---
id: mplfinance
version: "0.12.10b0"
license: BSD-style
license_treatment: permissive
maintenance: dormant
---

# mplfinance — Utilities for the visualization, and visual analysis, of financial data

License: permissive · Maintenance: dormant · Downloads: 810.5K/mo

## 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 above — verify before relying.

mplfinance provides matplotlib-based visualization for financial market data, enabling candlestick charts, OHLC plots, and technical analysis overlays from pandas DataFrames.

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

pip install mplfinance
uv add mplfinance
poetry add mplfinance

## Installing mplfinance

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.

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)

Data must be a pandas DataFrame with DatetimeIndex and Open, High, Low, Close columns; matplotlib and pandas must be installed.

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: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 810.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

candlestick chart plotting, ohlc financial visualization, stock market charting, technical analysis plotting, financial data visualization, trading chart library, price movement charts, financial-visualization, matplotlib-extension, technical-analysis

[View on SkillFed](https://skillfed.io/packages/mplfinance) · [View on PyPI](https://pypi.org/project/mplfinance/)