Khushi Choudhary

Trade & Operations Analyst

Talent

GurugramMember since June 17, 2026

PythonSqlLinuxShell ScriptingFinanceQuantitative AnalysisC++Ai

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PythonSqlLinuxShell ScriptingFinance

About

Detected 15+ execution anomalies in live algorithmic trading strategies by systematically analyzing low-latency infrastructure logs and real-time market data feeds, preventing an estimated INR 50L+ in trade slippage. Reduced mean latency by 40ms across trading systems through forensic Linux log analysis, diagnosing exchange connectivity failures and writing targeted scripts to eliminate manual infrastructure checks. Built and deployed a full-stack Trade Reports console (React/Next.js, Node.js, PostgreSQL, AWS RDS) — delivering frontend tooling used daily by the ops and trading teams to monitor PnL and flag reporting anomalies.

Experience

Trade & Operations Analyst at 26 Miles Capital (2025-06 – Present)

Detected 15+ execution anomalies in live algorithmic trading strategies by systematically analyzing low-latency infrastructure logs and real-time market data feeds, preventing an estimated INR 50L+ in trade slippage.
Reduced mean latency by 40ms across trading systems through forensic Linux log analysis, diagnosing exchange connectivity failures and writing targeted scripts to eliminate manual infrastructure checks.
Built and deployed a full-stack Trade Reports console (React/Next.js, Node.js, PostgreSQL, AWS RDS) — delivering frontend tooling used daily by the ops and trading teams to monitor PnL and flag reporting anomalies.
Automated 8 start-of-day readiness checks and manual monitoring workflows using Python, Bash, and Apache Airflow DAGs — eliminating 6+ hours/week of repetitive work and improving system observability before market open.
Deployed AI-assisted diagnostic tooling using LLM-based log summarization and anomaly classification, reducing mean time-to-diagnose on trading incidents by ∼30% and enabling faster stakeholder communication.
Took ownership of 20+ production trading incidents, coordinating cross-functionally with traders and developers; raised alerts and drove resolution — cutting mean time from 2–3 hours to 45 minutes.
Identified corrupted tick data impacting 60% of operational strategies through timestamp pattern correlation and data integrity verification; escalated findings with actionable remediation steps to stakeholders.