Build a Robo-Advisor with Python (From Scratch)
eBook - ePub

Build a Robo-Advisor with Python (From Scratch)

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Build a Robo-Advisor with Python (From Scratch)

About this book

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 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 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.

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Yes, you can access Build a Robo-Advisor with Python (From Scratch) by Rob Reider in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Manning
Year
2025
eBook ISBN
9781638357278
Edition
0

Table of contents

  1. Build a Robo-Advisor with Python (From Scratch)
  2. copyright
  3. contents
  4. preface
  5. acknowledgments
  6. about this book
  7. about the authors
  8. about the cover
  9. Part 1 Basic tools and building blocks
  10. 1 The rise of robo-advisors
  11. 2 An introduction to portfolio construction
  12. 3 Estimating expected returns and covariances
  13. 4 ETFs: The building blocks of robo-portfolios
  14. Part 2 Financial planning tools
  15. 5 Monte Carlo simulations
  16. 6 Financial planning using reinforcement learning
  17. 7 Measuring and evaluating returns
  18. 8 Asset location
  19. 9 Tax-efficient withdrawal strategies
  20. Part 3 Portfolio construction
  21. 10 Optimization and portfolio construction
  22. 11 Asset allocation by risk: Introduction to risk parity
  23. 12 The Black–Litterman model
  24. Part 4 Portfolio management
  25. 13 Rebalancing: Tracking a target portfolio
  26. 14 Tax-loss harvesting: Improving after-tax returns