Build a Robo-Advisor with Python (from Scratch).

Format:

Book

Author/Creator:

Reider, Rob.

Language:

English

Subjects (All):

Portfolio management--Software--Programming.

Portfolio management.

Investments--Software--Programming.

Investments.

Robots--Programming.

Robots.

Python (Computer program language).

Computer programming.

Artificial intelligence.

Physical Description:

1 online resource (266 pages)

Edition:

1st ed.

Place of Publication:

New York : Manning Publications Co. LLC, 2025.

Summary:

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.

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! Building 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 Fin Tech. When you finish, your powerful assistant will be able to create optimal asset allocations, rebalance investments while minimizing taxes, and more.

Contents:

Part 1. Basic tools and building blocks. The rise of robo-advisors

An introduction to portfolio construction

Estimating expected returns and covariances

ETFs: the building blocks of robo-portfolios

Part 2. Financial planning tools. Monte Carlo simulations

Financial planning using reinforcement learning

Measuring and evaluating returns

Asset location

Tax-efficient withdrawal strategies

Part 3. Portfolio construction. Optimization and portfolio construction

Asset allocation by risk: introduction to risk parity

The Black-Litterman model

Part 4. Portfolio management. Rebalancing: tracking a target portfolio

Tax-loss harvesting: improving after-tax returns.

Notes:

Description based on publisher supplied metadata and other sources.

Includes index.

ISBN:

9781638357278

1638357277

OCLC:

1514629857