Overview
What TextToQuant is, and how the pipeline turns a sentence into a backtest.
TextToQuant turns a sentence into a backtested trading strategy. You describe an idea in plain English, and the platform parses it into a precise, deterministic strategy specification, runs it bar by bar against real market data, and hands you an honest report: performance, risk, and an overfitting verdict.
These docs are organised by intent: learn the ideas, follow a guide to get a job done, or consult the reference for exact behaviour.
How it works#
Write your idea the way you'd say it out loud: asset, signal, exits, timeframe.
The query compiles deterministically into a fixed strategy spec. The AI never scores the result, so it can't steer toward a good looking number.
A bar by bar engine simulates the strategy on real data, using only closed bar information (no look ahead).
Metrics, chart, trade ledger, robustness checks and a letter grade, with everything you need to trust or reject the result.
Buy BTC when RSI(14) crosses above 30, exit at 3R or 2% stop, on the 4hRun in terminalStart here#
Every performance metric, what it means, and how to read it honestly.
TutorialsAcademyGuided, hands on lessons across Foundations, Validation and Risk.
What makes it different#
- Deterministic by construction. Same query ⇒ same spec fingerprint ⇒ same test. Results are reproducible, and the header shows a Reproducible chip to prove it.
- Overfitting aware. Beyond Sharpe and win rate, the report ships probabilistic and deflated statistics that correct for how many configurations you tried.
- No look ahead. Signals read only closed bar data, so a backtest can't cheat with information it wouldn't have had live.
Read the Metrics reference next: understanding what the numbers mean is the fastest way to get value from every backtest you run.