Explore Python code recipes to use market data for designing and deploying algorithmic trading strategies. By following step-by-step instructions, you'll be proficient in trading concepts and have hands-on experience in a live trading environment.

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Bibliographic Details

Title: Python for Algorithmic Trading Cookbook
Publisher: Packt Publishing Limited, GB
Publication Date: 2024
Language: English
ISBN 10: 1835084702
ISBN 13: 9781835084700
Binding: Paperback
Condition: New
Dimensions: 2.41 centimeters width by 19.05 centimeters height by 23.5 centimeters depth

About this title

Synopsis

Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment

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Key Features

Follow practical Python recipes to acquire, visualize, and store market data for market research
Design, backtest, and evaluate the performance of trading strategies using professional techniques
Deploy trading strategies built in Python to a live trading environment with API connectivity

Book Description

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.

What you will learn

Acquire and process freely available market data with the OpenBB Platform
Build a research environment and populate it with financial market data
Use machine learning to identify alpha factors and engineer them into signals
Use VectorBT to find strategy parameters using walk-forward optimization
Build production-ready backtests with Zipline Reloaded and evaluate factor performance
Set up the code framework to connect and send an order to Interactive Brokers

Who this book is for

Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be.

Acquire Free Financial Market Data with Cutting-edge Python Libraries
Analyze and Transform Financial Market Data with pandas
Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash
Store Financial Market Data on Your Computer
Build Alpha Factors for Stock Portfolios
Vector-Based Backtesting with VectorBT
Event-Based Backtesting Factor Portfolios with Zipline Reloaded
Evaluate Factor Risk and Performance with Alphalens Reloaded
Assess Backtest Risk and Performance Metrics with Pyfolio
Set Up the Interactive Brokers Python API

(N.B. Please use the Read Sample option to see further chapters)

About the Author

Jason Strimpel is the founder of PyQuant News and co-founder of Trade Blotter. His career America, Europe, and Asia over the last 20+ years. He previously traded for a Chicago-based hedge fund, was a risk manager at JPMorgan, and managed production risk technology for an energy derivatives trading firm in London. In Singapore, he served as APAC CIO for an agricultural trading firm and built the data science team for a global metals trading firm. Jason holds degrees in finance and economics and a Master's in Capitalize Quantitative Finance from the Illinois Institute of Technology. He shares his expertise through the PyQuant Newsletter, social media, and teaches Getting Started With Python for Quant Finance.

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