# joshyattridge/smart-money-concepts

Discover our Python package designed for algorithmic trading. It brings ICT's smart money concepts to Python, offering a range of indicators for your algorithmic trading strategies.

Repository: https://github.com/joshyattridge/smart-money-concepts
Canonical: https://ross.abutalabs.com/products/smart-money-concepts
Homepage: https://pypi.org/project/smartmoneyconcepts/
Language: Python
License: MIT
License Family: permissive
Topics: circle, concepts, crypto, forex, ict, inner, liquidity, money, ob, smart, stocks, trader, trading, fvg
Last push: 2026-04-03T14:16:51+00:00

## Health v2 (maintenance only)
Score: 63/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 75, release rhythm 40, longevity 76
- inputs: {"age_days": 1077, "days_push": 152, "days_rel": 548, "gap_med": 0, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1955, forks 844 (observed 2026-08-28T04:05:59.019731+00:00)

## What it is
A Python library implementing ICT (Inner Circle Trader) smart money concepts as technical indicators for algorithmic trading. It computes indicators like Fair Value Gaps, swing highs/lows, break of structure, change of character, order blocks, and liquidity levels from OHLCV data.

## Use cases
- calculate fair value gaps in python
- detect order blocks for trading strategies
- find swing highs and lows in candlestick data
- identify break of structure and change of character
- build smart money concept trading bots
- analyze liquidity levels in forex or crypto charts

## When to choose
- you trade forex, stocks, or crypto using ICT/smart money methodology
- you need ready-made SMC indicators as pandas DataFrames for backtesting
- you want a pip-installable library rather than reimplementing TradingView Pine scripts

## When to avoid
- you need traditional indicators like RSI or MACD
- you require a full backtesting or live-trading engine - this only computes indicators
- you need real-time streaming analysis rather than batch DataFrame input

## Facets
- artifact type: library
- maturity: active
- function: trading, data-science, analytics
- domain: fintech, data-science
- platform: python, cli
- tags: smart-money-concepts, ict, technical-indicators, algorithmic-trading, order-blocks, fair-value-gap, liquidity, forex, stocks, crypto, cryptocurrency

## Member repositories
- joshyattridge/smart-money-concepts (main) score 63

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:59.019731+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 2026-08-30T03:06:26.748216+00:00, confidence not recorded.
  - readme: https://github.com/joshyattridge/smart-money-concepts (fetched 2026-08-28T04:05:59.019731+00:00, sha e7370f9ce522)
  - homepage: https://pypi.org/project/smartmoneyconcepts/ (fetched 2026-08-29T10:46:15.787941+00:00, sha 4b4e8fead74a)
- Data as of 2026-08-30T08:39:29.467469+00:00.