Algorithmic trading platform · 2026
Quant Lab
A full-stack backtesting engine that lets traders test strategies on real market data — without writing code.
The problem
Retail traders and analysts want to validate a strategy before risking capital, but serious backtesting tools assume you can program.
The alternatives are spreadsheets that can't model real execution, or institutional platforms priced far out of reach.
Results are meaningless without proper risk analytics — a strategy that looks profitable can hide unacceptable drawdown.
What we built
A FastAPI backend running the backtest engine against historical and real-time market data, with a Next.js front end for configuration and analysis.
Pre-built strategy templates configured visually, so a trader with no Python can define, run and iterate on a strategy.
Multi-broker connectivity — Binance and Zerodha — so tests run against the data traders actually trade on.
A risk analytics layer computing Sharpe ratio, drawdown, win rate and related measures for every run.
JWT-authenticated accounts on PostgreSQL so strategies and results persist per user.
Inside the build
No coding required
Strategy templates configured through the UI, not a script file.
Multi-broker data
Binance and Zerodha integrations, extensible to more.
Historical + real-time
Test against past data or watch a strategy run on live feeds.
Risk analytics
Sharpe, drawdown, win rate and more computed per backtest.
Secure accounts
JWT auth over PostgreSQL — strategies and runs saved per user.
The result
Non-programmers can build and test an algorithmic strategy end to end.
Every run returns institutional-style risk metrics, not just a profit number.
Built on a production architecture — the same engine scales from one user to many.
Built with
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