smartmoneyconcepts

What it is and what it does

Smart Money Concepts is a Python library that implements technical indicators inspired by Inner Circle Trader (ICT) methodology for analyzing financial market data. It takes OHLC (open, high, low, close) price data as pandas DataFrames and computes indicators like fair value gaps, swing highs and lows, order blocks, liquidity zones, break of structure, and change of character—concepts used by traders to identify market structure, potential reversals, and order flow imbalances.

The library depends on pandas, numpy, and numba for efficient numerical computation on price arrays. It is actively maintained and has moderate adoption (113710 monthly downloads), though it is classified as Development Status 1 - Planning, indicating early-stage code. The package includes a disclaimer that it is for educational purposes only and should not be used as a sole decision-maker for trades.

Use it for:

• Identify fair value gaps in price action to locate potential support/resistance zones for entry or exit planning.
• Detect swing highs and lows to map market structure and determine trend direction.
• Find order blocks where large volumes congregated to anticipate potential price reversals or continuations.
• Locate liquidity clusters at swing points to identify where stop-losses or limit orders may be swept.
• Analyze break of structure and change of character to confirm shifts in market direction.
• Calculate retracement percentages from swing points to project support and resistance levels.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Computes technical indicators for financial market analysis based on Inner Circle Trader (ICT) concepts, including fair value gaps, swing highs/lows, order blocks, liquidity levels, and market structure breaks from OHLC price data.

Yes, with conditions. Install if you are building a trading analysis tool and want ICT-based indicators on OHLC data. The low install friction and active maintenance are positive signals. However, the unclear license status requires verification before commercial use, and the Development Status 1 - Planning classifier suggests the code is early-stage—test thoroughly and do not rely on it as your sole trading signal. The disclaimer explicitly warns against using it as a sole decision-maker.

Install

smartmoneyconcepts on PyPI

pip install smartmoneyconcepts

uv add smartmoneyconcepts

poetry add smartmoneyconcepts

Installing smartmoneyconcepts

Before you install

Low install friction with three common numeric dependencies (pandas, numpy, numba). Package is actively maintained with recent commits and moderate popularity (113710 monthly downloads). Marked as Development Status 1 - Planning, suggesting early-stage code.

License in practice

License status is unclear—no SPDX identifier or raw license text provided. Before depending on this package in production or commercial work, verify the actual license terms directly from the repository.

Quickstart

pip install smartmoneyconcepts

from smartmoneyconcepts import smc
import pandas as pd

# Prepare OHLC data with lowercase column names
ohlc = pd.DataFrame({"open": [...], "high": [...], "low": [...], "close": [...], "volume": [...]})
fvg_result = smc.fvg(ohlc, join_consecutive=False)

Requires OHLC data formatted as a pandas DataFrame with lowercase column names: ["open", "high", "low", "close"] and ["volume"] for volume-dependent indicators.

Verify before relying

• Minimum Python version requirement is unspecified; verify compatibility with your environment.
• Actual license terms are not declared; check the repository directly before using in commercial projects.
• Numerical accuracy and backtesting validation of ICT indicator implementations are not documented in the fact sheet.

Package facts

Evidence: smartmoneyconcepts-0.0.27-py3-none-any.whl

Keywords: smart, money, concepts, ict, indicators, trading, forex, stocks, crypto, order, blocks, liquidity

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