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-# MiadasTechnologies
+# Midas Technologies LLC
+
+## Overview
+
+Welcome to **Midas Technologies LLC**, an innovative company focused on developing sophisticated algorithmic trading solutions, primarily aimed at the financial markets. Our goal is to deliver above-market returns using a robust, data-driven approach powered by advanced technology, natural language processing (NLP), machine learning, and state-of-the-art trading algorithms.
+
+## Mission Statement
+
+Midas Technologies aims to build and manage a diversified portfolio of algorithmic trading strategies that maximize returns while managing risk effectively. With a commitment to continual improvement and innovation, we strive to provide top-tier trading systems capable of navigating volatile markets and delivering consistent profitability.
+
+## Business Model
+
+Our core product is an algorithmic trading platform that leverages real-time data to predict and execute trades based on crude oil price fluctuations. Our trading system integrates a multifaceted analysis of market trends, sentiment analysis, historical price patterns, and economic indicators to ensure precise market predictions.
+
+**Current Project: Oil Oracle 1.0**
+
+- **Purpose**: To predict and execute trades on oil prices with consistent accuracy.
+- **Technology Stack**:
+  - **Python** for core algorithm development.
+  - **Machine Learning Models** such as BERT and LSTM for sentiment analysis and volatility prediction.
+  - **Data Scraping** for real-time news and sentiment acquisition.
+  - **Technical Indicators** for validation of trade signals.
+  - **APIs** for live data integration and trade execution.
+
+## Key Components
+
+### 1. **Sentiment Analysis and News Scraper**
+   - **Objective**: Extract relevant oil-related news and analyze market sentiment.
+   - **Functionality**: Scrapes news at precise times, preprocesses data, performs sentiment analysis, and uses historical backtesting to validate accuracy.
+   - **Sentiment Scoring**: -1 to +1, representing sentiment strength and impact.
+
+### 2. **Confidence Scoring Module**
+   - **Objective**: Provide confidence scores for sentiment analysis results.
+   - **Methods**: Uses ensemble learning and backtested metrics to assign confidence scores, filtering out low-confidence predictions.
+
+### 3. **Pre-Market and Intraday Volatility Assessment**
+   - **Objective**: Estimate daily price movement and intraday volatility.
+   - **Tools**: Machine learning models like LSTM and XGBoost, volatility indicators, and pre-market analysis based on news strength.
+
+### 4. **Technical Analysis and Historical Pattern Matching**
+   - **Objective**: Validate sentiment-driven insights with technical analysis and historical patterns.
+   - **Indicators**: Includes Moving Averages, RSI, Bollinger Bands, and support/resistance levels to confirm sentiment-based trade signals.
+
+### 5. **Trade Execution and Monitoring**
+   - **Objective**: Execute trades based on projected price movement and risk management protocols.
+   - **Strategies**: Uses options trading with dynamic stop-losses, profit-taking, and trend reversal mechanisms for optimal performance.
+
+## Directory Structure
+
+```
+MidasTechnologiesLLC/
+├── src/
+│   ├── data-collection/        # Web scraping, data ingestion, and preprocessing
+│   ├── neural-network/          # Machine learning models for sentiment and volatility analysis
+│   ├── sentiment-analysis/      # Sentiment analysis and NLP processing
+│   ├── frontend/                # Visualization and UI components
+│   └── main.py                  # Main entry point for the program
+│
+├── docs/
+│   ├── BusinessDocumentation/   # Documents related to business plans, bylaws, and other formal records
+│   ├── PoliciesAndStandards/    # Guidelines for coding, Git usage, file-path standards, etc.
+│   └── ManPages/                # Global code documentation for the overarching program
+│
+├── config/                      # Configuration files and environment settings
+├── data/                        # Static data for the overarching program
+├── tests/                       # Unit and integration tests for code validation
+├── scripts/                     # Utility scripts for setup and deployment
+└── examples/                    # Sample scripts and example usage files
+```
+
+## Standards and Best Practices
+
+### 1. **Coding Standards**
+
+All code should adhere to **PEP8** standards for Python and follow industry best practices for maintainability and readability. Each root module must contain a `README.md` file with documentation on functionality and usage.
+
+- **Python Standards**: Use virtual environments (`venv`), ensure `requirements.txt` is up to date, and avoid committing environment-specific files.
+- **Interfacing with Other Languages**: Maintain consistency when interacting with languages like C, Rust, or Go.
+
+### 2. **Documentation Standards**
+
+Documentation is organized into three main areas within the `docs` folder:
+- **Business Documentation**: Legal, business, and corporate documents.
+- **Policies and Standards**: Coding guidelines, Git practices, file-path conventions, and more.
+- **Man Pages**: Comprehensive documentation of each part of the system.
+
+### 3. **Git Standards**
+
+- **Branching Strategy**: `main` is the production branch, `dev` is for development, and feature-specific branches are created off of `dev`.
+- **Commit Messages**: Follow a structured format and keep messages descriptive and clear.
+- **Pull Requests**: All changes must be submitted through pull requests, with relevant team members assigned as reviewers.
+
+## Communication and Collaboration
+
+To ensure a cohesive development process, Midas Technologies follows these key guidelines:
+
+- **GitHub Issues**: For tracking bugs, features, and tasks.
+- **Weekly Meetings**: Updates on progress, blockers, and upcoming tasks.
+- **Direct Messaging**: For urgent, immediate issues or clarifications.
+
+## Roadmap
+
+Our current focus is building a modular and scalable system capable of performing complex sentiment analysis and technical validation for trading. **Future goals** include expanding into other commodities and assets, refining machine learning models, and implementing additional risk management strategies.
+
+| Phase                       | Duration   | Goals |
+|-----------------------------|------------|-------|
+| **Phase 1: Initial Build**  | Weeks 1-4  | Develop core modules, news scraper, and basic sentiment analysis |
+| **Phase 2: Backtesting**    | Weeks 5-6  | Historical backtesting for reliability |
+| **Phase 3: Expansion**      | Weeks 7-8  | Introduce multi-asset support and advanced indicators |
+| **Phase 4: Live Trading**   | Ongoing    | Deploy and continuously improve trading algorithm |
+
+## Getting Started
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/MidasTechnologiesLLC/MidasTechnologies.git
+   ```
+2. **Set up the virtual environment**:
+   ```bash
+   python -m venv venv
+   source venv/bin/activate
+   ```
+3. **Install dependencies**:
+   ```bash
+   pip install -r requirements.txt
+   ```
+4. **Run tests**:
+   ```bash
+   pytest
+   ```
+
+## Contact
+
+For more information, please reach out to the Midas Technologies team.
+
+**Primary Contacts**:
+- **Chief Data Officer**: Griffin Witt
+- **Chief Technical Officer**: Collin Schaufele
+- **Chief Operations Officer**: Jacob Mardian
+
+**Note**: This project and all related files are private and for use by Midas Technologies LLC only. Unauthorized distribution or modification is strictly prohibited.
+