lstm-forecast 0.1.1

A package for LSTM-based financial time series forecasting

LSTM Forecast

Overview

This project implements an LSTM-based model for predicting cryptocurrency prices. It includes features for data loading, preprocessing, model training, hyperparameter optimization, backtesting, and API deployment.

Table of Contents

• LSTM Forecast
  • Overview
  • Table of Contents
  • Project Structure
  • Installation
  • Usage
    • Prepare Data
    • Optimize Hyperparameters and Feature Selection
    • Train the Model
    • Make Predictions
    • Start the API Server
    • General Usage
  • Configuration
  • API
  • Backtesting
  • Testing
  • License

Project Structure

.
├── LICENSE
├── README.md
├── config.json
├── data/
├── docs/
├── logs/
├── models/
│   ├── checkpoints/
│   └── optuna/
├── png/
├── pyproject.toml
├── reports/
├── requirements.txt
├── setup.py
├── src/
│   └── lstm_forecast/
│       ├── __init__.py
│       ├── api/
│       ├── backtesting/
│       ├── cli.py
│       ├── config.py
│       ├── data_loader.py
│       ├── early_stopping.py
│       ├── feature_engineering.py
│       ├── feature_selection.py
│       ├── generate_html.py
│       ├── hyperparameter_optimization.py
│       ├── logger.py
│       ├── model.py
│       ├── model_utils.py
│       ├── predict.py
│       ├── predict_utils.py
│       └── train.py
├── static/
└── tests/

Installation

1. Clone the repository:

   git clone https://github.com/your-username/lstm-price-prediction.git
   cd lstm-price-prediction
   

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Install the project in editable mode:

   pip install -e .
   

Usage

The lstm_forecast command-line interface provides several subcommands for different functionalities:

Prepare Data

To prepare the data for training and prediction:

lstm_forecast prepare --config path/to/config.json

Optimize Hyperparameters and Feature Selection

To run hyperparameter optimization and feature selection:

lstm_forecast optimize --config path/to/config.json [OPTIONS]

Options:

• --n_trials INTEGER: Number of trials for hyperparameter tuning (default: 100)
• --n_feature_trials INTEGER: Number of trials for feature selection (default: 15)
• --min_features INTEGER: Minimum number of features to select (default: 5)
• --force: Force re-run of Optuna study

Train the Model

To train the model:

lstm_forecast train --config path/to/config.json

Make Predictions

To make predictions using a trained model:

lstm_forecast predict --config path/to/config.json

Start the API Server

To start the API server:

lstm_forecast server --config path/to/config.json

General Usage

All commands require a configuration file specified with the --config option. This JSON file contains all the necessary settings for data processing, model architecture, training, and prediction.

For more information on any command, you can use the --help option:

lstm_forecast [COMMAND] --help

Replace [COMMAND] with any of the available commands (prepare, optimize, train, predict, server) to see specific help for that command.

Configuration

The config.json file contains all the necessary settings for data processing, model architecture, training, and prediction. Modify this file to adjust parameters such as:

• Data settings (ticker, date range, features)
• Model settings (hidden size, number of layers, dropout)
• Training settings (epochs, learning rate, batch size)
• Backtesting parameters

API

The project includes a FastAPI-based API for model inference. To start the API server:

uvicorn lstm_forecast.api.app:app --reload

API endpoints:

• /predict: Make predictions using the trained model
• /backtest: Run backtesting on historical data

Backtesting

The backtesting module allows you to evaluate the model's performance on historical data. It includes:

• Trading engine simulation
• Performance metrics calculation
• Visualization of results

To run a backtest:

lstm_forecast backtest --config path/to/config.json --model path/to/model.pth

Testing

To run the tests:

pytest tests/

License

This project is licensed under the MIT License - see the LICENSE file for details.