PBL
Bridgewater Project
Machine Learning for Financial Forecasting
Design and evaluate machine learning-based trading strategies using time series forecasting and AI tools for research and optimization.
Project
Bridgewater Project
Location
Online
Duration
8 Weeks
Upcoming Sessions
N/A
Outcomes
Develop algorithmic trading strategies using machine learning
Build time series forecasting models like ARIMA and LSTM
Apply backtesting techniques to evaluate trading performance
Use LLMs and GenAI for research and idea generation
Implement reinforcement learning for trading decision-making
You Will Get
Industry Guidance
Work directly with our project leads—experts and top researchers—who bring their real-world insights and expertise straight to your learning experience.
Research Experience
Collaborate with teammates and the project lead in a multi-week project to pursue novel questions in your research field.
Peer Networks
Engage with our PBL participants from all over the world. Collaborate with new peers and learn about their own research endeavours.
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A Strong Portfolio
Put your best foot forward in the PBL with a standout project and receive a PBL Evaluation Report that can be used as a recommendation letter for employers and grad schools.
Expert Guidance
Get personalized feedback to grow your research and innovation skills.
Deliverables
Real projects, lasting connections, and new opportunities beyond your program.
Project Deliverables
The final presentation of your 8 weeks could be a poster, written report, or a slide deck, all of which can be expanded on.
Research Extension
Utilize up to 5 additional meeting times with the project lead after the project’s conclusion to build your work out for publication or conference presentation.
Industry Network
Meet peers in your projects and participate in a global talent community both online and in-person.
Industry Application
Bridgewater Associates, one of the largest hedge funds globally, employs sophisticated algorithmic trading strategies similar to those developed in this PBL. Learners learning automated trading, market dynamics, and strategy development will find these skills highly relevant. Bridgewater’s focus on data-driven decision-making and risk management parallels the learning outcomes of this PBL, preparing learners for real-world applications in the financial industry.
Popular Industry Positions
Quantitative Analyst
Develops models to predict market movements.
Algorithmic Trader
Designs and implements automated trading strategies.
Risk Manager
Analyzes and mitigates financial risks within a portfolio.
Tracks
Track 1
AI-Assisted Custom Trading Strategy
Students will design and develop a personalized algorithmic trading strategy using machine learning models, with AI tools aiding research, coding, and evaluation.
Use LLMs and GenAI tools to define and plan trading strategies
Explore methods like reinforcement learning, NLP sentiment analysis, or computer vision for technical analysis
Select relevant datasets tailored to the chosen financial instruments
Develop, implement, and backtest custom ML-based trading models
Document and reflect on the use of AI tools and prompt engineering throughout the project
Track 2
Time Series Forecasting for Algorithmic Trading
Students will build time series forecasting models and apply them to create data-driven trading strategies, using both traditional and machine learning methods.
Develop forecasting models using ARIMA, Exponential Smoothing, LSTM
Apply time series cross-validation (TSCV) for tuning and model validation
Design trading strategies informed by forecasted trends
Backtest trading models on historical data to assess performance
Enhance modeling with AI tools for research, debugging, and feature engineering
PBL Journey
Online PBL Projects meet once a week for 8 weeks, and follow the research project format. Participants will meet the project lead, learn the conventions of the field and familiarize themselves with the tracks, then spend the middle portion of their time collaborating to develop their research.
At the end, participants will present their final project and receive feedback, with the opportunity to extend their timeline and develop the project in greater depth.
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Project Team
Our Academic Team plays a vital role in your PBL journey at Blended Learning. We are dedicated to enhancing your learning experience and ensuring your academic success. Our team consists of three distinct roles, each with a specific focus to support your Research Guidance, Project Progress, and Personal Growth.
Project Lead
Providing Industry and Research Guidance
Lead Applied Scientist at the Columbia-Dream Sports AI Innovation Center
He currently serves as a Senior Data Scientist at Netflix, where he focuses on enhancing the personalization algorithms that recommend content on users' homepages. Prior to joining Netflix, Michael worked as an Economist in Amazon Advertising, where he applied his expertise in economic modeling to digital advertising strategies. He earned his PhD from the MIT Sloan School of Management, specializing in the measurement of spillover effects in digital media. His research has been featured in prestigious academic journals, including the Proceedings of the National Academy of Sciences and Science Advances.
Academic Advisor
Tracking Your Project Development
The Academic Advisor is dedicated to your project completion success. They manage the progress of your PBL, guiding team formation, facilitating group discussions, and resolving conflicts. Additionally, the Academic Advisor ensures team member contributions are on track and provides logistical support, including attendance tracking, hosting recitation sessions, managing research support requests, and conducting student evaluations at the end of the PBL.
From Our Students
"After a night spent debugging, I suddenly discovered the program running perfectly. In that triumphant moment, you realize your true capability and success. The exhaustion fades, replaced by the thrill of knowing your skills and persistence led to this achievement, reaffirming your potential."
Nicole Y.
National University of Singapore
B.S. Economics
FAQs
What is the learning format of a PBL?
All PBLs are offered in an 8-week online format that begins with an orientation followed by subject setup overview of the different tracks. The majority of the session time is dedicated to project development, with a final presentation at the culmination of the 8 weeks. Many PBLs are also offered bi-annually in an on-campus format that consists of daily in-person meetings.
How long does each PBL cohort last？
One round of the Online PBL cohort lasts 8 weeks, preceded bys a pre-PBL orientation week. Each On-Campus PBL usually has 8 in person meetings, with intensive classroom education and collaboration. This means the biggest difference between online and on-campus PBLs is time participants have in between meetings.
How can I be more academically prepared before the PBL starts?
Review the Blended Learning Insights sent by the Academic Advisor and familiarize yourself with the project topic and pre-learning materials. Ensure you have all necessary softwares and other resources needed for the PBL.
For each PBL cohort, will I work in teams? Are PBL team members self-selected or assigned?
Yes, you will work in teams for each round of the PBL Cohort. Each team has 3 to 6 participants, organized by the Academic Team. The Academic Advisor will organize groupings based on students' backgrounds, preferred track, and skills.
Can I work with the Project Lead on my project after the PBL ends?
Yes, with your AI + X Research Plan, you may request up to five PBL Research Extension meetings, where you work with the project lead to develop your project into a working manuscript. To schedule a PBL Research Extension meeting, talk to your Academic Advisor at the conclusion of your PBL.
What do I receive at the end of the PBL?
At the conclusion of the PBL cohort, you can request a PBL Evaluation Report which summarizes the PBL content, the hours you spent, the track you chose, and includes a recommendation letter from the Project Lead (for eligible participants who completed the project successfully).
Is attendance mandatory for PBL Live Sessions and Recitation Sessions?
Yes, attendance is mandatory for both PBL Live Sessions and Recitation Sessions. Participants with three or more unexcused absences forfeit their eligibility for a PBL Evaluation Report.
Do I need to have my camera on during online PBL Live Sessions?
Yes, you must have your camera on during online PBL Live Sessions. Participants with cameras off will be marked as absent. This is meant to encourage active engagement and participation in meetings.