Assumptions and design choices

One of the assumptions of this project is as follows: whether we are thinking about Bitcoin trading in November 2016 with a price of about $700, or trading in November 2017 with a price in the $6500-7000 range, patterns in how people trade are similar. Now, we have several other assumptions, as described in the following points:

• Assumption one: From what has been said previously, we can ignore the actual price and rather look at its change. As a measure of this, we can take the delta between opening and closing prices. If it is positive, it means the price grew during that minute; the price went down if it is negative and stayed the same if delta = 0.
  In the following figure, we can see that Delta was -1.25 for the first minute observed, -12.83 for the second one, and -0.23 for the third one. Sometimes, the open price can differ significantly from the close price of the previous minute (although Delta is negative during all three of the observed minutes, for the third minute the shown price was actually higher than close for a second). But such things are not very common, and usually the open price doesn't change significantly compared to the close price of the previous minute.
• Assumption two: The next need to consider...  is predicting the price change in a black box environment. We do not use other sources of knowledge such as news, Twitter feeds, and others to predict how the market would react to them. This is a more advanced topic. The only data we use is price and volume. For simplicity of the prototype, we can focus on price only and construct time series data.
  Time series prediction is a prediction of a parameter based on the values of this parameter in the past. One of the most common examples is temperature prediction. Although there are many supercomputers using satellite and sensor data to predict the weather, a simple time series analysis can lead to some valuable results. We predict the price at T+60 seconds, for instance, based on the price at T, T-60s, T-120s and so on.
• Assumption three: Not all data in the dataset is valuable. The first 600,000 records are not informative, as price changes are rare and trading volumes are small. This can affect the model we are training and thus make end results worse. That is why the first 600,000 of rows are eliminated from the dataset.
• Assumption four: We need to Label our data so that we can use a supervised ML algorithm. This is the easiest measure, without concerns about transaction fees.