Stock Return Prediction

A pattern recognition project for predicting stock return from historical data using multiple machine learning models and techniques.

See the slide Here.

Dataset: S&P500, Yahoo Finance - yfinance

Outline

Data Preprocessing
Feature Engineering
- Technical Indicators
  - Relative Strength Index (RSI)
  - Bollinger Bands
  - Average True Range (ATR)
  - Moving Average Convergence/Divergence (MACD)
  - Momentum
  - Lagged Return
Data Splitting
- Training and Testing Set: 2014 - 2023 (9y)
- Trading Evaluation: 2023 - 2024 (1y)
Model
- Baseline: Naive Forecast
- Neural Network: Architecture 1 (Timeseries) & 2 (TS+Exogenous)
  - Long Short Term Memory (LSTM)
  - Gated Recurrent Unit (GRU)
- Classical: Regression & Classification
  - Linear regression
  - Logistic regression
  - Support Vector
  - Random Forest
  - Extreme Gradient Boosting
  - K-Nearest Neighbor
Hyper parameters Tuning
- Grid Search
- Random Search
Trading
- Baseline Strategy: Buy and Hold
- Equal Weight portfolio
- Sharpe Ratio
Analysis
- Feature Importance

Name		Name	Last commit message	Last commit date
Latest commit History 132 Commits
archive		archive
saved run models		saved run models
.gitignore		.gitignore
FeatureImportance.ipynb		FeatureImportance.ipynb
GRU1.ipynb		GRU1.ipynb
GRU1.py		GRU1.py
GRU2.ipynb		GRU2.ipynb
GRU2.py		GRU2.py
LICENSE		LICENSE
LSTM1.ipynb		LSTM1.ipynb
LSTM1.py		LSTM1.py
LSTM2.ipynb		LSTM2.ipynb
LSTM2.py		LSTM2.py
Presentation Slide.pdf		Presentation Slide.pdf
README.md		README.md
main.ipynb		main.ipynb
main.py		main.py