{"id":19973064,"url":"https://github.com/l1zle/quantitative_electricity","last_synced_at":"2025-09-19T18:31:08.241Z","repository":{"id":250031733,"uuid":"833281091","full_name":"L1ZLe/Quantitative_Electricity","owner":"L1ZLe","description":"Quantitative Analysis \u0026 Trading of the Electricity 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align=\"center\"\u003e\r\n  \u003cimg src=\"https://cdn-icons-png.flaticon.com/512/6295/6295417.png\" width=\"100\" /\u003e\r\n\u003c/p\u003e\r\n\u003cp align=\"center\"\u003e\r\n    \u003ch1 align=\"center\"\u003eQUANTITATIVE_ELECTRICITY\u003c/h1\u003e\r\n\u003c/p\u003e\r\n\u003cp align=\"center\"\u003e\r\n    \u003cem\u003e\u003ccode\u003e► An advanced platform for forecasting electricity prices and developing trading strategies.\u003c/code\u003e\r\n\tLink to the web app: (https://quantitative-electricity.streamlit.app/)\r\n    \u003c/em\u003e\r\n\u003c/p\u003e\r\n\u003cp align=\"center\"\u003e\r\n\t\u003cimg src=\"https://img.shields.io/github/license/L1ZLe/Quantitative_Electricity?style=flat\u0026color=0080ff\" alt=\"license\"\u003e\r\n\t\u003cimg src=\"https://img.shields.io/github/last-commit/L1ZLe/Quantitative_Electricity?style=flat\u0026logo=git\u0026logoColor=white\u0026color=0080ff\" alt=\"last-commit\"\u003e\r\n\t\u003cimg src=\"https://img.shields.io/github/languages/top/L1ZLe/Quantitative_Electricity?style=flat\u0026color=0080ff\" alt=\"repo-top-language\"\u003e\r\n\t\u003cimg src=\"https://img.shields.io/github/languages/count/L1ZLe/Quantitative_Electricity?style=flat\u0026color=0080ff\" alt=\"repo-language-count\"\u003e\r\n\u003cp\u003e\r\n\u003cp align=\"center\"\u003e\r\n\t\t\u003cem\u003eDeveloped with the software and tools below.\u003c/em\u003e\r\n\u003c/p\u003e\r\n\u003cp align=\"center\"\u003e\r\n\t\u003cimg src=\"https://img.shields.io/badge/Python-3776AB.svg?style=flat\u0026logo=Python\u0026logoColor=white\" alt=\"Python\"\u003e\r\n\u003c/p\u003e\r\n\u003chr\u003e\r\n\r\n## 🔗 Quick Links\r\n\r\n\u003e - [📍 Overview](#-overview)\r\n\u003e - [📦 Features](#-features)\r\n\u003e - [📂 Repository Structure](#-repository-structure)\r\n\u003e - [🧩 Modules](#-modules)\r\n\u003e - [⚙️ Installation](#️-installation)\r\n\u003e - [🤖 Running Quantitative_Electricity](#-running-Quantitative_Electricity)\r\n\u003e - [🛠 Project Roadmap](#-project-roadmap)\r\n\u003e - [🤝 Contributing](#-contributing)\r\n\u003e - [📄 License](#-license)\r\n\u003e - [👏 Acknowledgments](#-acknowledgments)\r\n\r\n---\r\n\r\n## 📍 Overview\r\n\r\n\u003ccode\u003e► Welcome to the Electricity Trading Strategy Project! This platform provides comprehensive tools and models to forecast electricity prices and develop trading strategies. We utilize advanced machine learning models including SARIMA and GRU to predict price movements and assess trading strategies. Explore live predictions, backtesting tools, and performance metrics to understand and improve trading strategies in the electricity market.\u003c/code\u003e\r\n\r\n---\r\n\r\n## 📦 Features\r\n\r\n► Explore a range of features designed to enhance your trading strategy:\r\n\r\n- **Model Overview**: Detailed descriptions of the models used for forecasting electricity prices.\r\n- **Data Exploration**: Interactive visualizations of historical electricity prices and influencing factors.\r\n- **Predictions**: Live forecasts of next-day electricity prices using various models.\r\n- **Trading Strategy**: Insights into the logic and implementation of trading strategies.\r\n- **Performance Metrics**: Evaluation of strategies using metrics such as Sharpe ratio, ROI, and more.\r\n- **Backtesting**: Assess the performance of strategies on historical data.\r\n\r\n---\r\n\r\n## 📂 Repository Structure\r\n\r\n```sh\r\n└── Quantitative_Electricity/\r\n    ├── README.md\r\n    ├── app.py\r\n    ├── assets\r\n    │   ├── ARIMA_predictions.png\r\n    │   ├── Average Electricity price by Month.png\r\n    │   ├── BOS.png\r\n    │   ├── Correlations between variables1.png\r\n    │   ├── Correlations between variables2.png\r\n    │   ├── Electricity seasonal decomposition.png\r\n    │   ├── LinearRegression.png\r\n    │   ├── Natural Gas seasonal decomposition.png\r\n    │   ├── Net_generated electricity and Temperature.png\r\n    │   ├── PACF_ACF.png\r\n    │   ├── RandomForest.png\r\n    │   ├── Relation between Electricity price and Temperature.png\r\n    │   ├── gru_predictions.png\r\n    │   └── lstm_predictions.png\r\n    ├── datasets\r\n    │   ├── Data_cleaned_Dataset.csv\r\n    │   ├── Net_generation_United_States_all_sectors_monthly.csv\r\n    │   ├── Net_generation_by places.csv\r\n    │   └── Retail_sales_of_electricity_United_States_monthly.csv\r\n    ├── model_module.py\r\n    ├── models\r\n    │   ├── price_ARIMA_model.pkl\r\n    │   ├── price_gru_model.h5\r\n    │   ├── price_lstm_model.h5\r\n    │   ├── scaler.pkl\r\n    │   ├── sign_LSTM_model.keras\r\n    │   ├── sign_gru_model.keras\r\n    │   ├── sign_linearRegression_model.pkl\r\n    │   └── sign_randomForest_model.pkl\r\n    ├── requirements.txt\r\n    ├── trading_strategies.py\r\n    └── visualizations.py\r\n```\r\n\r\n---\r\n\r\n## 🧩 Modules\r\n\r\n\u003cdetails closed\u003e\u003csummary\u003e.\u003c/summary\u003e\r\n\r\n| File                                                                                                       | Summary                         |\r\n| ---                                                                                                        | ---                             |\r\n| [model_module.py](https://github.com/L1ZLe/Quantitative_Electricity/blob/master/model_module.py)             | \u003ccode\u003e► Contains functions for model training and predictions.\u003c/code\u003e |\r\n| [visualizations.py](https://github.com/L1ZLe/Quantitative_Electricity/blob/master/visualizations.py)         | \u003ccode\u003e► Generates interactive visualizations for data exploration.\u003c/code\u003e |\r\n| [trading_strategies.py](https://github.com/L1ZLe/Quantitative_Electricity/blob/master/trading_strategies.py) | \u003ccode\u003e► Implements various trading strategies based on predictions.\u003c/code\u003e |\r\n| [app.py](https://github.com/L1ZLe/Quantitative_Electricity/blob/master/app.py)                               | \u003ccode\u003e► Main entry point for running the Streamlit application.\u003c/code\u003e |\r\n\r\n\u003c/details\u003e\r\n\r\n---\r\n\r\n### ⚙️ Installation\r\n\r\n1. Clone the Quantitative_Electricity repository:\r\n\r\n```sh\r\ngit clone https://github.com/L1ZLe/Quantitative_Electricity\r\n```\r\n\r\n2. Change to the project directory:\r\n\r\n```sh\r\ncd Quantitative_Electricity\r\n```\r\n\r\n3. Install the dependencies:\r\n\r\n```sh\r\npip install -r requirements.txt\r\n```\r\n\r\n### 🤖 Running Quantitative_Electricity\r\n\r\nUse the following command to run Quantitative_Electricity:\r\n\r\n```sh\r\nstreamlit run app.py\r\n```\r\n\r\n---\r\n\r\n## 🛠 Project Roadmap\r\n\r\n- [X] `► Initial setup and model development`\r\n- [X] `► Implementation of trading strategies`\r\n- [X] `► Enhance user interface and visualizations`\r\n- [X] `► Deploy and monitor application`\r\n\r\n---\r\n\r\n## 🤝 Contributing\r\n\r\nContributions are welcome! Here are several ways you can contribute:\r\n\r\n- **[Join the Discussions](https://github.com/L1ZLe/Quantitative_Electricity/discussions)**: Share your insights, provide feedback, or ask questions.\r\n- **[Report Issues](https://github.com/L1ZLe/Quantitative_Electricity/issues)**: Submit bugs found or log feature requests for Quantitative_Electricity.\r\n\r\n\u003cdetails closed\u003e\r\n    \u003csummary\u003eContributing Guidelines\u003c/summary\u003e\r\n\r\n1. **Fork the Repository**: Start by forking the project repository to your GitHub account.\r\n2. **Clone Locally**: Clone the forked repository to your local machine using a Git client.\r\n   ```sh\r\n   git clone https://github.com/L1ZLe/Quantitative_Electricity\r\n   ```\r\n3. **Create a New Branch**: Always work on a new branch, giving it a descriptive name.\r\n   ```sh\r\n   git checkout -b new-feature-x\r\n   ```\r\n4. **Make Your Changes**: Develop and test your changes locally.\r\n5. **Commit Your Changes**: Commit with a clear message describing your updates.\r\n   ```sh\r\n   git commit -m 'Implemented new feature x.'\r\n   ```\r\n6. **Push to GitHub**: Push the changes to your forked repository.\r\n   ```sh\r\n   git push origin new-feature-x\r\n   ```\r\n7. **Submit a Pull Request**: Create a PR against the original project repository. Clearly describe the changes and their motivations.\r\n\r\nOnce your PR is reviewed and approved, it will be merged into the main branch.\r\n\r\n\u003c/details\u003e\r\n\r\n---\r\n\r\n## 📄 License\r\n\r\nThis project is protected under the [MIT License](https://choosealicense.com/licenses/mit/). For more details, refer to the [LICENSE](LICENSE) file.\r\n\r\n---\r\n\r\n## 👏 Acknowledgments\r\n\r\n- **[Pandas](https://pandas.pydata.org/)**: For providing the data manipulation tools.\r\n- **[TensorFlow](https://www.tensorflow.org/)**: For enabling deep learning models.\r\n- **[Streamlit](https://streamlit.io/)**: For allowing easy deployment of the web application.\r\n- **[eia.gov](https://www.eia.gov/)**: For providing electricity data.\r\n\r\n---\r\n\r\n\u003cp align=\"center\"\u003e\r\n  \u003cem\u003eThank you for exploring Quantitative_Electricity. We hope you find it valuable for your trading strategy development!\u003c/em\u003e\r\n\u003c/p\u003e\r\n```\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fl1zle%2Fquantitative_electricity","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fl1zle%2Fquantitative_electricity","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fl1zle%2Fquantitative_electricity/lists"}