An analysis of historic stock datasets and building a machine learning algorithm to predict stock behavior.
database-agg/
Scripts to add data to database with proper time and json formatting, has firebase scripts that will add data directly to firestore.
datasets/
The collection of datasets we are using for sample analysis.
r-analysis/
Multiple R files with a reference inital markdown and inital .docx files to generate reports as well as understand how to analyze large CSVs with R.
real-time-data/
Python API that uses Yahoo Finance API to get real time stock data. viz.py has plotting peaks of stocks over max 1 year. The folder also includes Go scripts for real time finance data scraping.
These instructions will get you a copy of the project up and running on your local machine.
- Python 3.6.9 or above with pip3
- Jupyter Notebook
For Mac OS/Linux users:
git clone https://github.com/stocksmith/ml-research.git
sudo bash setup.sh
For windows users:
https://github.com/stocksmith/ml-research.git
setup.bat
- Jupyter - Prototyping with Python