Matplotlib Finance (mplfinance)

Warnings

• gotcha mplfinance expects input data as a Pandas DataFrame with a DatetimeIndex and specific column names (case-sensitive): 'Open', 'High', 'Low', 'Close', and optionally 'Volume'. Incorrect indexing or column naming will lead to errors.
  Fix: Ensure your DataFrame's index is `pd.DatetimeIndex` and column names are exactly 'Open', 'High', 'Low', 'Close', 'Volume'. Use `df.rename()` or `df.columns = [...]` if necessary.
• deprecated Older versions of mplfinance (prior to 0.12.9b7) may issue deprecation warnings or encounter compatibility issues when run with recent versions of Matplotlib or Pandas.
  Fix: Upgrade mplfinance to version 0.12.9b7 or newer to benefit from fixes addressing deprecation warnings and improved compatibility with modern Matplotlib/Pandas releases. Also ensure Matplotlib and Pandas are up-to-date.
• gotcha Direct manipulation of Matplotlib Axes objects returned by `mpf.plot()` can be challenging due to mplfinance's internal panel structure. For overlaying custom data series or indicators, the `addplot` kwarg is the idiomatic approach.
  Fix: Use `mpf.make_addplot()` to create 'addplot' objects for overlay data (e.g., scatter plots, lines), then pass a list of these objects to the `addplot` keyword argument of `mpf.plot()`. For general chart customization, explore `mpf.plot()`'s extensive keyword arguments (e.g., `style`, `marketcolors`).
• gotcha mplfinance frequently releases beta versions (e.g., `0.12.10b0`) which may introduce minor API changes or new features that are refined rapidly. While generally stable, users should monitor release notes carefully.
  Fix: When using pre-release versions, review the GitHub changelog for each update. For production environments requiring strict stability, consider pinning to exact minor versions.

Install

• pip install mplfinance Install stable version
• pip install --pre mplfinance Install latest pre-release (beta)

Imports

• mpf

  import mplfinance as mpf

Quickstart

This quickstart generates dummy OHLCV data using Pandas and NumPy, then plots a basic candlestick chart with volume using `mpf.plot()`. It demonstrates the required DataFrame format (DatetimeIndex, specific column names) and common plotting parameters like `type`, `style`, and `volume`. The `returnfig=True` argument allows access to the underlying Matplotlib Figure and Axes objects for further customization or saving.

import mplfinance as mpf
import pandas as pd
import numpy as np

# Create dummy OHLCV data with DatetimeIndex
dates = pd.date_range('2023-01-01', periods=50, freq='D')
np.random.seed(42)
open_price = np.random.rand(50) * 100 + 100
close_price = open_price + np.random.randn(50) * 5
high_price = np.maximum(open_price, close_price) + np.random.rand(50) * 2
low_price = np.minimum(open_price, close_price) - np.random.rand(50) * 2
volume = np.random.rand(50) * 1000000

df = pd.DataFrame({
    'Open': open_price,
    'High': high_price,
    'Low': low_price,
    'Close': close_price,
    'Volume': volume
}, index=dates)

# Plot a basic candlestick chart with volume
fig, axes = mpf.plot(df, 
                     type='candle', 
                     style='yahoo', 
                     volume=True, 
                     title='Sample Candlestick Chart',
                     ylabel='Price',
                     ylabel_lower='Volume',
                     returnfig=True
                    )

# To display the plot in a non-interactive environment or save it
# fig.savefig('candlestick_chart.png')
# import matplotlib.pyplot as plt
# plt.show() # Uncomment for interactive display