Engineering Systematic Discipline for the Indian Markets
Bridging the gap between institutional quantitative research and Indian equity swing traders through zero-code condition trees, realistic friction backtests, and automated daily execution.
Why 95% of Retail Swing Traders Lose Money
Every day, thousands of Indian traders stare at technical charts, drawing subjective diagonal lines, listening to television pundits, and trading based on emotional hunches. When a trade goes against them, they hope; when it goes in their favor, they exit prematurely out of fear.
Meanwhile, institutional quantitative hedge funds never trade on emotion. They build mathematically verified rules, measure historical drawdowns across a decade of market regimes, account for every rupee of transaction friction, and execute automatically without second-guessing.
Until now, building institutional systems for the National Stock Exchange (NSE) required writing thousands of lines of complex Python code, manually managing delisted stock databases to avoid survivorship bias, and setting up dedicated server crons.
Tradzy was founded to eliminate that barrier. We built the platform we always wished existed: a clean, institutional-grade quantitative workspace tailored specifically for Indian equities.
“Discretionary trading is gambling disguised as analysis. True profitability in swing trading requires three things: positive statistical expectancy, ruthless friction modeling, and autonomous algorithmic execution.”
Our Three Core Pillars
Evidence Over Emotion
If a rule cannot demonstrate statistical alpha across 10+ years of Indian bull, bear, and sideways regimes, it has no business risking your hard-earned capital. We believe in verification before deployment.
Ruthless Market Realism
We reject paper-gain illusions. Tradzy sizes in whole integer shares, isolates brokerage charges and adverse slippage drag, and reconstructs historical point-in-time index constituents to completely banish survivorship bias.
Autonomous Execution
A system is only as good as its discipline. Our automated three-cron daily pipeline syncs evening data, evaluates condition trees, and executes morning fills without human panic or hesitation.
Our Technology Stack
Built with modern, high-performance web and data engineering technologies designed for sub-second backtests.
FastAPI & Python 3.12
High-speed asynchronous API handling strategy tree interpretation, signal generators, and daily cron workers.
MySQL 8.4 & TiDB
ACID-compliant storage for historical OHLCV, constituent history tables, trade logs, and live execution journals.
Next.js 16 & Tailwind v4
App Router, React 19, and the Lumina design system providing an ultra-responsive, accessible desktop experience.
Parquet & Redis
Columnar Parquet storage for sub-second analytical aggregations across 15+ years of multi-million candle datasets.
Ready to Upgrade Your Swing Trading?
Explore the platform or reach out to our quantitative engineering team.