Overview
What this challenge is about.
Price American Options with a Deep Hedging Notebook. Expert-level challenge in research. Conducting rigorous research on real questions, earn a blockchain-ve...
The Brief
What you'll do, and what you'll demonstrate.
Build a reference notebook comparing deep hedging to Longstaff-Schwartz Monte Carlo on a vanilla American put with realistic transaction costs.
This is not a research exercise. It is the work a researcher does to produce findings that withstand scrutiny. That distinction matters to every hiring manager who has seen candidates summarize papers and none who have produced original findings under expert review.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
Earning criteria — what you'll demonstrate
- Implement Longstaff-Schwartz Monte Carlo for American option pricing
- Train a deep hedger with a variance-plus-cost objective
- Design a fair comparison between a classical and a learning-based method
- Write clean research commentary new colleagues can learn from
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
AI and Quantitative Finance
Master · Applied Ai
Strong alignment
This challenge maps to AI and Quantitative Finance at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Deep Learning
Design and train neural networks for complex pattern recognition tasks.
- Stochastic Modeling
Apply stochastic modeling to solve real industry problems and demonstrate production-level capability.
- Derivatives Pricing
Apply derivatives pricing to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Monte Carlo Methods
Apply monte carlo methods to solve real industry problems and demonstrate production-level capability.
- Research Writing
Apply research writing to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
ML Researcher
Reproducing a paper and writing honest commentary on its limits is the literal day-one task for an ML researcher inside a quant fund.
This challenge sharpens
- deep-learning
- research-writing
- monte-carlo-methods
Research Scientist
Designing a fair comparison and grounding it in primary literature is exactly the rigor expected from a junior research scientist's first internal review.
This challenge sharpens
- stochastic-modeling
- derivatives-pricing
- research-writing