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May 18 - 20, 2026

Python for Investment Professionals

Register Now Agenda Program
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Location
Prague, NH Hotel Prague
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Price
€ 1,560 + VAT
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Lecturer
Andreas Steiner
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Language
English
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Evaluation
New
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By the end of this course, participants will be able to:

Download live market data from stock exchanges and other sources
Clean and prepare messy financial data
Calculate returns, volatility, TE, Sharpe ratios, VaR, drawdowns, information ratios
Build minimum spanning trees for correlation analysis
Supplement quantitative analysis with qualitative aspects and interpretations
Perform bootstrap analysis for risk metric uncertainty
Generate professional visualizations
Create automated Excel reports with formatting
Write reusable Python scripts for daily workflows
Transition repetitive Excel tasks to automated Python solutions
The purpose of this course is to give the participants an understanding of how Python can be used to solve real-world investment analytics problems in a practical and efficient way. Participants will learn to automate their workflows, build reproducible financial analyses, and create professional, data-driven reports using practical Python tools relevant for portfolio management, risk analysis, and performance reporting.

Course Overview
Python Foundations and Data Handling
On the first day, participants are introduced to Python as a practical tool for investment professionals transitioning from Excel. The day focuses on understanding the Python workflow, using Jupyter Notebooks, and mastering essential programming concepts through hands-on exercises. Participants learn to work with NumPy and Pandas for efficient data manipulation and explore how to import, structure, and analyse real financial data from Excel and other sources.

Market Data, Portfolio Analytics and Risk Measurement
On the second day, the course moves into real-world financial data and portfolio construction. Participants build data pipelines from European and US exchanges, clean and process time series, and compute key portfolio metrics such as returns, volatility, and drawdowns. The afternoon is devoted to correlation analysis and diversification techniques using minimum spanning trees, followed by modules on Value at Risk (VaR), Conditional VaR, and bootstrap methods to assess the uncertainty of risk metrics.

Performance Reporting and Automation
The final day focuses on performance attribution, professional reporting, and workflow automation. Participants learn to perform Brinson-Fachler attribution, generate automated performance reports and fund factsheets, and export fully formatted Excel and PDF outputs. The course concludes with creating production-ready Python scripts, scheduling automated processes, and exploring advanced applications such as optimization, machine learning, and real-time dashboards.

Target Audience
Portfolio managers, analysts, risk managers, and investment professionals who currently rely on Excel but want to scale their analytical capabilities with Python.

Prerequisites
  • Excel skills
  • No prior programming experience needed
  • Willingness to embrace a new workflow
Course Philosophy
"Vibe Coding" - Learning by Doing

This is not a theoretical programming course. You'll build real solutions to problems you face daily. The focus is on practical application: by the end of this course, you'll have working code that solves actual investment analytics problems and that serves you as a starting point for your own Python solutions.
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PDF brochure with all details about the Python for Investment Professionals seminar is available on request.
Participants are requested to bring their own notebook with Python (version 3.9 or higher) and Jupyter Notebooks pre-installed. Microsoft Excel is also required for integration exercises and report generation.

Program of the seminar: Python for Investment Professionals

The seminar timetable follows Central European Time (CET).

09.00 - 09.10 Welcome

09.10 - 12.30

Introduction and Setup Verification

  • Ensure Python is working on all laptops
  • Setup of all relevant course materials

Python Mindset for Excel Users

Module 1: Why Python When You Have Excel?

Discussion Topics:

  • What Python does that Excel can't (or struggles with)
  • When to stay in Excel vs. when to use Python
  • The Python ecosystem for finance
  • Success stories: Investment firms using Python

Key Concepts:

  • Python as a complement, not replacement
  • Workflow integration strategies
  • Building institutional knowledge through code
Module 2: Jupyter Notebooks - Your New Workspace

Live Demonstration:

  • What is a Jupyter notebook? (Excel worksheet meets Word document)
  • Creating your first notebook
  • Understanding cells:
    • Code cells (where Python runs)
    • Markdown cells (documentation and notes)
  • Running cells (Shift+Enter is your friend)
  • Saving and organizing notebooks
  • Exporting to PDF/HTML for sharing

Practical Tips:

  • Documenting your thought process
  • Adding formulas and explanations
  • Creating professional reports directly in notebooks
Module 3: Python Basics - The Excel Translation

Interactive Coding (following along)

Variables and Basic Operations:

  • Variables (like Excel cell references)
  • Lists (like Excel columns)
  • Basic operations
  • Printing results (like seeing cell values)

Excel-Python Translation Guide:

  • Cell reference → Variable
  • Range → List
  • Formula → Function
  • Worksheet → DataFrame
  • Workbook → Multiple DataFrames
  • VLOOKUP → merge()
  • Pivot Table → groupby()
  • IF statements → Conditional logic

Common Operations:

  • Addition, subtraction, multiplication, division
  • Loops (for repeating operations)
  • Conditions (if/else logic)
  • Functions (reusable code blocks)

12.30 - 13.30 Lunch break

13.30 - 17.30

NumPy & Pandas - Your New Excel

Module 4: NumPy Arrays - Supercharged Ranges

Why NumPy?

  • Think of it as Excel ranges on steroids
  • Vectorized operations (no more copy-paste formulas down!)
  • Lightning-fast calculations on large datasets
  • Foundation for all numerical computing in Python

Case Study: Portfolio Returns Calculation

  • Calculate returns and risks for a 10-stock portfolio
Module 5: Pandas DataFrames - Excel Tables Evolved

Why Pandas?

  • DataFrames = Excel tables, but much more powerful
  • Handles dates, strings, numbers seamlessly
  • Built-in functions for everything you do in Excel
  • Industry standard for data analysis

Case Study: Building a Multi-Currency Portfolio Holdings Table & Pivot Operations

Excel vs Pandas Quick Reference:

  • Excel: Insert column → Pandas: df['new_col'] = calculation
  • Excel: AutoFilter → Pandas: df[condition]
  • Excel: VLOOKUP → Pandas: merge()
  • Excel: Pivot Table → Pandas: groupby()
  • Excel: Sort → Pandas: sort_values()

Real Data: Importing from Multiple Sources

Module 6: Python - Excel Integration

Why This Matters:

  • Your data is still in Excel
  • Your colleagues use Excel
  • Reports often need Excel format
  • Bridge between both worlds

Case Study: Reading Portfolio Data from Excel

09.00 - 12.30

Module 7: Market Data from European & US Exchanges

Data Beyond Yahoo Finance:

  • Direct access to exchange data
  • Higher data quality
  • Real-time capabilities
  • Regulatory compliance

Case Study: Building a Market Data Pipeline – real-time data, daily updates of security and index data

  • Market Data: Deutsche Börse (Xetra), Euronext (Paris, Amsterdam, Brussels), SIX Swiss Exchange
  • Economic Data: FRED for Economic Data
  • Data from National and International Financial Organisations: BIS, ECB, SNB

Portfolio Construction & Optimization

Module 8: Data Cleaning - The Unglamorous Essential

Why This Matters:

  • Real data is messy
  • Missing prices, corporate actions, data errors
  • Clean data = reliable analysis
  • "Garbage in, garbage out"

Case Study: Cleaning Historical Price Data

  • Finding Issues
  • Handling Missing Data
  • Outliers
  • Corporate Actions
  • Dates
  • Resampling
  • Time Aggregating (Daily to Monthly)
Module 9: Portfolio Metrics

Case Study: Building a Portfolio Analytics Dashboard With Risk & Return Metrics, Graphical Summary Analysis, Traffic-Lights to Identify Issues

  • Calculate portfolio returns from individual stocks
  • Compute all major performance metrics
  • Analyse drawdowns comprehensively
  • Create summary statistics tables
  • Build reusable functions for metrics

12.30 - 13.30 Lunch break

13.30 - 17.30

Module 10: Correlation Analysis & Minimum Spanning Trees

Why Minimum Spanning Trees?

  • Visualize portfolio diversification structure
  • Identify clusters of correlated assets
  • Network-based risk analysis
  • Understand which stocks move together
  • Regulatory applications (systemic risk)

Case Study: Visual Portfolio Diversification Analysis with JPMorgan Long-Term Capital Market Assumptions, Stability of Diversification Over Time

  • Calculate correlation matrices
  • Transform correlations to distances
  • Build minimum spanning trees
  • Visualize portfolio structure
  • Identify diversification opportunities
  • Detect asset clusters
  • Calculate diversification metrics
  • Interpret MST for portfolio construction

Risk Measurement & Bootstrap Analysis

Module 11: VaR & CVaR - Risk Metrics

Case Study: Portfolio Risk Dashboard with Historical, Parametric and Monte Carlo VaR, CvaR, VaR Limit Breaches, Backtesting VaR Models (Kupiec Test)

  • Calculate VaR using three methods
  • Compute CVaR/Expected Shortfall
  • Understand when each method is appropriate
  • Backtest VaR models
  • Visualize risk metrics
Module 12: Bootstrap Methods & Confidence Intervals

Why Bootstrapping?

  • Understand uncertainty in risk metrics
  • Don't assume normality (returns aren't normal!)
  • Generate confidence bands for any metric
  • Regulatory stress testing applications
  • Client communication: "here's the range of possible outcomes"

Case Study A: Direct Data from Stock Exchanges

Case Study B: Bootstrap Confidence Intervals for Maximum Drawdown

The Problem:

  • Max drawdown is a single historical number
  • But what's the uncertainty around it?
  • Could it have been worse with different ordering?
  • Bootstrap answers: "What's the range of likely drawdowns?"

Case Study C: Bootstrap for Multiple Risk Metrics

Practical Applications:

  • Risk disclosure to investors
  • Regulatory stress testing
  • Capital allocation decisions
  • Performance fee hurdle rates
  • Client reporting with uncertainty

When to Use Bootstrap:

  • Small sample sizes
  • Non-normal distributions
  • Metrics without closed-form formulas
  • Communicating uncertainty to non-technical audiences

09.00 - 12.30

Performance Attribution & Analysis

Module 13: Return Attribution

Case Study: Sector-Based Performance Attribution

Background: Decompose active return into allocation and selection effects

  • Perform Brinson-Fachler attribution
  • Decompose active returns
  • Analyze sector contributions
  • Create attribution reports
  • Identify sources of alpha
Module 14: Performance Analytics Suite

Case Study: Automated Performance Report

  • Build reusable metric functions
  • Calculate comprehensive performance stats
  • Automate monthly reporting
  • Create tearsheets

Professional Reporting & Visualization

Module 15: Creating Fund Factsheets

Case Study: Automated Monthly Factsheet

  • Create multi-panel factsheets
  • Generate professional charts
  • Export publication-quality images
  • Build repeatable templates
  • HTML, PDF output
Module 16: Excel Report Generation

Case Study: Formatted Excel Reports

  • Generate multi-sheet workbooks
  • Apply Excel formatting from Python
  • Create professional reports
  • Automate monthly processes

12.30 - 13.30 Lunch break

13.30 - 17.30

Production-Ready Scripts & Automation

Module 17: From Notebook to Script

Why Scripts vs Notebooks:

  • Notebooks = Exploration
  • Scripts = Production
  • Scripts can be scheduled and automated

Case Study: Production Portfolio Analytics Script

  • Structure code with functions
  • Add error handling
  • Implement logging
  • Make scripts command-line executable
Module 18: Scheduling & Automation
  • Schedule Python scripts
  • Automate data updates
  • Set up error notifications
  • Build production workflows
Module 19: Some Tips for Building Your Own Workflow
Module 20: Going Further

Next Steps:

  • Advanced optimization (cvxpy, scipy.optimize)
  • Machine learning (scikit-learn)
  • Real-time data (websockets)
  • Web dashboards (Streamlit, Dash)

Resources:

  • GitHub repository
  • Community Slack
  • Monthly Q&A (3 months)

Summary and Conclusions of the Course

Location and Registration

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Venue

Prague

The seminar will be held in an attractive destination in the very heart of Europe.

More Information
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Accommodation

NH Hotel Prague

Accommodation is not a part of the seminar. Upon request, we will book your priority accommodation.

Hotel booking
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Registration

Registration Deadline

May 4, 2026

Register Now
Training catalogue in PDF
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