The goal of this project is to identify optimal buy, sell, and hold points for any stock through a trained machine learning model.
The main class - MLAlgoStrat.py - loads the daily price data of a stock from a specified start date and end date (see SPYMLDataset.csv) and includes values from various technical indicators such as the moving averages, exponential moving averages, RSI, and more.
In order to train the machine learning model, I manually identified buy, sell, and hold points for SPY (see SPYClassifiedBuyPoints.csv) and fed the data into a Random Forest model.
The program can also be used to test user-defined algorithmic trading strategies, and view each trade and its performance metrics.