Optimize Stop-Loss Policies with Dynamic Programming at a Quant Fund
Overview
What this challenge is about.
Calibrate a Markov model, solve stop-loss actions with backward induction, back-test the DP policy, and earn your verifiable certificate.
The scenario
The fund (around 40 staff, around USD 600 million assets under management) had two double-digit drawdowns in the past 18 months that the current fixed-stop policy was meant to prevent.
The Brief
What you'll do, and what you'll demonstrate.
Beat the current fixed-percentage stop-loss policy on Sharpe and max drawdown using a state-dependent DP policy in back-test.
Earning criteria — what you'll demonstrate
- Calibrate a discrete Markov model from financial time series
- Implement backward induction for finite-horizon decision problems
- Run an honest back-test (out-of-sample, no peek-ahead)
- Communicate model risk to a risk-committee 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.
- Dynamic Programming
Apply dynamic programming to solve real industry problems and demonstrate production-level capability.
- Backward Induction
Apply backward induction to solve real industry problems and demonstrate production-level capability.
- State Modeling
Apply state modeling to solve real industry problems and demonstrate production-level capability.
- Back Testing
Apply back testing to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Risk Modeling
Apply risk modeling 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:
Applied AI Scientist
DP-based risk-policy design with honest back-testing is the kind of quantitative work applied AI scientists do at funds and fintechs.
This challenge sharpens
- dynamic-programming
- back-testing
- risk-modeling
Data Scientist
State-model calibration on financial time series is a foundational data-scientist skill on quant teams.
This challenge sharpens
- state-modeling
- back-testing
- python
ML Researcher
Finite-horizon DP is the on-ramp to deeper RL research; this challenge proves the student can ship the simpler primitive.
This challenge sharpens
- dynamic-programming
- backward-induction
- state-modeling