Python For Finance: Next Steps
Diving into financial modeling
Topic: Software Development
Python is a powerful tool for modeling due to its simplicity and robust capabilities, including libraries for mathematical operations, optimization, visualization, manipulation, and more. Combined with its user-friendly environment, Python is quite handy for financial modeling in particular.
Expert Abdullah Karasan walks you through performing more challenging financial tasks with Python, such as simulation analysis. You’ll dive into Monte Carlo simulation and its applicability, then get hands-on to conduct forecasting analysis via Monte Carlo simulation and generate a stock price using the Black-Scholes-Merton equation. Along the way, you’ll get an introduction to option pricing via Monte Carlo simulation—a very hot topic nowadays.
What you'll learn-and how you can apply it
By the end of this live online course, you’ll understand:
- How to effectively use Python in finance
- How and why to conduct simulation in finance
- How to forecast stock price via simulation
And you’ll be able to:
- Perform stock price simulation
- Price an option
- Optimize your portfolio
This training course is for you because...
- You’re a financial analyst who wants to improve your financial modeling skills.
- You want to improve your finance knowledge.
- You want to learn how to adapt Python to finance while building your Python skills.
- A basic understanding of statistics (central tendency and dispersion measures, distributions, etc.)
- A general knowledge of finance concepts (risk, return, correlation, covariance, etc.)
- Familiarity with Python (for loops and if clauses, pandas, NumPy, etc.)
- Take Python for Finance First Steps (live online training course with Abdullah Karasan)
About your instructor
Abdullah Karasan was born in Berlin, Germany. After he studied Economics and Business Administration, he obtained his master’s degree from the University of Michigan-Ann Arbor and his PhD in Financial Mathematics from Middle East Technical University (METU)-Ankara. He worked as a Treasury Controller at the Undersecretariat of the Treasury in Turkey. More recently, he has been working as Principal Data Science consultant and instructor at Thinkful and Magnimind.
His research fields are financial modeling, stochastic analysis, risk modeling machine learning, deep learning. Along with his researches, he is writing a book titled "Machine Learning for Financial Risk Management with Python.
The timeframes are only estimates and may vary according to how the class is progressing
Simulation analysis (50 minutes)
- Presentation: Introduction to simulation analysis; the logic of simulation; the Monte Carlo method for simulation; stock price simulations via Monte Carlo.
- Jupyter notebook exercise: Simulate stock prices using the Monte Carlo technique
Break (5 minutes)
Simulation analysis and portfolio optimization (50 minutes)
- Presentation: The Black-Scholes-Merton and Markowitz models in finance; how to price an option via Black-Scholes-Merton model using Monte Carlo simulation; how to form an optimum portfolio
- Jupyter notebook exercise: Price an option and find the optimum portfolio
Break (5 minutes)
Portfolio optimization and efficient frontiers (50 minutes)
- Presentation: Why we apply optimizations and have efficient frontiers; portfolio optimization and efficient frontier key concepts; obtaining an efficient frontier with Python
- Jupyter notebook exercise: Draw an efficient frontier and select the optimum portfolio in Python
Wrap-up and Q&A (10 minutes)