⚡ ferro-ta

Rust-powered Python technical analysis with a TA-Lib-compatible API

Focused on one primary job: fast, reproducible technical analysis for Python users who want TA-Lib-style ergonomics without native build friction.

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ferro-ta is a Rust-backed Python technical analysis library for NumPy-first workloads. It keeps TA-Lib-style ergonomics, ships pre-built wheels on supported targets, and publishes reproducible benchmark artifacts instead of blanket speed claims.

🚀 What ferro-ta is

	TA-Lib	ferro-ta
Primary product	C-backed Python TA library	Rust-backed Python TA library
API shape	talib.SMA(close, 20)	ferro_ta.SMA(close, 20)
Installation	Often requires native/system setup	Pre-built wheels on supported targets
Scope	Technical indicators	Technical indicators first; other tooling is optional and secondary

⚡ Benchmark evidence

The latest checked-in TA-Lib comparison artifact uses contiguous float64 arrays at 10k and 100k bars on an Apple M3 Max, CPython 3.13.5, and Rust 1.91.1.

• ferro-ta achieves competitive parity with TA-Lib, winning on 7 of 12 tested indicators at 100k bars (5 of 12 at 10k bars).
• Strong performance wins at 100k bars include MFI (3.25×), WMA (2.20×), BBANDS (1.97×), and SMA (1.93×) vs TA-Lib.
• TA-Lib maintains performance advantages on STOCH and ADX; EMA, ATR, and OBV are statistical ties.
• Compared to pure-Python libraries like Tulipy, ferro-ta provides 150-350x speedups through Rust-optimized implementations.

See the benchmark methodology and artifacts:

• benchmarks/README.md
• benchmarks/artifacts/latest/
• docs/benchmarks.rst

🎯 Core capabilities

• 160+ indicators with a TA-Lib-style public API.
• Batch and streaming APIs for multi-series and bar-by-bar workloads.
• NumPy-first execution with pandas and polars adapters.
• Pre-built wheels on the supported Python and OS matrix.
• Type stubs, error codes, examples, and reproducible benchmarks.

Adjacent and experimental surfaces such as derivatives analytics, MCP, GPU, plugins, and WASM remain opt-in and secondary to the core TA library story.

📦 Installation

pip install ferro-ta

Optional extras:

pip install "ferro-ta[pandas]"   # pandas.Series support
pip install "ferro-ta[polars]"   # polars.Series support
pip install "ferro-ta[gpu]"      # PyTorch-backed GPU helpers
pip install "ferro-ta[options]"  # derivatives analytics helpers
pip install "ferro-ta[mcp]"      # MCP server for agent/tool clients
pip install "ferro-ta[all]"      # most optional extras (excluding gpu)

⚡ Quick start

import numpy as np
from ferro_ta import SMA, EMA, RSI, MACD, BBANDS

close = np.array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10, 45.15,
                  43.61, 44.33, 44.83, 45.10, 45.15, 43.61, 44.33])

sma = SMA(close, timeperiod=5)
ema = EMA(close, timeperiod=5)
rsi = RSI(close, timeperiod=14)
macd_line, signal, histogram = MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
upper, middle, lower = BBANDS(close, timeperiod=5, nbdevup=2.0, nbdevdn=2.0)

📊 TA-Lib compatibility

• ferro-ta implements 100% of TA-Lib's function set (162+ indicators).
• Most functions are marked Exact or Close; the remaining notable non-exact categories are the Hilbert cycle indicators plus MAMA, SAR, and SAREXT.
• The full parity matrix and coverage summary now live in TA_LIB_COMPATIBILITY.md.

Migration and compatibility references:

• docs/migration_talib.rst
• docs/compatibility/talib.md
• docs/support_matrix.rst

🗺️ Docs map

Core guides:

• docs/quickstart.rst
• docs/migration_talib.rst
• docs/support_matrix.rst
• PLATFORMS.md

Evidence and APIs:

• benchmarks/README.md
• docs/batch.rst
• docs/streaming.rst
• docs/derivatives.rst

Optional and experimental surfaces:

• docs/mcp.md
• docs/adjacent_tooling.rst
• docs/plugins.rst
• wasm/README.md

Project and release docs:

• CONTRIBUTING.md
• CHANGELOG.md
• VERSIONING.md
• RELEASE.md

🛠️ Development

uv sync --extra dev
uv run pytest tests/unit tests/integration
uv run maturin build --release --out dist

More setup details live in CONTRIBUTING.md.