Codice 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.

Cosa si ottiene

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-gruppo-casuale — pool your blocks into the menus the builder samples from: Condition groups for rules, Value groups for prices and levels.
  • modello-di-strategia-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.
  • progetto-strategia-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

  • StrategyQuant X (build 144) — your own install; everything is derived from it
  • Codice 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” sul forum

Faccio trading da 15 anni e utilizzo StrategyQuant X dal 2014. Attualmente mi occupo di tre cose: sviluppo e metto alla prova strategie automatizzate, amplio le funzionalità di SQX con indicatori personalizzati, snippet e strumenti Python per l’apprendimento automatico e la ricerca quantitativa, e aiuto altri trader a raggiungere i loro obiettivi più rapidamente, evitando loro gli anni di costosi errori che io stesso ho commesso lungo il percorso.
La maggior parte delle strategie di trading al dettaglio sembra ottima nei backtest, ma fallisce quando viene applicata dal vivo. Il mio lavoro è esattamente l’opposto: strategie oneste, validate e sufficientemente solide da poter essere utilizzate con denaro reale. Scrivo di questo processo — gli aspetti positivi, quelli negativi e il fenomeno dell’overfitting di cui nessuno parla — sul mio sito, algotrading.space.
Se vuoi approfondire i temi della validazione, dello sviluppo personalizzato di SQX o della creazione di un vero vantaggio competitivo grazie a dati e codice, è proprio lì che condivido tutto ciò che so.  www.algotrading.space

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