# 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](#lstm-forecast)
- [Overview](#overview)
- [Table of Contents](#table-of-contents)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Install with pip](#install-with-pip)
- [Install from sources](#install-from-sources)
- [Usage](#usage)
- [Prepare Data](#prepare-data)
- [Optimize Hyperparameters and Feature Selection](#optimize-hyperparameters-and-feature-selection)
- [Train the Model](#train-the-model)
- [Make Predictions](#make-predictions)
- [Start the API Server](#start-the-api-server)
- [General Usage](#general-usage)
- [Configuration](#configuration)
- [API](#api)
- [Backtesting](#backtesting)
- [Testing](#testing)
- [License](#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
### Install with pip
If you want to use the latest stable version, you can install the package directly from PyPI:
```
pip install lstm-forecast
```
### Install from sources
If you want to use the latest development version or contribute to the project, you can install from the source:
1. Clone the repository:
```
git clone https://github.com/gianlucamazza/lstm_forecast.git
cd lstm_forecast
```
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 .
```
This way, you'll have the latest version of the code and be able to make changes if needed.
## 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](LICENSE) file for details.
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"description": "# LSTM Forecast\n\n \n## Overview\n\nThis 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.\n\n## Table of Contents\n\n- [LSTM Forecast](#lstm-forecast)\n - [Overview](#overview)\n - [Table of Contents](#table-of-contents)\n - [Project Structure](#project-structure)\n - [Installation](#installation)\n - [Install with pip](#install-with-pip)\n - [Install from sources](#install-from-sources)\n - [Usage](#usage)\n - [Prepare Data](#prepare-data)\n - [Optimize Hyperparameters and Feature Selection](#optimize-hyperparameters-and-feature-selection)\n - [Train the Model](#train-the-model)\n - [Make Predictions](#make-predictions)\n - [Start the API Server](#start-the-api-server)\n - [General Usage](#general-usage)\n - [Configuration](#configuration)\n - [API](#api)\n - [Backtesting](#backtesting)\n - [Testing](#testing)\n - [License](#license)\n\n## Project Structure\n\n```\n.\n\u251c\u2500\u2500 LICENSE\n\u251c\u2500\u2500 README.md\n\u251c\u2500\u2500 config.json\n\u251c\u2500\u2500 data/\n\u251c\u2500\u2500 docs/\n\u251c\u2500\u2500 logs/\n\u251c\u2500\u2500 models/\n\u2502 \u251c\u2500\u2500 checkpoints/\n\u2502 \u2514\u2500\u2500 optuna/\n\u251c\u2500\u2500 png/\n\u251c\u2500\u2500 pyproject.toml\n\u251c\u2500\u2500 reports/\n\u251c\u2500\u2500 requirements.txt\n\u251c\u2500\u2500 setup.py\n\u251c\u2500\u2500 src/\n\u2502 \u2514\u2500\u2500 lstm_forecast/\n\u2502 \u251c\u2500\u2500 __init__.py\n\u2502 \u251c\u2500\u2500 api/\n\u2502 \u251c\u2500\u2500 backtesting/\n\u2502 \u251c\u2500\u2500 cli.py\n\u2502 \u251c\u2500\u2500 config.py\n\u2502 \u251c\u2500\u2500 data_loader.py\n\u2502 \u251c\u2500\u2500 early_stopping.py\n\u2502 \u251c\u2500\u2500 feature_engineering.py\n\u2502 \u251c\u2500\u2500 feature_selection.py\n\u2502 \u251c\u2500\u2500 generate_html.py\n\u2502 \u251c\u2500\u2500 hyperparameter_optimization.py\n\u2502 \u251c\u2500\u2500 logger.py\n\u2502 \u251c\u2500\u2500 model.py\n\u2502 \u251c\u2500\u2500 model_utils.py\n\u2502 \u251c\u2500\u2500 predict.py\n\u2502 \u251c\u2500\u2500 predict_utils.py\n\u2502 \u2514\u2500\u2500 train.py\n\u251c\u2500\u2500 static/\n\u2514\u2500\u2500 tests/\n```\n\n## Installation\n\n### Install with pip\n\nIf you want to use the latest stable version, you can install the package directly from PyPI:\n\n```\npip install lstm-forecast\n```\n\n### Install from sources\n\nIf you want to use the latest development version or contribute to the project, you can install from the source:\n\n1. Clone the repository:\n ```\n git clone https://github.com/gianlucamazza/lstm_forecast.git\n cd lstm_forecast\n ```\n\n2. Create a virtual environment:\n ```\n python -m venv venv\n source venv/bin/activate # On Windows use `venv\\Scripts\\activate`\n ```\n\n3. Install the required packages:\n ```\n pip install -r requirements.txt\n ```\n\n4. Install the project in editable mode:\n ```\n pip install -e .\n ```\n\nThis way, you'll have the latest version of the code and be able to make changes if needed.\n\n## Usage\n\nThe `lstm_forecast` command-line interface provides several subcommands for different functionalities:\n\n### Prepare Data\n\nTo prepare the data for training and prediction:\n\n```\nlstm_forecast prepare --config path/to/config.json\n```\n\n### Optimize Hyperparameters and Feature Selection\n\nTo run hyperparameter optimization and feature selection:\n\n```\nlstm_forecast optimize --config path/to/config.json [OPTIONS]\n```\n\nOptions:\n- `--n_trials INTEGER`: Number of trials for hyperparameter tuning (default: 100)\n- `--n_feature_trials INTEGER`: Number of trials for feature selection (default: 15)\n- `--min_features INTEGER`: Minimum number of features to select (default: 5)\n- `--force`: Force re-run of Optuna study\n\n### Train the Model\n\nTo train the model:\n\n```\nlstm_forecast train --config path/to/config.json\n```\n\n### Make Predictions\n\nTo make predictions using a trained model:\n\n```\nlstm_forecast predict --config path/to/config.json\n```\n\n### Start the API Server\n\nTo start the API server:\n\n```\nlstm_forecast server --config path/to/config.json\n```\n\n### General Usage\n\nAll commands require a configuration file specified with the `--config` option. 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