This library binds the power of plotly with the flexibility of pandas for easy plotting.
This library is available on https://github.com/santosjorge/cufflinks
This tutorial assumes that the plotly user credentials have already been configured as stated on the getting started guide.
Support for Plotly 4.x
Cufflinks is no longer compatible with Plotly 3.x
Support for Plotly 3.0
New iplot
helper.
To see a comprehensive list of parameters
cf.help()
# 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
Removed dependecies on ta-lib. This library is no longer required. All studies have be rewritten in Python.
QuantFigure
is a new class that will generate a graph object with persistence. Parameters can be added/modified at any given point.
This can be as easy as:
df=cf.datagen.ohlc()
qf=cf.QuantFig(df,title='First Quant Figure',legend='top',name='GS')
qf.add_bollinger_bands()
qf.iplot()
- Technical Analysis Studies can be added on demand.
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()
rangeslider
to display a date range slider at the bottomcf.datagen.ohlc().iplot(kind='candle',rangeslider=True)
rangeselector
to display buttons to change the date range displayedcf.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})
- Customise annotions, with
fontsize
,fontcolor
,textangle
- Label mode
cf.datagen.lines(1,mode='stocks').iplot(kind='line', annotations={'2015-02-02':'Market Crash', '2015-03-01':'Recovery'}, textangle=-70,fontsize=13,fontcolor='grey')
- Explicit mode
cf.datagen.lines(1,mode='stocks').iplot(kind='line', annotations=[{'text':'exactly here','x':'0.2', 'xref':'paper','arrowhead':2, 'textangle':-10,'ay':150,'arrowcolor':'red'}])
- Label mode
Figure.iplot()
to plot figures- New high performing candle and ohlc plots
cf.datagen.ohlc().iplot(kind='candle')
- 'cf.datagen.choropleth()' to for sample choropleth data.
- 'cf.datagen.scattergeo()' to for sample scattergeo data.
- Support for choropleth and scattergeo figures in
iplot
- 'cf.get_colorscale' for maps and plotly objects that support colorscales
xrange
,yrange
andzrange
can be specified iniplot
andgetLayout
cf.datagen.lines(1).iplot(yrange=[5,15])
layout_update
can be set iniplot
andgetLayout
to explicitly update anyLayout
value
- Support for Python 3
- Support for pie charts
cf.datagen.pie().iplot(kind='pie',labels='labels',values='values')
- Generate Open, High, Low, Close data
datagen.ohlc()
- Candle Charts support
ohlc=cf.datagen.ohlc()
ohlc.iplot(kind='candle',up_color='blue',down_color='red')
- OHLC (Bar) Charts support
ohlc=cf.datagen.ohlc()
ohlc.iplot(kind='ohlc',up_color='blue',down_color='red')
- Support for logarithmic charts ( logx | logy )
df=pd.DataFrame([x**2] for x in range(100))
df.iplot(kind='lines',logy=True)
- Support for MulitIndex DataFrames
- Support for Error Bars ( error_x | error_y )
cf.datagen.lines(1,5).iplot(kind='bar',error_y=[1,2,3.5,2,2])
cf.datagen.lines(1,5).iplot(kind='bar',error_y=20, error_type='percent')
- Support for continuous error bars
cf.datagen.lines(1).iplot(kind='lines',error_y=20,error_type='continuous_percent')
cf.datagen.lines(1).iplot(kind='lines',error_y=10,error_type='continuous',color='blue')
- Technical Analysis Studies for Timeseries (beta)
- Simple Moving Averages (SMA)
cf.datagen.lines(1,500).ta_plot(study='sma',periods=[13,21,55])
- Relative Strength Indicator (RSI)
cf.datagen.lines(1,200).ta_plot(study='boll',periods=14)
- Bollinger Bands (BOLL)
cf.datagen.lines(1,200).ta_plot(study='rsi',periods=14)
- Moving Average Convergence Divergence (MACD)
cf.datagen.lines(1,200).ta_plot(study='macd',fast_period=12,slow_period=26, signal_period=9)
- Simple Moving Averages (SMA)
- Support of offline charts
cf.go_offline()
cf.go_online()
cf.iplot(figure,online=True)
(To force online whilst on offline mode)
- Support for secondary axis
fig=cf.datagen.lines(3,columns=['a','b','c']).figure()
fig=fig.set_axis('b',side='right')
cf.iplot(fig)
- Support for global theme setting
cufflinks.set_config_file(theme='pearl')
- New theme ggplot
cufflinks.datagen.lines(5).iplot(theme='ggplot')
- Support for horizontal bar charts barh
cufflinks.datagen.lines(2).iplot(kind='barh',barmode='stack',bargap=.1)
- Support for histogram orientation and normalization
cufflinks.datagen.histogram().iplot(kind='histogram',orientation='h',norm='probability')
- Support for area plots
cufflinks.datagen.lines(4).iplot(kind='area',fill=True,opacity=1)
- Support for subplots
cufflinks.datagen.histogram(4).iplot(kind='histogram',subplots=True,bins=50)
cufflinks.datagen.lines(4).iplot(subplots=True,shape=(4,1),shared_xaxes=True,vertical_spacing=.02,fill=True)
- Support for scatter matrix to display the distribution amongst every series in the DataFrame
cufflinks.datagen.lines(4,1000).scatter_matrix()
- Support for vline and hline for horizontal and vertical lines
cufflinks.datagen.lines(3).iplot(hline=[2,3])
cufflinks.datagen.lines(3).iplot(hline=dict(y=2,color='blue',width=3))
- Support for vspan and hspan for horizontal and vertical areas
cufflinks.datagen.lines(3).iplot(hspan=(-1,2))
cufflinks.datagen.lines(3).iplot(hspan=dict(y0=-1,y1=2,color='orange',fill=True,opacity=.4))
- Global setting for public charts
cufflinks.set_config_file(world_readable=True)
- Enhanced Spread charts
cufflinks.datagen.lines(2).iplot(kind='spread')
- Support for Heatmap charts
cufflinks.datagen.heatmap().iplot(kind='heatmap')
- Support for Bubble charts
cufflinks.datagen.bubble(4).iplot(kind='bubble',x='x',y='y',text='text',size='size',categories='categories')
- Support for Bubble3d charts
cufflinks.datagen.bubble3d(4).iplot(kind='bubble3d',x='x',y='y',z='z',text='text',size='size',categories='categories')
- Support for Box charts
cufflinks.datagen.box().iplot(kind='box')
- Support for Surface charts
cufflinks.datagen.surface().iplot(kind='surface')
- Support for Scatter3d charts
cufflinks.datagen.scatter3d().iplot(kind='scatter3d',x='x',y='y',z='z',text='text',categories='categories')
- Support for Histograms
cufflinks.datagen.histogram(2).iplot(kind='histogram')
- Data generation for most common plot types
cufflinks.datagen
- Data extraction: Extract data from any Plotly chart. Data is delivered in DataFrame
cufflinks.to_df(Figure)
- Integration with colorlover
- Support for scales
iplot(colorscale='accent')
to plot a chart using an accent color scale - cufflinks.scales() to see all available scales
- Support for scales
- Support for named colors
*
iplot(colors=['pink','red','yellow'])