Training course on Python for Financial Analysis

Module 1: Introduction to Python for Finance

Python environment setup (Anaconda, Jupyter, VS Code)

Key libraries for finance: NumPy, pandas, matplotlib, statsmodels, scikit-learn

Writing clean and efficient Python code

Module 2: Data Handling with Python

Importing financial data from Excel, CSV, SQL, and APIs

Data cleaning, transformation, and wrangling with pandas

Working with time series indices and date-time objects

Module 3: Core Financial Calculations in Python

Time value of money: NPV, IRR, annuities

Loan amortization schedules

Bond pricing and yield calculations

Module 4: Investment Analysis and Portfolio Theory

Calculating returns (simple, log, annualized)

Portfolio expected return, variance, and Sharpe ratio

Portfolio optimization with cvxpy or scipy.optimize

Module 5: Risk Management with Python

Value-at-Risk (VaR) and Conditional VaR

Monte Carlo simulations for portfolio risk

Stress testing and scenario analysis

Module 6: Financial Time Series Analysis

Stationarity and unit root testing

ARIMA, GARCH, and VAR models with statsmodels

Forecasting stock prices, interest rates, or FX rates

Module 7: Asset Pricing and Valuation Models

CAPM and multi-factor models

Option pricing with Black-Scholes and binomial trees

Discounted cash flow (DCF) modeling

Module 8: Data Visualization and Reporting

Plotting financial trends with matplotlib and seaborn

Interactive charts with plotly and dash

Automating financial reports in Python

Module 9: Python Integration with Other Tools

Linking Python with Excel using openpyxl and xlwings

Querying financial databases and APIs (Bloomberg, Yahoo Finance, FRED)

Deploying financial models into dashboards or apps

Module 10: Machine Learning Applications in Finance (Intro)

Classification and regression for credit scoring

Clustering for customer segmentation

Predictive modeling for financial markets

Module 11: Case Studies in Financial Analysis

Portfolio optimization under constraints

Credit risk analysis using logistic regression

Stress testing a loan portfolio with macroeconomic data

Module 12: Capstone Project and Hands-On Workshop

Building a complete financial analysis project in Python

Real-world dataset application (e.g., stock market, credit portfolio, macro data)

Presentation of project findings and recommendations