Forex Prediction Model

Implementation of a Random forest algorithm model to predict stock prices for the S&P 500 index.

Workflow

Here's a quick walkthrough on what to expect to do in this project:)

1. Data Preprocessing

• Load data using yfinance library and data cleaning techniques.
• Create a target column for training the model

2. Model Training

• Create Random Forrest Regressor model with
• Tune parameters for increased accuracy on predictions
• Train the model using predefined training set and continiously evalute for accuracy score, f1 score, and classification reports

3. Backtesting

• Develop a backtesting algorithm to evaluate model performance using large historical data (10+ years)
• Gather data from backtesting process for evaluation using the earlier mentioned metrics

4. Model Improvement

• Create new model parameters to improve the performace of the model
• Create new set of predictor variables to increase reliability and accuracy

5. Reporting

• Report model performance and reasoning behind improvement techniques adopted