# bukosabino/ta

Technical Analysis Library using Pandas and Numpy

Repository: https://github.com/bukosabino/ta
Canonical: https://ross.abutalabs.com/products/ta
Homepage: https://technical-analysis-library-in-python.readthedocs.io/en/latest/
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: trading, technical-analysis, python3, volume, technical-analysis-library, volatility, trend-analysis, momentum, fundamental-analysis, numpy, pandas, oscillator, financial, python, trend, series-datasets
Last push: 2026-03-18T13:04:55+00:00

## Health v2 (maintenance only)
Score: 65/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 72, release rhythm 35, longevity 100
- inputs: {"age_days": 3165, "days_push": 168, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5180, forks 1153 (observed 2026-08-28T04:09:11.786730+00:00)

## What it is
A Python technical analysis library built on Pandas and NumPy that computes 43 financial indicators (volume, volatility, trend, momentum) from OHLCV time series data. It is designed for feature engineering on financial datasets.

## Use cases
- compute technical indicators like RSI and MACD from stock price data
- feature engineering from OHLCV financial time series
- calculate Bollinger Bands and volatility indicators in Python
- add momentum and trend indicators to a pandas DataFrame of prices
- build features for a trading or backtesting strategy

## When to choose
- you need a lightweight, pure-Python indicator library on top of pandas DataFrames
- you want a broad set of standard technical indicators with a simple API
- you are doing feature engineering for financial machine learning

## When to avoid
- you need a full backtesting or live trading engine
- you need high-performance vectorized computation beyond pandas/numpy
- you need charting or broker integration

## Facets
- artifact type: library
- maturity: stable
- function: data-science, math, analytics
- domain: fintech, data-science, time-series
- platform: python
- tags: technical-analysis, pandas, numpy, financial-indicators, feature-engineering, trading, time-series

## Member repositories
- bukosabino/ta (main) score 65

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:11.786730+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:01:47.986487+00:00, confidence not recorded.
  - readme: https://github.com/bukosabino/ta (fetched 2026-08-28T04:09:11.786730+00:00, sha 645cde5b60d3)
  - registry_pypi: https://pypi.org/pypi/ta/json (fetched 2026-08-29T08:56:09.795382+00:00, sha 553388339a52)
- Data as of 2026-08-30T08:39:29.467469+00:00.