Quick Start

Install AlphaPy Pro from source:

git clone https://github.com/ScottFreeLLC/alphapy-pro.git
cd alphapy-pro
pip install -e .

Note

Please refer to Installation for detailed installation instructions.

Running Your First Model

  1. Navigate to the Kaggle example:

    cd projects/kaggle

  2. Run the AlphaPy pipeline:

    alphapy

This will train multiple models on the Titanic dataset and generate predictions.

  1. Check the results:

    ls -la runs/

You’ll see a new timestamped directory with your model outputs, plots, and predictions.

Working with Projects

AlphaPy Pro organizes work into projects. Each project contains:

  • config/model.yml - Model configuration

  • data/ - Input data files

  • runs/ - Output directories for each model run

Example Projects

The repository includes several example projects:

  • Kaggle - Titanic survival prediction

  • Shannon’s Demon - Trading strategy implementation

  • Time Series - Market prediction examples

  • Triple Barrier Method - Advanced labeling for financial ML

Quick Examples

Running a Market Analysis:

cd projects/time-series
mflow

Customizing Model Configuration:

Edit config/model.yml to change algorithms, features, or parameters:

model:
    algorithms: ['CATB', 'LGB', 'XGB']  # Choose algorithms
    cv_folds: 5                          # Cross-validation folds
    grid_search:
        option: True                     # Enable hyperparameter tuning
        iterations: 50                   # Number of iterations

Creating a New Project:

mkdir projects/my_project
cd projects/my_project
mkdir config data

# Copy a template configuration
cp ../kaggle/config/model.yml config/

# Add your data to the data/ directory
# Run alphapy

Working with Notebooks

You can also work with AlphaPy Pro in Jupyter notebooks:

from alphapy.model import create_model
from alphapy.frame import read_frame

# Load your data
train_df = read_frame('data/train.csv')
test_df = read_frame('data/test.csv')

# Create and train models
model = create_model(train_df, test_df, 'config/model.yml')

Note

AlphaPy Pro supports both command-line and notebook workflows. We recommend using the command line for production runs and notebooks for exploratory analysis.

Next Steps