Forecast Intraday FX Volatility for a London Liquidity Desk
Overview
What this challenge is about.
Forecast Intraday FX Volatility for a London Liquidity Desk. Advanced challenge in code. Writing production code that solves real engineering problems, earn ...
The Brief
What you'll do, and what you'll demonstrate.
Build and validate an intraday FX volatility forecaster that beats an EWMA baseline, especially around scheduled macro releases.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
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
- Apply machine learning to a realistic quant-finance forecasting problem
- Design walk-forward back-tests that avoid look-ahead bias
- Evaluate volatility models with both statistical and economic metrics
- Communicate model risk to a non-ML governance audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Time Series Forecasting
Apply time series forecasting to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Model Validation
Apply model validation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Quantitative Finance
Apply quantitative finance to solve real industry problems and demonstrate production-level capability.
- Backtesting
Apply backtesting 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:
Data Scientist
End-to-end feature engineering on tick data with honest evaluation builds the core data-scientist muscle of moving from raw data to a defensible model.
This challenge sharpens
- feature-engineering
- time-series-forecasting
- backtesting
Machine Learning Engineer
Productionizing this notebook would be the natural next step; the reproducible pipeline and risk checklist are the artifacts MLEs hand to model risk teams.
This challenge sharpens
- python
- backtesting
- model-validation