Daily average return - 0.2406%

Implied average annualized return - 140.4271%

Standard deviation (volatility measure) - 3.9660

Graph below shows the daily return % on the x-axis plotted against the number of its occurrences on the y-axis $BTC

^{}

Ultimately we can apply the Shapiro(

@benshapiro

?)-Wilk test to see if the data is normally distributed or not

If p<=0.05 then $BTC does not follow a normal distribution

Computing this gives p = 4.2434e-32

^{}

#Bitcoin does not follow a normal distribution, also yielding high risk to investors/hodlers - but who didn't know that

Now we've just quantified it

^{}

It will also be helpful to do this 'longer' part now as it will be useful to have it moving onwards

^{}

Pending for action :)

^{}

Other part is like: Vlad, you have six more hours in here, use it productively and don't be a lil' bitch

^{}

Most of these have different dates on which the data is presented

Need to sort it by time first, I think I'll go from 1st Jan 2018 to 31st Dec 2018

^{}

In the end managed to get matching data for about 3/4 a year ranging from

09/11/2017 to 11/06/2018

^{}

Gives a portfolio performance of 14.7% at the end of the sample after being up 234.0% on the 13-01-18 and starting underwater around -21.7% in Nov. 2017

^{}

Arguably the most interesting graph shared in this thread

Strong correlation on OMG & ETH, DASH & XMR as well as LTC & ETH

No negative or 0 correlation to be seen: if you invest in #crypto we all sink or rise

^{}

Using the formula below and taking into account the 213 days of the dataset, the portfolio volatility is a monster 97.55% assuming an equally distributed portfolio

^{}

Utter bs with little practical application and people still making a living off it smh

So we're going straight to Lesson 4 looking at $BTC Value at Risk :)

^{}

We're gonna start by looking at #Bitcoin Historical drawdown from Nov 2014

First we need to have the cumulative returns of the asset which closely resembles the price chart

^{}

Now we need to find our max & min $BTC price in the data

I've used both the min cumulative return and min value on the Low price column to find that min price is 152.4$

^{}

I should rather find the value between the beginning of the dataset to the max of the data and find its return

^{}

Max cum_return - 50.9933

Starting price - 376.42$ (Close on 29-11-2014)

Max price - 19187$ (Close on 16-12-2017)

Using these number we get 50.9723 which is not == but I'm happy that our cum_sum function is correct

^{}

-83.43%

Still better off than 'investing' most altcoins tbh

^{}

-6.31% (red line)

Historical Expected Shortfall - the expected loss of the worst case scenarios of returns, in this case, 5%

-9.40% (blue line)

^{}

^{}

^{}

Here are my first 4 random walks, depicting 100 runs for each random walk.

This one is for you moon boys :)

What I did not do here is to aggregate the data to actually look at min and max Bitcoin price in these

^{}

More precisely:

Mean - 33776$

Max - 2.068e+07$

Min - 10.77$

This is just for 10000 samples, it could be millions/billions

^{}

Analysing until 500000$ we see that we can zoom in further

This run has a mean of 14000$ which matches closer to our reality #Bitcoin

^{}

This thread is mostly for documentation purposes as some of the skillset learned/improved in this course will certainly be helpful in the future

Thank you @DataCamp :)

^{}