Dolat Capital Tech Stack

Multi-asset quantitative trading firm with ultra-low latency infrastructure

Financial Services Mumbai, Maharashtra 201–500 employees Founded 1970 Privately Held

Dolat Capital is a quantitative trading operation running C++ infrastructure optimized for sub-millisecond latency across equities, futures, options, commodities, currencies, and fixed income. The tech stack—Python, NumPy, TensorFlow, PyTorch, scikit-learn, PostgreSQL, and custom C++ systems—reveals a research-to-production pipeline heavy on statistical modeling and real-time execution. Active hiring across engineering and research teams, combined with projects in automated trading systems and ML market prediction, indicates the firm is scaling both infrastructure capacity and algorithmic sophistication to handle higher transaction throughput.

Tech Stack 15 technologies

Core StackPython Linux NumPy PostgreSQL C++ TensorFlow PyTorch scikit-learn MATLAB Bash Matplotlib Windows TCP/IP UDP R

What Dolat Capital Is Building

◆Challenges

High-transaction throughput
Scaling trading systems
System performance optimization
Low-latency/high throughput trading environment
Low-latency trading
Platform speed improvement
Real-time market inefficiencies
Improving research consumption
Strengthening institutional rankings
Enhancing institutional research brand

▲Active Projects

Low-latency trading system enhancement
High-conviction investment ideas generation
Historical market data and trading simulations
Risk-management and performance-tracking tools
Automated trading systems
Quantitative strategy development
Ai/ml model development
Ai/ml market prediction models
High-performance strategy optimization
Trading workflow applications

Hiring Activity

Decelerating10 roles · 3 in 30d

Department

Engineering

Research

Finance

Production

Seniority

Mid

Senior

Junior

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About Dolat Capital

Dolat Capital is a privately held quantitative trading firm founded in 1970, headquartered in Mumbai. The firm operates as a multi-strategy trader across all major asset classes, using mathematical and statistical techniques to generate returns. Core infrastructure is built in C++ for competitive latency performance. The organization spans 201–500 employees across engineering, research, finance, and production functions, with active development on low-latency trading systems, risk management tools, and AI/ML-driven strategy optimization. Current hiring velocity is decelerating but remains focused on engineering and research roles in India.

HeadquartersMumbai, Maharashtra

Company Size201–500 employees

Founded1970

Hiring MarketsIndia

Frequently Asked Questions

What tech stack does Dolat Capital use?

Python, NumPy, TensorFlow, PyTorch, scikit-learn, PostgreSQL, Linux, and custom C++ infrastructure for low-latency trading. Also uses MATLAB and R for quantitative research.

What is Dolat Capital working on?

Low-latency trading system enhancements, quantitative strategy development, AI/ML market prediction models, risk-management tools, and automated trading systems across multiple asset classes.