| Name | tradestream JSON |
| Version |
0.5.10
JSON |
| download |
| home_page | None |
| Summary | A Python application with a Dash frontend, services to fetch market data, and an API server. |
| upload_time | 2024-08-15 14:16:25 |
| maintainer | None |
| docs_url | None |
| author | Mac Anderson |
| requires_python | <4.0.0,>=3.12.5 |
| license | Proprietary |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
### Machine Learning Models
Tradestream uses a variety of machine learning models to predict the future price of a stock. The models are trained on historical data and use a variety of features to make predictions. The models are trained on a daily basis and the predictions are made on a minute-by-minute basis.
#### Machine Learning Libraries
Tradestream researched the following machine learning libraries:
- [TensorFlow](https://www.tensorflow.org/)
- [LSTM](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM)
- [GRU](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)
- [Transformer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Transformer)
- [PyTorch](https://pytorch.org/)
- [LSTM](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html)
- [GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html)
- [Transformer](https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html)
- [Scikit-learn](https://scikit-learn.org/)
- [Ridge](https://scikit-learn.org/stable/modules/linear_model.html#ridge-regression)
- [Lasso](https://scikit-learn.org/stable/modules/linear_model.html#lasso)
- [ElasticNet](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net)
- [RandomForest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)
- [GradientBoosting](https://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting)
- [AdaBoost](https://scikit-learn.org/stable/modules/ensemble.html#adaboost)
- [Stacking](https://scikit-learn.org/stable/modules/ensemble.html#stacking)
- [Voting](https://scikit-learn.org/stable/modules/ensemble.html#voting)
- [Bagging](https://scikit-learn.org/stable/modules/ensemble.html#bagging)
- [ExtraTrees](https://scikit-learn.org/stable/modules/ensemble.html#extra-trees)
- [IsolationForest](https://scikit-learn.org/stable/modules/ensemble.html#isolation-forest)
- [LocalOutlierFactor](https://scikit-learn.org/stable/modules/neighbors.html#local-outlier-factor)
- [XGBoost](https://xgboost.readthedocs.io/en/stable/)
- [XGBRegressor](https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBRegressor)
- [XGBClassifier](https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBClassifier)
- [LightGBM](https://lightgbm.readthedocs.io/en/latest/)
- [LGBMRegressor](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html)
- [LGBMClassifier](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html)
- [CatBoost](https://catboost.ai/)
- [CatBoostRegressor](https://catboost.ai/docs/concepts/python-reference_catboostregressor.html)
- [CatBoostClassifier](https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html)
- [Prophet](https://facebook.github.io/prophet/)
- [ProphetRegressor](https://facebook.github.io/prophet/docs/python/python_api.html#prophet.ProphetRegressor)
- [ProphetClassifier](https://facebook.github.io/prophet/docs/python/python_api.html#prophet.ProphetClassifier)
### Contributing to Tradestream
We welcome contributions to Tradestream! Please open an issue or submit a pull request with your changes. You can find the pull request template in the `.github/pull_request_template.md` file. If you have any questions, please open an issue and we will be happy to help. You can also find us on the [Tradestream Discord](https://discord.gg/tradestream) if you have any questions. It is very important that you follow the [Contributing Guidelines](CONTRIBUTING.md) when contributing to Tradestream. We look forward to seeing your contributions!
### Project Structure
Tradestream is a Python application that uses the Dash framework for the frontend and the Flask framework for the backend. The application is deployed to Heroku. The project is organized as follows:
``` python
tradestream/ # Main directory for the application
│
├── dash_app/ # Directory for the Dash app (frontend)
│ ├── __init__.py # Initialize the Dash app, include authentication
│ ├── layout.py # Define the layout of the Dash app
│ ├── callbacks.py # Define callbacks for interactivity
│ └── authentication.py # Handle user authentication
│
├── services/ # Directory for services that fetch real-time market data
│ ├── __init__.py # Initialization for services
│ ├── market_fetcher.py # Code to fetch real-time data from the markets
│ └── scheduler.py # Schedule tasks to fetch data at intervals
│
├── api/ # Directory for the API server
│ ├── __init__.py # Initialization for API server
│ ├── routes.py # Define API routes
│ ├── models.py # Define MongoDB models using ODM (like PyMongo or Motor)
│ └── views.py # API views (logic to handle requests)
├── .env # DOTENV file
├── Procfile # Define process types for Heroku (e.g., web, worker)
├── requirements.txt # Python dependencies
└── build.sh # Shell script to build the app
└── start.sh # Shell script to start the app
└── pyproject.toml # Python package configuration
```
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"description": "### Machine Learning Models\n\nTradestream uses a variety of machine learning models to predict the future price of a stock. The models are trained on historical data and use a variety of features to make predictions. The models are trained on a daily basis and the predictions are made on a minute-by-minute basis.\n\n#### Machine Learning Libraries\n\nTradestream researched the following machine learning libraries:\n\n- [TensorFlow](https://www.tensorflow.org/)\n- [LSTM](https://www.tensorflow.org/api_docs/python/tf/keras/layers/LSTM)\n- [GRU](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU)\n- [Transformer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Transformer)\n- [PyTorch](https://pytorch.org/)\n- [LSTM](https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html)\n- [GRU](https://pytorch.org/docs/stable/generated/torch.nn.GRU.html)\n- [Transformer](https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html)\n- [Scikit-learn](https://scikit-learn.org/)\n- [Ridge](https://scikit-learn.org/stable/modules/linear_model.html#ridge-regression)\n- [Lasso](https://scikit-learn.org/stable/modules/linear_model.html#lasso)\n- [ElasticNet](https://scikit-learn.org/stable/modules/linear_model.html#elastic-net)\n- [RandomForest](https://scikit-learn.org/stable/modules/ensemble.html#random-forests)\n- [GradientBoosting](https://scikit-learn.org/stable/modules/ensemble.html#gradient-boosting)\n- [AdaBoost](https://scikit-learn.org/stable/modules/ensemble.html#adaboost)\n- [Stacking](https://scikit-learn.org/stable/modules/ensemble.html#stacking)\n- [Voting](https://scikit-learn.org/stable/modules/ensemble.html#voting)\n- [Bagging](https://scikit-learn.org/stable/modules/ensemble.html#bagging)\n- [ExtraTrees](https://scikit-learn.org/stable/modules/ensemble.html#extra-trees)\n- [IsolationForest](https://scikit-learn.org/stable/modules/ensemble.html#isolation-forest)\n- [LocalOutlierFactor](https://scikit-learn.org/stable/modules/neighbors.html#local-outlier-factor)\n- [XGBoost](https://xgboost.readthedocs.io/en/stable/)\n- [XGBRegressor](https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBRegressor)\n- [XGBClassifier](https://xgboost.readthedocs.io/en/stable/python/python_api.html#xgboost.XGBClassifier)\n- [LightGBM](https://lightgbm.readthedocs.io/en/latest/)\n- [LGBMRegressor](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMRegressor.html)\n- [LGBMClassifier](https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html)\n- [CatBoost](https://catboost.ai/)\n- [CatBoostRegressor](https://catboost.ai/docs/concepts/python-reference_catboostregressor.html)\n- [CatBoostClassifier](https://catboost.ai/docs/concepts/python-reference_catboostclassifier.html)\n- [Prophet](https://facebook.github.io/prophet/)\n- [ProphetRegressor](https://facebook.github.io/prophet/docs/python/python_api.html#prophet.ProphetRegressor)\n- [ProphetClassifier](https://facebook.github.io/prophet/docs/python/python_api.html#prophet.ProphetClassifier)\n\n\n### Contributing to Tradestream\n\nWe welcome contributions to Tradestream! 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