Command Line Interface

The AlphaPy Pro Command Line Interface (CLI) provides a streamlined way to run machine learning pipelines. Simply navigate to your project directory and run the appropriate command.

Basic Usage

First, change to your project directory:

cd path/to/project

Train models and generate predictions:

alphapy

Core Commands

alphapy - Main ML Pipeline

Usage:

alphapy [options]

Options:

  • --train - Train new models and make predictions [Default]

  • --predict - Make predictions using saved models

  • --verbose - Enable verbose logging

  • --debug - Enable debug mode with detailed output

Example:

# Train models (default behavior)
alphapy

# Use a saved model for predictions
alphapy --predict

# Train with verbose output
alphapy --train --verbose

mflow - MarketFlow Pipeline

For financial market analysis and trading systems:

mflow [options]

Options:

  • --train - Train new models [Default]

  • --predict - Make predictions from saved models

  • --tdate YYYY-MM-DD - Training start date (Default: earliest date)

  • --pdate YYYY-MM-DD - Prediction date (Default: today)

  • --lookback N - Number of days for lookback window

  • --forecast N - Number of days to forecast

Examples:

# Train a market model
mflow

# Train from a specific date
mflow --tdate 2023-01-01

# Make predictions for a specific date
mflow --predict --pdate 2024-01-15

# Use lookback window
mflow --lookback 252 --forecast 20

Custom Pipelines

You can create custom domain-specific pipelines following the MarketFlow pattern. See the AlphaPy Pro architecture documentation for implementation details.

Project Structure Requirements

Before running commands, ensure your project follows this structure:

my_project/
├── config/
│   └── model.yml       # Model configuration
├── data/
│   ├── train.csv       # Training data
│   └── test.csv        # Testing data (optional)
└── runs/               # Output directory (auto-created)

Output Structure

After running a command, outputs are organized in timestamped directories:

runs/
└── run_YYYYMMDD_HHMMSS/
    ├── config/         # Configuration used
    ├── input/          # Input data snapshots
    ├── model/          # Trained models and metrics
    ├── output/         # Predictions and rankings
    └── plots/          # Visualizations

Common Workflows

1. Quick Model Training:

cd projects/my_project
alphapy

2. Hyperparameter Tuning:

First, edit config/model.yml to enable grid search:

model:
    grid_search:
        option: True
        iterations: 100

Then run:

alphapy --verbose

3. Batch Predictions:

# Train once
alphapy --train

# Use the model for multiple prediction sets
alphapy --predict

4. Market Backtesting:

# Run market analysis with specific dates
mflow --tdate 2020-01-01 --pdate 2023-12-31

Logging and Debugging

AlphaPy Pro creates detailed logs:

  • alphapy.log - Main pipeline log

  • market_flow.log - MarketFlow specific log (when using mflow)

To increase log verbosity:

alphapy --verbose --debug

Tips and Best Practices

  1. Always verify your data before running models

  2. Start with small iterations for grid search, then increase

  3. Use dated runs for reproducibility

  4. Keep model.yml under version control

  5. Review plots in the output directory for insights

For more details on configuration options, see Project Structure.