Python for Finance

Name: Python for Finance Course
Price: 36 USD

with Martin Ganchev and Ned Krastev

4.8/5

(478)

Blend investment analysis skills with Python programming: Master financial analysis using Python

6 hours of content 9162 students

Start for free

What you get:

6 hours of content
27 Interactive exercises
224 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Python for Finance

A course by Martin Ganchev and Ned Krastev

Start for free

What you get:

6 hours of content
27 Interactive exercises
224 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Start for free

What you get:

6 hours of content
27 Interactive exercises
224 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

What you learn

Obtain the efficient frontier in Python to visualize optimal portfolios.
Calculate and compare rates of return and associated risk of securities.
Measure portfolio risk and identify idiosyncratic and market risk types.
Apply Markowitz optimization to construct optimal investment portfolios.
Leverage CAPM and Monte Carlo simulations to estimate return and risk.

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

Python for Finance is the crossing point where programming in Python blends with financial theory. Together, they give you the know-how to apply that theory into practice and real-life scenarios. In a world where individuals and companies are aiming to become more and more autonomous, your ability to combine programming skills with financial data will allow you to create independent analyses. And that competency will give you an edge over your competitors or in your personal investments. To prepare you for these multi-faceted challenges, this course provides the relevant topics in financial theory and their hands-on application in Python.

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1.1 Course Introduction

4 min

1.2 Must-Have Packages for Finance and Data Science

5 min

1.3 Working with Arrays

6 min

1.4 Generating Random Numbers

3 min

1.5 Important Note on Using Online Financial Data Sources

1 min

1.6 Using Financial Data in Python

3 min

Curriculum

1. Useful Tools

11 Lessons 42 Min

In this course, we assume that you have already gone through our Introduction to Python course. However, you can add more layers to what you have learnt there. In this section, we will add more Python programming skills to your arsenal, explaining OOP, importing modules, in particular - the pandas-datareader module, as it allows us to download financial data and more.

Course Introduction
4 min
Must-Have Packages for Finance and Data Science
5 min
Working with Arrays
6 min
Generating Random Numbers
3 min
Important Note on Using Online Financial Data Sources Read now
1 min
Using Financial Data in Python
3 min
Importing and Organizing Data in Python - Part I
4 min
Importing and Organizing Data in Python - Part II
7 min
Importing and Organizing Data in Python - Part III
4 min
Changing the Index of Your Time-Series Data
3 min
Restarting the Jupyter Kernel
2 min
2. Calculating and Comparing Rates of Return in Python

11 Lessons 46 Min

As an investor, you would like to be able to compare the performance of the stocks in your portfolio. One of the most important measures that will allow you to do that is the rate of return of the stock. This section will explain the relevant theory in detail and will provide you with the tools to do that yourself using Python.

Considering Both Risk and Return
3 min
What Are We Going to See Next
3 min
Calculating a Security's Rate of Return
6 min
Calculating a Security's Rate of Return in Python - Simple Returns - Part I
5 min
Calculating a Security's Rate of Return in Python - Simple Returns - Part II
3 min
Calculating a Security's Rate of Return in Python - Logarithmic Returns
4 min
What Is a Portfolio of Securities and How to Calculate Its Rate of Return
3 min
Using 'Loc' and 'Iloc' - Notes Read now
1 min
Calculating the Rate of Return of a Portfolio of Securities
9 min
Popular Stock Indices
4 min
Calculating the Rate of Return of Indices
5 min
3. Meаsuring Investment Risk

10 Lessons 41 Min

Achieving a high rate of return of your stocks doesn’t come at no cost. Every investment is associated with a certain level of risk, and an investor must be well aware of it before putting their money in a certain basket. This section will teach you how to understand and measure such risk using Python.

How Do We Measure a Security's Risk
6 min
Calculating a Security's Risk in Python
6 min
The Benefits of Portfolio Diversification
3 min
Calculating the Covariance Between Securities
4 min
Measuring the Correlation between Stocks
4 min
Calculating Covariance and Correlation
5 min
Considering the Risk of Multiple Securities in a Portfolio
3 min
Calculating Portfolio Risk
3 min
Understanding Systematic vs. Idiosyncratic Risk
3 min
Calculating Diversifiable and Non-diversifiable Risk of a Portfolio
4 min
4. Using Regressions for Financial Analysis

4 Lessons 22 Min

Understanding rates of return and risk is not all there is about finance. Working with regression analysis is a must, and you will see that Python only helps you to be quicker and more precise when doing such estimations.

The Fundamentals of Simple Regression Analysis
4 min
Running a Regression in Python
7 min
Are All Regressions Created Equal? Learning How to Distinguish Good Regressions
5 min
Computing Alpha, Beta, and R Squared in Python
6 min
5. Markowitz Portfolio Optimization

4 Lessons 20 Min

One of the main pillars of modern finance is the Markowitz Portfolio Theory. It relates to building a portfolio optimization model, which is quite a complex task mathematically. However, you can see once more how Python can make such a challenge manageable, so long as we stick to theory and are careful at each step while coding.

Markowitz Portfolio Theory - One of the main Pillars of Modern Finance
7 min
Obtaining the Efficient Frontier in Python - Part I
6 min
Obtaining the Efficient Frontier in Python - Part II
5 min
Obtaining the Efficient Frontier in Python - Part III
2 min
6. The Capital Asset Pricing Model

8 Lessons 26 Min

As good or bad your portfolio may be, it is not self-sufficient. It is always part of a bigger picture – the market. That’s why you’d always want to compare the performance of your portfolio to the market. The Capital Asset Pricing Model (CAPM), the Beta of a stock, the Sharpe ratio and other measures will come in handy… and will be applied to real data with Python!

The Intuition behind the Capital Asset Pricing Model (CAPM)
5 min
Understanding and Calculating a Security's Beta
4 min
Calculating the Beta of a Stock
4 min
The CAPM Formula
4 min
Calculating the Expected Return of a Stock (CAPM)
2 min
Introducing the Sharpe Ratio and the Way It Can Be Applied in Practice
2 min
Obtaining the Sharpe Ratio in Python
1 min
Measuring Alpha and Verifying How Good (or Bad) a Portfolio Manager Is Doing
4 min
7. Multivariate Regression Analysis

2 Lessons 12 Min

While in Section 4 we deal with simple regression analysis, here we will take this technique to the next level. As you can guess, multivariate regression analysis is more advanced, but is also more interesting, as it allows you to deal with more complex financial problems.

Multivariate Regression Analysis - a Valuable Tool for Finance Practitioners
6 min
Running a Multivariate Regression in Python
6 min
8. Monte Carlo Simulations as a Decision-Making Tool

15 Lessons 60 Min

You can’t consider yourself a full-fledged investor if you don’t know how to use Monte Carlo Simulations. All tools you’ve learnt so far in the course will be essential to your ability to embrace the advantages of this technique for visualizing the potential outcomes of financial operations and improving your estimation of the risk associated with them.

The Essence of Monte Carlo Simulations
3 min
What is a Normal Distribution? - Note Read now
1 min
Monte Carlo Applied in a Corporate Finance Context
3 min
Monte Carlo: Predicting Gross Profit - Part I
6 min
Monte Carlo: Predicting Gross Profit - Part II
3 min
Forecasting Stock Prices with a Monte Carlo Simulation
4 min
Another Way to Calculate Simple and Log Returns - Note Read now
1 min
Monte Carlo: Forecasting Stock Prices - Part I
4 min
Monte Carlo: Forecasting Stock Prices - Part II
5 min
Monte Carlo: Forecasting Stock Prices - Part III
4 min
An Introduction to Derivative Contracts
7 min
The Black-Scholes Formula for Option Pricing
5 min
Monte Carlo: Black-Scholes-Merton
6 min
Monte Carlo: Euler Discretization - Part I
6 min
Monte Carlo: Euler Discretization - Part II
2 min

Topics

PythonData AnalysisFinancial AnalysisProgrammingInvestment AnalysisMontecarlo SimulationData PreprocessingRegression AnalysisMultivariate RegressionData VisualizationTheoryFinance SkillsIndustry Specialization

Tools & Technologies

Course Requirements

Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
Basic familiarity with Python programming is required.
Familiarity with NumPy is helpful but not mandatory.

Who Should Take This Course?

Level of difficulty: Beginner

Aspiring investment analysts, financial analysts, data analysts, data scientists
Current investment analysts, financial analysts, data analysts, data scientists who are passionate about acquiring domain-specific knowledge in investment analysis

Exams and Certification

A 365 Financial Analyst Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Martin Ganchev

Worked at

15 Courses

41532 Reviews

612714 Students

Martin Ganchev has been creating high-quality online education content since 2016, helping over one million students worldwide gain valuable business, finance, and data skills. As the second employee at 365 Careers, he played a key role in building some of the company's most successful courses, known for their clarity, practical value, and engaging delivery. With a Master of Science in Economic and Social Sciences from Bocconi University, Martin specializes in statistics, econometrics, Python, SQL, and business intelligence—equipping students to analyze data rigorously, automate workflows, and turn complex results into clear business insights.