Borrowing few funds from taxes to yield some revenue

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import yfinance as yf
import mplfinance as mpf
from scipy import stats
import seaborn as sns

# get data from yahoo finance
df = yf.download(
    'VWRL.AS', # Vandguard global ETF
    start='2012-06-01', 
    end='2021-06-15', 
    progress=False,
    interval='1mo'
)
# calc "revenue" between months. not sure why close and next open are not equal,
# unless close is from the same day (1st of the month, quite unlikely though) 
df['rev'] = df.Open.shift(-1) / df.Open
df.dropna(inplace=True)
df.tail() # why is High and Low data missing from the first rows?

# "succinct" viz. about how it is performing
f, ax = plt.subplots(ncols=2, nrows=2,figsize=(15,15))

# 1st fig, historical price # candles broken due to data integrity
mpf.plot(df, ax=ax[0,0], type='candle', style='yahoo')

# 2nd fig, monthly revenue
df.rev.plot(ax=ax[1,0])
ax[1,0].set_ylabel('fraction revenue')
ax[1,0].axhline(df.rev.mean(), ls='--', c='g', 
                label=f'mean {round(df.rev.mean(),4)}')
ax[1,0].axhline(1, ls=':', c='gray');
ax[1,0].legend()

# 3rd, viz distribution
df.rev.hist(ax=ax[0,1], bins=40)
ax[0,1].axvline(1, c='k', ls=':')

# 4th, month superposition
(df.groupby(df.index.month)['rev'].agg(mean='mean', sem=stats.sem).
plot(ax=ax[1,1],y='mean', yerr='sem', legend=False, marker='o', 
    ls='none', xlabel='month', ylabel='profit (monthly)',
    capsize=4, color='b'))
ax[1,1].axhline(1, ls=':', c='gray')
ax[1,1].scatter(
    df.index.month+np.random.normal(scale=0.1, size=len(df)), 
    df.rev, color=(0,0,0,0), edgecolor='gray' )

# set titles
ax = ax.flatten()
for i, title in enumerate(
    ['time candles', 'revenue distribution', 'frac. rev vs time', 'rev. by month']
    ):
    ax[i].set_title(title)

# one last viz
plt.figure(figsize=(8,7))
sns.heatmap(
    df.pivot_table(index=df.index.year, columns=df.index.month, values="rev")*100-100,
    cmap='coolwarm', center=0, annot=True, fmt='.1f', linewidths=.5, 
    annot_kws={"fontsize":8}, vmin=-15, vmax=15
).set(xlabel='month', ylabel='year');

# simul year 2019, declared on 2020 Jul (1st) & Nov (2nd payment)
ex = df.loc[('2018-12-31'< df.index) & (df.index < '2020-11-01')] 
ex.tail()

# get revenue per each month (accu prod/ cum prod)
revs = ex.rev.values[:12][::-1].cumprod()[::-1] 
# we flip it to cumprod and then flip again so that december's 
# returns are perceived by all of the (100€) investments. 
# November's all but 1, etc.
nyear = (revs * 100).sum() # 100€ each month * each revenue
nyear # 1316 rather than 1200, not bad

# now add further interest till Jul (tax campaign ends at the end of June)
money = nyear * ex.rev.values[12:18].prod() # oh shit dang global pandemics
# not adding more money because they fall in next(current) year taxes
# we pay 60% of the taxes (.6*1200)
money -= (1200 * .6)
# remaining interest [Jul to oct, early nov we need to finish tax payment]
money *= ex.rev.values[18:].prod()
money -= (1200 * .4)
# remove from 19 to 21 taxes in those earnings
money * 0.81 # taxes to capital gains (from 19 to 21%)
# not entirely sure it works the same way with losses

def IRPF_debt_rev(total_money, year, df=df):
    """
    total_money: total IRPF debt, assumes it is distributed uniformly
    year: year to test
    df: dataframe with column called 'rev' with monthly revenue, 
        indexed with datetime index
    """
    ex = df.loc[(f'{year-1}-12-31'< df.index) & (df.index < f'{year+1}-11-01')]
    assert len(ex)==22, f'is df complete? filtered df len must be 22'
    monthly_debt = total_money / 12
    revs = ex.rev.values[:12][::-1].cumprod()[::-1]
    money = (revs * monthly_debt).sum() 
    money *= ex.rev.values[12:18].prod()
    money -= (total_money * .6) # 1st payment
    money *= ex.rev.values[18:].prod()
    money -= (total_money * .4) # 2nd payment
    # remove from 19 to 21 taxes in those earnings
    return money * 0.81

returns = []
for y in range(2013,2020): # test from 2013 to 2020
    returns += [IRPF_debt_rev(1200,y)]
    print(f'In {y} it would have returned {round(returns[-1],2)}€'\
        f' ({round(100*returns[-1]/1200,2)}%)')
    
print(f'\non average it represents {round(np.mean(returns),2)}€/year')
print(f'which is {round(100 * np.mean(returns)/1200,2)}% '\
    'of the "borrowed" money')