Collection of 3 quantitative finance projects in Python that uses algorithmic trading.
I completed these projects by watching a full course by the software developer Nick McCullum on
YouTube.
The projects consist of instructions given on a Jupyter notebook from
this repo
and I did the coding part by following the course.
- Building An Equal-Weight SP 500 Index Fund
- Building A Quantitative Momentum Investing Strategy
- Building A Quantitative Value Investing Strategy
All three projects use the sandbox version of IEX Cloud API. The free sandbox version is for testing purposes and gives random values.
The goal of this project is to build an equal-weight version of the S&P 500 index fund by taking the value of a portfolio as input and tell how many shares of each S&P 500 stock should be purchased. The script exports an excel file with the equal-weight version of the index fund as a result.
The goal of this project is to build a quantitative momentum strategy that decides how many of each of the best 50 stocks to buy based on their HQM scores (high-quality momentum) that indicate that they have increased in price the most.
The goal of this project is to build a quantitative value strategy that decides how many of each of the best 50 stocks to buy based on their RV scores (robust value) that indicate how cheap they are relative to common measures of business value.
