# peerchemist/finta

Common financial technical indicators implemented in Pandas.

Repository: https://github.com/peerchemist/finta
Canonical: https://ross.abutalabs.com/products/finta
Language: Python
License: LGPL-3.0
License Family: copyleft
Topics: pandas, trading-algorithms, fintech, python, technical-analysis, trading, algorithmic-trading, algotrading, trading-strategy
Archived: true
Last push: 2022-07-24T08:40:51+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3653, "days_push": 1501, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2263, forks 716 (observed 2026-08-28T04:06:32.023916+00:00)

## What it is
FinTA is a Python library implementing over 80 common financial technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands, etc.) on top of Pandas DataFrames. It is aimed at algorithmic trading and financial analysis workflows.

## Use cases
- calculate RSI and MACD for stock price data in pandas
- compute moving averages for a trading strategy backtest
- add technical indicators like Bollinger Bands to OHLCV data
- build an algorithmic trading bot with indicator signals
- compute VWAP and ATR for crypto price series
- generate trading signals from stochastic oscillator and ADX

## When to choose
- you already use pandas and want pure-Python technical indicators
- you need a wide catalog of 80+ indicators with a simple API
- you are prototyping trading strategies in Python

## When to avoid
- you need high-performance or GPU-accelerated indicator computation
- you require guaranteed numerical accuracy for production trading
- you need actively maintained software with frequent releases

## Facets
- artifact type: library
- maturity: maintenance
- function: data-science, math
- domain: fintech, data-science
- platform: python
- tags: technical-analysis, trading-indicators, pandas, algorithmic-trading, fintech, algorithms

## Member repositories
- peerchemist/finta (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:32.023916+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 2026-08-30T02:42:29.605164+00:00, confidence not recorded.
  - readme: https://github.com/peerchemist/finta (fetched 2026-08-28T04:06:32.023916+00:00, sha 4e2a2d4bbc97)
  - registry_pypi: https://pypi.org/pypi/finta/json (fetched 2026-08-29T10:22:53.049589+00:00, sha d3865eb19419)
- Data as of 2026-08-30T08:39:29.467469+00:00.