import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import investpy
import cryptocompare
from datetime import datetime
# For plotting
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.dates as md
import seaborn as sns
sns.set_theme(style='darkgrid', palette='deep')
df = pd.read_csv('Data.csv')
df
| coin | amount | price_EUR | fees_EUR | date_purchased | |
|---|---|---|---|---|---|
| 0 | BTC | 0.005842 | 72.16 | 2.84 | 21/08/2020 |
| 1 | BTC | 0.008702 | 190.75 | 0.00 | 07/06/2020 |
| 2 | BTC | 0.009843 | 88.16 | 2.84 | 14/04/2020 |
| 3 | BTC | 0.016705 | 221.75 | 0.00 | 03/03/2020 |
| 4 | DNT | 243.160569 | 56.01 | 4.99 | 21/02/2020 |
| 5 | DNT | 500.160569 | 25.01 | 4.99 | 25/02/2020 |
| 6 | ETH | 0.082635 | 72.16 | 9.84 | 21/04/2020 |
| 7 | ETH | 0.226705 | 135.25 | 2.74 | 23/07/2020 |
| 8 | ETH | 0.012076 | 152.97 | 2.26 | 07/08/2020 |
| 9 | LINK | 2.089757 | 68.69 | 1.99 | 21/09/2020 |
| 10 | LINK | 7.052657 | 22.69 | 1.99 | 25/09/2020 |
initialPriceEUR = sum(df['price_EUR'])
totalFees = sum(df['fees_EUR'])
df['coin']
groupedByType = df.groupby('coin')['price_EUR'].sum()
groupedByType
coin BTC 572.82 DNT 81.02 ETH 360.38 LINK 91.38 Name: price_EUR, dtype: float64
plt.pie(groupedByType,labels=('BTC','DNT','ETH','LINK'),autopct='%1.1f%%')
plt.title('Portfolio Holdings by Initial Price Payed in Euro')
plt.axis('equal')
plt.tight_layout()
plt.show()
The following uses the CryptoCompare API to retrieve the current price for each respective Crypto Currency:
dfBTC = cryptocompare.get_price('BTC')['BTC']['EUR']
dfETH = cryptocompare.get_price('ETH')['ETH']['EUR']
dfLINK = cryptocompare.get_price('LINK')['LINK']['EUR']
dfDNT = cryptocompare.get_price('DNT')['DNT']['EUR']
groupedByAmount = df.groupby('coin')['amount'].sum()
print(groupedByAmount)
btc = groupedByAmount[0]*(dfBTC)
dnt = groupedByAmount[1]*(dfDNT)
eth = groupedByAmount[2]*(dfETH)
link = groupedByAmount[3]*(dfLINK)
currentValueNoFees = btc + dnt + eth + link
currentValueFeesDeducted = currentValueNoFees - totalFees
print("Current total value of the portfolio, fees deducted:")
currentValueFeesDeducted
coin BTC 0.041092 DNT 743.321139 ETH 0.321416 LINK 9.142413 Name: amount, dtype: float64 Current total value of the portfolio, fees deducted:
2758.18094926908
print("Percentage difference from initial purchase of the entire portfolio, fees deducted:")
((currentValueFeesDeducted - initialPriceEUR) / initialPriceEUR) * 100.0
Percentage difference from initial purchase of the entire portfolio, fees deducted:
149.4736748615304
Capital gains tax is assumed to be 19 percent.
print("Profit from initial purchase of the entire portfolio, fees and capital gains tax deducted")
(currentValueFeesDeducted - initialPriceEUR) - (0.19 * (currentValueNoFees - initialPriceEUR))
Profit from initial purchase of the entire portfolio, fees and capital gains tax deducted
1332.039368907955
This section retrieves the historical price information of each crypto currency within the portfolio since January 1st 2020, up until the present date that the code is run.
todaysDate = datetime.today().strftime('%d/%m/%Y')
dfBTC_info = investpy.crypto.get_crypto_information(crypto='bitcoin', as_json=False)
dfBTC_hist_info = investpy.get_crypto_historical_data(crypto='bitcoin', from_date='01/01/2020', to_date=todaysDate)
dfETH_info = investpy.crypto.get_crypto_information(crypto='ethereum', as_json=False)
dfETH_hist_info = investpy.get_crypto_historical_data(crypto='ethereum', from_date='01/01/2020', to_date=todaysDate)
dfDNT_info = investpy.crypto.get_crypto_information(crypto='district0x', as_json=False)
dfDNT_hist_info = investpy.get_crypto_historical_data(crypto='district0x', from_date='01/01/2020', to_date=todaysDate)
dfLINK_info = investpy.crypto.get_crypto_information(crypto='chainlink', as_json=False)
dfLINK_hist_info = investpy.get_crypto_historical_data(crypto='chainlink', from_date='01/01/2020', to_date=todaysDate)
# prepare the figure
fig, ax = plt.subplots(figsize = (15, 7))
sns.lineplot(x=dfBTC_hist_info.index, y=dfBTC_hist_info['Close']).set_title('BTC Historical USD Price')
# specify the position of the major ticks at the first day of the month, with a month interval of 1
ax.xaxis.set_major_locator(md.MonthLocator(bymonthday=1, interval=1))
# specify the format of the labels as 'year-month-day'
ax.xaxis.set_major_formatter(md.DateFormatter('%Y-%m-%d'))
# rotate by 90° the labels in order to improve their spacing
plt.setp(ax.xaxis.get_majorticklabels(), rotation = 90)
# set axes labels
plt.xlabel('Date')
plt.ylabel('Close Price, USD')
# show the plot
plt.show()
fig, ax = plt.subplots(figsize = (15, 7))
sns.lineplot(x=dfETH_hist_info.index, y=dfETH_hist_info['Close']).set_title('ETH Historical USD Price')
ax.xaxis.set_major_locator(md.MonthLocator(bymonthday=1, interval=1))
ax.xaxis.set_major_formatter(md.DateFormatter('%Y-%m-%d'))
plt.setp(ax.xaxis.get_majorticklabels(), rotation = 90)
plt.xlabel('Date')
plt.ylabel('Close Price, USD')
plt.show()
fig, ax = plt.subplots(figsize = (15, 7))
sns.lineplot(x=dfDNT_hist_info.index, y=dfDNT_hist_info['Close']).set_title('DNT Historical USD Price')
ax.xaxis.set_major_locator(md.MonthLocator(bymonthday=1, interval=1))
ax.xaxis.set_major_formatter(md.DateFormatter('%Y-%m-%d'))
plt.setp(ax.xaxis.get_majorticklabels(), rotation = 90)
plt.xlabel('Date')
plt.ylabel('Close Price, USD')
plt.show()
fig, ax = plt.subplots(figsize = (15, 7))
sns.lineplot(x=dfLINK_hist_info.index, y=dfLINK_hist_info['Close']).set_title('LINK Historical USD Price')
ax.xaxis.set_major_locator(md.MonthLocator(bymonthday=1, interval=1))
ax.xaxis.set_major_formatter(md.DateFormatter('%Y-%m-%d'))
plt.setp(ax.xaxis.get_majorticklabels(), rotation = 90)
plt.xlabel('Date')
plt.ylabel('Close Price, USD')
plt.show()