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Quantitative Finance

Machine Learning Credit Risk & Macro Stress Frameworks

Blend interpretable ML with macro stressors, calibrate PD/LGD models, and automate scenario libraries for regulatory-grade risk.

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Machine Learning Credit Risk & Macro Stress Frameworks
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What You'll Learn

  • •Develop advanced quantitative research pipelines with institutional-grade tools.
  • •Structure resilient portfolios that adapt to liquidity, risk, and execution constraints.
  • •Automate diagnostics across factor decay, forecast drift, and regime shifts.
  • •Ship production-ready research artefacts with peer-reviewed notebooks and lab critiques.

Who This Course Is For

  • Experienced quants scaling into portfolio leadership roles.
  • Systematic traders exploring new asset classes and risk overlays.
  • Risk engineers modernising analytics infrastructure.
  • PhD candidates converting theoretical advances into production alpha.

Course Syllabus

7-week studio · Advanced research · 5 projects · 2 certificates

Module 1 · Advanced Research Foundations

4 lessons · 80 minutes

  • Strategic kickoff & research roadmap
    18 min
  • Notebook showcase: institutional repo design
    20 min
  • Lab: project scaffolding starter kitLab
    28 min
  • Live cohort AMALive
    18 min

Module 2 · Signal Validation & Diagnostics

4 lessons · 84 minutes

  • Forecast decay analytics
    16 min
  • Residual stress testing playbook
    18 min
  • Lab: validation dashboardLab
    26 min
  • Peer review sessionLive
    16 min

Module 3 · Portfolio Construction Systems

4 lessons · 88 minutes

  • Adaptive allocation architectures
    17 min
  • Liquidity-aware optimization patterns
    18 min
  • Lab: allocator notebookLab
    27 min
  • Desk clinic: scaling strategies
    17 min

Module 4 · Risk & Capital Analytics

4 lessons · 92 minutes

  • Regulatory-ready risk analytics
    16 min
  • Scenario libraries & governance
    19 min
  • Lab: risk automation harnessLab
    25 min
  • Fireside: leading risk teamsLive
    14 min

Module 5 · Execution & Microstructure

4 lessons · 96 minutes

  • Execution stack architecture
    16 min
  • Transaction cost attribution
    17 min
  • Lab: microstructure simulatorLab
    24 min
  • Roundtable: vendor integrationLive
    15 min

Module 6 · Automation & Governance

4 lessons · 100 minutes

  • Alerting & observability patterns
    15 min
  • Scheduling & orchestration workflows
    18 min
  • Lab: automation pipelineLab
    26 min
  • Governance checklist workshopWorkshop
    15 min

Module 7 · Case Study Workshop

4 lessons · 104 minutes

  • Case clinic: real-world transformation
    20 min
  • Panel: lessons from production incidentsLive
    22 min
  • Lab: deploy the reference playbookLab
    28 min
  • Progress retro & Q&ALive
    18 min

Module 8 · Capstone & Demo Day

3 lessons · 108 minutes

  • Capstone briefing & rubric walkthrough
    18 min
  • Studio working sprintWorkshop
    60 min
  • Demo day with mentor feedbackLive
    42 min

Frequently Asked Questions

What background do I need to succeed here?

Comfort with Python and experience modeling markets is expected. Orientation modules refresh probability, optimization, and execution microstructure before lessons begin.

Can I access the material after the cohort ends?

Yes, you retain lifetime access to all videos, labs, community recordings, and updates.

Is there any community support?

Each cohort pairs you with a micro-studio for weekly critiques, plus optional mentor office hours for deeper dives.

Machine Learning Credit Risk & Macro Stress Frameworks | Free Quant Finance Course | QuantEdX