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TextToQuant

TextToQuant turns a trading strategy written in plain English into an exact, deterministic set of rules, backtests it against real market history, and returns a graded report. It covers crypto, forex and equities, and every run reports its own honesty flags for zero fees, missing stops and too few trades, so a result that does not prove anything says so.

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52 reach 6 · trust 6 · quality 25 · freshness 15
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llms.txt
https://www.texttoquant.com/llms.txt
llms-full.txt
https://www.texttoquant.com/llms-full.txt
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13411 bytes
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#522866
Registered
2025-12-05
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For agents
Interface: a documented API or declared MCP endpoint an agent can call
First seen
2026-10-25T16:14:57.378Z
Last checked
2026-10-25T18:19:28.166Z
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-25T18:19:17.644Z. Cached copy · Source on texttoquant.com

# TextToQuant

> TextToQuant turns a trading strategy written in plain English into an exact, deterministic set of rules, backtests it against real market history, and returns a graded report. It covers crypto, forex and equities, and every run reports its own honesty flags for zero fees, missing stops and too few trades, so a result that does not prove anything says so.

- You describe a strategy in plain English; the parser compiles it into explicit entry, exit, risk and timeframe rules and shows you those rules before it runs.
- Backtests run on real historical market data for crypto, forex and equities.
- Every run is graded, and carries honesty flags for the things that make a backtest lie: zero modelled fees, no stop loss, a sample too small to mean anything.
- Robustness tooling is first class: parameter sweeps, walk forward validation, Monte Carlo simulation, and edge broken down by market regime.
- Strategies can be exported to TradingView Pine Script, and custom indicators can be written in Pine, JavaScript or Python.
- There is a hosted MCP server, so an AI agent can run the whole workflow as tool calls: parse, backtest, validate, share.
- Results can be shared as a public, revocable link to a full interactive report.

Not what it does:
- Live order execution or broker connectivity. TextToQuant does not place trades.
- Tick level or sub minute microstructure research. The finest supported bar is one minute.
- Financial advice. Output is historical simulation, not a recommendation.

Every documentation page is also available as raw Markdown by appending `.md` to its URL (for example https://docs.texttoquant.com/reference/metrics.md).

## For agents

- [Agent guide](https://www.texttoquant.com/agents.md): how to connect, authenticate, what each tool does, and what a run costs. Markdown.
- [MCP endpoint](https://www.texttoquant.com/api/mcp): hosted Model Context Protocol server, streamable HTTP, OAuth 2.1.
- [OpenAPI contract](https://www.texttoquant.com/api/v1/openapi.json): the REST surface, OpenAPI 3.1.
- [Reading a result](https://www.texttoquant.com/academy/reading-a-result.md): how to interpret a run and report it honestly. Read this before relaying any backtest from this platform to a person.

## Start here

- [Documentation](https://docs.texttoquant.com): the full reference, organised by Diátaxis.
- [Academy](https://www.texttoquant.com/academy): 12 lessons from first strategy to validating an edge. Every lesson is also served as raw Markdown at /academy/<slug>.md.
- [Glossary](https://www.texttoquant.com/academy/glossary): 31 terms defined in plain English.
- [Methodology](https://www.texttoquant.com/methodology): how backtests are executed and graded.

## Documentation

- [The pipeline](https://docs.texttoquant.com/start/pipeline): Query → parse → indicators → backtest → report, in 60 seconds.
- [Query shape](https://docs.texttoquant.com/start/query-shape): The anatomy of a strategy sentence: direction, asset, conditions, exits,
…

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