PySteps 6-weeks Bootcamp:
Use Your Computer to Make Informed Decisions in Stock Trading
A gentle introduction to stock market trading, Python programming in Colab, analytics, and data visualisation
Why join?
Zero barriers to start
We use Google Colab notebooks. It is a copy-past solution, one-click init environment, and easy to extend.
No need to have a finance background
We explain our ideas with a plain language without complicated models and assumptions from theoretic finance.
Build a semi-automatic trading system
You'll create a set of notebooks with your ideas that can be applied for any broker account.
Make unbiased decisions
You'll try to validate the hypothesis at scale: from one example to hundreds of stocks observed.
Collaboration
Working in pairs and talking to like-minded people interested in investing, analysis, and programming.
Confident trading
You'll get a minimum working knowledge to generate your ideas and test them on the market.
Who should attend?
We believe the most common use cases are:
• home traders and individual investors can get a market macro view develop a statistical intuition;
• students can practice the coding skills and try to solve the mathematical puzzles of the financial markets;
• analysts/developers who want to trade could get a brief introduction to the financial markets;
• analysts who want to get the coding skills can try the web scrapping, time-series analysis, and prediction modelling on the large sets of data
• students can practice the coding skills and try to solve the mathematical puzzles of the financial markets;
• analysts/developers who want to trade could get a brief introduction to the financial markets;
• analysts who want to get the coding skills can try the web scrapping, time-series analysis, and prediction modelling on the large sets of data
Prerequisites
You should have:
• some coding experience at least in one modern programming language;
• some trading experience / interest in Fin markets for the immediate application of the obtained knowledge;
• data-driven decisions thinking;
• some trading experience / interest in Fin markets for the immediate application of the obtained knowledge;
• data-driven decisions thinking;
Format of a course
• You will be asked to read through the Medium/Long-read article before each
lesson, check the code, and form the basic understanding of the computation flow
• There will be Youtube screencasts to watch that help you to start with the code
• You will copy the full exercise notebook, try to run it on the last available data, and try to extend it
• Each part has a questionnaire in the end
lesson, check the code, and form the basic understanding of the computation flow
• There will be Youtube screencasts to watch that help you to start with the code
• You will copy the full exercise notebook, try to run it on the last available data, and try to extend it
• Each part has a questionnaire in the end
Results
After completing the course you will be able to:
• find new ways of automatic checking the facts at scale
• use Python to scrape, test, visualise the data, and create customised trading algorithms
• you will find like-minded peers, who will help you to achieve better long-standing results
• find new ways of automatic checking the facts at scale
• use Python to scrape, test, visualise the data, and create customised trading algorithms
• you will find like-minded peers, who will help you to achieve better long-standing results
Course syllabus
Weekend 1
The Philosophy of the Course and Practical Introduction
What is data-driven decisions? What is a landscape of potential personal investments? Where do we focus our attention? Risk-reward? We will do Colab setup and downloading your first financial data using the Finance APIs. We will discuss the principles to choose a right API and when you need to start paying for it.
What is data-driven decisions? What is a landscape of potential personal investments? Where do we focus our attention? Risk-reward? We will do Colab setup and downloading your first financial data using the Finance APIs. We will discuss the principles to choose a right API and when you need to start paying for it.
Weekend 2
The core libraries for data analysis in Python
Numpy, Pandas, and Matplotlib (Seaborn, Altair, Plotly).
You will be provided with a dataset and build your own visualisations.
Numpy, Pandas, and Matplotlib (Seaborn, Altair, Plotly).
You will be provided with a dataset and build your own visualisations.
Weekend 3
Sentiment Analysis of Financial Views
In this module we will learn how to use New API, get the last month news entries, and define what is a good or bad piece of news. We will discuss the recent influential news and stock jumps and see what evidence can be found in the articles in particular. We will use then the Vader algorithm to analyse its contents.
In this module we will learn how to use New API, get the last month news entries, and define what is a good or bad piece of news. We will discuss the recent influential news and stock jumps and see what evidence can be found in the articles in particular. We will use then the Vader algorithm to analyse its contents.
Weekend 4
Scraping Earnings Per Share (EPS)
We will show how to get the data from Websites using scrapping. We will discuss about the important EPS financial metric, and see how it evolved during the last reporting period. Technically we will learn how to combine the observations from different timestamps to one dataframe, and how to combine different dataframes together.
We will show how to get the data from Websites using scrapping. We will discuss about the important EPS financial metric, and see how it evolved during the last reporting period. Technically we will learn how to combine the observations from different timestamps to one dataframe, and how to combine different dataframes together.
Weekend 5
Long-term EPS and Short Term Investment Strategy
We will look at the all available history for a selected set of stocks. We will expand the previous lesson into a massive data dump and will try to find practical recipes for an individual investor.
We will look at the all available history for a selected set of stocks. We will expand the previous lesson into a massive data dump and will try to find practical recipes for an individual investor.
Weekend 6
IPOs
Year 2020 was a year of IPOs and 2021 promises to have even more companies going public. We learn how to get the website data to a table with one command, how to clean stats, and how understand what is an opportunity for an individual investor to buy a new stock.
Year 2020 was a year of IPOs and 2021 promises to have even more companies going public. We learn how to get the website data to a table with one command, how to clean stats, and how understand what is an opportunity for an individual investor to buy a new stock.
Are you interested to take an instructor-led course?
Please fill in a short survey and leave your details
By clicking the button you agree to our Privacy Policy
Ready to book a call?
Let's discuss your motivation, experience, and ideas
before you join the course!
before you join the course!
Blog articles
Explore the recent research algorithms, including interactive charts and code on the Github
Reach Out
Phone
Follow me
Sign up for our newsletter to get the latest news
