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This is the code repository for Python for Algorithmic Trading Cookbook, published by Packt.

Recipes for designing, building, and deploying algorithmic trading strategies with Python

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.

This book covers the following exciting features:

• 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

If you feel this book is for you, get your copy today!

All of the code is organized into folders.

The code will look like the following:

import datetime as dt
import pandas as pd
from openbb_terminal.sdk import openbb

Following is what you need for this book: 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.

With the following software and hardware list you can run all code files present in the book (Chapter 1-13).

• Page 34 (Code block snippet under section 5): asset_2.volume.mean() should be asset_2.volume[asset_2.index[5:10]].mean().astype(int)
• Page 34 (second code block snippet under section 5): asset_2.iat[10, 5] should be asset_2.iat[10,4]
• Page 34 (line 3 which is after second code block snippet): The result is a scalar value representing the mean volume between indexes 5 and 10. should be The result is a scalar value corresponding to the mean of the 'volume' column, assigned to the cell located at row 10 and column 4 (which corresponds to the 'volume' column).

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