Pythonfin

Investing
in Python

Master Python for investing and financial analytics to make financial decisions with confidence.

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Online course

Study at a convenient time from anywhere in the world

Pace

Choose for yourself the speed at which you complete your training.

Practice

Apply your skills on real cases

Who is a Python developer and what does he do?

A Python developer is a specialist who writes code in Python to create applications, automate processes, and analyze data.

The Python language is used in a variety of areas: from creating programs to working with analytics.

You will learn

Programming in Python and working with scientific packages such as NumPy and Pandas.

Conducting in-depth investment analysis and creating investment portfolios.

Calculation of risk and return of individual securities and investment portfolios.

Application of regression analysis and Monte Carlo simulation for the valuation of financial instruments.

This course is ideal for

Beginners aspiring to a career in IT.

You will master Python programming, learn to work with different frameworks and databases.

IT professionals looking to change their profile.

The course will give you the opportunity to learn new skills and put them into practice, giving you access to new projects and opportunities.

Python development engineers looking to update their knowledge.

We carefully analyze the needs and supplement the material so that even professionals with relevant experience can get the most out of the training.

What makes this course different from other programming and finance courses?

This course will teach you how to program in Python and apply those skills to finance.

Practicing experts. We understand how difficult it is to apply theoretical knowledge without practical experience, so all theory is illustrated with examples from real-world projects.

Full training. We cover all the key topics needed to get started programming in Python and solving the financial problems presented in the course (and there are quite a few!).

Course authors and experts

More than 15 years of experience in finance and programming. He worked as an analyst in large investment companies, and is now a leading Python developer at an international consulting firm.

More than 10 years in the field of financial technology. He started as a financial analyst, then moved into development and is now a Python expert at a company that automates financial processes.

More than 8 years of experience in programming and financial analysis. He worked for several large banks, and now he is a Python developer at a startup specializing in financial applications.

Course program

Video

Text

Module 1. Introduction

What is this course about?

Download useful resources - exercises and solutions

Part I. PYTHON

Module 2. Introduction to Python Programming

Programming explanation in 5 minutes

Why Jupiter?

Jupyter interface – Prerequisites for coding

Programming explanation in 5 minutes

Installing Python and Jupiter

Python 2 and Python 3: what is the difference?

Why Python?

Jupyter Interface – Control Panel

Module 3. Variables and Python Data Types Variables - Exercise #1

Variables - Exercise #2

Numbers and logical values - Exercise #3

Strings - Exercise #2

Variables - Exercise #3

Numbers and logical values - Exercise #4

Strings - Exercise #3

Variables - Exercise #4

Numbers and logical values - Exercise #5

Strings - Exercise number 4

Variables

Numbers and logical values

Strings - Exercise #5

Numbers and logical values

Strings

An Introduction to Anaconda Artificial Intelligence

Numbers and Logical values - Exercise #1

Strings - Exercise #1

Using Anaconda Assistant: Strings

Numbers and logical values - Exercise #2

Module 4. Basic Python Syntax

Arithmetic operators

Reassigning values — Exercise #3

Reassign values

Arithmetic operators - Exercise #1

Reassigning values — Exercise #4

Reassigning values is Exercise #1

Arithmetic operators - Exercise #2

Reassign values

Reassigning values is Exercise #2

Arithmetic operators - Exercise #3

Add comments

Reassigning values — Exercise #3

Arithmetic operators - Exercise #4

Continuation of the line

Reassigning values — Exercise #4

Arithmetic operators - Exercise #5

Continuation of the line - Exercise #1

Reassign values

Arithmetic operators - Exercise #6

Indexing of elements

Add comments

Arithmetic operators - Exercise #7

Indexing elements is Exercise #1

Continuation of the line

Arithmetic operators - Exercise #8

Indexing elements is Exercise #2

Continuation of the line - Exercise #1

Arithmetic operators

Structure your code with indentation

Indexing of elements

A double equal sign

Structure your Code with indentation — Exercise #1

Indexing elements is Exercise #1

The double equal sign - Exercise #1

Structure your code with indentation

Indexing elements is Exercise #2

A double equal sign

Structure your code with indentation

Reassign values

The double equal sign - Exercise #1

Structure your Code with indentation — Exercise #1

Reassigning values is Exercise #1

A double equal sign

Structure your code with indentation

Reassigning values is Exercise #2

Module 5. Python Operators -continued

Comparison operators

Comparison Operators - Exercise #4

Logical and identity operators - Exercise #3

Comparison Operators — Exercise #1

Logical and identification operators

Logical and identity operators - Exercise #4

Comparison Operators — Exercise #2

Logical and identity operators - Exercise #1

Logical and identity operators - Exercise #5

Comparison Operators - Exercise #3

Logical and identity operators - Exercise #2

Logical and identity operators - Exercise #6

Module 6. Conditional statements

Introduction to the IF statement

Add the ELSE operator

Otherwise, If, For short, ELIF

IF - Exercise #1

OTHERWISE, Exercise number 1

A note about boolean values

IF - Exercise #2

Module 7. Python Functions

Defining a Function in Python

Combining Conditional Statements and Functions

Known Built-in Functions in Python - Exercise #4

Creating a Function with a Parameter

Combining Conditional Statements and Functions - Exercise #1

Known Built-in Functions in Python - Exercise #5

Creating a Function - Exercise #1

Creating Functions that Contain Multiple Arguments

Known Built-in Functions in Python - Exercise #6

Creating a Function - Exercise #2

Known Built-in Functions in Python

Known Built-in Functions in Python - Exercise #7

Another Way to Define a Function

Known Built-in Functions in Python - Exercise #1

Known Built-in Functions in Python - Exercise #8

Another Way to Define a Function - Exercise #1

Known Built-in Functions in Python - Exercise #2

Known Built-in Functions in Python - Exercise #9

Using a Function within Another Function

Known Built-in Functions in Python - Exercise #3

Functions

Using a Function within Another Function - Exercise #1

Module 8. Sequence

Lists

Slicing the list

Tuples - Exercise #2

Lists - Exercise #1

Slicing up the list is Exercise #1

Tuples - Exercise #3

Lists - Exercise #2

Slicing the list is Exercise #2

Tuples - Exercise #4

Lists - Exercise #3

Slicing the list is Exercise #3

Dictionaries

Lists - Exercise #4

Slicing the list is Exercise #4

Dictionaries - Exercise #1

Lists - Exercise #5

Slicing the list is Exercise #5

Dictionaries - Exercise #2

Using methods

Slicing the list is Exercise #6

Dictionaries - Exercise #3

Using methods is Exercise #1

Slicing up the list is Exercise #7

Dictionaries - Exercise #4

Using methods is Exercise #2

Tuples

Dictionaries - Exercise #5

Using methods is Exercise #3

Tuples - Exercise #1

Dictionaries - Exercise #6

Using Methods - Exercise #4

Module 9. Using Iterations in Python

For Loops

Creating Lists with the range() Function - Exercise #3

Conditional Statements, Functions, and Loops - Exercise #1

For Loops - Exercise #1

Using Conditional Statements and Loops Together

Using Anaconda Assistant: Several Python Tools

For Loops - Exercise #2

Combining Conditional Statements and Loops - Exercise #1

Iterating Over Dictionaries

While Loops and Incrementing

Combining Conditional Statements and Loops - Exercise #2

Iterating Over Dictionaries - Exercise #1

While Loops and Incrementing - Exercise #1

Iterating Over Dictionaries - Exercise #2

Creating Lists with the range() Function

Combining Conditional Statements and Loops - Exercise #3

Using Anaconda Assistant: Dictionaries

Creating Lists with the range() Function - Exercise #1

All in One – Conditional Statements, Functions, and Loops

Creating Lists with the range() Function - Exercise #2

Part II. Finance

Module 10. Advanced Python Tools

Object-Oriented Programming

Importing Modules - Test

Accessing Notebook Files

Object-Oriented Programming - Test

Essential Packages for Finance and Data Science

Importing and Organizing Data in Python – Part I

Modules and Packages

Essential Packages - Test

Importing and Organizing Data in Python – Part II.A

Modules - Test

Working with Arrays

Importing and Organizing Data in Python – Part II.B

Standard Library

Generating Random Numbers

Importing and Organizing Data in Python – Part III

Standard Library - Quiz

Note on Using Financial Data in Python

Changing the Index of Your Time Series Data

Importing Modules

Sources of Financial Data

Restarting the Jupyter Kernel

Module 11. Calculation and comparison of profitability rates in Python

Taking into account both risk and profitability

Calculating the profitability of securities in Python – Simple Returns – Part II

Calculation of the profitability of the securities portfolio

Risk and Profitability - A test

Calculation of securities profitability in Python – logarithmic profitability

Popular Stock Indexes that can help us understand Financial Markets

What will we see next?

Calculation of the security yield rate

What is a securities portfolio and how to calculate its profitability

Which of the above is not an index? - Test

Calculating the profitability of securities in Python – Simple Returns – Part I

What is a securities portfolio and how to calculate its profitability - Quiz

Calculation of the rate of return of indices

Module 12. Measuring investment risk

How do we measure the risk of a security?

Calculation of the covariance between securities

Accounting for the risk of multiple securities in a portfolio

Which of the following sentences is correct? - Test

Covariance Test

Portfolio risk calculation

Calculating the risk of a security in Python

Measuring the correlation between stocks

Understanding systematic and idiosyncratic risk

Advantages of portfolio diversification

Correlation Test

A Diversified Risk Test

Investing in Stocks - Quiz

Calculation of covariance and correlation

Calculation of the diversified and non-diversified portfolio risk

Module 13. Using regressions for financial analysis

Basics of simple regression analysis

Running regression in Python

Regression Test

Are all regressions created equal? Learning to distinguish between good regressions

Calculating Alpha, Beta, and R Squared in Python

Module 14. Markowitz Portfolio Optimization

Markowitz Portfolio Theory

Obtaining the Efficient Frontier in Python – Part I

Obtaining the Efficient Frontier in Python – Part III

Markowitz - Quiz

Obtaining the Efficient Frontier in Python – Part II

Module 15. Capital asset valuation model

The intuition behind the Capital Asset Pricing Model (CAPM)

Calculating the beta coefficient of stocks

Sharpe Coefficients - Test

CAPM Test

The CAPM formula

Getting the Sharpe coefficient in Python

Understanding and calculating the beta coefficient of a security

Calculation of expected stock returns (CAPM)

Measuring alpha and checking how well (or poorly) a portfolio manager is performing

Beta Quiz

Getting to know the Sharpe coefficient and how to apply it in practice

Alpha Quiz

Module 16. Multivariate Regression Analysis

Multivariate regression analysis is a valuable tool for finance professionals

Multivariate Regressions - Quiz

Running Multivariate Regression in Python

Module 17. Monte Carlo Simulation as a Decision-Making Tool

The Essence of Monte Carlo Simulation

Forecasting Stock Prices Using Monte Carlo Simulation

Derivatives - Quiz

Monte Carlo - Quiz

Monte Carlo Simulation - Test

Black-Scholes Formula for Option Pricing

Monte Carlo Applied in the Context of Corporate Finance

Monte Carlo: Forecasting Stock Prices. Part I

Monte Carlo: Black-Scholes-Merton

Monte Carlo in Corporate Finance - Test

Monte Carlo: Forecasting Stock Prices. Part II

Using Monte Carlo with Black-Scholes-Merton - Test

Monte Carlo: Forecasting Gross Profit – Part I

Monte Carlo: Forecasting Stock Prices. Part III

Monte Carlo: Euler Discretization - Part I

Monte Carlo: Forecasting Gross Profit – Part II

Introduction to Derivative Contracts

Monte Carlo: Euler Discretization - Part II

Module 18: Application - Basics of Pandas

Pandas Series - Introduction

Pandas Series - Exercise #10

pandas DataFrames - Introduction - Exercise #1

Note on completing upcoming coding exercises

pandas - Working with Methods - Part I

pandas DataFrames - Introduction - Exercise #2

Pandas Series - Exercise #1

pandas - Working with Methods - Part II

pandas DataFrames - Introduction - Part II

Pandas Series - Exercise #2

pandas - Working with Methods - Exercise #1

Using Anaconda Assistant: Importing Data with pandas

Pandas Series - Exercise #3

pandas - Working with Methods - Exercise #2

pandas DataFrames - Introduction - Exercise #3

Pandas Series - Exercise #4

pandas - Using Parameters and Arguments

pandas DataFrames - Introduction - Exercise #4

Pandas Series - Exercise #5

pandas - Using Parameters and Arguments - Exercise #1

pandas DataFrames - Introduction - Exercise #5

Pandas Series - Exercise #6

pandas - Using Parameters and Arguments - Exercise #2

pandas DataFrames - General Attributes

Pandas Series - Exercise #7

pandas DataFrames - Data Selection

Pandas Series - Exercise #8

Pandas Series - .unique() and .nunique()

Pandas Series - .sort_values()

pandas DataFrames - Data Selection with .iloc[]

Pandas Series - Exercise #9

pandas DataFrames - Introduction - Part I

Module 19: Application - Technical Analysis

Technical Analysis - Principles, Applications, Assumptions

Common Chart Patterns

Non-Price Indicators

Charts Used in Technical Analysis

Price Indicators

Technical Analysis - Cycles

Other Tools Used in Technical Analysis

Momentum Oscillators

Intermarket Analysis

Trendlines, Support, and Resistance

Module 20: Bonus

Bonus Lecture: Next Steps

All software and data used in the course are free of charge.

Tariffs

Python

You will get acquainted with the Python programming language, learning where, when, and how it is used.

$32

Part I - Python

10 modules

Practical exercises

Feedback

Access - 3 months

No certificate

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Finance

Study Python for finance with an emphasis on its practical, operational application.

$36

Part II

7 modules

Practical exercises

Feedback

Access - 6 months

No certificate

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Advanced

You will master advanced Python technologies in finance, progressing from beginner to professional.

$68

2 parts (Python + Finance)

17 modules

Practical exercises

Bonus module

Tests

Feedback

Access - 9 months

Certificate

Place Order

VIP

Master the profession and all its components with the help of mentors and curators.

$96

2 parts (Python + Finance)

Application + 2 Modules

Tests

Bonus module

Expert feedback

Curator support

Access - 12 months

Certificate

Place Order

Corporate

During the training, company employees will improve their development skills, create real projects, and become confident specialists.

$500

Groups of 5-10 people

All course materials

Bonus module

Practical exercises

Tests

Group chat

Communication with curators

Feedback

Access - 12 months

Certificate

Place Order

What results can you expect at the end of your studies?

$32

graduates of the course achieve their goal and find work in IT

37%

students receive development orders already during their studies

53%

grow to the middle level faster than a year after completing the course

Reviews from our graduates

Egor

Very good course if you want to learn the basics of Python in general, as well as for data analysis and presentation. In the finance section, they start with the fundamentals and move on to more intermediate topics such as Monte Carlo simulation, option pricing models, and technical analysis. The course is very didactic and easy to understand. Recommended for those looking to learn Python while studying or strengthening their knowledge in finance.

Savely

Great course for beginners! It covers all the essential aspects of Python and provides practical assignments that help reinforce the material. I especially enjoyed the section dedicated to financial applications, where we studied real examples. I recommend it to anyone looking to start their journey in programming and finance!

Mark

The course is very informative and structured. I was pleasantly surprised at how easy it is to learn Python thanks to clear explanations and practical exercises. The finance section of the course was also useful, especially the topics related to data analysis. Definitely worth taking!

Nick

I took this course and was very satisfied! The program covers both the basics of Python and more complex topics such as technical analysis. The practical assignments helped me better understand how to apply the knowledge gained in practice. I recommend it to anyone looking to improve their skills in programming and finance.

Paul

Very useful course! It is perfect for those who want to learn Python from scratch and apply it in the financial field. I especially appreciated the practical assignments that helped me solidify my knowledge. The course is accessible and understandable, making it a great choice for beginners.

Alex

The course exceeded my expectations! I learned a lot about Python and its applications in finance. The program is well-structured, and the practical assignments make learning more engaging. I recommend it to anyone looking to develop in this field!

Vyacheslav

Wonderful course for learning Python! It covers all the necessary topics and provides plenty of practical exercises. I especially appreciated the sections dedicated to financial analysis and modeling. The course is suitable for both beginners and those looking to deepen their knowledge.

We will refund your money in full if the course is not suitable

Money can be returned at any time. During the first three lessons, we will refund you the full amount, and starting from the fourth, we will calculate the refund amount or help you choose another course instead.

We answer questions

What level of preparation is required to take the course?

The course is suitable for both beginners and those who already have basic knowledge of Python. We start with the fundamentals and gradually move on to more complex topics, so everyone can find something useful for themselves.

What topics are covered in the course?

The course covers a wide range of topics, including the basics of Python, data analysis, financial models, technical analysis, and Monte Carlo simulation. We also provide practical assignments to reinforce the material.

How long will I have access to the course materials?

Access to the course materials is provided for a period of 3 to 12 months, depending on the chosen package. This allows you to learn at your own pace.

Will I receive a certificate upon completion of the course?

Yes, upon completion of the course, you will receive.

How can I evaluate my learning progress?

The course includes tests and practice assignments to help you assess your progress. You will also receive feedback from your teachers, which will allow you to understand what to focus on.

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