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Robo-Advisor with Python

Robo-Advisor with Python

By : Aki Ranin
4.2 (5)
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Robo-Advisor with Python

Robo-Advisor with Python

4.2 (5)
By: Aki Ranin

Overview of this book

Robo-advisors are becoming table stakes for the wealth management industry across all segments, from retail to high-net-worth investors. Robo-advisors enable you to manage your own portfolios and financial institutions to create automated platforms for effective digital wealth management. This book is your hands-on guide to understanding how Robo-advisors work, and how to build one efficiently. The chapters are designed in a way to help you get a comprehensive grasp of what Robo-advisors do and how they are structured with an end-to-end workflow. You’ll begin by learning about the key decisions that influence the building of a Robo-advisor, along with considerations on building and licensing a platform. As you advance, you’ll find out how to build all the core capabilities of a Robo-advisor using Python, including goals, risk questionnaires, portfolios, and projections. The book also shows you how to create orders, as well as open accounts and perform KYC verification for transacting. Finally, you’ll be able to implement capabilities such as performance reporting and rebalancing for operating a Robo-advisor with ease. By the end of this book, you’ll have gained a solid understanding of how Robo-advisors work and be well on your way to building one for yourself or your business.
Table of Contents (22 chapters)
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1
Part 1: The Basic Elements of Robo-Advisors
6
Part 2: Building Your Own Robo-Advisor
15
Part 3: Running and Operating Your Own Robo-Advisor

Summary

In this chapter, we rounded out our suite of Robo-advisor capabilities by adding batch processes for dividends and fees. We started with processing dividends. First, we received a dividend payout file from the custodian. We ran through that file using pandas to extract which dividends should be allocated to which goals. This turned out as a reusable function that can be scheduled to run on a daily basis on your platform.

The second part of the chapter talked about fees. We focused on a scenario of calculating monthly platform AUM fees that we could send to the broker to extract on our behalf. We started off by calculating today’s AUM, and then expanded that to cover the average daily AUM for the last month using market data from yfinance. Finally, we calculated fees using a 10 bps AUM fee. We put all this together into a reusable function that generates a CSV file that could be sent to the broker to execute on.

The final chapter of this book will discuss the impact...

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Robo-Advisor with Python
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