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