We’re building AI-Quant Studio, a no-code backtesting platform that lets traders describe strategies in plain English — and see fully executed backtests in seconds.
Most backtesting tools today are built for programmers, not traders. They require knowledge of scripting, platforms like Pine Script or Backtrader, and involve frustrating trial-and-error.
Our insight:
Traders don’t want to code. They want answers.
We’ve built a system where users input strategies like:
“Buy after 3 red candles and RSI < 35”
Our engine parses, translates, and runs the logic — outputting performance metrics, equity curves, and trade logs immediately.
How it works:
A custom logic parser built on LLMs + deterministic templates
Vectorized Python engine for fast multi-scenario backtests
A validation layer that filters out vague or risky logic
Closed beta of 100 users completed — thousands of backtests run
Daily active traders, strong retention
High intent waitlist and early interest from prop firm traders
Fully bootstrapped and moving fast
What’s next:
Expanding beta
Layering in optimization and multi-condition testing
Exploring integrations with platforms like TradingView or MetaTrader
Looking to connect with:
Fellow builders in fintech, AI tooling, and low-code/no-code who are solving similar parsing, validation, or strategy evaluation challenges.
Aman4312•7mo ago
Most backtesting tools today are built for programmers, not traders. They require knowledge of scripting, platforms like Pine Script or Backtrader, and involve frustrating trial-and-error.
Our insight: Traders don’t want to code. They want answers. We’ve built a system where users input strategies like:
“Buy after 3 red candles and RSI < 35” Our engine parses, translates, and runs the logic — outputting performance metrics, equity curves, and trade logs immediately.
How it works:
A custom logic parser built on LLMs + deterministic templates
Vectorized Python engine for fast multi-scenario backtests
A validation layer that filters out vague or risky logic
Beautiful, interactive visual output (Plotly, equity curves, trade insights)
Where we are now:
Closed beta of 100 users completed — thousands of backtests run
Daily active traders, strong retention
High intent waitlist and early interest from prop firm traders
Fully bootstrapped and moving fast
What’s next:
Expanding beta
Layering in optimization and multi-condition testing
Exploring integrations with platforms like TradingView or MetaTrader
Looking to connect with: Fellow builders in fintech, AI tooling, and low-code/no-code who are solving similar parsing, validation, or strategy evaluation challenges.