Trading Signals with Technical Indicators and Machine Learning

Problem Definition

Build a machine learning model to classify buy and sell signals for AAPL stock using technical indicator data on SMA, EMA, MACD and RSI.

Getting this Notebook

git clone https://github.com/jaredpek/Technical-Analysis-Signal-Classification

Dataset Used

• Yahoo Finance daily AAPL stock price from 1 January 2015 to 31 December 2019
  • date = Date of trading day
  • open = Price of the first trade of the day
  • close = Price of the last trade of the day
  • adjclose = Final close price after accounting for corporate actions
  • low = Minimum trade price of the day
  • high = Maximum trade price of the day
  • volume = Number of shares transacted on the day
  • ticker = 'AAPL' ticker

Models Used

• Random Forest Classifier
• Decision Tree Classifier
• Stochastic Gradient Descent Classifier
• Logistic Regression

Conclusion

• LogisticRegression was the best out of the 4 models to classify buy and sell signals based on provided technical indicators, with a very high prediction accuracy of 0.956.
• Tree-based models are more prone to overfitting, hence we must use hyperparameter tuning to mitigate it.

Disclaimer

This notebook is best viewed using JupyterNotebook or VisualStudioCode as plotly charts are unable to render via github's online notebook view.