Pratik Bhadane
307beeca02
Prepare the first public 1.0.0 release and finish the remaining CI hardening work. Highlights: - align Python, Rust, WASM, Conda, API, MCP, and docs version metadata to 1.0.0 - promote package metadata to Production/Stable and update stability/versioning docs for the stable series - move the accumulated Unreleased notes into a dated 1.0.0 changelog section and keep a fresh top-level Unreleased block - strengthen the changelog checker so it validates a single top-level Unreleased section - fix the CI/package support mismatch by declaring Python >=3.10 consistently and gating pandas-ta extras to Python 3.12+ - restore Sphinx autodoc compatibility for documented ferro_ta.<module> imports by registering module aliases - make the TA-Lib benchmark guardrail less flaky by checking median and tail-percentile speedups instead of failing on a single mild outlier - switch PyPI publishing to OIDC-only trusted publishing and wire the changelog check into the required CI gate - apply the Ruff-driven cleanup across the Python and test tree and refresh uv/cargo lockfiles Validated locally: - python3 scripts/check_changelog.py - uv run --with ruff ruff check python tests - uv run --with ruff ruff format --check python tests - uv lock --check - sphinx-build -b html docs docs/_build -W --keep-going - build/install the ferro_ta 1.0.0 wheel successfully
84 lines
3.1 KiB
ReStructuredText
84 lines
3.1 KiB
ReStructuredText
ferro-ta Documentation
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======================
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.. toctree::
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:maxdepth: 2
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:caption: Contents
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quickstart
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migration_talib
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pandas_api
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error_handling
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api/index
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streaming
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extended
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batch
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benchmarks
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plugins
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changelog
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contributing
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Overview
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--------
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**ferro-ta** is a fast Technical Analysis library — a drop-in alternative to TA-Lib
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powered by Rust and PyO3.
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Features:
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- 160+ indicators covering all TA-Lib categories
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- 10 extended indicators not in TA-Lib (VWAP, Supertrend, Ichimoku Cloud, …)
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- Batch execution API — run indicators on 2-D arrays of multiple series
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- Pure Rust core library (``crates/ferro_ta_core``) — no PyO3 / numpy dependency
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- Streaming / bar-by-bar API for live trading
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- Transparent pandas.Series support
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- Math operators and transforms
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- Type stubs (.pyi) for IDE auto-completion
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- WASM binding for browser/Node.js use
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- Options/IV helpers (IV rank, IV percentile, IV z-score) — see `Options/IV Helpers <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/options-volatility.md>`_
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- Agentic workflow and LangChain tool wrappers — see `Agentic guide <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/agentic.md>`_
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- MCP server for Cursor/Claude integration — see `MCP guide <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_
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- Sphinx documentation
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Installation
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~~~~~~~~~~~~
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.. code-block:: bash
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pip install ferro-ta
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Quick Start
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~~~~~~~~~~~
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.. code-block:: python
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import numpy as np
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from ferro_ta import SMA, EMA, RSI, MACD, BBANDS
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close = np.array([10.0, 11.0, 12.0, 13.0, 14.0, 13.5, 12.5])
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print(SMA(close, timeperiod=3))
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# Batch: run SMA on 5 symbols at once
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from ferro_ta.batch import batch_sma
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data = np.random.rand(100, 5)
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result = batch_sma(data, timeperiod=10)
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Further Reading
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~~~~~~~~~~~~~~~
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- `Architecture <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/architecture.md>`_ — Rust/Python layout, two-crate design, binding flow.
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- `Performance Guide <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/performance.md>`_ — when to use raw numpy vs pandas/polars, batch notes, tips.
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- `API Stability <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/stability.md>`_ — stability tiers, versioning, and deprecation policy.
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- `Rust-First Policy <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/rust_first.md>`_ — all compute logic belongs in Rust; how to add new indicators.
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- `Out-of-Core Execution <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/out-of-core.md>`_ — chunked processing and Dask integration.
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- `Options/IV Helpers <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/options-volatility.md>`_ — IV rank, IV percentile, IV z-score.
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- `Agentic Workflow <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/agentic.md>`_ — tools.py, workflow.py, LangChain integration.
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- `MCP Server <https://github.com/pratikbhadane24/ferro-ta/blob/main/docs/mcp.md>`_ — run ferro-ta as an MCP server in Cursor/Claude.
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Indices and tables
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==================
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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