additional tests, and update last example in fill_between.ipynb

Author: DanielGoldfarb

examples/fill_between.ipynb

@@ -148,193 +148,50 @@ def test_fill_between05(bolldata):
 
 
 
-##  print('''
-##  
-##  Use a dict to specify two y values, or two series, (y1 and y2) for `fill_between`:
-##  
-##  ''')
-##  
-##  mpf.plot(daily,figscale=0.7,fill_between=dict(y1=3090,y2=3120))
-##  mpf.plot(daily,figscale=0.7,fill_between=dict(y1=3100,y2=daily['Close'].values))
-##  mpf.plot(daily,figscale=0.7,fill_between=dict(y1=daily['Low'].values,y2=daily['High'].values))
-##  
-##  print('''
-##  
-##  Use a dict to specify other attributes (kwargs) for `fill_between`:
-##  
-##  To demonstrate use of the `where` kwarg to display a holding period,
-##  we convert the datetime index into a dataframe, and use that to generate a boolean array:
-##  
-##  `where_values = pd.notnull(dates_df[ (dates_df>=buy_date) & (dates_df <= sell_date) ])['Date'].values`
-##  
-##  ''')
-##  
-##  dates_df     = pd.DataFrame(daily.index)
-##  buy_date     = pd.Timestamp('2019-11-06')
-##  sell_date    = pd.Timestamp('2019-11-19')
-##  
-##  where_values = pd.notnull(dates_df[ (dates_df>=buy_date) & (dates_df <= sell_date) ])['Date'].values
-##  
-##  y1values = daily['Close'].values
-##  y2value  = daily['Low'].min()
-##  
-##  mpf.plot(daily,figscale=0.7,
-##           fill_between=dict(y1=y1values,y2=y2value,where=where_values,alpha=0.5,color='g')
-##          )
-##  
-##  print('''
-##  
-##  Use `panel=` in the `fill_between` dict to place the fill_between on a panel other than panel 0:
-##  
-##  In this example, we `fill_between` on the volume panel, 
-##  filling between the volume and the average volume.
-##  
-##  ''')
-##  
-##  mpf.plot(daily,volume=True,panel_ratios=(1.1,1),
-##           type='candle',tight_layout=True,figratio=(1,1),
-##           fill_between=dict(y1=daily['Volume'].values,
-##                             y2=daily['Volume'].mean(),
-##                             panel=1,alpha=0.5,color='lime'))
-##  
-##  print('''
-##  
-##  There are two ways to do multiple `fill_betweens`:
-##  (1) Specify a list of `fill_between` dicts.
-##  (2) Specify a fill_between (or list of fill_betweens) for each `mpf.make_addplot()`
-##  
-##  Here, for example, we specify a `fill_between=` a list of fill between dicts:
-##  
-##  ''')
-##  
-##  fb1 = dict(y1=daily['Open'].values  , y2=daily['Close'].values , panel=0, alpha=0.3, color='magenta')
-##  fb2 = dict(y1=daily['Volume'].values, y2=daily['Volume'].mean(), panel=1, alpha=0.5, color='lime')
-##  
-##  mpf.plot(daily,volume=True,panel_ratios=(1.1,1),
-##           type='candle',tight_layout=True,figratio=(1,1),
-##           fill_between=[fb1,fb2])
-##  
-##  print('''
-##  
-##  We can accomplish the same thing by specifying one fill_between in `mpf.plot()`
-##  and the other in `mpf.make_addplot()`.   This is useful if we are already using
-##  `make_addplot()` to plot some additional data.  
-##  
-##  NOTE: Since make_addplot() accepts a panel argument, one should NOT specify
-##        panel in the fill_between dict used by make_addplot.
-##  
-##  ''')
-##  
-##  fb1 = dict(y1=daily['Open'].values  , y2=daily['Close'].values , alpha=0.4, color='magenta')
-##  fb2 = dict(y1=daily['Volume'].values, y2=daily['Volume'].mean(), alpha=0.5, color='lime')
-##  
-##  avol = [daily['Volume'].mean()]*len(daily)
-##  
-##  ap = mpf.make_addplot(avol,panel=1,fill_between=fb2,color='k',linestyle='-.',width=0.25)
-##  
-##  mpf.plot(daily,volume=True,panel_ratios=(1.1,1),
-##           type='candle',tight_layout=True,figratio=(1,1),
-##           fill_between=fb1,addplot=ap)
-##  
-##  print('''
-##  
-##  We can specify effectively a "multi-color" fill_between, by breaking it into
-##  two separate fill_betweens, with two separate colors, each with a "where" clause to
-##  indicate where the fill_between color should and should not appear along the datetime axis.
-##  
-##  This is useful, for example, if we want to highlight where a given value is 
-##  above or below the average value.
-##  
-##  Notice that when using multiple where clauses like this, it is helpful
-##  to set `interpolate=True` in the `fill_between` dict, so that the space 
-##  between True values and False values also gets filled.
-##  
-##  ''')
-##  
-##  fb_above = dict(y1=daily['Volume'].values, 
-##                  y2=daily['Volume'].mean(), 
-##                  alpha=0.4, color='lime', 
-##                  interpolate=True,
-##                  where=(daily['Volume'] > daily['Volume'].mean()).values)
-##  
-##  fb_below = fb_above.copy()
-##  fb_below['color'] = 'magenta'
-##  fb_below['where'] = (daily['Volume'] < daily['Volume'].mean()).values
-##  
-##  avol = [daily['Volume'].mean()]*len(daily)
-##  ap = mpf.make_addplot(avol,panel=1,fill_between=[fb_above,fb_below],color='k',linestyle='-.',width=0.25)
-##  
-##  mpf.plot(daily,volume=True,panel_ratios=(0.8,1),
-##           type='candle',tight_layout=True,figratio=(1,1),addplot=ap)
-##  
-##  print('''
-##  
-##  Here, as an additional example, we create "multi-color" fill_between for both panels:
-##  
-##  ''')
-##  
-##  fbvolume_above = dict(y1=daily['Volume'].values, 
-##                  y2=daily['Volume'].mean(), 
-##                  alpha=0.4, color='lime', 
-##                  interpolate=True,
-##                  where=(daily['Volume'] > daily['Volume'].mean()).values)
-##  
-##  fbvolume_below = fbvolume_above.copy()
-##  fbvolume_below['color'] = 'magenta'
-##  fbvolume_below['where'] = (daily['Volume'] < daily['Volume'].mean()).values
-##  
-##  avol = [daily['Volume'].mean()]*len(daily)
-##  ap = mpf.make_addplot(avol,panel=1,fill_between=[fbvolume_above,fbvolume_below],color='k',linestyle='-.',width=0.25)
-##  
-##  fbclose_above = dict(y1=daily['Open'].values  , y2=daily['Close'].values , alpha=0.4, 
-##                       interpolate=True,
-##                       color='lime',
-##                       where=(daily['Close']>daily['Open']).values
-##                      )
-##  
-##  fbclose_below = fbclose_above.copy()
-##  fbclose_below['color'] = 'magenta'
-##  fbclose_below['where'] = (daily['Close']<daily['Open']).values
-##  
-##  mpf.plot(daily,volume=True,panel_ratios=(1,1),
-##           type='candle',tight_layout=True,figratio=(1,1),
-##           fill_between=[fbclose_above,fbclose_below],
-##           addplot=ap)
-##  
-##  print('''
-##  
-##  Finally, as a more pratical example, we use `fill_between` to color a MACD plot:
-##  
-##  ''')
-##  
-##  df = pd.read_csv('data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True).loc['2011-07-01':'2011-12-30',:]
-##  
-##  # =======
-##  #  MACD:
-##  
-##  exp12     = df['Close'].ewm(span=12, adjust=False).mean()
-##  exp26     = df['Close'].ewm(span=26, adjust=False).mean()
-##  macd      = exp12 - exp26
-##  signal    = macd.ewm(span=9, adjust=False).mean()
-##  histogram = macd - signal
-##  
-##  fb_green = dict(y1=macd.values,y2=signal.values,where=signal<macd,color="#93c47d",alpha=0.6,interpolate=True)
-##  fb_red   = dict(y1=macd.values,y2=signal.values,where=signal>macd,color="#e06666",alpha=0.6,interpolate=True)
-##  fb_green['panel'] = 1
-##  fb_red['panel']   = 1
-##  fb       = [fb_green,fb_red]
-##  
-##  apds = [mpf.make_addplot(exp12,color='lime'),
-##          mpf.make_addplot(exp26,color='c'),
-##          mpf.make_addplot(histogram,type='bar',width=0.7,panel=1,
-##                           color='dimgray',alpha=1,secondary_y=True),
-##          mpf.make_addplot(macd,panel=1,color='fuchsia',secondary_y=False),
-##          mpf.make_addplot(signal,panel=1,color='b',secondary_y=False)#,fill_between=fb),
-##         ]
-##  
-##  s = mpf.make_mpf_style(base_mpf_style='classic',rc={'figure.facecolor':'lightgray'})
-##  
-##  mpf.plot(df,type='candle',addplot=apds,figscale=1.6,figratio=(6,5),title='\n\nMACD',
-##           style=s,volume=True,volume_panel=2,panel_ratios=(3,4,1),fill_between=fb)
-##  
-##  def test_fill_between02(bolldata):
+def test_fill_between06(bolldata):
+
+    #df = _get_data_subset(bolldata)
+    df = bolldata
+    fname,tname,rname = _get_file_names('06')
+
+    # =======
+    #  MACD:
+    
+    exp12     = df['Close'].ewm(span=12, adjust=False).mean()
+    exp26     = df['Close'].ewm(span=26, adjust=False).mean()
+    macd      = exp12 - exp26
+    signal    = macd.ewm(span=9, adjust=False).mean()
+    histogram = macd - signal
+    
+    fb_12up = dict(y1=exp12.values,y2=exp26.values,where=exp12>exp26,color="#93c47d",alpha=0.6,interpolate=True)
+    fb_12dn = dict(y1=exp12.values,y2=exp26.values,where=exp12<exp26,color="#e06666",alpha=0.6,interpolate=True)
+    fb_exp12 = [fb_12up,fb_12dn]
+    
+    fb_macd_up = dict(y1=macd.values,y2=signal.values,where=signal<macd,color="#93c47d",alpha=0.6,interpolate=True)
+    fb_macd_dn = dict(y1=macd.values,y2=signal.values,where=signal>macd,color="#e06666",alpha=0.6,interpolate=True)
+    #fb_macd_up['panel'] = 1
+    #fb_macd_dn['panel'] = 1
+    
+    fb_macd = [fb_macd_up,fb_macd_dn]
+    
+    apds = [mpf.make_addplot(exp12,color='lime'),
+            mpf.make_addplot(exp26,color='c'),
+            mpf.make_addplot(histogram,type='bar',width=0.7,panel=1,
+                             color='dimgray',alpha=0.65,secondary_y=True),
+            mpf.make_addplot(macd,panel=1,color='fuchsia',secondary_y=False),
+            mpf.make_addplot(signal,panel=1,color='b',secondary_y=False,fill_between=fb_macd),
+           ]
+    
+    s = mpf.make_mpf_style(base_mpf_style='blueskies',facecolor='aliceblue')#,rc={'figure.facecolor':'lightcyan'})
+    
+    mpf.plot(df,type='candle',addplot=apds,figscale=1.6,figratio=(1,1),title='\n\nMACD',
+             style=s,volume=True,volume_panel=2,panel_ratios=(3,4,1),tight_layout=True,
+             fill_between=fb_exp12,
+             savefig=tname)
+    
+    _report_file_sizes(tname,rname)
+
+    result = compare_images(rname,tname,tol=IMGCOMP_TOLERANCE)
+    if result is not None:
+       print('result=',result)
+    assert result is None