smartmoneyconcepts
Getting indicators based on smart money concepts or ICT
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires OHLC data formatted as a pandas DataFrame with lowercase column names: ["open", "high", "low", "close"] and ["volume"] for volume-dependent indicators.
- Low install friction with three common numeric dependencies (pandas, numpy, numba).
- Package is actively maintained with recent commits and moderate popularity (113710 monthly downloads).
License · maintenance · safety
(unclear) — 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.
last release 2026-04-03 (133 days) · last repo commit 2026-04-03 · 1,934 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 113,710 downloads/mo, #12,334 on PyPI
Alternatives
Verify before relying
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)- 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.
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 on it.
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
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.
Requires OHLC data formatted as a pandas DataFrame with lowercase column names: ["open", "high", "low", "close"] and ["volume"] for volume-dependent indicators.
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)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
| License | Not declared unclear |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagespandasnumpynumba |
| Maintenance | Actively maintained 133 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 113,710 / month, #12,334 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 1 - PlanningIntended Audience :: DevelopersOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: UnixProgramming Language :: Python :: 3 |
Evidence: smartmoneyconcepts-0.0.27-py3-none-any.whl
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