Market Intelligence OS is an independent applied AI and workflow systems project exploring how live market data, options-chain intelligence, portfolio visibility, model outputs, regime indicators, diagnostics, and operational dashboards can be combined into a unified research and decision-support environment.
This project is intentionally positioned as a research, workflow, and decision-support prototype rather than an automated trading platform. Automatic order execution remains disabled unless explicitly and safely implemented later.
The purpose of Market Intelligence OS is to explore how operational dashboards, AI-assisted workflows, live data systems, and research coordination tools can improve market research visibility, information routing, and decision-support workflows.
The project focuses on:
- Live market-data workflow concepts
- Options-chain inspection and analysis
- Watchlist-based research workflows
- Candidate evaluation and scoring concepts
- AI/model output visibility
- Regime and macro-monitoring concepts
- Portfolio visibility and operational dashboards
- Diagnostics and system-status tracking
- Workflow-safe separation between research and execution
- AI-assisted market research and decision-support concepts
- Live market-data ingestion concepts
- Tick-to-minute data transformation
- CSV and JSON processing pipelines
- Watchlist-driven data organization
- Market snapshot and status monitoring
- Data-quality and source-status diagnostics
- Options-chain inspection workflows
- Candidate review and filtering concepts
- Strategy comparison dashboards
- Contract-level visibility concepts
- Risk/reward and scenario-review concepts
- Research-oriented options workflow organization
- Model-output visibility
- Scenario comparison and review workflows
- Candidate scoring concepts
- Backtest-style research logs
- Paper-test signal review
- Train / validation / test style evaluation concepts
- Cost sensitivity and drawdown review concepts
- Multi-panel dashboard concepts
- Research workflow coordination
- Operational status tracking
- Diagnostics and alert visibility
- GUI-based research tools
- Frontend/backend architecture exploration
- Structured logging for later review
1. Collect or receive live market data
2. Normalize and structure the data
3. Generate technical and custom research features
4. Run selected research or scenario modules
5. Display model outputs and diagnostics in a GUI/dashboard
6. Log decisions, signals, events, and review data
7. Evaluate results before any production or execution use
Market Data Sources
↓
Data Ingestion / File Watchers
↓
Normalization and Feature Engineering
↓
Research Modules / Scenario Evaluation
↓
Dashboard and GUI Review Layer
↓
Paper-Test Logs / Decision-Support Outputs
- Project Overview
- Architecture Notes
- Operational Workflow
- AI and Model Visibility
- Safety and Sanitization
- Screenshots and Visuals
- Python
- C#
- Tkinter
- FastAPI / React concepts
- REST API concepts
- Brokerage API integration concepts
- CSV and JSON data pipelines
- Market-data workflows
- Options-chain analysis concepts
- Technical feature engineering
- Scenario testing
- Dashboard design
- Model-status tracking
- Workflow orchestration
- AI-assisted research workflows
- Operational intelligence systems
Status: Research prototype / showcase project
This repository is intended to demonstrate applied AI, workflow automation, dashboard architecture, data-pipeline design, and decision-support system concepts.
It is not intended to provide financial advice, investment recommendations, or live automated trading functionality.
Screenshots and diagrams are available in:
Suggested visuals include:
- Main dashboard
- Market data status view
- Options-chain inspection view
- Candidate review workflow
- Model-output visibility panel
- Diagnostics / system log view
- Paper-test signal log
- Architecture diagram
Example:
market-intelligence-os-showcase/
│
├── README.md
├── docs/
│ ├── architecture.md
│ ├── project-overview.md
│ ├── operational-workflow.md
│ ├── ai-and-model-visibility.md
│ ├── safety-and-sanitization.md
│ ├── screenshots.md
│ └── images/
│
├── sample_data/
│ └── sanitized_sample_market_data.csv
│
├── examples/
│ ├── sample_signal_log.csv
│ ├── sample_candidate_review.json
│ └── sample_dashboard_output.csv
│
├── src/
│ ├── data_pipeline/
│ ├── dashboard/
│ ├── research_modules/
│ └── utils/
│
└── LICENSE
Before publishing or sharing any project materials, remove or redact:
- API keys
- OAuth tokens
- Brokerage credentials
- Account IDs
- Live portfolio values
- Personal file paths
- Raw proprietary data
- Order logs
- Sensitive financial records
- Virtual environments
node_modules- Large cache folders
This repository should be maintained as a sanitized showcase repository focused on architecture, workflow design, applied AI concepts, and decision-support functionality.
Market Intelligence OS demonstrates experience in:
- Applied AI workflow design
- Operational dashboard architecture
- Data ingestion and transformation
- Decision-support system design
- Scenario-based research workflows
- Model-output visibility
- Local automation and GUI tooling
- Research-to-review pipeline development
- Operational intelligence concepts
The project reflects a broader interest in using AI-assisted systems to improve how complex information is collected, organized, evaluated, and presented for decision-making.
This project is for educational, research, and portfolio demonstration purposes only.
It does not provide financial advice, trading recommendations, investment recommendations, or automated order execution. Any market-related examples are used solely to demonstrate data engineering, dashboard architecture, workflow orchestration, and decision-support concepts.