{% set name = "ferro-ta" %}
{% set version = "1.1.4" %}

package:
  name: {{ name|lower }}
  version: {{ version }}

source:
  # Build from PyPI wheel (simplest approach; no Rust toolchain required).
  # Replace with url/sha256 of the specific wheel for your platform or
  # use `pip_install: true` to let conda-build fetch it.
  pip_install: true
  packages:
    - ferro-ta=={{ version }}

build:
  number: 0
  # Use noarch: python only if wheels are already compiled. For source builds
  # remove noarch and add the maturin build steps below.
  noarch: python
  script: |
    {{ PYTHON }} -m pip install ferro-ta=={{ version }} --no-deps --ignore-installed -vv

requirements:
  host:
    - python
    - pip
  run:
    - python >=3.10
    - numpy >=1.20

test:
  imports:
    - ferro_ta
  commands:
    - python -c "from ferro_ta import SMA, RSI; import numpy as np; print(SMA(np.array([1.0,2.0,3.0,4.0,5.0]), timeperiod=3))"

about:
  home: https://github.com/pratikbhadane24/ferro-ta
  license: MIT
  license_family: MIT
  summary: Rust-powered Python technical analysis library with a TA-Lib-compatible API
  description: |
    ferro-ta is a Rust-powered Python technical analysis library with a
    TA-Lib-compatible API and pre-compiled wheels for the supported platforms.
    It provides 155+ indicators via a Rust core and PyO3 bindings, with
    optional pandas and streaming APIs.
  doc_url: https://github.com/pratikbhadane24/ferro-ta
  dev_url: https://github.com/pratikbhadane24/ferro-ta

extra:
  recipe-maintainers:
    - pratikbhadane24