technical
Technical Indicators for Financial Analysis
Decision gist · record as of 2026-08-14
Yes, if you are building Freqtrade strategies or need a curated collection of technical indicators for pandas-based financial analysis. The active maintenance, low install friction, and lack of known vulnerabilities make it reliable. GPLv3 licensing requires that any derivative work also be open-source; if that is incompatible with your use case, do not install. TA-lib dependency can be a blocker on some platforms.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires TA-lib to be installed separately (a compiled C extension); Python 3.11 or later.
- Low install friction with a pure-Python wheel distribution.
- Actively maintained with a recent release (24 days old) and 1027 repository stars.
License · maintenance · safety
GPLv3 (copyleft) — Licensed under GPLv3 (copyleft). Any derivative work or distribution must also be released under GPLv3; proprietary use requires separate licensing or relicensing of your code.
last release 2026-07-21 (24 days) · last repo commit 2026-08-14 · 1,027 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 264,907 downloads/mo, #8,332 on PyPI
Alternatives
Verify before relying
pip install technical
from technical.indicators import accumulation_distribution
from technical.util import resample_to_interval, resampled_merge
dataframe_long = resample_to_interval(dataframe, 240)
dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)- Whether TA-lib installation is straightforward on all platforms (known to be problematic on some systems).
- Performance characteristics when working with large dataframes or many indicators in sequence.
What it is and what it does
Technical is a companion library for Freqtrade that bundles technical indicators and utilities for building trading strategies. It wraps TA-lib and provides custom indicators (consensus models, Ichimoku cloud, Supertrend, etc.) alongside standard ones (SMA, EMA, Bollinger Bands, RSI) all designed to work directly on pandas dataframes with OHLCV columns. The library also includes utilities for resampling dataframes to different timeframes and merging them back, allowing strategies to incorporate signals from multiple intervals.
The package is actively maintained, supports Python 3.11 through 3.14, and has no known vulnerabilities. It depends on TA-lib and pandas at runtime. Most indicators are wrapped to accept a dataframe and period parameters, making them straightforward to integrate into backtesting or live trading workflows.
Use it for
- Build multi-timeframe trading strategies by resampling OHLCV data and merging signals from 4h or daily indicators into 1h candles.
- Backtest Freqtrade strategies using a unified API for technical indicators without writing custom wrappers.
- Combine consensus-based indicators (e.g., moving average consensus) to filter entry/exit signals across multiple indicators.
- Calculate volume-weighted indicators and trend-following metrics (Supertrend, Ichimoku) on historical price data.
- Access custom indicators like VFI, ALMA, and SSL Channel that are not in standard TA-lib.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building Freqtrade strategies or need a curated collection of technical indicators for pandas-based financial analysis.
The active maintenance, low install friction, and lack of known vulnerabilities make it reliable. GPLv3 licensing requires that any derivative work also be open-source; if that is incompatible with your use case, do not install. TA-lib dependency can be a blocker on some platforms.
Install
technical on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. Actively maintained with a recent release (24 days old) and 1027 repository stars. Requires TA-lib and pandas as runtime dependencies.
Requires TA-lib to be installed separately (a compiled C extension); Python 3.11 or later.
License in practice
Licensed under GPLv3 (copyleft). Any derivative work or distribution must also be released under GPLv3; proprietary use requires separate licensing or relicensing of your code.
Quickstart
pip install technical
from technical.indicators import accumulation_distribution
from technical.util import resample_to_interval, resampled_merge
dataframe_long = resample_to_interval(dataframe, 240)
dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)Verify before relying
- Whether TA-lib installation is straightforward on all platforms (known to be problematic on some systems).
- Performance characteristics when working with large dataframes or many indicators in sequence.
Package facts
| License | GPLv3 copyleft |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesTA-libpandas |
| Maintenance | Actively maintained 24 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 264,907 / month, #8,332 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchLicense :: OSI Approved :: GNU General Public License v3 (GPLv3)Programming Language :: Python :: 3Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Office/Business :: Financial :: Investment |
Evidence: technical-1.7.0-py3-none-any.whl
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