FA 22 SEC Insider Trading Project
We all know what insider trading is, but do we know how to detect it? What makes a trade suspicious? How does the SEC go about detecting suspicious activity from filings? Can we do better? These are some questions we will try to answer!
This is an open-ended project where we analyze, synthesize, and display findings from SEC filings. Specifically, we will be exploring filings released for Q1 2020 by the SEC. Here is a sample filing for Elon Musk.
Some areas towe will explore
- Python webscraping development
- Data visualization for SEC filings
- Interactive data analysis tools (build a website?!)
- anything that interests you!
10/2, Meeting 1: Kickoff!
- Introductions
- Setup
- Exploratory Data Analysis
10/9, Meeting 2: More EDA
- Webscraping tutorial on Yahoo Finance data
- Further information on SEC form 4 filings
- Encourage sub-group formulation to tailor to everyones goals
- Group work
10/23, Meeting 3: Group work
- Sub-teams!
- work on web scrapers/models/visualizations
10/30, Meeting 4: Group work
11/6, Meeting 5: Group work
11/13, Meeting 6: Group work
- Re-evaluating goals to ensure completion by Meeting 7
11/20, Meeting 7: Penultimate
- Start write-up
12/4, Meeting 8: Final Touches
- Finish write-up
- Prepare for expo
There are not many dependencies needed for this project, so if you already have a virtual environment that contains what is specified in requirements.txt, feel free to skip this section.
We are going to initialize a Python virtual environment with all the required packages. We use a virtual environment to isolate our development environment from the rest of our computer. This is helpful because it standardizes the environment we run our program in across computers.
First create a Python 3.8 virtual environment. The virtual environment creation code for Linux/MacOS is below:
python3 -m venv venvNow that you have a virtual environment installed, you need to activate it. This may depend on your system, but on Linux/MacOS, this can be done using
source ./venv/bin/activateNow your computer will know to use the Python installation in the virtual environment rather than your default installation.
After the virtual environment has been activated, we can install the required dependencies into this environment using
pip install -r requirements.txtDataset:
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