# 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 
- **Chief Technical Officer**: Collin Aka KleinPanic
- **Chief Operations Officer**: Jacob 

**Note**: This project and all related files are private and for use by Midas Technologies LLC only. Unauthorized distribution or modification is strictly prohibited.

## License

For the license file, please navigate to the docs/BusinessDocumentation/LICENSE and read it there. 

``` Author
KleinPanic
```