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:

   git clone https://github.com/MidasTechnologiesLLC/MidasTechnologies.git
   

2. Set up the virtual environment:

   python -m venv venv
   source venv/bin/activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run tests:

   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.

KleinPanic