Forecast Intraday FX Volatility for a London Liquidity Desk
Overview
What this challenge is about.
Resample FX tick data, engineer volatility features, train two models, and back-test with walk-forward splits for a verifiable certificate.
The scenario
The desk quotes around USD 6 billion notional per day across the six pairs and books most of its P&L from spread capture; a 10 percent better volatility forecast around scheduled events would meaningfully tighten their quoting policy.
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.
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