Tag: AAPL

  • Risk-Return Analysis and LSTM Price Predictions of 4 Major Tech Stocks in 2023

    Risk-Return Analysis and LSTM Price Predictions of 4 Major Tech Stocks in 2023

    The open-source Python workflow breaks down our investigation into the following 4 steps: (1) invoke yfinance to import real-time stock information into a Pandas dataframe; (2) visualize different dataframe columns with Seaborn and Matplotlib; (3) compare stock risk/return using historical data; (4) predict stock prices in 2023 with the trained LSTM model. Input Data Let’s…

  • Bear vs. Bull Portfolio Risk/Return Optimization QC Analysis

    Bear vs. Bull Portfolio Risk/Return Optimization QC Analysis

    Based on the Portfolio Allocation and Optimization Algorithm discussed earlier and the related portfolio management, let’s run the Bear vs. Bull QC test of the portfolio P=[MSFT, AAPL, NDAQ] in terms of the Risk/Return Ratio (RRR). We have got a Sharpe ratio of less than one that is considered unacceptable or bad. The risk the…

  • AAPL Stock Technical Analysis 2 June 2022

    AAPL Stock Technical Analysis 19 May, 2022. Both annual and monthly linear regression trends of the AAPl stock performance vs CPI change show a clear positive gradient. This means that this dividend stock is a good candidate for your inflation-resistant portfolio for reasons beyond its dividend.

  • Inflation-Resistant Stocks to Buy

    Inflation-Resistant Stocks to Buy AAPL Example Python workflow Download 3 historical datasets – stock price and monthly/annual CPI Compute the monthly/annual stock performance (%) and CPI rate (%) Apply linear regression to the stock vs CPI performance cross-plot Check the slope or gradient of the linear trend – positive, negative or zero.

  • ML/AI Regression for Stock Prediction – AAPL Use Case

    1. Install Yahoo finance library 2. Call all dependencies that we will use for this exercise  3. Define the ticker you will use 4. Let’s look at the data table 5. Data Exploration Phase 6. Data Preparation, Pre-Processing & Manipulation 7. Apply Linear Regression 8. Perform ML QC Analysis 9. Final Output