Page 11 - MONECO Financial Training Catalogue

PYTHON FOR INVESTMENT PROFESSIONALS

Module 3: Python Basics – The Excel Translation • Your colleagues use Excel
Interactive Coding (following along) • Reports often need Excel format
Variables and Basic Operations: • Bridge between both worlds
• Variables (like Excel cell references) Case Study: Reading Portfolio Data from Excel
• Lists (like Excel columns)
• Basic operations TUESDAY, MAY 19
• Printing results (like seeing cell values)
00
09 –12 30
Excel-Python Translation Guide:
• Cell reference → Variable Module 7: Market Data from European & US Exchanges
• Range → List Data Beyond Yahoo Finance:
• Formula → Function • Direct access to exchange data
• Worksheet → DataFrame • Higher data quality
• Workbook → Multiple DataFrames • Real-time capabilities
• VLOOKUP → merge() • Regulatory compliance
• Pivot Table → groupby() Case Study: Building a Market Data Pipeline – real-time
• IF statements → Conditional logic
data, daily updates of security and index data
Common Operations: • Market Data: Deutsche Börse (Xetra), Euronext (Paris,
• Addition, subtraction, multiplication, division Amsterdam, Brussels), SIX Swiss Exchange
• Loops (for repeating operations) • Economic Data: FRED for Economic Data
• Conditions (if/else logic) • Data from National and International Financial
• Functions (reusable code blocks) Organisations: BIS, ECB, SNB
Portfolio Construction & Optimization
30
30
12 –13
Lunch break Module 8: Data Cleaning – The Unglamorous Essential
30
13 –17 30 Why This Matters:
NumPy & Pandas – Your New Excel • Real data is messy
• Missing prices, corporate actions, data errors
Module 4: NumPy Arrays – Supercharged Ranges • Clean data = reliable analysis
Why NumPy? • “Garbage in, garbage out”
• Think of it as Excel ranges on steroids
• Vectorized operations (no more copy-paste formulas down!) Case Study: Cleaning Historical Price Data
• Lightning-fast calculations on large datasets • Finding Issues
• Foundation for all numerical computing in Python • Handling Missing Data
• Outliers
Case Study: Portfolio Returns Calculation • Corporate Actions
• Calculate returns and risks for a 10-stock portfolio
• Dates
• Resampling
Module 5: Pandas DataFrames – Excel Tables Evolved • Time Aggregating (Daily to Monthly)
Why Pandas?
• DataFrames = Excel tables, but much more powerful
• Handles dates, strings, numbers seamlessly Module 9: Portfolio Metrics
• Built-in functions for everything you do in Excel Case Study: Building a Portfolio Analytics Dashboard
• Industry standard for data analysis With Risk & Return Metrics, Graphical Summary Analysis,
Traffic-Lights to Identify Issues
Case Study: Building a Multi-Currency Portfolio Holdings • Calculate portfolio returns from individual stocks
Table & Pivot Operations • Compute all major performance metrics
Excel vs Pandas Quick Reference: • Analyse drawdowns comprehensively
• Excel: Insert column → Pandas: df[‘new_col’] = calculation • Create summary statistics tables
• Excel: AutoFilter → Pandas: df[condition] • Build reusable functions for metrics
• Excel: VLOOKUP → Pandas: merge()
30
30
• Excel: Pivot Table → Pandas: groupby() 12 –13
• Excel: Sort → Pandas: sort_values() Lunch break
30
13 –17 30
Real Data: Importing from Multiple Sources
Module 6: Python – Excel Integration Module 10: Correlation Analysis & Minimum Spanning
Why This Matters: Trees
• Your data is still in Excel Why Minimum Spanning Trees?
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