooks and all Python code files for easy cloning and local usage are available on Github. Just clone the Github repo and run the codes within Jupyter Notebook or Jupyter Lab.

LEVELING UP

We are offering comprehensive Python for Finance online training programs — leading to University Certificates — about Financial Data Science, Algorithmic Trading, Computational Finance, and Asset Management. In addition, we also offer customized corporate training classes. See https://certificate.tpq.io or just get in touch below.


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The program covers a wide range of important topics in Python for Asset Management, Algorithmic Trading, and Computational Finance. A strong focus lies on Machine Learning, Deep Learning, and Reinforcement Learning.

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