QuantForge AI is an institutional-grade options analytics and quantitative research platform designed for the Indian derivatives market.
The platform collects live NIFTY and BANKNIFTY option-chain data, computes advanced quantitative indicators, models volatility dynamics, analyzes dealer positioning, and generates AI-powered market intelligence.
Unlike traditional retail trading tools, QuantForge AI focuses on providing institutional-style analytics such as Gamma Exposure (GEX), Delta Exposure (DEX), volatility surface modeling, market regime detection, and machine learning-driven forecasting.
Democratize institutional-quality quantitative research and options analytics for traders, analysts, and financial professionals.
- Live NIFTY Option Chain Collection
- Live BANKNIFTY Option Chain Collection
- India VIX Tracking
- Historical Data Warehouse
- Real-Time Data Processing
- Put Call Ratio (PCR)
- Open Interest Analysis
- OI Build-Up Detection
- Max Pain Calculation
- Support & Resistance Detection
- Volume Imbalance Analysis
- Delta
- Gamma
- Vega
- Theta
Powered by:
- QuantLib
- py_vollib
Institutional-grade analytics including:
- Gamma Exposure (GEX)
- Delta Exposure (DEX)
- Gamma Flip Levels
- Dealer Hedging Analysis
- Implied Volatility Surface
- Volatility Skew
- Volatility Smile
- Surface Shift Detection
- Volatility Regime Analysis
Machine Learning Models:
- LightGBM
- XGBoost
- Ensemble Models
Predictions:
- Probability of Up Move
- Probability of Down Move
- Probability of Neutral Move
Trading Intelligence:
- Long Signals
- Short Signals
- Neutral Signals
Built using:
- Market Structure
- Volatility Conditions
- Dealer Positioning
- Machine Learning Forecasts
Performance Metrics:
- CAGR
- Sharpe Ratio
- Sortino Ratio
- Win Rate
- Profit Factor
- Maximum Drawdown
Frameworks:
- VectorBT
- Backtrader
Live Market Data
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Data Collection Layer
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PostgreSQL + TimescaleDB
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Feature Engineering Engine
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Greeks Engine
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Dealer Positioning Engine
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Volatility Intelligence Engine
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Machine Learning Layer
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Signal Generation Engine
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Backtesting Engine
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Dashboard & APIs
- Live Option Chain Collection
- Historical Data Warehouse
- PCR Analytics
- OI Analytics
- Max Pain Engine
- Dashboard V1
- Greeks Calculation
- Gamma Exposure
- Delta Exposure
- Gamma Flip Detection
- IV Surface Modeling
- Volatility Dashboard
- Feature Store
- LightGBM Models
- XGBoost Models
- Ensemble Forecasting
- Signal Engine
- Backtesting Infrastructure
Python
FastAPI
PostgreSQL
TimescaleDB
Redis
Pandas
NumPy
Polars
LightGBM
XGBoost
Scikit-Learn
PyTorch
QuantLib
py_vollib
VectorBT
Next.js
React
TailwindCSS
Docker
AWS EC2
AWS RDS
GitHub Actions
quantforge-ai/
├── data/
│ ├── collectors/
│ ├── pipelines/
│ └── storage/
│
├── analytics/
│ ├── pcr/
│ ├── max_pain/
│ ├── oi_analysis/
│ └── support_resistance/
│
├── greeks/
│ ├── delta/
│ ├── gamma/
│ ├── theta/
│ └── vega/
│
├── dealer_positioning/
│ ├── gex/
│ ├── dex/
│ └── gamma_flip/
│
├── volatility/
│ ├── iv_surface/
│ ├── skew/
│ └── smile/
│
├── ml/
│ ├── feature_store/
│ ├── lightgbm/
│ ├── xgboost/
│ └── ensemble/
│
├── signals/
│
├── backtesting/
│
├── api/
│
├── dashboard/
│
├── docs/
│
└── tests/
- Multi-Asset Expansion
- BANKNIFTY Intelligence
- Sector Index Analytics
- API Marketplace
- Quant Research Terminal
- Institutional Research Suite
Abha Mahato
Research Intern, HITLAB (Toronto) B.Tech CSE (Data Science) Machine Learning & Quantitative Research Enthusiast
MIT License
This project is intended for research and educational purposes only. It does not constitute financial advice, investment recommendations, or trading guarantees. Users are responsible for their own investment decisions.