Build a Robo Advisor with Python (From Scratch) you own this product

Rob Reider and Alexander Michalka
  • MEAP began January 2023
  • Publication in Fall 2023 (estimated)
  • ISBN 9781633439672
  • 300 pages (estimated)
  • printed in black & white
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$499.99
only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free eBook every time you renew
  • choose twelve free eBooks per year
  • exclusive 50% discount on all purchases
  • Build a Robo Advisor with Python (From Scratch) eBook for free

Covers a lot of details and in great depth. Having built a lot of the same tools myself, wish I had this book 5 years ago when I started.

Philip Patterson
Look inside
Take control of your wealth management by building your own reliable, effective, and automated financial advisor tool.

In Build a Robo Advisor with Python (From Scratch) you’ll learn how to:

  • Measure returns and estimate the benefits of robo advisors
  • Use Monte Carlo simulations to build and test financial planning tools
  • Construct diversified, efficient portfolios using optimization and other advanced methods
  • Implement and evaluate rebalancing methods to track a target portfolio over time
  • Decrease taxes through tax-loss harvesting and optimized withdrawal sequencing
  • Use reinforcement learning to find the optimal investment path up to, and after, retirement

Every day automated digital advisors, also called robo advisors, make financial decisions worth millions of dollars. Build a Robo Advisor with Python (From Scratch): Automate your financial and investment decisions teaches you how to construct a Python-based financial advisor of your very own! You’ll develop a flexible tool that’s capable of managing a real investing strategy—all with popular free Python libraries.

about the technology

Automated “robo advisors” are commonplace in financial services, thanks to their ability to give high-quality investment advice at a fraction of the cost of human advisors. Your own robo advisor will be a real asset for your financial planning, whether you’re saving for retirement, creating a diversified portfolio, or trying to ensure your tax efficiency.

about the book

In Build a Robo Advisor with Python (From Scratch), you’ll design and develop a working financial advisor that can manage a real investing strategy. You’ll add new features to your advisor chapter-by-chapter, including determining the optimal weight of cryptocurrency in your portfolio, rebalancing to keep your investments on target while minimizing taxes, and using reinforcement learning to find a “glide path” that can maximize how long your money will last in retirement. Best of all, the skills you learn in reinforcement learning, convex optimization, and Monte Carlo methods can be applied to numerous lucrative fields beyond the domain of finance.

about the reader

The book is accessible to anyone with a basic knowledge of Python and finance—no special skills required.

about the authors

Rob Reider has been a quantitative hedge fund portfolio manager for over 15 years. He holds a PhD in Finance from The Wharton School and is an Adjunct Professor at NYU, where he teaches a graduate course in the Math-Finance department called “Time Series Analysis and Statistical Arbitrage.” He has built asset allocation models, financial planning tools, and optimal tax strategies for a robo advisor. Rob has given numerous lectures that combine Python with finance, as well as developing an online course entitled “Time Series Analysis in Python.” As a hedge fund manager, Rob has been involved in all aspects of the investment process, from discovering new trading strategies to backtesting, executing, and managing the risk.

Alex Michalka has worked in finance and technology since 2006. He began his career developing weather derivative pricing models at Weatherbill, spent six years conducting research on quantitative equity portfolio construction at AQR Capital Management, and currently leads the investments research group at Wealthfront. He holds a BA in applied mathematics from UC Berkeley and a PhD in operations research from Columbia University.

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A good deep dive into financial portfolio management using technology for non-tech-savvy users.

Mohana Krishna

Provides significantly more value in teaching readers how to generate application code that can be used immediately than what one would encounter while reading through a typical portfolio theory book.

Marcus H. Young

Does a very good job of easing the reader gradually to the various steps in portfolio construction.

Keerthi Shetty

A balanced exposition involving simulation, mathematical modeling, clearly explained assumption and shortcoming of the models.

Marco Carnini
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