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Core Technical - Advanced - Front Office Investment

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ZISHI
ZISHI
CO₂ prevented 11.595 kg

Awarded to graduates in the Investment Division who have successfully completed the Phase 3 in-region technical training for the Front Office stream. This badge recognises advanced capability in financial markets, investment products, derivatives, credit analysis, advanced financial modelling and Python programming for investment applications.

 

Modules Covered:

  • Insights into Global Commodity Markets (Parts 1 & 2)
  • Advanced Strategies in Structured Credit (Parts 1 & 2)
  • Maximizing Returns in Complex Markets (Parts 1 & 2)
  • Demystifying the Securities Trade Lifecycle (Parts 1 & 2)
  • Navigating Derivatives: Swap Products Simplified (Parts 1 & 2)
  • Credit Derivatives: Understanding Structures & Risks (Parts 1 & 2)
  • Credit Analysis: Thinking Beyond the Ratios (Parts 1 & 2)
  • Structured Success: Unlocking Investment Products (Parts 1 & 2)
  • Python Power-Up: Level Up Your Coding Skills
  • From Rows to Results: Building with Python DataFrames (Advanced)
  • Python for Time-Based Trends: A Timeseries Toolkit (Advanced)
  • Python Playbook: Grouping, Merging & Mastering Market Data (Advanced)
  • Trade It and Test It: Backtesting with Python (Advanced)
  • Optimize This: Smarter Portfolios with Python (Advanced)
  • The Pythonic Way: Writing Cleaner, Smarter Code (Advanced)
  • Pair Programming with Copilot: AI as Your Coding Assistant (Advanced)
  • Modelling Advanced: M&A
  • Modelling Advanced: Real Estate
  • Modelling Advanced: DCF
  • Modelling Advanced: Infrastructure
  • Modelling Advanced: Debt & LBO

Learning Outcomes

By earning this badge, participants will be able to:

  • Analyse the drivers and trends within global commodity markets.
  • Apply advanced structured credit concepts to evaluate investment opportunities and risks.
  • Evaluate opportunities in complex markets and assess portfolio performance under varying market conditions.
  • Explain the securities trade lifecycle and its operational considerations.
  • Assess the structure, applications and risks of derivative products, including swaps and credit derivatives.
  • Conduct advanced credit analysis using both quantitative metrics and qualitative judgement.
  • Evaluate the characteristics and applications of investment products within portfolio and client contexts.
  • Apply Python programming techniques to analyse, manipulate and interpret financial data, including DataFrames, grouping, merging and time series analysis.
  • Build, test and evaluate investment strategies using backtesting techniques.
  • Apply portfolio optimisation concepts to support investment decision-making.
  • Build and interpret advanced financial models across M&A, real estate, infrastructure, DCF and leveraged finance (debt & LBO) contexts.
  • Use AI-assisted coding tools responsibly to improve productivity, collaboration and code quality.
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Issue information
Completed on 25/08/2026
Expires on Does not expire