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Build a Robo-Advisor with Python
Take control of your wealth management by building your own reliable, effective, and automated financial advisor tool.
Automated digital financial advisors—also called robo-advisors—manage billions of dollars in assets. Follow the step-by-step instructions in this hands-on guide, and you’ll learn to build your robo-advisor capable of managing a real investing strategy.
In Build a Robo-Advisor with Python (From Scratch) you’ll learn how
• 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 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
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. Build a Robo-Advisor with Python (From Scratch) teaches you to develop one of these powerful, flexible tools using popular and free Python libraries. You’ll master practical Python skills in demand in financial services, and financial planning skills that will help you take the best care of your money. All examples are accompanied by working Python code, and are easy to adjust for investors anywhere in the world.
About the technology
Millions of investors use robo-advisors as an alternative to human financial advisors. In this one-of-a-kind guide, you’ll learn how to build one of your own. Your robo-advisor will assist you with all aspects of financial planning, including saving for retirement, creating a diversified portfolio, and decreasing your tax bill. And along the way, you’ll learn a lot about Python and finance!
About the book
Build a Robo-Advisor with Python (From Scratch) guides you step-by-step, feature-by-feature as you create a robo-advisor from the ground up. As you go, you’ll dive into techniques like reinforcement learning, convex optimization, and Monte Carlo methods that you can apply even outside the field of FinTech. When you finish, your powerful assistant will be able to create optimal asset allocations, rebalance investments while minimizing taxes, and more.
What's inside
• Advanced portfolio construction techniques
• Tax-loss harvesting, sequencing of retirement withdrawals, and asset location
• Financial planning using AI and Monte Carlo simulations
• Rebalancing methods to track a portfolio over time
About the reader
Accessible to anyone with a basic knowledge of Python and finance—no special skills required.
About the author
Rob Reider is a quantitative hedge fund portfolio manager. He holds a PhD in Finance from The Wharton School and is an Adjunct Professor at NYU. Alex Michalka is head of investments research at Wealthfront. He holds a PhD from Columbia University.
Automated digital financial advisors—also called robo-advisors—manage billions of dollars in assets. Follow the step-by-step instructions in this hands-on guide, and you’ll learn to build your robo-advisor capable of managing a real investing strategy.
In Build a Robo-Advisor with Python (From Scratch) you’ll learn how
• 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 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
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. Build a Robo-Advisor with Python (From Scratch) teaches you to develop one of these powerful, flexible tools using popular and free Python libraries. You’ll master practical Python skills in demand in financial services, and financial planning skills that will help you take the best care of your money. All examples are accompanied by working Python code, and are easy to adjust for investors anywhere in the world.
About the technology
Millions of investors use robo-advisors as an alternative to human financial advisors. In this one-of-a-kind guide, you’ll learn how to build one of your own. Your robo-advisor will assist you with all aspects of financial planning, including saving for retirement, creating a diversified portfolio, and decreasing your tax bill. And along the way, you’ll learn a lot about Python and finance!
About the book
Build a Robo-Advisor with Python (From Scratch) guides you step-by-step, feature-by-feature as you create a robo-advisor from the ground up. As you go, you’ll dive into techniques like reinforcement learning, convex optimization, and Monte Carlo methods that you can apply even outside the field of FinTech. When you finish, your powerful assistant will be able to create optimal asset allocations, rebalance investments while minimizing taxes, and more.
What's inside
• Advanced portfolio construction techniques
• Tax-loss harvesting, sequencing of retirement withdrawals, and asset location
• Financial planning using AI and Monte Carlo simulations
• Rebalancing methods to track a portfolio over time
About the reader
Accessible to anyone with a basic knowledge of Python and finance—no special skills required.
About the author
Rob Reider is a quantitative hedge fund portfolio manager. He holds a PhD in Finance from The Wharton School and is an Adjunct Professor at NYU. Alex Michalka is head of investments research at Wealthfront. He holds a PhD from Columbia University.
- GenresFinanceComputer Science
334 pages, Kindle Edition
Published April 22, 2025
5 people are currently reading
19 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