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Python for Algorithmic Trading Cookbook. Recipes for designing, building, and deploying algorithmic trading strategies with Python

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Python for Algorithmic Trading Cookbook. Recipes for designing, building, and deploying algorithmic trading strategies with Python Jason Strimpel - okladka książki

Python for Algorithmic Trading Cookbook. Recipes for designing, building, and deploying algorithmic trading strategies with Python Jason Strimpel - okladka książki

Serie wydawnicze:
Cookbook
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
404
Dostępne formaty:
     PDF
     ePub

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Do przechowalni

Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading.
Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details.
By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.
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O autorze książki

Jason Strimpel, founder of PyQuant News and co-founder of Quant Science, teaches widely-followed courses on Python for quant finance, algorithmic trading, and market data analysis.
Prior to his current ventures, he traded professionally for a Chicago-based hedge fund and served as a risk manager at JPMorgan. He also managed credit and market risk technology for an energy derivatives trading firm in London and held the role of APAC Chief Information Officer for an agricultural trading firm in Singapore. In addition, he established the data science, analytics, and engineering team for a global metals trading firm.
With undergraduate degrees in Finance and Economics and a Master's in Quantitative Finance from the Illinois Institute of Technology, Jason's career has taken him across America, Europe, and Asia. He shares his insights through the PyQuant Newsletter, social media, and teaches the popular course Getting Started With Python for Quant Finance.

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