Pratik Bhadane
108 lines
3.6 KiB
ReStructuredText
108 lines
3.6 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: Core Library
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quickstart
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migration_talib
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support_matrix
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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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batch
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extended
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.. toctree::
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:maxdepth: 2
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:caption: Evidence and Releases
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benchmarks
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changelog
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.. toctree::
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:maxdepth: 2
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:caption: Adjacent and Experimental
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derivatives
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adjacent_tooling
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plugins
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contributing
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Overview
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--------
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**ferro-ta** is a Rust-powered Python technical analysis library focused on a
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TA-Lib-compatible API for NumPy-centered workloads.
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.. important::
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Performance varies by indicator, array layout, warmup, build flags, and
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machine. ferro-ta is often faster on selected indicators, not universally
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faster. See :doc:`benchmarks` for the reproducible workflow, methodology
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notes, and the indicators where TA-Lib still wins or ties in the current
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checked-in artifact.
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Core library:
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- 160+ indicators covering all TA-Lib categories
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- TA-Lib-style imports such as ``ferro_ta.SMA(close, timeperiod=20)``
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- Pre-built wheels for the supported Python/OS matrix
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- Pure Rust core library (``crates/ferro_ta_core``) — no PyO3 / numpy dependency
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- Batch execution API — run indicators on 2-D arrays of multiple series
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- Streaming / bar-by-bar API for live trading
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- Transparent pandas.Series support
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- Type stubs (.pyi) for IDE auto-completion
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- 10 extended indicators not in TA-Lib (VWAP, Supertrend, Ichimoku Cloud, ...)
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Adjacent and experimental tooling:
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- Derivatives analytics — see :doc:`derivatives`
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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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- WASM, plugins, and other optional surfaces — see :doc:`adjacent_tooling`
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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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- :doc:`support_matrix` — parity status, tested wheel targets, supported Python versions, and experimental modules.
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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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- :doc:`derivatives` — IV helpers, options pricing/Greeks/IV, futures analytics, strategy schemas, and payoff helpers.
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- :doc:`adjacent_tooling` — optional surfaces such as derivatives, MCP, WASM, GPU, plugins, and agent-oriented integrations.
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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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