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Build a Robo-Advisor with Python (From Scratch): Automate your financial and investment decisions
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
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 author
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.
In Build a Robo Advisor with Python (From Scratch) you’ll learn how
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 author
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.
- GenresFinanceComputer Science
336 pages, Paperback
Published February 18, 2025
7 people are currently reading
20 people want to read
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Displaying 1 - 4 of 4 reviews
March 19, 2025
The book Build a Robo-Advisor with Python by Rob Reider and Alex Michalka is a recommended read for professionals and students interested in finance, automation, and programming. As a university professor specializing in Artificial Intelligence, Computer Science, and Data Science, I find this book to offer a well-structured and tailored approach for students to learn how to automate financial processes using Python.
The book covers fundamental concepts in automated financial advising, financial tools and planning, and portfolio construction and management. The authors present a clear methodology for applying advanced programming tools to real-world financial problems. The use of libraries such as NumPy, Pandas, SciPy, and CVXPY enables readers to efficiently implement optimization and financial analysis algorithms.
Additionally, this book is highly valuable for advanced courses in data science applied to finance, as well as for professionals seeking to automate investment and portfolio management processes. Its project-based approach reinforces active learning, making it ideal for hands-on financial programming classes. Furthermore, the book delves into advanced models such as Black-Litterman and Risk Parity, essential tools for those aiming to design optimal investment strategies based on data and quantitative models.
A particularly innovative aspect of the book is its inclusion of reinforcement learning to optimize investment strategies and financial planning, an emerging trend in fintech.
The book covers fundamental concepts in automated financial advising, financial tools and planning, and portfolio construction and management. The authors present a clear methodology for applying advanced programming tools to real-world financial problems. The use of libraries such as NumPy, Pandas, SciPy, and CVXPY enables readers to efficiently implement optimization and financial analysis algorithms.
Additionally, this book is highly valuable for advanced courses in data science applied to finance, as well as for professionals seeking to automate investment and portfolio management processes. Its project-based approach reinforces active learning, making it ideal for hands-on financial programming classes. Furthermore, the book delves into advanced models such as Black-Litterman and Risk Parity, essential tools for those aiming to design optimal investment strategies based on data and quantitative models.
A particularly innovative aspect of the book is its inclusion of reinforcement learning to optimize investment strategies and financial planning, an emerging trend in fintech.
February 23, 2025
This book is a practical & comprehensive guide to building a robo-advisor using Python for DIY investors.
What I liked about the book:
- It discusses advanced techniques like reinforcement learning, convex optimization, Monte Carlo methods, and Black-Litterman model with clear explanations.
- It covers topics such as tax-efficient withdrawal strategies, tax-aware rebalancing and tax-loss harvesting which I have not seen elsewhere.
- It includes Python code examples that illustrate and implement the algorithms that are employed.
The github repo acccompanying the book has the code examples. But I would have liked to see more flushed out notebooks that show end-to-end implementations rather than just code listings by chapter. I would also like to see (in an updated edition) how LLMs/AI agents can be used to develop and automate these strategies even further.
What I liked about the book:
- It discusses advanced techniques like reinforcement learning, convex optimization, Monte Carlo methods, and Black-Litterman model with clear explanations.
- It covers topics such as tax-efficient withdrawal strategies, tax-aware rebalancing and tax-loss harvesting which I have not seen elsewhere.
- It includes Python code examples that illustrate and implement the algorithms that are employed.
The github repo acccompanying the book has the code examples. But I would have liked to see more flushed out notebooks that show end-to-end implementations rather than just code listings by chapter. I would also like to see (in an updated edition) how LLMs/AI agents can be used to develop and automate these strategies even further.
February 19, 2025
I enjoyed the book. It is engaging. I learned from the book both some Python tricks and a lot of savings / investments good advices. I live in Europe rather than the states, and hence some specific details are different for me, yet I can easily follow and make the necessary adjustments for my situation.
February 25, 2025
This book provides an excellent financial explanation of concepts with deep understanding. Its a fine balance of the Financial concepts for investing and application of Python to automate the Investment Advice. It would be better if someone has some financial background to grasp this book fully but it is surely an excellent resource for anybody looking towards different techniques of investing.
Displaying 1 - 4 of 4 reviews