[Pdf/ePub] Learn Algorithmic Trading with

Learn Algorithmic Trading with Python: Build Automated Electronic Trading Systems using Python by Jamal Sinclair O'Garro

• Learn Algorithmic Trading with Python: Build Automated Electronic Trading Systems using Python
• Jamal Sinclair O'Garro
• Page: 276
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781484249345
• Publisher: Apress

Learn Algorithmic Trading with Python: Build Automated Electronic Trading Systems using Python

Book downloads for free Learn Algorithmic Trading with Python: Build Automated Electronic Trading Systems using Python

Overview

Develop and deploy an automated electronic trading system with Python and the SciPy ecosystem. This book introduces you to the tools required to gather and analyze financial data through the techniques of data munging and data visualization using Python and its popular libraries: NumPy, pandas, scikit-learn, and Matplotlib. You will create a research environment using Jupyter Notebooks while leveraging open source back-testing software to analyze and experiment with several trading strategies. Next, you will measure the level of return and risk of a portfolio using measures such as Alpha, Beta, and the Sharpe Ratio. This will set the stage for the use of open source backtesting and scientific computing libraries such as zipline, NumPy, and scikit-learn to develop models that will help you identify, buy, and sell signals for securities in your portfolio and watch-list. With Learn Algorithmic Trading with Python you will explore key techniques used to analyze the performance of a portfolio and trading strategies and write unit tests on Python code that will send live orders to the market. What You'll Learn Analyze financial data with Pandas • Use Python libraries to perform statistical reviews Review algorithmic trading strategies • Assess risk management with NumPy and StatsModels Perform paper and Live Trading with IB Python API • Write unit tests and deploy your trading system to the Cloud Who This Book Is For Software developers, data scientists, or students interested in Python and the SciPy ecosystem

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