Page 10 - MONECO Financial Training Catalogue

PYTHON FOR INVESTMENT PROFESSIONALS

DATES: May 18 – 20, 2026 • PRICES: € 1,560 In-class • LOCATION: Prague, NH Hotel Prague
By the end of this course, participants will be able to:

• Download live market data
from stock exchanges and Purpose of the Course
The purpose of this course is to give the participants an understanding of how Python
other sources can be used to solve real-world investment analytics problems in a practical and efficient
• Clean and prepare messy way. Participants will learn to automate their workflows, build reproducible financial
financial data analyses, and create professional, data-driven reports using practical Python tools
• Calculate returns, volatility, relevant for portfolio management, risk analysis, and performance reporting.
TE, Sharpe ratios, VaR, Course Overview
drawdowns, information Python Foundations and Data Handling
ratios On the first day, participants are introduced to Python as a practical tool for investment
• Build minimum spanning professionals transitioning from Excel. The day focuses on understanding the Python
trees for correlation analysis workflow, using Jupyter Notebooks, and mastering essential programming concepts
• Supplement quantitative through hands-on exercises. Participants learn to work with NumPy and Pandas for
analysis with qualitative efficient data manipulation and explore how to import, structure, and analyse real
financial data from Excel and other sources.
aspects and interpretations
• Perform bootstrap analysis Market Data, Portfolio Analytics and Risk Measurement
for risk metric uncertainty On the second day, the course moves into real-world financial data and portfolio
• Generate professional construction. Participants build data pipelines from European and US exchanges, clean
and process time series, and compute key portfolio metrics such as returns, volatility,
visualizations and drawdowns. The afternoon is devoted to correlation analysis and diversification
• Create automated Excel techniques using minimum spanning trees, followed by modules on Value at Risk (VaR),
reports with formatting Conditional VaR, and bootstrap methods to assess the uncertainty of risk metrics.
• Write reusable Python scripts Performance Reporting and Automation
for daily workflows The final day focuses on performance attribution, professional reporting, and workflow
• Transition repetitive Excel automation. Participants learn to perform Brinson-Fachler attribution, generate
tasks to automated Python automated performance reports and fund factsheets, and export fully formatted
solutions 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.

MONDAY, MAY 18 • Workflow integration strategies
00
09 – 09 10 • Building institutional knowledge through code
Welcome
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09 –12 30 Module 2: Jupyter Notebooks – Your New Workspace
Introduction and Setup Verification Live Demonstration:
• Ensure Python is working on all laptops • What is a Jupyter notebook? (Excel worksheet meets Word
• Setup of all relevant course materials document)
Python Mindset for Excel Users • Creating your first notebook
• Understanding cells:
Module 1: Why Python When You Have Excel? – Code cells (where Python runs)
Discussion Topics: – Markdown cells (documentation and notes)
• What Python does that Excel can’t (or struggles with) • Running cells (Shift+Enter is your friend)
• When to stay in Excel vs. when to use Python • Saving and organizing notebooks
• The Python ecosystem for finance • Exporting to PDF/HTML for sharing
• Success stories: Investment firms using Python
Practical Tips:
Key Concepts: • Documenting your thought process
• Python as a complement, not replacement • Adding formulas and explanations
• Creating professional reports directly in notebooks
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