Cufflinks Documentation

You can configure global settings for all charts generated by cufflinks:

• Themes: Use cufflinks.set_config_file(theme='theme_name') to set a global theme (e.g., 'pearl', 'ggplot'). You can also pass a theme argument directly to .iplot().
• World Readable: Use cufflinks.set_config_file(world_readable=True) for public charts.
• Config File: Use cufflinks.set_config_file() to manage various global settings.

To use Cufflinks, import the library as cf. Note that this tutorial assumes the package is already installed in your environment.

import cufflinks as cf

You can add annotations to your plots using two modes:

1. Label Mode: Pass a dictionary to annotations where keys are dates/labels and values are the text. Use textangle, fontsize, and fontcolor to style them.
2. Explicit Mode: Pass a list of dictionaries to annotations for precise control over position (x, y), reference (xref), arrowheads, and text styling.

Cufflinks supports interactive date range controls for time-series plots:

• rangeslider: Displays a date range slider at the bottom of the chart. Set rangeslider=True in .iplot().
• rangeselector: Displays buttons to quickly change the displayed date range. Pass a dictionary to the rangeselector parameter in .iplot() containing 'steps', 'bgcolor', 'x', and 'y'.

# Enable rangeslider
cf.datagen.ohlc().iplot(kind='candle',rangeslider=True)

# Enable rangeselector
cf.datagen.ohlc(500).iplot(kind='candle', rangeselector={ 'steps':['1y','2 months','5 weeks','ytd','2mtd','reset'], 
     'bgcolor' : ('grey',.3), 'x': 0.3 , 'y' : 0.95})

Cufflinks allows you to switch between online and offline Plotly modes:

• cf.go_offline(): Switch to offline mode.
• cf.go_online(): Switch to online mode.
• cf.iplot(figure, online=True): Force online mode for a specific plot even if the global setting is offline.

You can inspect the available figure types and the specific parameters supported by each figure using the cf.help() method.

• Call cf.help() to see a comprehensive list of all supported figures.
• Call cf.help('figure_name') to see the parameters available for a specific figure (e.g., 'scatter', 'candle').

# For a list of supported figures
cf.help()

# Or to see the parameters supported that apply to a given figure try
cf.help('scatter')
cf.help('candle') #etc

You can generate sample Open-High-Low-Close-Volume (OHLCV) data using the cf.datagen.ohlcv() method for testing and prototyping.

df=cf.datagen.ohlcv()

The cf.colors module provides utilities to convert colors between HEX, RGB, RGBA, and named color formats.

• Named Colors: Access a list of pre-defined color names using cf.colors.cnames.
• HEX to RGB: Use cf.colors.hex_to_rgb(color).
• RGB to HEX: Use cf.colors.rgb_to_hex(color).
• To RGBA (Transparency): Use cf.colors.to_rgba(color, alpha) where alpha is the transparency level.
• RGBA to RGB (Flattening): Use cf.colors.rgba_to_rgb(rgba_color, transparency_color) to flatten transparency. By default, the transparency color is assumed to be 'white'.

Enhance a QuantFig object by adding various technical indicators before calling .iplot(). Supported indicators include:

• add_sma(periods, width, color, legendgroup): Adds Simple Moving Averages.
• add_rsi(periods, color): Adds Relative Strength Index.
• add_bollinger_bands(periods, boll_std, colors, fill): Adds Bollinger Bands.
• add_volume(): Adds volume bars.
• add_macd(): Adds Moving Average Convergence Divergence.

qf.add_sma([10,20],width=2,color=['green','lightgreen'],legendgroup=True)
qf.add_rsi(periods=20,color='java')
qf.add_bollinger_bands(periods=20,boll_std=2,colors=['magenta','grey'],fill=True)
qf.add_volume()
qf.add_macd()
qf.iplot()