# cinar/indicator

Indicator Go delivers a rich set of technical analysis indicators, customizable strategies, and a powerful backtesting framework. No dependencies, just pure simplicity. ✨ See how! 👀

Repository: https://github.com/cinar/indicator
Canonical: https://ross.abutalabs.com/products/indicator
Language: Go
License: AGPL-3.0
License Family: copyleft
Topics: indicators, technical-analysis, stock-analysis, stock-market, technical-analysis-library, technical-analysis-indicators, bollinger-bands, macd, indicator, trading-algorithms, trading-strategies, quant, quantative-finance, quantative-trading, finance, financial-instruments, yahoo-finance, trading, quantitative-finance, algorithmic-trading
Last push: 2026-08-24T09:36:11+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 99, longevity 100
- inputs: {"age_days": 1909, "days_push": 9, "days_rel": 9, "gap_med": 5, "n_releases_24m": 36}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1229, forks 199 (observed 2026-08-28T04:04:03.673387+00:00)

## What it is
Indicator is a Go library providing 80+ technical analysis indicators, configurable trading strategies, and a backtesting framework with real-time strategy execution. It operates on data streams via Go channels, has no external dependencies, and includes MCP support for AI tool integration.

## Use cases
- calculate technical indicators like MACD and Bollinger Bands on stock price data
- backtest algorithmic trading strategies in Go
- build a quantitative trading bot with real-time strategy execution
- analyze stock market data with a dependency-free Go library
- integrate technical analysis into AI tools via MCP
- stream market data through indicators using Go channels

## When to choose
- you need a pure-Go technical analysis library with no external dependencies
- you want to backtest or run trading strategies with streaming data
- you need a large catalog of configurable indicators with generics support

## When to avoid
- you need a TypeScript or Python trading library instead
- you require broker connectivity or live order execution out of the box
- the AGPL-3.0 license is incompatible with your project

## Facets
- artifact type: library
- maturity: active
- function: sdk, data-science, trading
- domain: fintech, data-science, developer-tools
- platform: go, cross-platform
- tags: technical-analysis, indicators, backtesting, algorithmic-trading, quantitative-finance, mcp, streaming, zero-dependency, cryptocurrency

## Member repositories
- cinar/indicator (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:03.673387+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 2026-08-30T06:15:06.098169+00:00, confidence not recorded.
  - readme: https://github.com/cinar/indicator (fetched 2026-08-28T04:04:03.673387+00:00, sha e1fb735a7fd3)
- Data as of 2026-08-30T08:39:29.467469+00:00.