Profile

Paras Parkash

Mumbai, Maharashtra

Open To Opportunities

Developing Quantitative Models for Hedging using Derivatives leveraging Agentic AI for Risk Management and Market Screening

Proficient in developing and implementing profitable High-Frequency Trading (HFT) and Medium-Frequency Trading (MFT) strategies, as well as portfolio construction, while incorporating ML/AI techniques for high accuracy. Proven ability to leverage advanced statistical methods, low-latency systems, and performance optimization for alpha generation and risk management.

Mumbai, Maharashtra, India

Building @QuantCraft

Advanced HFT/MFT trading strategies with ML/AI integration for alpha generation and risk management

Python/C++ML/AIHFT Systems

Working as

Quant Researcher

@QuantEdX Research • 2023 - Present

Developing Quantitative Models for Hedging using Derivatives leveraging Agentic AI for Risk Management and Market Screening

GitHub Contributions - 2025

GitHub contributions chart showing Paras Parkash's coding activity for 2025

  • anonP2P

    GitHub

    A secure peer-to-peer communication framework designed for anonymous interactions, implementing advanced encryption and privacy-preserving protocols for decentralized networking.

    PythonPeer-to-PeerCryptographyPrivacy

  • Zerodha Data Collector

    GitHub

    A data collection and pipeline management tool for Zerodha Kite API that handles market data fetching, processing, and storage for quantitative trading applications.

    PythonTrading APIData PipelineKite Connect

  • Quantitative Momentum Backtesting

    GitHubLive Demo

    A comprehensive quantitative momentum backtesting tool that implements momentum-based investment strategies for algorithmic trading, featuring real-time strategy performance analysis and risk metrics.

    PythonQuant FinanceMomentumStreamlit

- Recent Posts