A modular quantitative options research platform integrating volatility modeling, options strategy research, portfolio analytics, risk management, and automated research documentation.
The Quant Option Research Platform is a research-oriented quantitative framework designed to build a complete and reproducible workflow for systematic options research.
The platform connects the full quantitative research lifecycle:
The objective of this project is to bridge the gap between quantitative research prototypes and a structured research infrastructure.
The framework focuses on:
- Volatility research
- Options strategy development
- Portfolio analytics
- Risk monitoring
- Automated technical documentation
The volatility research module focuses on analyzing option market structures.
Main capabilities:
- Implied volatility calculation
- Volatility smile analysis
- Volatility surface construction
- Term structure analysis
- ATM volatility monitoring
- Moneyness-based volatility analysis
Research outputs include:
- IV surface visualization
- Smile evolution analysis
- Term structure monitoring
- Volatility signal generation
The strategy layer adopts a modular registry-based architecture.
Each strategy is represented through a structured research record containing:
- Strategy motivation
- Construction rules
- Entry and exit logic
- Greeks profile
- Backtest evidence
- Performance summary
- Limitations
- Future extensions
Current research strategies include:
- Volatility strategies
- Strangle structures
- Calendar spreads
- Butterfly structures
The framework allows future strategies to be added without redesigning the research infrastructure.
The portfolio layer provides systematic portfolio analytics:
- Position tracking
- NAV calculation
- Exposure aggregation
- Portfolio Greeks calculation
- Risk snapshot generation
Core components:
- PositionBook
- Portfolio Engine
- NAV Engine
The risk module evaluates portfolio exposure through option Greeks.
Covered risk measures include:
- Delta exposure
- Gamma exposure
- Vega exposure
- Portfolio risk status
The system generates:
- Exposure reports
- Risk dashboards
- Portfolio monitoring summaries
A key feature of this project is automated technical documentation generation.
The reporting framework transforms research outputs into structured documents.
Workflow:
Research Data
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Analysis Pipeline
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Visualization Generation
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Automated Report Builder
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Technical Documentation
Generated documents include:
- English technical white paper
- Chinese technical white paper
- Strategy documentation
- Research summaries
The project follows a modular architecture:
Quant Option Research Platform
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├── Data Layer
| Market data loading and preprocessing
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├── Analysis Layer
| Research analysis modules
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├── Volatility Engine
| IV / Smile / Surface / Term Structure
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├── Strategy Framework
| Strategy registry and backtesting
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├── Portfolio Engine
| Position and NAV management
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├── Risk Engine
| Greeks and exposure monitoring
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├── Monitoring Layer
| Risk status and reporting
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└── Reporting Framework
Automated documentation generation
The published research uses intraday Chinese index option and futures data from 2026-01-02 to 2026-06-10. The Phase I prototype completed 4 trades, with 3 winners, a reported cumulative return of 30.5%, and a maximum drawdown of -0.92%.
The expanded strategy evaluation reports:
| Strategy | Win Rate | Average Return | Best Return | Worst Return |
|---|---|---|---|---|
| Long ATM Strangle | 28.57% | -2.1074% | 6.8016% | -9.4435% |
| Long Call Butterfly | 100.00% | 8.0018% | 17.7778% | 0.0000% |
| Calendar Spread | 13.33% | -29.5095% | 61.6839% | -87.6428% |
These are preliminary results from a short 2026 sample, not evidence of stable out-of-sample profitability. Raw licensed market data and generated research tables are excluded from the public repository, so third parties cannot fully reproduce the reported values from the repository alone. See the Phase I report and technical white paper.
Quant-Option-Research-Platform
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├── analysis/
| Research analysis modules
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├── config/
| Configuration and research settings
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├── framework/
| Core quantitative research framework
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├── scripts/
| Research pipelines and automation scripts
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├── docs/
| Research design documents and figures
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└── research/
Reports and documentation
Complete technical documentation is available in:
research/reports/quant_option_technical_white_paper_v3_0.docx
research/reports/quant_option_technical_white_paper_cn_v1_0.docx
The documents cover:
- Research methodology
- Volatility analysis
- Strategy framework
- Portfolio management
- Risk monitoring
- System architecture
Clone the repository:
git clone https://github.com/mdldpc/Quant-Option-Research-Platform.git
cd Quant-Option-Research-PlatformCreate a virtual environment:
python -m venv venvActivate environment:
venv\Scripts\activatesource venv/bin/activateInstall dependencies:
pip install -r requirements.txtpython scripts/<script_name>.pyEnglish:
python -m scripts.rebuild.build_documentation_v3_1Chinese:
python -m scripts.rebuild.build_documentation_cn_v1_0The pipeline automatically generates:
- Research figures
- Tables
- Technical documentation
1. Prepare Market Data
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2. Build Research Dataset
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3. Analyze Volatility Structure
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4. Generate Strategy Signals
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5. Run Backtests
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6. Evaluate Portfolio Risk
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7. Generate Research Report
The project includes a regime-aware meta-labeling workflow designed for short option histories. It uses purged and embargoed expanding walk-forward tests, PCA/GMM regimes, regularized and nonlinear baselines, portfolio-level metrics, block-bootstrap uncertainty, calibration, and reproducibility manifests.
Potential extensions:
- Additional volatility strategies
- More comprehensive historical datasets
- Advanced portfolio optimization
- Machine learning based volatility forecasting
- Real-time market data integration
- Cloud-based research deployment
This project is developed for quantitative research and educational purposes.