This is the code repository for Learn Algorithmic Trading , published by Packt.
Build and deploy algorithmic trading systems and strategies using Python and advanced data analysis
It’s now harder than ever to get a significant edge over competitors in terms of speed and efficiency when it comes to algorithmic trading. Relying on sophisticated trading signals, predictive models and strategies can make all the difference. This book will guide you through these aspects, giving you insights into how modern electronic trading markets and participants operate.
This book covers the following exciting features: Understand the components of modern algorithmic trading systems and strategies Apply machine learning in algorithmic trading signals and strategies using Python Build, visualize and analyze trading strategies based on mean reversion, trend, economic releases and more Quantify and build a risk management system for Python trading strategies Build a backtester to run simulated trading strategies for improving the performance of your trading bot Deploy and incorporate trading strategies in the live market to maintain and improve profitability
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import pandas as pd
from pandas_datareader import data
Following is what you need for this book: This book is for software engineers, financial traders, data analysts, and entrepreneurs. Anyone who wants to get started with algorithmic trading and understand how it works; and learn the components of a trading system, protocols and algorithms required for black box and gray box trading, and techniques for building a completely automated and profitable trading business will also find this book useful.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| All | Python 2.7+ | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
- Page 144 (Bullet pont 4, line 9 of code): MIN_PROFIT_TO_CLOSE = 10 should be MIN_PROFIT_TO_CLOSE = 10*NUM_SHARES_PER_TRADE
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Mastering Python for Finance - Second Edition [Packt] [Amazon]
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Hands-On Machine Learning for Algorithmic Trading [Packt] [Amazon]
Sebastien Donadio Sebastien Donadio is the Chief Technology Officer at Tradair, responsible for leading the technology. He has a wide variety of professional experience, including being head of software engineering at HC Technologies, partner and technical director of a high-frequency FX firm, a quantitative trading strategy software developer at Sun Trading, working as project lead for the Department of Defense. He also has research experience with Bull SAS, and an IT Credit Risk Manager with Société Générale while in France. He has taught various computer science courses for the past ten years in the University of Chicago, NYU and Columbia University. His main passion is technology but he is also a scuba diving instructor and an experienced rock-climber.
Sourav Ghosh Sourav Ghosh has worked in several proprietary high-frequency algorithmic trading firms over the last decade. He has built and deployed extremely low latency, high throughput automated trading systems for trading exchanges around the world, across multiple asset classes. He specializes in statistical arbitrage market-making, and pairs trading strategies for the most liquid global futures contracts. He works as a Senior Quantitative Developer at a trading firm in Chicago. He holds a Masters in Computer Science from the University of Southern California. His areas of interest include Computer Architecture, FinTech, Probability Theory and Stochastic Processes, Statistical Learning and Inference Methods, and Natural Language Processing.
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