ft-pandas-ta
An easy to use Python 3 Pandas Extension with 130+ Technical Analysis Indicators. Can be called from a Pandas DataFrame or standalone like TA-Lib. Correlation tested with TA-Lib.
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you need a lightweight, Pandas-native technical analysis toolkit for trading or financial analysis. The low dependency footprint and permissive license make it easy to integrate. However, maintenance is aging (last release 319 days ago), so verify that the indicator set and accuracy meet your specific requirements before relying on it for production trading. No known vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- DataFrame index must be a DatetimeIndex for some indicators like VWAP.
- TA-Lib must be installed separately to access all candlestick patterns.
- Low friction install with only numpy and pandas as runtime dependencies.
License · maintenance · safety
The MIT License (MIT) (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary trading systems.
last release 2025-09-29 (319 days) · last repo commit 2025-09-29 · 25 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 256,885 downloads/mo, #8,454 on PyPI
Alternatives
Verify before relying
import pandas as pd
import numpy as np
df = pd.read_csv('ohlcv_data.csv')
df.set_index(pd.DatetimeIndex(df['datetime']), inplace=True)
# Calculate indicators and append to DataFrame
df.ta.sma(append=True)
df.ta.bbands(append=True)- Whether all 130+ indicators are fully functional or if some require optional TA-Lib installation
- Performance characteristics when processing large datasets or using multiprocessing strategy mode
- Accuracy correlation with TA-Lib for shared indicators beyond what the description claims
- Specific parameter values and defaults for commonly used indicators like sma, macd, bbands
What it is and what it does
Pandas TA is a Pandas extension library that adds technical analysis capabilities to financial DataFrames. It wraps 130+ indicators (moving averages, momentum oscillators, volatility bands, volume measures, and trend tools) plus candlestick patterns. You call indicators either as methods on a DataFrame or as standalone functions, and results append as new columns. The library is a maintained fork of the original pandas-ta, updated to work with modern Python and Pandas versions.
The package is designed for traders and quantitative analysts building backtesting systems or signal generators. It integrates with Pandas workflows and optionally uses TA-Lib for accelerated computation if installed. It supports multiprocessing for strategy evaluation and allows custom indicators. Candlestick patterns require TA-Lib; without it, only built-in patterns are available.
Use it for
- Calculate moving averages, MACD, Bollinger Bands, and other standard indicators on OHLCV data for signal generation
- Backtest trading strategies by applying multiple indicators via the DataFrame strategy method with multiprocessing
- Detect candlestick patterns in price data for entry/exit signals (requires TA-Lib for full pattern library)
- Build custom chained strategies by combining indicators with prefixes and suffixes for clarity
- Analyze financial time series for volatility, momentum, and trend without leaving the Pandas ecosystem
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you need a lightweight, Pandas-native technical analysis toolkit for trading or financial analysis. The low dependency footprint and permissive license make it easy to integrate. However, maintenance is aging (last release 319 days ago), so verify that the indicator set and accuracy meet your specific requirements before relying on it for production trading. No known vulnerabilities.
Install
ft-pandas-ta on PyPI
Before you install
Low friction install with only numpy and pandas as runtime dependencies. Maintenance status is aging—last release was 319 days ago—but the package is actively maintained by the Freqtrade team as a fork of the discontinued original project.
DataFrame index must be a DatetimeIndex for some indicators like VWAP. TA-Lib must be installed separately to access all candlestick patterns.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for both open-source and proprietary trading systems.
Quickstart
import pandas as pd
import numpy as np
df = pd.read_csv('ohlcv_data.csv')
df.set_index(pd.DatetimeIndex(df['datetime']), inplace=True)
# Calculate indicators and append to DataFrame
df.ta.sma(append=True)
df.ta.bbands(append=True)Verify before relying
- Whether all 130+ indicators are fully functional or if some require optional TA-Lib installation
- Performance characteristics when processing large datasets or using multiprocessing strategy mode
- Accuracy correlation with TA-Lib for shared indicators beyond what the description claims
- Specific parameter values and defaults for commonly used indicators like sma, macd, bbands
Package facts
| License | The MIT License (MIT) permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpypandas |
| Maintenance | Aging 319 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 256,885 / month, #8,454 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Office/Business :: FinancialTopic :: Office/Business :: Financial :: InvestmentTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Information Analysis |
Evidence: ft_pandas_ta-0.3.16-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “trading signals candlestick patterns”
- ft-pandas-taProvides 130+ technical analysis indicators and candlestick patterns…
- pandas-ta-classicPandas TA Classic provides 193 technical indicators and 62 native…
- pandas-taProvides over 150 technical analysis indicators and 60 candlestick…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also pandas-ta · pandas-ta-classic · stockstats · ta · TA-Lib · smartmoneyconcepts · technical · mplfinance · tradingview-ta · dtale