Course Outline
Introduction
Algorithmic Trading Core Concepts
- What is algorithmic trading?
- Markets and trading
- Textual data and analysis
Python, R, and Stata
- Stock trading
- Bond trading
- Investment analysis
Preparing the Development Environment
- Installing Quandl
- Installing quantmod
- Installing and configuring Stata
Algorithmic Trading and Python
- Importing data
- Using Quandl
- Working with financial data
- Creating databases for financial data
Algorithmic Trading and R
- Importing data
- Using quantmod
- Working with regressions
Algorithmic Trading and Stata
- Importing and cleaning data
- Testing strategies
- Working with regressions
Summary and Conclusion
Requirements
- Experience with R
- Python experience
Audience
- Business Analysts
Testimonials (5)
The fact of having more practical exercises using more similar data to what we use in our projects (satellite images in raster format)
Matthieu - CS Group
Course - Scaling Data Analysis with Python and Dask
Very good preparation and expertise of a trainer, perfect communication in English. The course was practical (exercises + sharing examples of use cases)
Monika - Procter & Gamble Polska Sp. z o.o.
Course - Developing APIs with Python and FastAPI
It was a though course as we had to cover a lot in a short time frame. Our trainer knew a lot about the subject and delivered the content to address our requirements. It was lots of content to learn but our trainer was helpful and encouraging. He answered all our questions with good detail and we feel that we learned a lot. Exercises were well prepared and tasks were tailored accordingly to our needs. I enjoyed this course
Bozena Stansfield - New College Durham
Course - Build REST APIs with Python and Flask
Trainer develops training based on participant's pace
Farris Chua
Course - Data Analysis in Python using Pandas and Numpy
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.