Código Claude

Meet sqx-lab: The AI Authoring Toolkit for StrategyQuant, Now Free to Download

In July I wrote about building StrategyQuant blocks, groups and templates with AI — four AI skills that turn plain English into import-ready StrategyQuant artifacts, each one validated against your own installation before it ever reaches you. At the time, the toolkit was something I demonstrated. Today it is something you can install.

sqx-lab — the SQX Authoring Toolkit — is now packaged as a single Claude Code plugin, free to download from the StrategyQuant Codebase.

O que você recebe

Four skills, covering the whole authoring chain that feeds the StrategyQuant builder:

  • sqx-custom-block — describe an atomic trading rule (“close above the upper Bollinger band, with its opposite”) and get validated custom-block XML, built only from indicators your install actually has.
  • sqx-grupo-aleatório — pool your blocks into the menus the builder samples from: Condition groups for rules, Value groups for prices and levels.
  • modelo-de-estratégia-sqx — describe a thesis (“breakouts only in the trend direction, entered on a stop at the prior-period high”) and get a buildable .sqx template wired to your clean groups, with a full exit stack.
  • projeto-de-estratégia-sqx — wrap templates into a runnable project.cfx with build tasks, data, timeframes and analysis wired in.

The chain runs block → group → template → project. Each output feeds the next, or any skill works alone. And the principle behind all four is unchanged: the AI proposes, but your StrategyQuant install disposes. Every artifact is derived from a catalog of what your build actually supports and validated before hand-off — no phantom indicators, no hand-written XML, no plausible-looking files that quietly refuse to build.

New since the July article

The release version (1.2) is built around a simple idea: setup should be one command, and failure should be loud.

  • /sqx-setup

    — point it at your StrategyQuant X folder once. It validates the folder (and refuses anything that isn’t a real SQX install), stores the path for all four skills, bootstraps every catalog, and reports what it found. A wrong folder can no longer silently break the toolkit.
  • /sqx-doctor

    — a one-command health check: Python, install, catalogs, and whether the block → group → template → project chain is actually intact. It specifically hunts broken random groups — groups referencing custom blocks that aren’t in your install. One broken Value group used to silently disable entire classes of strategy shapes; now the doctor names it, lists the missing blocks, and the Custom Block Builder’s Repair mode rebuilds them.
  • Settings survive updates — your install path lives outside the plugin now, so updating is: download, reinstall, re-run
    /sqx-setup

    . Seconds.

What you need

  • EstratégiaQuant X (build 144) — your own install; everything is derived from it
  • Código Claude — the free plugin installs into it
  • Python 3.8+ — standard library only, no pip installs

Everything runs locally. Your strategies, catalogs and data never leave your machine.

Get it

Download the zip, unzip, and install inside Claude Code:

/plugin marketplace add /absolute/path/to/sqx-lab
/plugin install sqx-lab@sqx-lab
/sqx-setup

That’s the whole setup. From then on you don’t invoke skills by name — you just describe the artifact you want, and Claude picks the right layer.

Download the SQX Authoring Toolkit from the Codebase →

The full manual — six-step tutorial, per-skill reference, FAQ — lives on the Codebase entry alongside the download.


Ivan Hudec (clonex) has published 135+ free extensions in the StrategyQuant Codebase and writes about the research behind them at algotrading.space.

clonex / Ivan Hudec

Ivan Hudec — “Clonex” no fórum

Negocio há 15 anos e venho desenvolvendo com o StrategyQuant X desde 2014. Hoje em dia, faço três coisas: desenvolvo e testo estratégias automatizadas em condições reais de mercado, amplio o próprio SQX com indicadores personalizados, trechos de código e ferramentas em Python para aprendizado de máquina e pesquisa quantitativa, e ajudo outros traders a chegarem lá mais rápido — sem passar pelos anos de erros caros que cometi ao longo do caminho.
A maioria das estratégias de trading parece ótima em um backtest, mas desmorona na prática. Meu trabalho é exatamente o oposto: estratégias que são honestas, validadas e robustas o suficiente para que se possa confiar nelas com dinheiro real. Escrevo sobre esse processo — o que dá certo, o que dá errado e o sobreajuste de que ninguém fala — no meu site, algotrading.space.
Se você quiser se aprofundar no tema da validação, no desenvolvimento personalizado de SQX ou na criação de uma vantagem competitiva real com dados e código, é lá que eu compartilho tudo isso.  www.algotrading.space

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