Python Finance Analyzing Bitcoin

Question

Hello, i'm struggling with the last Code Repo task for the Python Finance Course:

Tasks
1. Calculate the volatility of Bitcoin and output the risk %.
2. Calculate and output the Sharpe ratio.

My code is a mess with lot of trial and error:

import numpy as np
import numpy_financial as npf
import matplotlib.pyplot as plt
import yfinance as yf
#Task1: Calculate the Volatility of Bitcoin and output the Risk %
#Task2: Calculate tand output the Sharpe Ratio
#Need to calculate the returns

data = yf.Ticker('BTC-USD')

#Daily return
d = yf.download('BTC-USD', start='2020-01-01')

#Calculate percentage change
pc = d['Close'].pct_change()

x = data.history('1y')['Close']

start = 1000 / x[0]

#Volatility
#To do this use the std.dv of my returns
vol = (np.std(d))

print(vol)

annual_std = np.std(vol) * np.sqrt(252)

print(annual_std)

y = np.multiply(start, x)
plt.plot(y)
plt.savefig('plot.png')

Answer 1

Please put the code in Code Playground and then pass the link.
I know a person who has already passed the course and knows a lot about the subject:
Lobo Solitario

Answer 2

import yfinance as yf
import numpy as np

data = yf.Ticker('BTC-USD')
x = data.history('1y')['Close']

ret = x
risk = np.std(ret) * np.sqrt(252)
sharpe = (np.mean(ret) / np.std(ret))*np.sqrt(252)

print("Risk", risk)
print("Sharpe", sharpe)

Answer 3

I do not know if you are still in need of help but here is one alternative to solving the Question :)

import yfinance as yf
import numpy as np
import matplotlib.pyplot as plt

# Define the ticker symbol
tickerSymbol = 'BTC-USD'

# Get data on this ticker
tickerData = yf.Ticker(tickerSymbol)

# Get the historical prices for this ticker for the last 2 years
tickerDf = tickerData.history(period='2y')

# Use pct_change to calculate daily returns
daily_returns = tickerDf['Close'].pct_change()

# Use cumprod to calculate cumulative returns
cumulative_returns = (1 + daily_returns).cumprod()

# Print cumulative returns
print(cumulative_returns)

# Plot cumulative returns
plt.figure(figsize=(14, 7))
plt.plot(cumulative_returns)
plt.title(f'Cumulative Returns of {tickerSymbol} over the last 2 years')
plt.xlabel('Date')
plt.ylabel('Cumulative Returns')
plt.show()

Answer 4

Pontus Henriksson

I know this question is old, but the code you shared works in IDLE as 2023-07-08.
However, the exercises ask us to
1. Calculate the volatility of Bitcoin and output the risk %.
2. Calculate and output the Sharpe ratio.
From the slides on the course, in order to find the answer we need to get the portfolio_return first with following code

data = yf.Ticker('BTC-USD')
df = data.history(start='2018-01-01') # I don't know why, but I need this statement to make daily_return statement working
daily_return = df['Close'].pct_change()
portfolio_return = daily_return.sum(axis=1)

And it raise an exception with the last statement
raise ValueError(f"No axis named {axis} for object type {cls.__name__}")
ValueError: No axis named 1 for object type Series

If I omit axis=1, then the standard deviation of portfolio_return will become 0, and make calculating annual volatility impossible.
Any idea how to accomplish the exercise as asked?

Answer 5

Great

Answer 6

Bitcoin Price

Time to get some values!

Task
1. Get and output Close prices of Tesla stock for the last 5 days.

Hint
Use history() function on your data object and pass the period parameter with the appropriate value for the specified period.
Don't forget to add the ['Close'] filter at the end.