Module 4 Project - Stock Buy/Sell Predictions

Presentation

Goals

The purpose of stock predictions is to outperform the market. This means generating returns that would be greater than simply buying and holding a security. In order to accomplish this we can try to:

• Predict ideal entry and exit points for trading securities.
• Forecast future prices

Data used: Yahoo Finance library to pull stock data

Project Objective:

• Forecast prices for better trades

  • SVR Model
  • ARIMA Model
  • Price Forecast

• If forecasting is not accurate, predict entries and exits or long and short positions

  • Ichimoku Cloud
  • Logistic Regression

Metrics Used:

• Cross-validation score mean

Methods Used

• Statistics
• Data Cleaning
• Data Organizing/Exploring
• Feature Engineering
• Machine Learning
• Data Visualization
• Predictive Modeling
• Logistic Regression

Solutions:

• Selecting the right features and checking score results of models
• Use noncolinear technical indicators to improve accuracy and reduce overfitting
• Complicated problems do not always have to have complicated solutions

Project Findings:

• Using more features did not necessarily imporve the model
• Trying to forecast future prices was difficult
• More optimal to find long/short entries
• Model predicted 85% accuracy for short positions, 68% for long positions
• Features and models are better at choosing shorts rather than longs
• Other models were not used as they were less accurate or provided returns worse than buying and holding

Recommendations for further developments:

• Develop other models to improve accuracy
• More stocks to analyze
• Predict long/short positions closer to trade time vs. backtesting
• Build web scraper to scan for ideal stocks
• Similar model for opions trading?

  Further Analysis

• Improve accuracy for long entries
• Build algorithm using predictions to execute trades