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Gain insight into trading cryptocurrency markets and investing in Bitcoin with a simple cryptocurrency trading method by Vlad The Crypto Trader.
7 Jan 20
Everyone knows about the importance of volume but how can we detect anomalous volume on the charts?

The goal of this thread is to discover just that
This idea came about after coming across the following chart

You can see clear signs of accumulation in the form of those high volume spikes coming into 2020 but how can we automate such process?

Is anomalous volume for 1D enough? Or do we need to aggregate into 3D? Maybe 1W? Image
First we need to get the data from the exchange, in this case #Binance

Having a quick glance on data retrieval from Binance using Python, it took me about 1h to get the volume data from this pair

Here it is

Hmm so spiky and unclear, let's see if we can make sense of it Image
Read 22 tweets
14 Dec 19
While @ work

Decided to take a Intro to Portfolio Risk Management in Python @DataCamp

Gonna apply the lessons to #Bitcoin price from Nov 2014 onwards /thread
@DataCamp General Bitcoin data so far:

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
@DataCamp Does #Bitcoin follow a normal distribution?

For that we will need to measure the tilt of the distribution (Skewness) and the probability of outliers (Kurtosis)

Skewness - 0.2085

Kurtosis - 6.5293

This suggests that the is a greater probability of outliers & +ve returns
Read 27 tweets