diff --git a/README.md b/README.md index 2140e0e..4a24ce1 100644 --- a/README.md +++ b/README.md @@ -1 +1,142 @@ -# 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. +