[FIXED] How to adjust the size of a Japanese candlestick chart made with MatPlotLibFinance so that all moving averages get properly-displayed on Python3?

Issue

Introduction

I recently learned how to plot Simple Moving Averages (abbreviated "SMA") using the MatPlotLibFinance library on Python3. Simple Moving Averages are trend lines that They help the investor in determining the best times to enter to buy or sell an asset.

Data

The following variables contain the data used in the Script to plot the price action, the first variable was named df_trading_pair and contains the following information:

Index	Start Date	Open Price	High Price	Low Price	Close Price	Volume	End Date	Abs((CP-OP)/CP)*100	Low SMA 9	Close SMA 25	High SMA 99
0	2022-10-23 23:42:00	29.24	29.28	29.24	29.25	2145.0	2022-10-23 23:44:59.999	0.03	29.195555555555554	29.236400000000003	28.95191919191919
1	2022-10-23 23:45:00	29.25	29.27	29.24	29.24	2233.0	2022-10-23 23:47:59.999	0.03	29.192222222222224	29.239199999999997	28.95848484848485
2	2022-10-23 23:48:00	29.24	29.24	29.23	29.23	1399.0	2022-10-23 23:50:59.999	0.03	29.193333333333335	29.2316	28.96454545454545
3	2022-10-23 23:51:00	29.23	29.24	29.21	29.21	2603.0	2022-10-23 23:53:59.999	0.07	29.19888888888889	29.2284	28.97060606060606
4	2022-10-23 23:54:00	29.22	29.3	29.22	29.25	5576.0	2022-10-23 23:56:59.999	0.1	29.209999999999997	29.228	28.977575757575757
5	2022-10-23 23:57:00	29.24	29.28	29.23	29.26	3848.0	2022-10-23 23:59:59.999	0.07	29.221111111111114	29.226799999999997	28.983636363636364
6	2022-10-24 00:00:00	29.26	29.34	29.25	29.27	9973.0	2022-10-24 00:02:59.999	0.03	29.22666666666667	29.2288	28.990202020202016
7	2022-10-24 00:03:00	29.28	29.36	29.26	29.34	11754.0	2022-10-24 00:05:59.999	0.2	29.234444444444446	29.233600000000003	28.996969696969696
8	2022-10-24 00:06:00	29.34	29.44	29.33	29.41	28414.0	2022-10-24 00:08:59.999	0.24	29.245555555555555	29.24	29.003939393939394
9	2022-10-24 00:09:00	29.42	29.48	29.4	29.43	21753.0	2022-10-24 00:11:59.999	0.03	29.263333333333335	29.248800000000003	29.011414141414143
10	2022-10-24 00:12:00	29.43	29.43	29.28	29.28	9341.0	2022-10-24 00:14:59.999	0.51	29.26777777777778	29.2528	29.018787878787876
11	2022-10-24 00:15:00	29.29	29.3	29.25	29.26	3000.0	2022-10-24 00:17:59.999	0.1	29.27	29.2556	29.024040404040406
12	2022-10-24 00:18:00	29.26	29.29	29.25	29.28	3065.0	2022-10-24 00:20:59.999	0.07	29.27444444444445	29.2588	29.029393939393938
13	2022-10-24 00:21:00	29.27	29.29	29.26	29.27	754.0	2022-10-24 00:23:59.999	0.0	29.278888888888886	29.2612	29.034444444444443
14	2022-10-24 00:24:00	29.28	29.33	29.28	29.33	2657.0	2022-10-24 00:26:59.999	0.17	29.284444444444446	29.266	29.039292929292927
15	2022-10-24 00:27:00	29.33	29.39	29.32	29.33	3722.0	2022-10-24 00:29:59.999	0.0	29.29222222222222	29.2676	29.04484848484848
16	2022-10-24 00:30:00	29.34	29.41	29.34	29.4	3906.0	2022-10-24 00:32:59.999	0.2	29.30111111111111	29.2716	29.051010101010103
17	2022-10-24 00:33:00	29.39	29.39	29.34	29.34	3269.0	2022-10-24 00:35:59.999	0.17	29.302222222222227	29.274	29.056767676767677
18	2022-10-24 00:36:00	29.34	29.38	29.26	29.28	5719.0	2022-10-24 00:38:59.999	0.2	29.286666666666665	29.276	29.061818181818182
19	2022-10-24 00:39:00	29.28	29.29	29.23	29.25	2118.0	2022-10-24 00:41:59.999	0.1	29.281111111111116	29.2788	29.066060606060606
20	2022-10-24 00:42:00	29.24	29.24	29.21	29.23	1875.0	2022-10-24 00:44:59.999	0.03	29.276666666666667	29.2832	29.069999999999997
21	2022-10-24 00:45:00	29.23	29.25	29.21	29.24	6155.0	2022-10-24 00:47:59.999	0.03	29.272222222222222	29.284000000000002	29.074242424242424
22	2022-10-24 00:48:00	29.23	29.23	29.18	29.19	1913.0	2022-10-24 00:50:59.999	0.14	29.263333333333335	29.281999999999996	29.077777777777776
23	2022-10-24 00:51:00	29.19	29.2	29.13	29.14	6363.0	2022-10-24 00:53:59.999	0.17	29.246666666666663	29.278	29.081111111111113
24	2022-10-24 00:54:00	29.14	29.17	29.12	29.17	8608.0	2022-10-24 00:56:59.999	0.1	29.224444444444444	29.275199999999998	29.084444444444447
25	2022-10-24 00:57:00	29.17	29.21	29.17	29.19	2111.0	2022-10-24 00:59:59.999	0.07	29.20555555555556	29.272799999999997	29.087979797979795
26	2022-10-24 01:00:00	29.2	29.2	29.16	29.19	2259.0	2022-10-24 01:02:59.999	0.03	29.185555555555556	29.270800000000005	29.091313131313132
27	2022-10-24 01:03:00	29.18	29.21	29.18	29.21	1634.0	2022-10-24 01:05:59.999	0.1	29.176666666666662	29.27	29.094242424242424
28	2022-10-24 01:06:00	29.21	29.23	29.2	29.22	3276.0	2022-10-24 01:08:59.999	0.03	29.173333333333332	29.2704	29.0979797979798
29	2022-10-24 01:09:00	29.21	29.21	29.19	29.2	837.0	2022-10-24 01:11:59.999	0.03	29.171111111111113	29.2684	29.101717171717173

Also, another variable called df_trading_pair_date_time_index contains the same information as the previous variable with slight modifications, since it can only be used in this way in the script below:

def set_DateTimeIndex(df_trading_pair):
    df_trading_pair = df_trading_pair.set_index('Start Date', inplace=False)
    # Rename the column names for best practices
    df_trading_pair.rename(columns = { "Open Price" : 'Open',
                                       "High Price" : 'High',
                                       "Low Price" : 'Low',
                                       "Close Price" :'Close',
                              }, inplace = True)
            
    return df_trading_pair
 # Create another df just to properly plot the data
 df_trading_pair_date_time_index = set_DateTimeIndex(df_trading_pair)

Script

The following script will essentially plot a Japanese candlestick chart using the information stored in the df_trading_pair and df_trading_pair_date_time_index variables, the main details of such procedure are explained as comments within the script:

import pandas as pd
import mplfinance as mpf
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

trading_pair = "SOLBUSD"
# Plotting
# Create my own `marketcolors` style:
mc = mpf.make_marketcolors(up='#2fc71e',down='#ed2f1a',inherit=True)
# Create my own `MatPlotFinance` style:
s  = mpf.make_mpf_style(base_mpl_style=['bmh', 'dark_background'],marketcolors=mc, y_on_right=True)    

# Plot it
# First create a dictionary to store the plots to add
subplots = {'Low SMA 9': mpf.make_addplot(df_trading_pair['Low SMA 9'], width=1, color='#F0FF42'),
            'Close SMA 25': mpf.make_addplot(df_trading_pair['Close SMA 25'], width=1.5, color='#EA047E'),
            'High SMA 99': mpf.make_addplot(df_trading_pair['High SMA 99'], width=2, color='#00FFD1')}

trading_plot, axlist = mpf.plot(df_trading_pair_date_time_index,
                    figratio=(10, 6),
                    type="candle",
                    style=s,
                    tight_layout=True,
                    datetime_format = '%H:%M',
                    ylabel = "Precio ($)",
                    returnfig=True,
                    show_nontrading=True,
                    addplot=list(subplots.values())
                    )

# Add Title
symbol = trading_pair.replace("BUSD","")+"/"+"BUSD"
axlist[0].set_title(f"{symbol} - 3m", fontsize=25, style='italic', fontfamily='fantasy')

# Find which times should be shown every 6 minutes starting at the last row of the df
x_axis_minutes = []
for i in range (1,len(df_trading_pair_date_time_index),2):
    x_axis_minutes.append(df_trading_pair_date_time_index.index[-i].minute)

# Set the main "ticks" to show at the x axis
axlist[0].xaxis.set_major_locator(mdates.MinuteLocator(byminute=x_axis_minutes))

# Set the x axis label
axlist[0].set_xlabel('Zona Horaria UTC')

# Set the SMA legends
# First set the amount of legends to add to the legend box
axlist[0].legend([None]*(len(subplots)+2)) 
# Then Store the legend objects in a variable called "handles", based on this script, your objects to legend will appear from the third element in this list
handles = axlist[0].get_legend().legendHandles
# Finally set the corresponding names for the plotted SMA trends and place the legend box to the upper left corner of the bigger plot
axlist[0].legend(handles=handles[2:],labels=list(subplots.keys()), loc = 'upper left')

Finally, this script will produce the following image:

Problem

When comparing the chart printed by my script against the chart displayed by Binance:

It is evident that the largest moving average (the one of 99 value) was not plotted as such, or it was, but I think because of the size set (figratio=(10, 6)) for the same plot it ended up not appearing.

The Question

How could I make my script do a kind of Zoom out so that when printing the graph it shows the moving average of 99 without affecting the display of the other elements printed in the graph?.

Solution

I ended up finding an alternative which uses the set_ylim() method from MatPlotLib, the secret sauce lies within the following lines:

# Set the y axis range 
ymin_value = df_trading_pair[['Low Price','Low SMA 9','Close SMA 25', 'High SMA 99']].min(axis=1).min()
ymax_value = df_trading_pair[['High Price','Low SMA 9','Close SMA 25', 'High SMA 99']].max(axis=1).max()
axlist[0].set_ylim([ymin_value,ymax_value]) #this solves the issue

By setting a fixed range of values that includes the minimum value from the ['Low Price','Low SMA 9','Close SMA 25', 'High SMA 99'] columns and the maximum value from the ['High Price','Low SMA 9','Close SMA 25', 'High SMA 99'] columns, it assures that this program will properly handle the plotting of the data stored in df_trading_pair and df_trading_pair_date_time_index for any given context (I suppose)

Script

# Plotting
# Create my own `marketcolors` style:
mc = mpf.make_marketcolors(up='#2fc71e',down='#ed2f1a',inherit=True)
# Create my own `MatPlotFinance` style:
s  = mpf.make_mpf_style(base_mpl_style=['bmh', 'dark_background'],marketcolors=mc, y_on_right=True)    

# Plot it
# First create a dictionary to store the plots to add
subplots = {'Low SMA 9': mpf.make_addplot(df_trading_pair['Low SMA 9'], width=1, color='#F0FF42'),
            'Close SMA 25': mpf.make_addplot(df_trading_pair['Close SMA 25'], width=1.5, color='#EA047E'),
            'High SMA 99': mpf.make_addplot(df_trading_pair['High SMA 99'], width=2, color='#00FFD1')}

trading_plot, axlist = mpf.plot(df_trading_pair_date_time_index,
                    figratio=(10, 6),
                    type="candle",
                    style=s,
                    tight_layout=True,
                    datetime_format = '%H:%M',
                    ylabel = "Precio ($)",
                    returnfig=True,
                    show_nontrading=True,
                    addplot=list(subplots.values())
                    )

# Add Title
symbol = trading_pair.replace("BUSD","")+"/"+"BUSD"
axlist[0].set_title(f"{symbol} - 3m", fontsize=25, style='italic', fontfamily='fantasy')

# Find which times should be shown every 6 minutes starting at the last row of the df
x_axis_minutes = []
for i in range (1,len(df_trading_pair_date_time_index),2):
    x_axis_minutes.append(df_trading_pair_date_time_index.index[-i].minute)

# Set the main "ticks" to show at the x axis
axlist[0].xaxis.set_major_locator(mdates.MinuteLocator(byminute=x_axis_minutes))

# Set the x axis label
axlist[0].set_xlabel('Zona Horaria UTC')
# Set the y axis range 
ymin_value = df_trading_pair[['Low Price','Low SMA 9','Close SMA 25', 'High SMA 99']].min(axis=1).min()
ymax_value = df_trading_pair[['High Price','Low SMA 9','Close SMA 25', 'High SMA 99']].max(axis=1).max()
axlist[0].set_ylim([ymin_value,ymax_value])

# Set the SMA legends
# First set the amount of legends to add to the legend box
axlist[0].legend([None]*(len(subplots)+2)) 
# Then Store the legend objects in a variable called "handles", based on this script, your objects to legend will appear from the third element in this list
handles = axlist[0].get_legend().legendHandles
# Finally set the corresponding names for the plotted SMA trends and place the legend box to the upper left corner in the bigger plot
axlist[0].legend(handles=handles[2:],labels=list(subplots.keys()), loc = 'upper left', fontsize = 15)

Output:

Answered By - NoahVerner