M. Mehboob N. Meghani

Quantitative Developer · Systems Infrastructure

High-performance trading infrastructure, memory-mapped data pipelines, and low-latency streaming architectures.

// Calibrated System & Performance Benchmarks Verified Production & Replay Specs
Market Data Delay < 10 ms Real-Time Tick & Quote Streaming

Published real-time stock market tick and quote data to hundreds of clients using low-level socket programming

Memory Reduction ~ 80% Memory-Mapped Files + Blittable Structs

Reduced memory usage by ~80% through memory-mapped files and blittable structs

Operational Cost Reduction 98.8% Cloud-Based Trading Migration

Developed and deployed a dynamic cloud-based trading migration system that reduced operational costs by 98.8%

Client Reach 100s Real-Time Market Data

Built streaming infrastructure delivering real-time tick and quote data consumed by hundreds of clients

Market Domains 3 Indian · Crypto · FX

Contributed to trading-stack expansion across Indian, cryptocurrency, and foreign-exchange markets

Years of Experience 6+ Quantitative & Trading Systems

Professional experience spanning quantitative development, algorithmic trading, market data, cloud infrastructure, and software engineering

Production Trading Infrastructure & Case Studies

Selected engineering work spanning quantitative trading infrastructure, real-time market data, performance optimization, and cloud-based trading systems.

SYSTEM [01] Internal Production · Trading Systems

High-Performance Algorithmic Trading & Backtesting Infrastructure

C# .NET Unmanaged C++ Interop Memory-Mapped Files SignalR
Context & Problem

The trading platform required a more scalable architecture as the firm expanded its trading capabilities across Indian, Crypto, and FX markets, while large financial-data workloads created significant memory overhead.

Led a major rewrite of the trading stack using C#, .NET, and unmanaged C++ interoperability. Introduced memory-mapped files and blittable structs for more efficient data handling and integrated SignalR for real-time market data streaming to web dashboards.

Hardware & Runtime Constraints
  • Improving scalability of the existing trading stack
  • Reducing memory overhead in data-intensive workloads
  • Maintaining a modular quantitative architecture while introducing performance-oriented components
Trading Infrastructure Flow
STEP 01 Market & Historical Data Financial Data Data Processing
STEP 02 Data Infrastructure Memory-Mapped Files Efficient Data Handling
STEP 03 Quantitative Components C# / C++ Interop Performance-Critical Processing
STEP 04 Strategy Infrastructure Trading & Backtesting Quantitative Workflows
STEP 05 Web Distribution SignalR Real-Time Dashboard Data
Key Engineering Decisions & Tradeoffs

Memory-Mapped Data Handling

Used memory-mapped files to work with large financial datasets more efficiently and reduce conventional managed-memory overhead.

Blittable Structs

Applied compact value-type data structures to improve memory efficiency and reduce unnecessary allocation overhead.

Managed / Unmanaged Interoperability

Integrated unmanaged C++ components with the C#/.NET architecture where lower-level performance capabilities were required.

Real-Time Dashboard Integration

Integrated SignalR to stream market data from the trading infrastructure into web-based dashboards.

~ 80% Memory usage reduced by approximately 80% through memory-mapped files and blittable structs.
SYSTEM [02] Production System · Wallstreet Algo Trading

Real-Time Market Data Distribution Pipeline

C# .NET Low-Level Sockets Multithreading SignalR WebSockets
Context & Problem

Real-time stock market tick and quote data needed to be distributed reliably across a large client base while maintaining very low delivery delay.

Built real-time market data streaming infrastructure using low-level socket programming and distributed the resulting tick and quote data to client applications and web-based consumers.

Hardware & Runtime Constraints
  • Handling real-time market data flow with minimal delay
  • Supporting distribution to hundreds of downstream clients
  • Maintaining reliable communication across the streaming pipeline
Market Data Distribution Flow
STEP 01 Market Data Tick & Quote Data Real-Time Ingress
STEP 02 Socket Layer Low-Level Networking Data Reception
STEP 03 Processing Layer Market Data Processing Data Handling
STEP 04 Distribution Layer Real-Time Streaming Client Broadcast
STEP 05 Client Applications Hundreds of Clients <10ms Delay
Key Engineering Decisions & Tradeoffs

Low-Level Socket Programming

Used low-level socket programming to provide direct control over real-time market-data communication.

Real-Time Data Distribution

Designed the streaming flow to publish live tick and quote information to a broad set of downstream consumers.

Web Dashboard Integration

Connected real-time market data infrastructure with web-facing distribution through SignalR and related streaming components.

< 10 ms Real-time stock market tick and quote data delivered to hundreds of clients with less than 10ms delay.
SYSTEM [03] Production System · Wallstreet Algo Trading

Dynamic Cloud Trading Migration & Infrastructure Optimization

Cloud Infrastructure Trading Systems Resource Optimization
Context & Problem

The trading infrastructure required a more efficient cloud-based operating model as the system evolved and trading workloads expanded.

Developed and deployed a dynamic system for cloud-based trading migration, focusing on more efficient infrastructure utilization and operational support.

Hardware & Runtime Constraints
  • Reducing ongoing infrastructure costs
  • Supporting migration of trading workloads into a cloud environment
  • Improving operational efficiency without compromising trading-system support
Key Engineering Decisions & Tradeoffs

Dynamic Cloud Migration

Designed a migration approach that adapted cloud infrastructure to the operational requirements of the trading environment.

Infrastructure Optimization

Focused on reducing unnecessary infrastructure expenditure while improving the efficiency of supporting trading workloads.

Operational Efficiency

Improved the operational support model alongside the infrastructure migration.

98.8% Operational costs reduced by 98.8% while improving support efficiency.

Systems Architecture & Engineering Decisions

Architectural mechanisms, trade-offs, and design decisions applied across quantitative research, trading systems, real-time market data, and cloud infrastructure.

Multi-Source Financial Data Pipeline & Validation

System: Historical Market Data Infrastructure
Problem Solved

Trading research depended on reliable historical datasets across multiple financial data types, making data consistency and validation essential before analysis.

Mechanism & Implementation

Built pipelines to download, verify, clean, and process tick, quote, fundamentals, EOD, minute, ETF, and daily market data.

Architectural Trade-off

Added dedicated validation and reporting stages to improve data quality before it reached downstream research and strategy workflows.

Measured Result

Created reusable data pipelines with automated verification reports for accuracy and consistency.

Unified Live & Historical Strategy Execution

System: Algorithmic Strategy & Backtesting Infrastructure
Problem Solved

Strategy development needed to work across both live trading and historical analysis without maintaining completely separate workflows.

Mechanism & Implementation

Developed trading strategies and supporting pipelines for live and backtesting environments, alongside Python-based analysis, visualization, and market-scanning tools.

Architectural Trade-off

Required common data and strategy workflows across different execution environments in exchange for a more consistent research-to-production process.

Measured Result

Supported strategy development across live trading, backtesting, market analysis, and scanning workflows.

Memory-Mapped Files & Blittable Structs

System: Trading Infrastructure & Data Processing
Problem Solved

Financial data workloads created significant memory overhead when large datasets were processed through conventional managed-memory structures.

Mechanism & Implementation

Engineered memory-mapped files and blittable structs to handle financial data more efficiently while reducing managed-memory overhead.

Architectural Trade-off

Introduced more explicit memory and data-layout handling in exchange for substantially lower resource consumption.

Measured Result

~80% reduction in memory usage across the system.

C# / C++ Interoperability

System: Production Trading & Quantitative Models
Problem Solved

Performance-sensitive quantitative components needed efficient execution while the broader trading platform remained flexible within the .NET ecosystem.

Mechanism & Implementation

Integrated unmanaged C++ components with C#/.NET trading infrastructure to support performance-critical workloads.

Architectural Trade-off

Added complexity at the managed and unmanaged boundary in exchange for greater control over performance-critical components.

Measured Result

Combined C#/.NET strategy architecture with native C++ performance capabilities.

Low-Level Socket-Based Market Data Streaming

System: Real-Time Market Data Distribution Pipeline
Problem Solved

Real-time tick and quote data needed to reach a large client base with minimal delivery delay.

Mechanism & Implementation

Built a low-level socket-based streaming architecture for real-time stock market tick and quote distribution.

Architectural Trade-off

Required lower-level network programming and explicit data-flow handling in exchange for tighter control over the streaming path.

Measured Result

Delivered real-time market data to hundreds of clients with <10ms delay.

Dynamic Cloud Trading Migration

System: Cloud-Based Trading Infrastructure
Problem Solved

Trading infrastructure required a more dynamic cloud approach to improve operational efficiency and support migration.

Mechanism & Implementation

Developed and deployed a dynamic system for cloud-based trading migration adapted to operational requirements.

Architectural Trade-off

Introduced additional migration and deployment orchestration in exchange for substantially lower infrastructure costs.

Measured Result

Reduced operational costs by 98.8% while improving support efficiency.

Production Chronology

Six-year engineering progression at Wallstreet Algo Trading, evolving from internal tooling and financial data engineering into quantitative development and production trading infrastructure.

Quantitative Developer · Wallstreet Algo Trading Private Limited

Nov 2023 – Present
Focus System: Trading Infrastructure & Quantitative Systems

Focused on building and evolving performance-oriented trading infrastructure and quantitative systems across the firm's expanding market footprint. • Designed and evolved trading infrastructure using C#, .NET, and unmanaged C++ interoperability. • Led a major rewrite of the trading stack to improve scalability and support expansion into Indian, Crypto, and FX markets. • Engineered memory-mapped files and blittable structs, reducing memory usage by ~80%. • Developed financial models and quantitative components for strategy and risk-management workflows. • Integrated SignalR to stream real-time market data to web-based dashboards.

Stock Market Analyst for Strategies · Wallstreet Algo Trading Private Limited

Jul 2022 – Nov 2023
Focus System: Strategy Research & Market Analysis

Worked closely with market data and strategy research, turning structured observations into datasets and analytical insights for systematic strategy development. • Conducted daily analysis using progressive and non-progressive time features. • Generated pivot-based insights for comparing individual symbols against benchmark indices. • Curated datasets to support strategy development and market research. • Identified recurring market patterns through structured data analysis. • Contributed analytical findings that supported the development and refinement of trading strategies.

Cloud Architect Engineer · Wallstreet Algo Trading Private Limited

May 2022 – Jul 2022
Focus System: Cloud-Based Trading Migration

Focused on improving the operational model of the firm's trading infrastructure through a dynamic cloud migration approach. • Developed a dynamic system for cloud-based trading migration. • Designed the solution around the operational requirements of trading infrastructure. • Improved the efficiency of supporting cloud-based trading workloads. • Reduced operational infrastructure costs by 98.8%. • Improved support efficiency as part of the migration effort.

Market Data Streamer · Wallstreet Algo Trading Private Limited

Mar 2022 – Apr 2022
Focus System: Real-Time Market Data Distribution

Built infrastructure for distributing real-time market information to a broad client base, with an emphasis on delivery speed and reliable data flow. • Published real-time stock market tick and quote data. • Implemented the streaming layer using low-level socket programming. • Designed the distribution system for concurrent downstream consumption. • Served real-time market data to hundreds of clients. • Achieved delivery delay of less than 10ms.

Stock Scanner & Screener · Wallstreet Algo Trading Private Limited

Jan 2022 – Mar 2022
Focus System: Market Scanning & Screening

Contributed to the development of a market-scanning system designed to process results and surface structured insights for further analysis. • Mentored in the development of a stock scanner tool. • Worked with market results and feature-selection-based analysis. • Supported the generation of pivots from selected market features. • Helped structure scanner outputs for easier analysis. • Contributed to tooling used for systematic market screening.

Software Engineer · Wallstreet Algo Trading Private Limited

May 2021 – Jan 2022
Focus System: Trading Strategies & Analysis Tools

Moved deeper into trading-system development, combining strategy implementation with analytical tooling for both live and historical workflows. • Developed trading strategies for live and backtesting environments. • Built supporting pipelines for strategy execution and analysis. • Developed Python-based visualization tools for market scanning. • Created analysis utilities for exploring market behavior and strategy results. • Built scanners with complex filters for NYSE stocks.

Software Developer · Wallstreet Algo Trading Private Limited

May 2020 – Mar 2021
Focus System: Financial Data Engineering

Focused on building reliable data-processing workflows for large financial datasets, establishing the data engineering foundation for later trading and quantitative development work. • Developed Python and C# projects for large-scale financial datasets. • Built pipelines to download historical market data from multiple sources. • Processed tick, quotes, fundamentals, EOD, minute, ETF, and daily datasets. • Added verification and cleaning stages to improve data quality. • Automated verification reports for monitoring data accuracy and consistency.

Intern · Wallstreet Algo Trading Private Limited

Nov 2019 – May 2020
Focus System: Internal Tools & Development

Established the initial foundation in professional software development through hands-on work with internal tools, debugging, and core programming concepts. • Developed foundational skills in C# and Python. • Applied object-oriented programming through project-based development. • Assisted with the development of internal tools. • Supported debugging and troubleshooting of existing applications. • Built practical experience working within an in-house engineering environment.

Engineering Foundation

A formal foundation in computer engineering & electronics and telecommunications that shaped my approach to systems and software.

Bachelor of Engineering — Electronics & Telecommunication

M. H. Saboo Siddik College of Engineering, Mumbai · 2020 – 2024

Engineering foundation spanning electronics and telecommunication, & computer-oriented systems.

Diploma in Computer Engineering

M. H. Saboo Siddik Polytechnic, Mumbai · 2016 – 2020

Early technical foundation in computer engineering and software development.

Tools & Engineering Utilities

Supporting projects and engineering utilities built across APIs, data extraction, automation, and developer tooling.

Let's Build with an Edge

For quantitative systems, trading infrastructure, performance engineering, or technical collaboration, reach out directly or send a project brief.

Direct Email er.mehboobmeghani@gmail.com
Direct Phone / WhatsApp +91 7021 111 528
Location Mumbai, India
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