create-indicator

Create Indicator

You create a pure numeric time-series component.

Scope

Default built-in paths:

• source: src/oxq/indicators/{snake_name}.py
• tests: tests/indicators/test_{snake_name}.py
• package export: src/oxq/indicators/__init__.py
• built-in registry: src/oxq/core/registry.py

If the user is building a third-party extension instead of a built-in, use dynamic oxq.register_indicator() in that package and state that it is not persistently built into open-xquant.

Phase 1: Read Existing Patterns

Read before editing:

• src/oxq/core/types.py
• src/oxq/indicators/sma.py
• one similar indicator module
• one existing test in tests/indicators/
• src/oxq/indicators/__init__.py
• the indicator registration block in src/oxq/core/registry.py

Confirm:

• class name is PascalCase
• file name is snake_case
• name equals the registry name
• compute() returns pd.Series
• computation is pure and deterministic

Phase 2: Define Behavior

State the design before coding:

• formula
• input columns
• parameters and defaults
• output units and sign
• NaN behavior
• constant-price behavior
• dependency requirements

Ask the user if the formula or defaults are ambiguous.

Phase 3: Test First

Write tests with hand-calculated expectations. Include:

• protocol compliance with Indicator
• output is a pd.Series with same index
• at least one hand-calculated value
• insufficient-history or NaN behavior
• constant-price or zero-denominator edge case when relevant
• name value

Run the new test and confirm it fails for the missing implementation before writing source when this is a new built-in.

uv run pytest tests/indicators/test_{snake_name}.py -v

Phase 4: Implement

Use pandas, numpy, and stdlib by default. For optional third-party dependency:

• add or reuse an optional dependency group in pyproject.toml
• import lazily or raise a clear install message
• validate with the extra installed

Implementation skeleton:

"""Short description indicator."""

from __future__ import annotations

import pandas as pd


class ClassName:
    """Short behavior description."""

    name = "ClassName"

    def compute(
        self,
        mktdata: pd.DataFrame,
        column: str = "close",
        period: int = 20,
    ) -> pd.Series:
        """Return the indicator series."""
        ...