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Divyansh Dixit
@divyansh_dx
Quantitative Systems Architect
India
Inglese, Hindi
Chi sono
I bridge the gap between complex trading logic and institutional-grade algorithmic infrastructure. Operating as a Quantitative Systems Architect, I use advanced AI-augmented engineering frameworks to architect, prototype, and deploy high-performance financial systems at 10x traditional development speeds. If you are a fund manager, private syndicate, or high-net-worth operator who needs custom algorithmic tools and modular data pipelines built without massive corporate overhead, I design your end-to-end ecosystem.
Competenze
Divyansh Dixit
offline •
Tempo di risposta medio: 1 ora
Consulta i miei servizi
Sviluppo bot di trading
I will build an institutional algorithmic trading bot or custom quant system
Portfolio
Esperienza lavorativa
Lead Quantitative Systems Architect
Self Employed • Freelance
Dec 2024 - Present • 1 yr 6 mos
Operating as a Lead Quantitative Systems Architect specializing in the design and connection of multi-layered algorithmic crypto infrastructure. By pioneering an AI-augmented engineering paradigm, I utilize advanced multi-model sessional prompting pipelines to completely automate low-level syntax generation. This structural shift moves human manual typing into pure system orchestration, mathematical modeling, and rigid quality control—deploying institutional-grade systems at 10x traditional development speeds. Core Infrastructure & Technical Implementations: • High-Throughput Data Architecture: Engineered automated ingestion and data repair pipelines to process massive, nested raw text datasets (JSON Lines), converting them into highly compressed Parquet files optimized for backtesting multi-resolution historical trade data. • Market Topology & Mathematical Networks: Designed multi-timeframe spatial proximity mesh networks to mathematically map structural liquidity zones—specifically isolating High-Volume Nodes (HVN), Low-Volume Nodes (LVN), and Points of Control (POC) derived from Auction Market Theory. • Advanced Pattern Filtration: Integrated machine learning-driven filtration systems, utilizing optimized XGBoost gradient boosting models to analyze market states and isolate probability distributions on granular execution timeframes. • Low-Latency Visualization & Streaming: Developed real-time asynchronous streaming dashboards using Flask, Socket.IO, and secure remote tunneling protocols to maintain consistent sub-second terminal state refreshes for active pipeline monitoring. By operating as the core engine director and utilizing AI as an advanced framework compiler for complex mathematical structures, I bridge the gap between abstract algorithmic logic and high-performance financial execution ecosystems.
