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first draft test_fill_between.py 
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‎tests/reference_images/fill_between01.png

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‎tests/reference_images/fill_between02.png

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‎tests/test_fill_between.py

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#!/usr/bin/env python
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# coding: utf-8
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import os
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import os.path
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import glob
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import mplfinance as mpf
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import matplotlib.pyplot as plt
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from matplotlib.testing.compare import compare_images
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print('mpf.__version__ =',mpf.__version__) # for the record
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print("plt.rcParams['backend'] =",plt.rcParams['backend']) # for the record
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base='fill_between'
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tdir = os.path.join('tests','test_images')
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refd = os.path.join('tests','reference_images')
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globpattern = os.path.join(tdir,base+'*.png')
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oldtestfiles = glob.glob(globpattern)
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for fn in oldtestfiles:
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try:
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os.remove(fn)
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except:
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print('Error removing file "'+fn+'"')
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# IMGCOMP_TOLERANCE = 7.0 # this works fine for linux
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IMGCOMP_TOLERANCE = 11.0 # required for a windows pass. (really 10.25 may do it).
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def _get_scaled_down_dataset(bolldata):
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start = int(0.2*len(bolldata))
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end = 2*start
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df = bolldata.iloc[start:end]
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scaled_down_data = df/df['Close'].mean()
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scaled_down_data -= 0.9*scaled_down_data['Low'].min()
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return scaled_down_data
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def test_fill_between01(bolldata):
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scaled_down_data = _get_scaled_down_dataset(bolldata)
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fname = base+'01.png'
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tname = os.path.join(tdir,fname)
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rname = os.path.join(refd,fname)
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mpf.plot(scaled_down_data,figscale=0.7,
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fill_between=scaled_down_data['Close'].values,
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savefig=tname)
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tsize = os.path.getsize(tname)
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print(glob.glob(tname),'[',tsize,'bytes',']')
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rsize = os.path.getsize(rname)
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print(glob.glob(rname),'[',rsize,'bytes',']')
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result = compare_images(rname,tname,tol=IMGCOMP_TOLERANCE)
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if result is not None:
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print('result=',result)
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assert result is None
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def test_fill_between02(bolldata):
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scaled_down_data = _get_scaled_down_dataset(bolldata)
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fname = base+'02.png'
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tname = os.path.join(tdir,fname)
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rname = os.path.join(refd,fname)
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mpf.plot(scaled_down_data,figscale=0.7,
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fill_between={'y1':scaled_down_data['Close'].values,'alpha':0.75},
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savefig=tname)
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tsize = os.path.getsize(tname)
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print(glob.glob(tname),'[',tsize,'bytes',']')
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rsize = os.path.getsize(rname)
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print(glob.glob(rname),'[',rsize,'bytes',']')
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result = compare_images(rname,tname,tol=IMGCOMP_TOLERANCE)
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if result is not None:
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print('result=',result)
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assert result is None
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## import pandas as pd
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##
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## # ### Read in daily data for the S&P 500 from November of 2019
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##
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## daily = pd.read_csv('data/SP500_NOV2019_Hist.csv',index_col=0,parse_dates=True)
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## daily.index.name = 'Date'
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## daily.shape
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## daily.head(3)
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##
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## import mplfinance as mpf
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## mpf.__version__
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##
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## print('''
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##
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## A single y-value or series **assumes** we fill between ZERO and that single y-value or series.
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##
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## Therefore, scale down the data, close to zero, so that we can demonstrate specifying
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## a single y-value, or single y-series for fill_between:
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##
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## ''')
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## scaled_down_data = (daily/3000)-1.0
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##
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## mpf.plot(scaled_down_data,figscale=0.7)
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## mpf.plot(scaled_down_data,figscale=0.7,fill_between=0.03)
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## mpf.plot(scaled_down_data,figscale=0.7,fill_between=scaled_down_data['Close'].values)
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##
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## print('''
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##
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## Use a dict to specify two y values, or two series, (y1 and y2) for `fill_between`:
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##
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## ''')
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##
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## mpf.plot(daily,figscale=0.7,fill_between=dict(y1=3090,y2=3120))
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## mpf.plot(daily,figscale=0.7,fill_between=dict(y1=3100,y2=daily['Close'].values))
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## mpf.plot(daily,figscale=0.7,fill_between=dict(y1=daily['Low'].values,y2=daily['High'].values))
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##
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## print('''
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##
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## Use a dict to specify other attributes (kwargs) for `fill_between`:
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##
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## To demonstrate use of the `where` kwarg to display a holding period,
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## we convert the datetime index into a dataframe, and use that to generate a boolean array:
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##
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## `where_values = pd.notnull(dates_df[ (dates_df>=buy_date) & (dates_df <= sell_date) ])['Date'].values`
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##
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## ''')
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##
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## dates_df = pd.DataFrame(daily.index)
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## buy_date = pd.Timestamp('2019-11-06')
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## sell_date = pd.Timestamp('2019-11-19')
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##
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## where_values = pd.notnull(dates_df[ (dates_df>=buy_date) & (dates_df <= sell_date) ])['Date'].values
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##
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## y1values = daily['Close'].values
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## y2value = daily['Low'].min()
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##
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## mpf.plot(daily,figscale=0.7,
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## fill_between=dict(y1=y1values,y2=y2value,where=where_values,alpha=0.5,color='g')
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## )
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##
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## print('''
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##
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## Use `panel=` in the `fill_between` dict to place the fill_between on a panel other than panel 0:
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##
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## In this example, we `fill_between` on the volume panel,
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## filling between the volume and the average volume.
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##
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## ''')
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##
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## mpf.plot(daily,volume=True,panel_ratios=(1.1,1),
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## type='candle',tight_layout=True,figratio=(1,1),
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## fill_between=dict(y1=daily['Volume'].values,
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## y2=daily['Volume'].mean(),
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## panel=1,alpha=0.5,color='lime'))
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##
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## print('''
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##
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## There are two ways to do multiple `fill_betweens`:
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## (1) Specify a list of `fill_between` dicts.
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## (2) Specify a fill_between (or list of fill_betweens) for each `mpf.make_addplot()`
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##
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## Here, for example, we specify a `fill_between=` a list of fill between dicts:
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##
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## ''')
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##
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## fb1 = dict(y1=daily['Open'].values , y2=daily['Close'].values , panel=0, alpha=0.3, color='magenta')
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## fb2 = dict(y1=daily['Volume'].values, y2=daily['Volume'].mean(), panel=1, alpha=0.5, color='lime')
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##
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## mpf.plot(daily,volume=True,panel_ratios=(1.1,1),
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## type='candle',tight_layout=True,figratio=(1,1),
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## fill_between=[fb1,fb2])
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##
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## print('''
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##
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## We can accomplish the same thing by specifying one fill_between in `mpf.plot()`
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## and the other in `mpf.make_addplot()`. This is useful if we are already using
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## `make_addplot()` to plot some additional data.
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##
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## NOTE: Since make_addplot() accepts a panel argument, one should NOT specify
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## panel in the fill_between dict used by make_addplot.
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##
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## ''')
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##
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## fb1 = dict(y1=daily['Open'].values , y2=daily['Close'].values , alpha=0.4, color='magenta')
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## fb2 = dict(y1=daily['Volume'].values, y2=daily['Volume'].mean(), alpha=0.5, color='lime')
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##
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## avol = [daily['Volume'].mean()]*len(daily)
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##
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## ap = mpf.make_addplot(avol,panel=1,fill_between=fb2,color='k',linestyle='-.',width=0.25)
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##
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## mpf.plot(daily,volume=True,panel_ratios=(1.1,1),
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## type='candle',tight_layout=True,figratio=(1,1),
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## fill_between=fb1,addplot=ap)
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##
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## print('''
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##
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## We can specify effectively a "multi-color" fill_between, by breaking it into
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## two separate fill_betweens, with two separate colors, each with a "where" clause to
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## indicate where the fill_between color should and should not appear along the datetime axis.
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##
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## This is useful, for example, if we want to highlight where a given value is
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## above or below the average value.
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##
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## Notice that when using multiple where clauses like this, it is helpful
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## to set `interpolate=True` in the `fill_between` dict, so that the space
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## between True values and False values also gets filled.
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##
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## ''')
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##
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## fb_above = dict(y1=daily['Volume'].values,
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## y2=daily['Volume'].mean(),
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## alpha=0.4, color='lime',
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## interpolate=True,
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## where=(daily['Volume'] > daily['Volume'].mean()).values)
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##
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## fb_below = fb_above.copy()
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## fb_below['color'] = 'magenta'
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## fb_below['where'] = (daily['Volume'] < daily['Volume'].mean()).values
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##
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## avol = [daily['Volume'].mean()]*len(daily)
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## ap = mpf.make_addplot(avol,panel=1,fill_between=[fb_above,fb_below],color='k',linestyle='-.',width=0.25)
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##
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## mpf.plot(daily,volume=True,panel_ratios=(0.8,1),
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## type='candle',tight_layout=True,figratio=(1,1),addplot=ap)
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##
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## print('''
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##
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## Here, as an additional example, we create "multi-color" fill_between for both panels:
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##
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## ''')
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##
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## fbvolume_above = dict(y1=daily['Volume'].values,
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## y2=daily['Volume'].mean(),
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## alpha=0.4, color='lime',
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## interpolate=True,
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## where=(daily['Volume'] > daily['Volume'].mean()).values)
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##
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## fbvolume_below = fbvolume_above.copy()
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## fbvolume_below['color'] = 'magenta'
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## fbvolume_below['where'] = (daily['Volume'] < daily['Volume'].mean()).values
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##
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## avol = [daily['Volume'].mean()]*len(daily)
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## ap = mpf.make_addplot(avol,panel=1,fill_between=[fbvolume_above,fbvolume_below],color='k',linestyle='-.',width=0.25)
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##
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## fbclose_above = dict(y1=daily['Open'].values , y2=daily['Close'].values , alpha=0.4,
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## interpolate=True,
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## color='lime',
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## where=(daily['Close']>daily['Open']).values
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## )
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##
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## fbclose_below = fbclose_above.copy()
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## fbclose_below['color'] = 'magenta'
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## fbclose_below['where'] = (daily['Close']<daily['Open']).values
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##
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## mpf.plot(daily,volume=True,panel_ratios=(1,1),
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## type='candle',tight_layout=True,figratio=(1,1),
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## fill_between=[fbclose_above,fbclose_below],
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## addplot=ap)
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##
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## print('''
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##
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## Finally, as a more pratical example, we use `fill_between` to color a MACD plot:
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##
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## ''')
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##
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## df = pd.read_csv('data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True).loc['2011-07-01':'2011-12-30',:]
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##
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## # =======
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## # MACD:
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##
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## exp12 = df['Close'].ewm(span=12, adjust=False).mean()
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## exp26 = df['Close'].ewm(span=26, adjust=False).mean()
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## macd = exp12 - exp26
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## signal = macd.ewm(span=9, adjust=False).mean()
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## histogram = macd - signal
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##
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## fb_green = dict(y1=macd.values,y2=signal.values,where=signal<macd,color="#93c47d",alpha=0.6,interpolate=True)
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## fb_red = dict(y1=macd.values,y2=signal.values,where=signal>macd,color="#e06666",alpha=0.6,interpolate=True)
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## fb_green['panel'] = 1
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## fb_red['panel'] = 1
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## fb = [fb_green,fb_red]
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##
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## apds = [mpf.make_addplot(exp12,color='lime'),
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## mpf.make_addplot(exp26,color='c'),
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## mpf.make_addplot(histogram,type='bar',width=0.7,panel=1,
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## color='dimgray',alpha=1,secondary_y=True),
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## mpf.make_addplot(macd,panel=1,color='fuchsia',secondary_y=False),
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## mpf.make_addplot(signal,panel=1,color='b',secondary_y=False)#,fill_between=fb),
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## ]
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##
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## s = mpf.make_mpf_style(base_mpf_style='classic',rc={'figure.facecolor':'lightgray'})
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##
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## mpf.plot(df,type='candle',addplot=apds,figscale=1.6,figratio=(6,5),title='\n\nMACD',
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## style=s,volume=True,volume_panel=2,panel_ratios=(3,4,1),fill_between=fb)
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##
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## def test_fill_between02(bolldata):

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