Black Box Tactics

Use algorithmic trading techniques to improve your AI

Product screenshot: the book cover, on a mockup

Why should you read this book?

Algorithmic ("algo") trading an AI have a lot in common. Both fields use data analysis and advanced mathematical models in the pursuit of profit.

The difference is that algorithmic trading has a long head start on AI, applying analysis and modeling to billions of dollars in transactions annually. During this time, traders have developed deep knowledge about the "black box" systems they deploy into the financial markets. They've learned what works and what doesn't. And they've honed the practices that allow them to turn a profit in this fast-paced, volatile space while protecting themselves from financial ruin.

If it works for them, it can work for you. Your company's AI efforts will benefit from algo traders' best practices. This book will show you how.

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Who should read this book?

This material touches on matters of strategy, as well as higher-order tactical ideas.

As such, this book will prove most helpful to CTOs, Chief Data Officers, Heads of AI, and similar roles. These are the people responsible for making AI work in a company, handling everything from managing R&D to supporting models in production.

It will also be a useful read for CEOs and Heads of Product, in order to understand the realities of bringing AI into an organization and integrating it into the company's products and services.

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What you'll read

Topics covered in this book

With this book, you'll learn key practices that algo traders use to:

Optimize model R&D
Traders organize model R&D to make the most of their time and avoid costly overruns.
Manage operations

Once that model makes it to production, it's responsible for real-world activity. Learn how traders keep their bots and data feeds in working order.

Identify and manage risk

Traders develop layers of protection so they can trust automated systems to handle real money while operating at high speeds.

Topics not covered in this book

Other resources can help you with:

How to become a data scientist or ML engineer
There's no technical material in here. You won't learn how to train models or write code for data pipelines. Those are important topics to explore after absorbing what's in this book.
How to develop your data/AI strategy
The material here will work alongside your data/AI strategy: as you identify projects and guide them to production, you should periodically refer to this book to keep everything on-track.

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Who's behind this?

I'm Q McCallum. I help companies navigate their AI journey from zero to production and beyond. I’ve been in the tech industry since the Dot-Com era, with a focus on data since the terms "predictive analytics" and "Big Data" were still gaining traction.

I've helped clients with matters such as providing strategic AI guidance to executives and product teams, surfacing AI use cases, developing AI models, and performing due diligence.

I'm a published author with multiple bylines from O'Reilly Media, including articles on their Radar platform on topics such as AI, business models, and N-sided marketplaces. I've also delivered talks at major industry conferences and private events.

My secret? It's not a secret at all. I've been very open in telling people that a small stint in the trading world has had tremendous impact on my career. Lessons I learned from finance have guided my work in data science, ML, and AI.

I've spoken and written about these ideas before. I routinely apply these best practices to my consulting work. And now I've pulled it all together into one slim, convenient read.

Learn from the algo traders.
Supercharge your AI today.

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