gradient_background

1.py

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
from sklearn.preprocessing import MinMaxScaler

nifty = pd.read_csv('nifty.csv', usecols=['Date', 'Close'], parse_dates=True)

#nifty =  Nifty data from Jan 1, 2020 to 18 Sep, 2020
nifty = nifty.loc[(nifty['Date'] >= "2020-01-01") & (nifty['Date'] <= "2020-09-18")].copy()

'''
slump = Nifty data from 19 Feb, 2020 to 18 May, 2020.
This is the period where Nifty slumped due to Covid-19.
'''
slump = nifty.loc[(nifty['Date'] >= "2020-02-19") & (nifty['Date'] <= "2020-05-18")].copy()


'''
recovery = Nifty data from 18 May, 2020 till the end i.e. 18 Sep, 2020.
This is the period where Nifty showed a decisive recovery from to Covid-19.
'''
recovery = nifty.loc[(nifty['Date'] > "2020-05-18")].copy()

2.py

fig, ax = plt.subplots(1, 1, figsize=(14,6), facecolor='white')

# setting the axes color to light gray
ax.margins(x=0)
ax.spines['bottom'].set_color('lightgray')
ax.spines['top'].set_color('lightgray')
ax.spines['left'].set_color('lightgray')
ax.spines['right'].set_color('lightgray')
ax.tick_params(axis='both', colors='lightgray')


ax.plot(nifty['Date'], nifty['Close'], color='black') # plotting nifty
ax.plot(slump['Date'], slump['Close'], color='red') # plotting slump
ax.plot(recovery['Date'], recovery['Close'], color='limegreen') # plotting recovery

# selecting every 5th date to display x ticks.
x_ticks = [*nifty.Date.reset_index(drop=True)[np.arange(0, len(nifty), 5)]]
# selecting every 400th value of close to display y ticks.
y_ticks = np.arange(nifty.Close.min(), nifty.Close.max()+400, 400)
plt.xticks(x_ticks, rotation=90, color='gray')
plt.yticks(y_ticks, color='gray')

# date when slump started (2020-02-19)
slump_start_date = [*nifty['Date']][35]
# value as on slump_start_date (12,125.9)
slump_start_value = np.round(float(nifty.loc[nifty['Date'] == slump_start_date, 'Close']), 2)


plt.annotate(text=f"Dip due to Covid on\n{slump_start_date} at\n{slump_start_value:,}", xy=[38,12000],color='black',fontsize=11)

# date when Nifty began recovering (2020-05-18)
recovery_start_date = [*nifty['Date']][91]
# value as on recovery_start_date (8,823.25)
recovery_start_value = np.round(float(nifty.loc[nifty['Date'] == recovery_start_date, 'Close']), 2)
# % drop from top (12,125.9) to recovery_start_value (8,823.25)
recovery_pct_change_from_top = np.abs(np.round(((recovery_start_value / slump_start_value) - 1) * 100, 2))


plt.annotate(text=f"Began recovering from Covid on\n{recovery_start_date} at "+
                  f"{recovery_start_value:,}\nstill down {recovery_pct_change_from_top}% from the top",
             xy=[92,8300],color='black', fontsize=11)

# bottom (7,610.25)
bottom = nifty.Close.min()
# % drop from top (12,125.9) to bottom (7,610.25)
bottom_pct_change_from_top = np.abs(np.round(((bottom / slump_start_value) - 1) * 100, 2))


plt.annotate(text=f"Bottom at {bottom:,}\ndown {bottom_pct_change_from_top}%\nfrom the top", xy=[59,7500],color='black',fontsize=11)

# difference in days from (time delta) from slump to recovery
recovery_time = (datetime.strptime(recovery_start_date, '%Y-%m-%d') - datetime.strptime(slump_start_date, '%Y-%m-%d')).days
plt.annotate(text=f"{recovery_time}", xy=[61,11200],color='dimgray',fontsize=35, fontweight='bold')
plt.annotate(text="days of\ncarnage", xy=[59,10600],color='dimgray',fontsize=15, fontweight='bold')


plt.title("NIFTY'S JOURNEY THROUGH COVID-19", fontdict={'fontsize': 20,
                                                        'fontweight': 'bold',
                                                        'color': 'dimgray',
                                                        'verticalalignment': 'top'}, 
                                                loc='left');

3.py

fig,ax = plt.subplots(1,1, figsize=(14,6), facecolor='white')

# setting the axes color to light gray
ax.margins(x=0)
ax.spines['bottom'].set_color('lightgray')
ax.spines['top'].set_color('lightgray')
ax.spines['left'].set_color('lightgray')
ax.spines['right'].set_color('lightgray')
ax.tick_params(axis='both', colors='lightgray')


ax.plot(nifty['Date'], nifty['Close'], color='black') # plotting nifty
ax.plot(slump['Date'], slump['Close'], color='red') # plotting slump
ax.plot(recovery['Date'], recovery['Close'], color='green') # plotting recovery

# selecting every 5th date to display x ticks.
x_ticks = [*nifty.Date.reset_index(drop=True)[np.arange(0, len(nifty), 5)]]
# selecting every 400th value of close to display y ticks.
y_ticks = np.arange(nifty.Close.min(), nifty.Close.max()+400, 400)
plt.xticks(x_ticks, rotation=90, color='gray')
plt.yticks(y_ticks, color='gray')


# date when slump started (2020-02-19)
slump_start_date = [*nifty['Date']][35]
# value as on slump_start_date (12,125.9)
slump_start_value = np.round(float(nifty.loc[nifty['Date'] == slump_start_date, 'Close']), 2)

##################################################
### Code to generate gradient background START ###
##################################################

# Calculating 3-day moving average of the time series during the slump
low_color_range = slump[['Close']].rolling(3).mean().dropna().copy()
# normalizing the moving average values and repeating each value 10 times   
low_gradient = [*np.repeat(MinMaxScaler(feature_range=(0.15, 1)).fit_transform(low_color_range).ravel(), 10)]
# adding the last value 20 times to acommodate the shortfall in the values due to moving average calculation
low_gradient.extend([low_gradient[-1]] * 20)
# plotting vertical lines by varying the alpha according to low_gradient 
for n, i in zip(np.arange(0, len(low_gradient), 1), np.arange(35, 91, 0.1)):
    plt.axvline(x=i, linestyle='-', linewidth=0.3, color='tomato', alpha=np.max(low_gradient) - low_gradient[n])
plt.annotate(text=f"Dip due to Covid on\n{slump_start_date} at\n{slump_start_value:,}", xy=[38,12000],color='black',fontsize=11)


# Calculating 3-day moving average of the time series during the recovery
recovery_color_range = recovery[['Close']].rolling(3).mean().dropna().copy()
# normalizing the moving average values and repeating each value 10 times   
recovery_gradient = [*np.repeat(MinMaxScaler(feature_range=(0.15, 1)).fit_transform(recovery_color_range).ravel(), 10)]
# adding the last value 20 times to acommodate the shortfall in the values due to moving average calculation
recovery_gradient.extend([recovery_gradient[-1]] * 20)
# plotting vertical lines by varying the alpha according to recovery_gradient 
for n, i in zip(np.arange(0, len(recovery_gradient), 1), np.arange(91, len(nifty), 0.1)):
    plt.axvline(x=i, linestyle='-', linewidth=0.3, color='limegreen', alpha=recovery_gradient[n])

##################################################
#### Code to generate gradient background END ####
##################################################

# date when Nifty began recovering (2020-05-18)
recovery_start_date = [*nifty['Date']][91]
# value as on recovery_start_date (8,823.25)
recovery_start_value = np.round(float(nifty.loc[nifty['Date'] == recovery_start_date, 'Close']), 2)
# % drop from top (12,125.9) to recovery_start_value (8,823.25)
recovery_pct_change_from_top = np.abs(np.round(((recovery_start_value / slump_start_value) - 1) * 100, 2))
plt.annotate(text=f"Began recovering from Covid on\n{recovery_start_date} at "+
                  f"{recovery_start_value:,}\nstill down {recovery_pct_change_from_top}% from the top", 
             xy=[92,8300],color='black', fontsize=11)


# bottom (7,610.25)
bottom = nifty.Close.min()
# % drop from top (12,125.9) to bottom (7,610.25)
bottom_pct_change_from_top = np.abs(np.round(((bottom / slump_start_value) - 1) * 100, 2))
plt.annotate(text=f"Bottom at {bottom:,}\ndown {bottom_pct_change_from_top}%\nfrom the top", xy=[59,7500],color='black',fontsize=11)

# difference in days from (time delta) from slump to recovery
recovery_time = (datetime.strptime(recovery_start_date, '%Y-%m-%d') - datetime.strptime(slump_start_date, '%Y-%m-%d')).days
plt.annotate(text=f"{recovery_time}", xy=[61,11200],color='dimgray',fontsize=35, fontweight='bold')
plt.annotate(text="days of\ncarnage", xy=[59,10600],color='dimgray',fontsize=15, fontweight='bold')


plt.title("NIFTY'S JOURNEY THROUGH COVID-19", fontdict={'fontsize': 20,
                                                        'fontweight': 'bold',
                                                        'color': 'dimgray',
                                                        'verticalalignment': 'top'}, 
                                              loc='left');