updated README
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klein panic
163
README.md
163
README.md
@@ -10,104 +10,113 @@ Midas Technologies aims to build and manage a diversified portfolio of algorithm
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## Business Model
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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.
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Our core product is a modular algorithmic trading platform. The current focus is **MidasV1**, a trading bot for contracts and options, with functionality spanning real-time data collection, market analysis, and automated execution.
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**Current Project: Oil Oracle 1.0**
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### **Current Project: MidasV1**
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- **Purpose**: To predict and execute trades on oil prices with consistent accuracy.
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- **Technology Stack**:
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- **Python** for core algorithm development.
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- **Machine Learning Models** such as BERT and LSTM for sentiment analysis and volatility prediction.
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- **Data Scraping** for real-time news and sentiment acquisition.
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- **Technical Indicators** for validation of trade signals.
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- **APIs** for live data integration and trade execution.
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#### Purpose
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MidasV1 automates trading decisions by combining advanced market analysis techniques, sentiment scoring, and option chain evaluation to ensure optimal trading performance.
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#### Workflow / Program Design
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1. **Module 1: Initial System Checks**
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- **Operating System Check**: Ensures compatibility with the host system (default: Linux).
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- **Dependency Check**: Verifies that all required libraries and tools are installed.
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- **Connectivity Check**: Confirms secure integration with IBJTS or IB Gateway.
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2. **Module 2: IBJTS List Petitioner**
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- Scans and refines a list of stocks meeting initial volume, change, and percent change criteria.
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- Filters stocks based on share price, options availability, volatility, and configurable thresholds.
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3. **Module 3: Stock Information Retrieval**
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- Gathers historical and intraday trading data (datetime, high, low, close, volume).
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- Implements a strategy counter to determine the best indicators (e.g., RSI, MACD, ADX) for market analysis.
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4. **Module 4: Option Chain Trading and Risk Management**
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- Evaluates option chain data for selected bullish and bearish stocks.
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- Executes trades with dynamic stop-losses and real-time risk assessment.
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5. **General Features**
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- Supports configurable flags for verbosity, enabling logs or console output.
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- Integrates a modular structure to simplify future enhancements.
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## Key Components
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### 1. **Sentiment Analysis and News Scraper**
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- **Objective**: Extract relevant oil-related news and analyze market sentiment.
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- **Functionality**: Scrapes news at precise times, preprocesses data, performs sentiment analysis, and uses historical backtesting to validate accuracy.
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- **Sentiment Scoring**: -1 to +1, representing sentiment strength and impact.
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### Sentiment Analysis and News Integration
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- **Objective**: Enhance market predictions with NLP-powered sentiment scoring.
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- **Models Used**: BERT, LSTM.
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- **Sentiment Scoring**: Range of -1 to +1 for precise market insights.
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### 2. **Confidence Scoring Module**
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- **Objective**: Provide confidence scores for sentiment analysis results.
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- **Methods**: Uses ensemble learning and backtested metrics to assign confidence scores, filtering out low-confidence predictions.
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### Technical Analysis and Strategy Development
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- Combines RSI, MACD, ADX, and EMA indicators for market determination.
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- Provides real-time confidence scoring and strategy refinement.
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### 3. **Pre-Market and Intraday Volatility Assessment**
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- **Objective**: Estimate daily price movement and intraday volatility.
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- **Tools**: Machine learning models like LSTM and XGBoost, volatility indicators, and pre-market analysis based on news strength.
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### 4. **Technical Analysis and Historical Pattern Matching**
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- **Objective**: Validate sentiment-driven insights with technical analysis and historical patterns.
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- **Indicators**: Includes Moving Averages, RSI, Bollinger Bands, and support/resistance levels to confirm sentiment-based trade signals.
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### 5. **Trade Execution and Monitoring**
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- **Objective**: Execute trades based on projected price movement and risk management protocols.
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- **Strategies**: Uses options trading with dynamic stop-losses, profit-taking, and trend reversal mechanisms for optimal performance.
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### Automated Trading Execution
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- Implements configurable risk management protocols.
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- Supports modular evaluation of live data for buy/sell signals.
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## Directory Structure
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```
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MidasTechnologiesLLC/
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├── src/
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│ ├── data-collection/ # Web scraping, data ingestion, and preprocessing
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│ ├── neural-network/ # Machine learning models for sentiment and volatility analysis
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│ ├── sentiment-analysis/ # Sentiment analysis and NLP processing
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│ ├── frontend/ # Visualization and UI components
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│ └── main.py # Main entry point for the program
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│
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├── assets/
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│ └── MidasTechnologiesLogo.JPG
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├── data/
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│ └── HistoricalData.json
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├── docs/
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│ ├── BusinessDocumentation/ # Documents related to business plans, bylaws, and other formal records
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│ ├── PoliciesAndStandards/ # Guidelines for coding, Git usage, file-path standards, etc.
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│ └── ManPages/ # Global code documentation for the overarching program
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│
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├── config/ # Configuration files and environment settings
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├── data/ # Static data for the overarching program
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├── tests/ # Unit and integration tests for code validation
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├── scripts/ # Utility scripts for setup and deployment
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└── examples/ # Sample scripts and example usage files
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│ ├── BusinessDocumentation/
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│ ├── PoliciesAndStandards/
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│ ├── ManPages/
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│ └── README.md
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├── logs/
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│ └── MidasV1.log
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├── scripts/
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│ └── README.md (Setup scripts and tools)
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├── src/
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│ ├── griffin-stuff/
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│ ├── MidasV1/
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│ │ ├── config/
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│ │ │ └── config.config
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│ │ ├── logs/
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│ │ │ └── MidasV1.log
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│ │ ├── modules/
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│ │ │ ├── initial_checks.py
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│ │ │ ├── stock_list_petitioner.py
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│ │ │ └── __pycache__/
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│ │ ├── tests/
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│ │ │ ├── test_connection.py
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│ │ │ └── test_stock_retriever.py
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│ │ └── main.py
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│ ├── WebScraper/
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│ │ ├── data/
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│ │ ├── scrapers/
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│ │ └── main.py
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│ └── README.md
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└── README.md
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```
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## Standards and Best Practices
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### 1. **Coding Standards**
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### Coding Standards
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- Adheres to **PEP8** for Python.
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- Modular structure ensures maintainability and scalability.
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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.
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### Documentation Standards
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- **Business Documentation**: Legal, corporate, and policy-related files.
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- **Man Pages**: Comprehensive technical references for all modules.
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- **Python Standards**: Use virtual environments (`venv`), ensure `requirements.txt` is up to date, and avoid committing environment-specific files.
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- **Interfacing with Other Languages**: Maintain consistency when interacting with languages like C, Rust, or Go.
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### 2. **Documentation Standards**
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Documentation is organized into three main areas within the `docs` folder:
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- **Business Documentation**: Legal, business, and corporate documents.
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- **Policies and Standards**: Coding guidelines, Git practices, file-path conventions, and more.
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- **Man Pages**: Comprehensive documentation of each part of the system.
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### 3. **Git Standards**
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- **Branching Strategy**: `main` is the production branch, `dev` is for development, and feature-specific branches are created off of `dev`.
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- **Commit Messages**: Follow a structured format and keep messages descriptive and clear.
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- **Pull Requests**: All changes must be submitted through pull requests, with relevant team members assigned as reviewers.
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## Communication and Collaboration
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To ensure a cohesive development process, Midas Technologies follows these key guidelines:
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- **GitHub Issues**: For tracking bugs, features, and tasks.
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- **Weekly Meetings**: Updates on progress, blockers, and upcoming tasks.
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- **Direct Messaging**: For urgent, immediate issues or clarifications.
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### Git Standards
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- **Branching Strategy**: `main` for production, `dev` for development, and feature-specific branches off `dev`.
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- **Commit Messages**: Follow structured and descriptive formats.
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- **Pull Requests**: Require reviewer approval for all major changes.
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## Roadmap
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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.
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| Phase | Duration | Goals |
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|-----------------------------|------------|-------|
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| **Phase 1: Initial Build** | Weeks 1-4 | Develop core modules, news scraper, and basic sentiment analysis |
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| **Phase 2: Backtesting** | Weeks 5-6 | Historical backtesting for reliability |
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| **Phase 3: Expansion** | Weeks 7-8 | Introduce multi-asset support and advanced indicators |
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| **Phase 4: Live Trading** | Ongoing | Deploy and continuously improve trading algorithm |
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|-----------------------------|------------|------------------------------------------|
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| **Phase 1: Initial Build** | Weeks 1-4 | Core modules, sentiment analysis, scraper |
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| **Phase 2: Backtesting** | Weeks 5-6 | Validate reliability and performance |
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| **Phase 3: Expansion** | Weeks 7-8 | Support additional assets and strategies |
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| **Phase 4: Live Trading** | Ongoing | Deploy trading bot and refine algorithms |
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## Getting Started
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@@ -135,14 +144,14 @@ For more information, please reach out to the Midas Technologies team.
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**Primary Contacts**:
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- **Chief Data Officer**: Griffin
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- **Chief Technical Officer**: Collin Aka KleinPanic
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- **Chief Technical Officer**: Collin (KleinPanic)
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- **Chief Operations Officer**: Jacob
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**Note**: This project and all related files are private and for use by Midas Technologies LLC only. Unauthorized distribution or modification is strictly prohibited.
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## License
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For the license file, please navigate to the docs/BusinessDocumentation/LICENSE and read it there.
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For the license file, please navigate to the `docs/BusinessDocumentation/LICENSE`.
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``` Author
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KleinPanic
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