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Use this file as the starting point for Codex, another AI agent, or a manual strategy author. It is declarative: choose approved module kind values and parameters; do not embed executable code.

Authoring tutorial

Build and validate a strategy one section at a time.

Strategy examples

Read three complete strategies with different modules and execution policies.
The always-current template can be downloaded from GET /api/v1/ai-trading/strategy-template.

Authoring workflow

  1. Follow Write your own strategy
  2. Fetch the live module registry
  3. Copy the template and change one concern at a time
  4. Submit the complete JSON to /strategies/validate
  5. Inspect the returned normalized strategy and node graph; hover nodes for module responsibilities and verify every typed port-to-port connection
  6. Backtest an existing published slug across several date ranges
  7. Review configured risk/reward separately from realized average win/loss
  8. Save, deploy, and arm inside the authenticated Hizz application only after human review

Important field semantics

risk.riskReward is the configured initial take-profit distance divided by the initial stop distance. It is not the realized payoff ratio. Trailing exits, reversals, timeouts, gaps, and fees can make realized average win / average loss very different from the configured value. feeBps is a per-fill uniform execution assumption for the generic definition. Published maker strategies can add an executionPolicy so backtests preserve separate maker and taker costs. Hard stops remain taker exits even when routine entries and exits are maker orders. data.frames controls market coverage. A provider ticker such as Crypto.ZEC/USD is not interchangeable with the Strike symbol ZEC-USD; Hizz validates and maps those namespaces before a backtest runs.