The document describes Dustin Jasmin's goal to develop a machine learning model to predict stock returns for Apple (AAPL) shares using the relative strength index (RSI) and moving average convergence divergence (MACD) indicators. It discusses importing stock data, pre-processing the data by adding it to dataframes and converting it to NumPy arrays, normalizing the data between 0 and 1, and developing a multi-layer perceptron model with an input, output, and hidden layer to make predictions. It concludes by noting the model learning loss sharply declined initially and steadily declined over time, while epoch duration decreased, indicating the model performed decently based on the loss data.

1. AAPL STOCK PREDICTION MODEL
DUSTIN JASMIN

2. GOAL
• Develop a Machine Learning model that uses:
• Relative Strength Index (RSI)
• Moving Average Convergence Divergence (MACD)
• To predict future % returns on AAPL shares
• Build trading bot that uses model to make buy and sell orders based on live
market data

3. PROCESS
• Imported many libraries
• Notables:
• Technical Analysis (TA)
• NumPy, Pandas
• Alpaca API
• CometML
• Pytorch, Keras, Tensorflow

4. PROCESS, CONTINUED
• Used Alpaca to import AAPL stock data (2016-2018):
• Each data point was in 4-hour intervals
• Used RSI, MACD and Closing Share Price for each data point
• Converted each dataset to NumPy arrays
• Developed Multi-Layered Perceptron
• Feed forward neural network
• Consists of three types of layers—the input layer, output layer and hidden layer
• The input layer receives the input signal to be processed.
• Output layer performs prediction and classification
• An arbitrary number of hidden layers that are placed in between the input and output layer are the true computational engine
of the MLP.

5. CODE, PRE-PROCESSING
• Added data to DataFrames
• Close series = 2 years of AAPL closing
price
• Added close series to RSI() and
MACD() object
• Converted to NumPy array

6. CODE, NORMALIZE
• Set data to between 0 and 1 for easier
computation
• Used unsqueeze() to format data for training

7. CODE, MODEL & PREDICTION

8. PERFORMANCE
• Model Learning Loss has
sharp decline in beginning
• Steady decline afterwards
• Epoch duration (time to
cycle through dataset)
decreases over time

9. SUMMARY
• Did not get to develop trading bot
• Personal Assessment:
• Based off loss data, model seemed to performed decently
• Project made me research/learn more about ML
• Data processing is toughest part in ML