About

I'm Divyasshree, a researcher, engineer, and builder working at the intersection of mathematics, trading systems, and on-chain markets.

Cryptogrammar is where I publish that work. It exists because most crypto writing skips the math. I'm interested in the parts that don't: how execution degrades at scale, why liquidity behaves the way it does, what the data actually shows when you strip out the narrative.

Background

I started as a software engineer at VMware, building Salesforce applications on the Partners Team, designing Lightning and Apex components, shipping a Partner Value Registration system that streamlined onboarding, and developing an automatic language translation feature for partner forms across regions. It was good production engineering work, and it taught me that the hardest part of software is always the interface between a system and the people who depend on it.

After a stretch of freelance technical writing for B2B SaaS clients, I joined Bitquery in January 2023 to build docs,support and sales, and I still work there.

Today I am on the core team: I join product roadmap discussions, run POCs for new directions, and stay close to traders and enterprise customers who rely on our APIs. I answer their technical questions end to end. I also build AI agent skills on Bitquery data, Model Context Protocol (MCP) integrations, and trading-oriented algorithms and examples for those same audiences.

In February 2026 I started Cryptogrammar as an independent publication alongside my role at Bitquery. The research here focuses on mid-frequency trading on on-chain data, liquidity dynamics across decentralized markets, and applied mathematics for market structure analysis. I've also built Proactive Mule Detector, a tool for on-chain fraud investigation.

Alongside this, I completed a Master of Science in Management at the University of Illinois Urbana-Champaign via their part-time option (Beta Gamma Sigma, 2024 to 2025).

My technical stack spans Python, Go, C++, Java, and JavaScript. I work with AWS, Apache Kafka, and streaming architectures for data infrastructure. I've done enough of both the systems side and the markets side to care about where they meet.

What This Publication Covers

The research here concentrates on a few overlapping areas:

DEX execution and slippage. How trades actually clear, where liquidity ceilings appear, and why large orders behave so differently from small ones.
Market microstructure. Regime changes, stationarity, mean reversion after liquidity shocks.
MEV and order flow. Relay dynamics, sandwich mechanics, and how extraction compounds slippage.
Infrastructure. Orderbooks in C++, execution engines, observability with OpenTelemetry, and the systems that sit underneath the markets.
Security. Exploit analysis, chain forensics, smart contract vulnerabilities.

The common thread is quantitative analysis grounded in real on-chain data, not commentary about prices.