Stock Portfolio Optimization with Python
Workshop Details
Brought to you by the analytics engineers and founding team of Sectors, Indonesia's financial data suite.
Language
English
Date
21st November, 2024
Time
1800 to 2100 (GMT+7)
Medium
Zoom Conferencing (Online)
Bundled Learning Experience
A practical, hands-on introduction to using Python for stock portfolio construction, leveraging real-time financial data, data-driven analysis, and a deep dive into various portfolio optimization models in Python.
During the workshop, your instructor will guide you through the process of building a stock portfolio optimization model using Python. You will learn how to leverage financial data, apply key optimization techniques like Mean-Variance Optimization, and use APIs to create data-driven investment strategies. By the end, you'll have the skills to construct and optimize your own stock portfolios based on real-time market data.
Add $5 for Workshop + Practicum
For an extra $5, you can upgrade your participation to the Workshop + Practicum tier (optional). Here's the offer:
- $9 workshop admission included
- $98 worth of Sectors API subscription included
- $39 worth of Supertype Fellowship+ included (peer to peer learning group, mentorship, post-workshop implementation support)
After the workshop, all Practicum members are invited to join the Supertype Fellowship, where you can continue building your financial analytics dashboard, access live financial data, and collaborate with other community members using the free API credits provided to you. Whether you're new to working with live financial data, looking to enhance your financial analysis skills, or simply curious about creating interactive dashboards with a low-code approach, this workshop is designed for you.
Follow along the last 1 hour of the workshop as a Workshop + Practicum member to earn a certificate of completion. We issue this certificate of completion as proof of demonstrable proficiency, with a difficulty level suited for non-programmers. You will be asked to submit your dashboard, and a mentor will assess it and award a certificate as acknowledgment of your competency.
Your Instructor
Rian Yan
Hailing from sunny Lisbon, Portugal, Rian is a data science consultant at Supertype with a strong passion in finance and investing. From his tech consulting gig at Robert Walters and LG Electronics (both in Lisbon), he has accumulated a wealth of experience and consistently awarded for his work in market research and data analysis. He is now pursuing his second Master's degree, in Computer Science and Data Science from University of Wolverhampton, UK.
Rian is also an individual contributor to the Sectors project, Indonesia's most comprehensive financial data platform, where his chief contribution is in modelling the IDX Greed-and-Fear Index, the only such index in Indonesia that measures the market sentiment of retail investors. As the lead instructor of this workshop, Rian will guide you through a series of quantitative and portfolio optimization techniques, from Mean-Variance Optimization to the Monto Carlo Simulation; from calculating the Sharpe Ratio, to rating your porfolio with the Efficient Frontier model. UK.
Rian has lived in three different countries (Portugal, China, and Sweden), travelled to more than 30 countries, and speak English, Mandarin Chinese, Portuguese and German.
More about the Workshop
The Organizers
This code-along workshop is created and organized by Supertype, a data science and analytics consultancy based in Jakarta, Indonesia. Supertype is a team of data scientists, engineers, and analytics developers who are passionate about helping businesses make better decisions through data.
Supertype is the developer of Sectors, a financial analytics platform built for the Indonesian stock market.
This workshop is supported by Algoritma, a leader in data science and analytics education in Indonesia. Algoritma offers a wide range of courses in data science, analytics, and programming, and has trained thousands of professionals in the region.
Workshop Topic & Coverage
We cover key technical aspects of stock portfolio optimization and financial data analysis, walking you through the steps to build data-driven investment models using Python. Participants will learn to leverage financial APIs, apply optimization techniques like Mean-Variance Optimization and Monte Carlo Simulation, and integrate real-time market data. By the end, you'll be equipped with practical skills and code examples to construct and optimize your own investment portfolios.
Syllabus
Introduction to Portfolio Optimization
Understand the basics of stock investing and the role of portfolio optimization in managing risk and returns.
Portfolio Diversification Models
How to build a diversified portfolio that maximizes returns and minimizes risks, using live financial data from Sectors API in Python
Portfolio Allocation & Scipy Optimization
How to use Scipy's optimization functions to allocate your portfolio based on your risk tolerance and investment goals
Working with Financial APIs
Learn how to extract and prepare stock data using Sectors Financial API for analysis.
Sharpe Ratio & Efficient Frontiers
How to calculate the Sharpe Ratio and use it to rate your portfolio, and how to build the Efficient Frontier model to optimize your portfolio
Advanced Optimization Techniques
Use Python’s Scipy library for precise optimization and compare different modeling approaches.
