saving a few examples and experiments into the repository

examples/mpf_demo_autoclose.py

@@ -0,0 +1,21 @@
+import pandas as pd
+import mplfinance as mpf
+
+import time
+import matplotlib.pyplot as plt
+
+infile = 'data/yahoofinance-SPY-20200901-20210113.csv'
+
+df = pd.read_csv(infile, index_col=0, parse_dates=True)
+
+# print('len(df)=',len(df))
+
+
+for jj in (0,1,2):
+    start = jj*35
+    stop  = start + 35
+    tdf = df.iloc[start:stop]
+    fig,_ = mpf.plot(tdf,type='candle',volume=True,mav=(10,20),figscale=1.5,returnfig=True)
+    plt.pause(4)
+    plt.close(fig)
+    del fig

examples/mpf_demo_axlabelsize.py

@@ -0,0 +1,28 @@
+import pandas as pd
+import mplfinance as mpf
+
+infile = 'data/yahoofinance-SPY-20200901-20210113.csv'
+
+df = pd.read_csv(infile, index_col=0, parse_dates=True).iloc[0:60]
+
+# mpf.plot(df,figscale=1.5,type='candle',mav=(10,20))
+
+
+#mpf.plot(df,type='candle',figscale=1.5)
+
+#df = pd.read_csv(infile, index_col=0, parse_dates=True).iloc[0:180]
+#mpf.plot(df,type='renko',figscale=1.5)
+#mpf.plot(df,type='pnf',figscale=1.5)
+
+#mpf.plot(df,type='candle',figscale=1.5,mav=10) 
+
+mystyle=mpf.make_mpf_style(base_mpf_style='yahoo',rc={'axes.labelsize':'small'})
+mpf.plot(df,type='candle',volume=True,mav=(10,20),figscale=1.5,style=mystyle)
+
+mystyle=mpf.make_mpf_style(base_mpf_style='yahoo',rc={'axes.labelsize':'medium'})
+mpf.plot(df,type='candle',volume=True,mav=(10,20),figscale=1.5,style=mystyle)
+
+mystyle=mpf.make_mpf_style(base_mpf_style='yahoo',rc={'axes.labelsize':18})
+mpf.plot(df,type='candle',volume=True,mav=(10,20),figscale=1.5,style=mystyle)
+
+

examples/scratch_pad/mpl.zorderbar.py

@@ -0,0 +1,28 @@
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+
+fig.patch.set_alpha(0.0)
+
+ax0 = fig.add_axes( [0.1,0.1,0.8,0.8] )
+ax1 = fig.add_axes( [0.1,0.1,0.8,0.8] )
+
+print('ax0 zorder=',ax0.get_zorder())
+print('ax1 zorder=',ax1.get_zorder())
+
+ax0.set_zorder(0.1)
+
+X  = [0,1,2,3,4]
+
+Y0 = [2,4,3,5,3.5]
+Y1 = [4600,4400,4800,4800,5000]
+
+
+ax0.plot(X, Y0, linewidth=5, color='lime')
+ax1.plot(X, Y1, linewidth=5, color='magenta')
+
+# ax1.set_zorder(1)
+# ax0.set_zorder(2)
+# ax0.patch.set_visible(False)
+
+plt.show()

examples/scratch_pad/multicursor_macd_ginput_save.py

@@ -0,0 +1,98 @@
+import pandas as pd
+import mplfinance as mpf
+import dill
+import os
+from matplotlib.widgets import MultiCursor
+
+# read the data:
+idf = pd.read_csv('../data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True)
+df  = idf.loc['2011-07-01':'2011-12-30',:]
+
+
+# macd related calculations:
+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
+
+# initial plot:
+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=False),
+        mpf.make_addplot(macd,panel=1,color='fuchsia',secondary_y=True),
+        mpf.make_addplot(signal,panel=1,color='b',secondary_y=True),
+       ]
+
+# For some reason, which i have yet to determine, MultiCursor somehow
+# causes ymin to be set to zero for the main candlestick Axes, but we
+# can correct that problem by passing in specific values:
+ymin = min(df['Low'])  * 0.98
+ymax = max(df['High']) * 1.02
+
+# initial plot with cursor:
+if os.path.exists('lines.dill'):
+    alines = dill.load(open('lines.dill','rb'))
+    fig, axlist = mpf.plot(df,type='candle',addplot=apds,figscale=1.25,figratio=(8,6),title='\nMACD', ylim=(ymin,ymax),
+                           alines=dict(alines=alines,colors='r'),
+                           style='blueskies',volume=True,volume_panel=2,panel_ratios=(6,3,2),returnfig=True)
+else:
+    alines = []
+    fig, axlist = mpf.plot(df,type='candle',addplot=apds,figscale=1.25,figratio=(8,6),title='\nMACD', ylim=(ymin,ymax),
+                           alines=dict(alines=alines,colors='r'),
+                           style='blueskies',volume=True,volume_panel=2,panel_ratios=(6,3,2),returnfig=True)
+multi = MultiCursor(fig.canvas, axlist[0:2], horizOn=True, vertOn=True, color='pink', lw=1.2)
+
+fig.canvas.draw_idle()
+
+# ---------------------------------------------------
+# set up an event loop where we wait for two
+# mouse clicks, and then draw a line in between them,
+# and then wait again for another two mouse clicks.
+
+# This is a crude way to do it, but its quick and easy.
+# Disadvantage is: user has 8 seconds to provide two clicks
+# or the first click will be erased.  But the 8 seconds
+# repeats as long as the user does not close the Figure,
+# so user can draw as many trend lines as they want.
+# The advantage of doing it this way is we don't have
+# to write all the mouse click handling stuff that's
+# already written in `Figure.ginput()`.
+
+
+not_closed = True
+def on_close(event):
+    global not_closed
+    global alines
+    dill.dump(alines, open('lines.dill','wb'))   
+    not_closed = False
+
+fig.canvas.mpl_connect('close_event', on_close)
+
+while not_closed:
+
+    vertices = fig.ginput(n=2,timeout=8)
+    if len(vertices) < 2:
+        continue
+    p1 = vertices[0]
+    p2 = vertices[1]
+
+    d1 = df.index[ round(p1[0]) ]
+    d2 = df.index[ round(p2[0]) ]
+
+    alines.append( [ (d1,p1[1]), (d2,p2[1]) ] )
+
+    apds = [mpf.make_addplot(exp12,color='lime',ax=axlist[0]),
+            mpf.make_addplot(exp26,color='c',ax=axlist[0]),
+            mpf.make_addplot(histogram,type='bar',width=0.7,panel=1,ax=axlist[2],color='dimgray',alpha=1),
+            mpf.make_addplot(macd,panel=1,color='fuchsia',ax=axlist[3]),
+            mpf.make_addplot(signal,panel=1,color='b',ax=axlist[3])
+           ]
+
+    mpf.plot(df,ax=axlist[0],type='candle',addplot=apds,ylim=(ymin,ymax),
+             alines=dict(alines=alines,colors='r'),
+             style='blueskies',volume=axlist[4],volume_panel=2,panel_ratios=(6,3,2))
+
+    fig.canvas.draw_idle()
+

examples/scratch_pad/simple_matplotlib.py

@@ -0,0 +1,10 @@
+import matplotlib.pyplot as plt
+import random
+
+fig = plt.figure(figsize=(6,6))
+ax = fig.add_axes([0.1,0.1,0.8,0.8])
+x = [x for x in range(0,50)]
+y = [random.randint(10,30) for y in range(0,50)]
+ax.bar(x,y)
+
+plt.show()

examples/scratch_pad/time_to_plot_line_vs_bar.py

@@ -0,0 +1,24 @@
+import matplotlib.pyplot as plt
+import timeit
+import random
+
+def pbar():
+    fig = plt.figure(figsize=(5,2))
+    ax = fig.add_axes([0.1,0.1,0.8,0.8])
+    x = [x for x in range(0,150)]
+    y = [random.randint(10,30) for y in range(0,150)]
+    ax.bar(x,y)
+
+def pline():
+    fig = plt.figure(figsize=(5,2))
+    ax = fig.add_axes([0.1,0.1,0.8,0.8])
+    x = [x for x in range(0,150)]
+    y = [random.randint(10,30) for y in range(0,150)]
+    ax.plot(x,y)
+
+timeline = timeit.timeit(pline,number=5)
+timebar  = timeit.timeit(pbar,number=5)
+print('timeline=',timeline)
+print('timebar =',timebar)
+print('\ntimebar/timeline=',timebar/timeline)
+