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Sensex Options AI

AI-driven algorithmic trading system for Sensex options execution on AngelOne platform. Leverages ML for strike selection.

Project Overview

An advanced AI-driven algorithmic trading system specifically designed for Sensex options execution on the AngelOne platform. The system leverages machine learning algorithms to optimize strike selection, timing, and position sizing. Built with a focus on low-latency execution and real-time market data processing, it implements sophisticated risk management protocols and backtesting frameworks.

Timeline: 6 months
Status: Production

Key Features

Real-time strike selection using ML models

Automated position sizing based on volatility

Multi-timeframe analysis integration

Risk-adjusted portfolio optimization

Backtesting engine with historical data

Live market data streaming and processing

Technology Stack

PythonAngelOne APIML

Architecture

Backend

  • Python 3.11
  • Pandas/NumPy
  • Scikit-learn
  • AngelOne SmartAPI

Infrastructure

  • Redis for caching
  • PostgreSQL for trade logs
  • Docker containers

Challenges Overcome

Handling API rate limits during high volatility
Optimizing ML model inference latency to <50ms
Managing slippage in fast-moving markets
Implementing robust error recovery mechanisms

Key Outcomes

Achieved 68% win rate in backtesting
Reduced execution latency to 120ms average
Processed 10,000+ trades with 99.8% uptime
Generated consistent alpha over benchmark

Interested in this project?

View the source code on GitHub or explore more projects in my portfolio.

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