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:

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?.

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: