A long ago I want to experiment and deal with a question of how Google trends can be implemented to analyze the financial markets. Here the opportunity has just turned up.
The main idea consists in the following: As the price is functioning of supply and demand, increase in demand for an asset will cause the growth of the price. The decrease in demand, or increase in the supply, respectively attracts reduction of the price. The assumption which I will check in at research is that the statistics of search queries on “hot trends” can correlate and advance price dynamics of a relevant “hot” asset.
In the beginning, I have decided to observe BITCOIN cryptocurrency. Today he more than “hot” because of the media interest and big volatility in a price.
We will need the following tools:
1. Data from the Google trend.
Google trends tools – the excellent example of a BIG DATA implementation. It shows dynamics of the popularity of a particular search query in time. Also, service provides tools for the analysis changes in inquiry and allows to compare keywords among themselves.
2. Historical quotes of BTC/USD
For that end, it can be used the quotas export from the MT4 terminal which is provided by the BTCe exchange.
After export of all data and formatting, I applied both lines in one chart. Here is the following graph:
The blue line is BTC/USD price, orange – the frequency of “bitcoin” query in Google search engine.
Already at this stage it evident that there is the correlation between both lines and dependence can be calculated. Here are the outputs:
We see that the correlation is 80.74%. It is evident that there are connections.
Unfortunately, Google doesn’t provide detailed statistics for the entire period, so it can’t be calculated more even. With this data, there is a high error of approximation. Most precisely the dependence is reflected by exponential and polynomial regression:
Unfortunately, no conclusions at this stage can be drawn. While the data volume is extremely deficient, it can’t be the forecasting tool. Of Course, I need to consider the broader array of factors. For example, the decision of the Central Bank of Japan about legalization cryptocurrency increased interest to Bitcoin, which affect the price of BTCUSD.
At the following stage, I will try to find the big database which can be applied to the analysis effectively. I’ll see if they’re available open sources solution, such as Quandl, for example.
