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.

PDF brožura s podrobnostmi o Python for Investment Professionals semináři je k dispozici na vyžádání.

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 semináře: Python for Investment Professionals

Seminář probíhá podle středoevropského času (CET).

Monday, May 18

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

Tuesday, May 19

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

Wednesday, May 20

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)

18. - 20. 5. 2026

Python for Investment Professionals

Místo

Cena

Lektor

Jazyk

Hodnocení

By the end of this course, participants will be able to:

Course Overview

Target Audience

Prerequisites

Course Philosophy

PDF brožura s podrobnostmi o Python for Investment Professionals semináři je k dispozici na vyžádání.

Program semináře: Python for Investment Professionals

09.00 - 09.10 Welcome

09.10 - 12.30

Introduction and Setup Verification

Python Mindset for Excel Users

Module 1: Why Python When You Have Excel?

Discussion Topics:

Key Concepts:

Module 2: Jupyter Notebooks - Your New Workspace

Live Demonstration:

Practical Tips:

Module 3: Python Basics - The Excel Translation

Interactive Coding (following along)

Variables and Basic Operations:

Excel-Python Translation Guide:

Common Operations:

12.30 - 13.30 Lunch break

13.30 - 17.30

NumPy & Pandas - Your New Excel

Module 4: NumPy Arrays - Supercharged Ranges

Why NumPy?

Module 5: Pandas DataFrames - Excel Tables Evolved

Why Pandas?

Excel vs Pandas Quick Reference:

Real Data: Importing from Multiple Sources

Module 6: Python - Excel Integration

Why This Matters:

09.00 - 12.30

Module 7: Market Data from European & US Exchanges

Data Beyond Yahoo Finance:

Portfolio Construction & Optimization

Module 8: Data Cleaning - The Unglamorous Essential

Why This Matters:

Module 9: Portfolio Metrics

12.30 - 13.30 Lunch break

13.30 - 17.30

Module 10: Correlation Analysis & Minimum Spanning Trees

Why Minimum Spanning Trees?

Risk Measurement & Bootstrap Analysis

Module 11: VaR & CVaR - Risk Metrics

Module 12: Bootstrap Methods & Confidence Intervals

Why Bootstrapping?

The Problem:

Practical Applications:

When to Use Bootstrap:

09.00 - 12.30

Performance Attribution & Analysis

Module 13: Return Attribution

Background: Decompose active return into allocation and selection effects

Module 14: Performance Analytics Suite

Professional Reporting & Visualization

Module 15: Creating Fund Factsheets

Module 16: Excel Report Generation

12.30 - 13.30 Lunch break

13.30 - 17.30

Production-Ready Scripts & Automation

Module 17: From Notebook to Script

Why Scripts vs Notebooks:

Module 18: Scheduling & Automation

Module 19: Some Tips for Building Your Own Workflow

Module 20: Going Further

Next Steps:

Resources:

Summary and Conclusions of the Course

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