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+# Mean Reversion Trading System for EUR/USD
+
+Welcome to the Mean Reversion Trading System project, specifically tailored for the EUR/USD forex pair. This project combines the precision of machine learning (ML), the robustness of Backtrader for strategy evaluation, and the real-world applicability of trading via the Oanda platform. It’s structured for scalability, leveraging containerization for development consistency and serverless architecture for operational efficiency.
+
+## Project Overview
+
+This system is designed for traders and developers interested in exploring and deploying automated trading strategies. It focuses on the mean reversion principle, a well-regarded concept in finance that suggests asset prices and returns eventually move back towards the mean or average. This project aims to capitalize on this phenomenon using historical EUR/USD data, predictive modeling, and live execution.
+
+## Project Structure
+
+```
+/mean-reversion-trading
+|-- /data
+|   |-- /raw                      # Store raw historical data from Oanda
+|   `-- /processed                # Processed data, ready for ML
+|
+|-- /models
+|   |-- /trained                  # Location for storing trained models
+|   `-- model_training.py         # Script for ML model training
+|
+|-- /strategies
+|   |-- mean_reversion_strategy.py  # Strategy implementation for Backtrader
+|
+|-- /backtesting
+|   |-- backtest.py               # Backtesting script using Backtrader
+|
+|-- /trading
+|   |-- live_trade.py             # Live trading execution script
+|
+|-- /utils
+|   |-- data_fetcher.py           # Utility script for data retrieval
+|   |-- feature_engineering.py    # Data processing and feature engineering
+|   `-- indicators.py             # Custom technical indicators
+|
+|-- Dockerfile                    # For containerization
+|-- requirements.txt              # Python package dependencies
+`-- README.md                     # Project documentation
+```
+
+### Detailed Component Overview
+
+- **/data**: Contains both raw and processed datasets. Raw data is directly fetched from Oanda, while processed data includes features engineered to enhance model training.
+- **/models**: This directory is pivotal for the ML lifecycle, encompassing scripts for training, validation, and serialization of models.
+- **/strategies**: Implements trading strategies within the Backtrader framework, enabling a seamless transition from theory to practical testing.
+- **/backtesting & /trading**: These separate concerns between historical strategy evaluation and real-world application, ensuring a clear pathway from concept to execution.
+- **/utils**: A collection of utility scripts support the data pipeline, from fetching and cleaning to feature engineering, alongside custom indicator development for strategic analysis.
+
+## Getting Started
+
+### Installation
+
+Ensure Python 3.8+ is installed on your system. Clone this repository and navigate into the project directory. Set up a virtual environment and install the required dependencies:
+
+```sh
+python -m venv venv
+source venv/bin/activate  # or venv\Scripts\activate on Windows
+pip install -r requirements.txt
+```
+
+### Environment Configuration
+
+Create a `.env` file in the project root. Add your Oanda API credentials here:
+
+```plaintext
+OANDA_ACCOUNT_ID=<your_account_id>
+OANDA_API_KEY=<your_api_key>
+```
+
+### Running the Project
+
+#### Data Preparation
+
+Fetch and prepare your data for analysis and model training:
+
+```sh
+python utils/data_fetcher.py
+```
+
+#### Model Training
+
+Train your model using historical data:
+
+```sh
+python models/model_training.py
+```
+
+#### Backtesting
+
+Evaluate your strategy with Backtrader:
+
+```sh
+python backtesting/backtest.py
+```
+
+#### Live Trading
+
+Execute your strategy in the live market:
+
+```sh
+python trading/live_trade.py
+```
+
+## Deployment
+
+For containerization, build the Docker image with:
+
+```sh
+docker build -t mean-reversion-trading .
+```
+
+To run the containerized application:
+
+```sh
+docker run -it mean-reversion-trading
+```
+
+For serverless deployment, refer to your cloud provider's documentation for deploying Docker containers and setting up serverless functions for tasks like scheduled data fetching and model retraining.
+
+## Contributing
+
+We welcome contributions! Whether it's feature requests, bug reports, or code contributions, please feel free to reach out or submit a pull request.
+
+## License
+
+Distributed under the MIT License. See `LICENSE` for more information.