Build a Robo Advisor with Python (From Scratch) (MEAP V01)

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.