151 lines
7.6 KiB
Markdown
151 lines
7.6 KiB
Markdown
# Midas Technologies LLC
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## Overview
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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.
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## Mission Statement
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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.
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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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**Current Project: Oil Oracle 1.0**
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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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## 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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### 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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### 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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## 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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├── 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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```
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## Standards and Best Practices
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### 1. **Coding Standards**
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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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- **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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## 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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## Getting Started
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1. **Clone the repository**:
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```bash
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git clone https://github.com/MidasTechnologiesLLC/MidasTechnologies.git
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```
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2. **Set up the virtual environment**:
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```bash
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python -m venv venv
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source venv/bin/activate
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```
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3. **Install dependencies**:
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```bash
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pip install -r requirements.txt
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```
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4. **Run tests**:
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```bash
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pytest
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```
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## Contact
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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 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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``` Author
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KleinPanic
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```
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