mplfinance
Utilities for the visualization, and visual analysis, of financial data
Decision gist · record as of 2026-08-14
Yes, if you need quick financial charting from pandas DataFrames and accept dormant maintenance. The package is stable, has no known vulnerabilities, and low install friction. However, do not rely on it for active bug fixes or new features—test compatibility with your matplotlib and pandas versions before production use, and consider it a mature but no-longer-developed tool.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Data must be a pandas DataFrame with DatetimeIndex and Open, High, Low, Close columns; matplotlib and pandas must be installed.
- Low install friction with only two runtime dependencies (matplotlib and pandas).
- Maintenance is dormant—last release was 2023-08-02, over a year ago, and no commits since 2024-08-08—so expect no active bug fixes or feature development.
License · maintenance · safety
BSD-style (permissive) — BSD-style permissive license allows commercial and private use with minimal restrictions, making it suitable for most projects without legal friction.
last release 2023-08-02 (1108 days) · last repo commit 2024-08-08 · 4,421 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 810,548 downloads/mo, #5,007 on PyPI
Alternatives
Verify before relying
import pandas as pd
import mplfinance as mpf
# Load OHLC data into a DataFrame with DatetimeIndex
data = pd.read_csv('data.csv', index_col=0, parse_dates=True)
# Plot candlestick chart with volume
mpf.plot(data, type='candle', volume=True)- Whether dormant maintenance status affects compatibility with recent matplotlib or pandas versions.
- Current state of the beta release (0.12.10b0) and whether it is production-ready or still experimental.
- Actual download volume and user base size to validate stability claims.
What it is and what it does
mplfinance is a matplotlib wrapper that simplifies financial chart creation by automating the matplotlib boilerplate typically required for candlestick, OHLC, and price-movement plots. It accepts pandas DataFrames with Open, High, Low, Close data indexed by date and generates publication-ready financial visualizations with minimal code.
The package handles common financial charting tasks: rendering multiple plot types (candlestick, OHLC, Renko, Point & Figure), overlaying technical indicators like moving averages, displaying volume bars, and managing non-trading day gaps. It integrates tightly with pandas, making it natural for workflows that already use DataFrames for market data manipulation.
Use it for
- Plot daily or intraday candlestick charts with volume for stock or futures data stored in pandas.
- Generate technical analysis charts with moving averages, support/resistance lines, and fill-between overlays.
- Create Renko or Point & Figure charts for alternative price-movement analysis without manual matplotlib configuration.
- Build chart exports for trading dashboards or financial reports with custom styling.
- Visualize market data with non-trading day handling for weekend and holiday gaps.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need quick financial charting from pandas DataFrames and accept dormant maintenance.
The package is stable, has no known vulnerabilities, and low install friction. However, do not rely on it for active bug fixes or new features—test compatibility with your matplotlib and pandas versions before production use, and consider it a mature but no-longer-developed tool.
Install
mplfinance on PyPI
Before you install
Low install friction with only two runtime dependencies (matplotlib and pandas). Maintenance is dormant—last release was 2023-08-02, over a year ago, and no commits since 2024-08-08—so expect no active bug fixes or feature development.
Data must be a pandas DataFrame with DatetimeIndex and Open, High, Low, Close columns; matplotlib and pandas must be installed.
License in practice
BSD-style permissive license allows commercial and private use with minimal restrictions, making it suitable for most projects without legal friction.
Quickstart
import pandas as pd
import mplfinance as mpf
# Load OHLC data into a DataFrame with DatetimeIndex
data = pd.read_csv('data.csv', index_col=0, parse_dates=True)
# Plot candlestick chart with volume
mpf.plot(data, type='candle', volume=True)Verify before relying
- Whether dormant maintenance status affects compatibility with recent matplotlib or pandas versions.
- Current state of the beta release (0.12.10b0) and whether it is production-ready or still experimental.
- Actual download volume and user base size to validate stability claims.
Package facts
| License | BSD-style permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesmatplotlibpandas |
| Maintenance | Dormant 1,108 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 810,548 / month, #5,007 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaFramework :: MatplotlibIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Financial and Insurance IndustryIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Office/Business :: FinancialTopic :: Office/Business :: Financial :: InvestmentTopic :: Scientific/Engineering :: Information AnalysisTopic :: Scientific/Engineering :: Visualization |
Evidence: mplfinance-0.12.10b0-py3-none-any.whl
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