The portfolio will consist of 5 equally weighted stocks. We chose MSFT (Microsoft), AMZN (Amazon), BRK.B (Berkshire Hathaway B), XOM (Exxon Mobile), and K (Kellogg's).
The portfolio will have $50,000 total, and we will put $10,000 into each stock.
It's better to train different machine learning models to outperform a standard portfolio, using historical stock closing prices.
Loops were also utilized to create Autocorrelation and Partial Autocorrelation graphs for each stock in the porfolio.
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ARMA - Autoregressive Moving Average
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ARIMA - Autoregressive Integrated Moving Average
Amazon was substituted for Tesla, because Alpaca API was unable to get adjusted closing prices for Tesla. This proved problematic, because Tesla had a stock split of 5-1, which affected the price from $2,000 to $400.