Data Mining to Find the Drivers Behind Bitcoin Price

Bitcoin is an open-source, digital currency that has caught the public imagination for reasons good and bad in the last couple of years. Bitcoins are created as a reward for the computationally intensive work of verifying and recording payments in a public ledger.

This work is called mining and the more computing power that is put in, the more bitcoins that are created in return. This process will continue until 21 million bitcoins have been produced at a rate that will take the process well into the next century.

Although entirely digital, bitcoins are designed to function like a conventional currency. They can be used to purchase goods and can also be exchanged for conventional currencies such as U.S. dollars or Chinese renminbi.

But one curious feature of this market is that the price of bitcoins has soared from about $5 each in 2011 to about $600 each today. At one point late last year, a single bitcoin was worth more than $1000. Buying and holding bitcoins could therefore have realized a profit of more than 9,000 percent in less than a year.

It’s easy to imagine that this kind of price rise is simply the result of pure speculation. But perhaps there are other more traditional economic forces at work, such as demand and supply and so on.

So exactly what forces have determined the price of bitcoins? Today we get an answer thanks to the work of Ladislav Kristoufek at the Charles University in Prague, Czech Republic, who has studied the link between bitcoin prices and various other financial yardsticks. He says the data clearly reveals the factors that have influenced the price of bitcoins and those that haven’t.

link.

Data Mining Mt GOX Bitcoin Transactions

When hackers posted 750 megabytes of data pilfered from the bankrupt bitcoin exchange Mt. Gox, many people seized on it as a kind of treasure map, hoping it would help locate the nearly half a billion dollars in digital currency that went missing from the exchange.

But not Kai Chang and Mary Becica.

Chang is a design technologist at San Francisco visualization studio Stamen Design, and Becica is a product manager at cloud management outfit AppDirect, also in San Francisco. Both love to geek out on data. When the Gox data was released into the wild, they weren’t interested in finding the allegedly stolen bitcoins. They went looking for patterns in the way the digital currency was flowing across the net. The result is a set of some 500 visualizations that show the activity of the 500 Gox accounts that traded the most bitcoin, a graphical representation of the exchange that shows how it progressed from a casual trading table for a small group of cryptocurrency enthusiasts to a hyper-speed market dominated by fairly sophisticated traders. “As you get later in the data,” Chang says, you see the development of “more consistent techniques.”

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Data Mining 22 Months of Kepler Data Produces 472 New Potential Exoplanet Candidates

Planetary Candidates Observed by Kepler IV: Planet Sample From Q1-Q8 (22 Months)

Authors:

Burke et al

Abstract:

We provide updates to the Kepler planet candidate sample based upon nearly two years of high-precision photometry (i.e., Q1-Q8). From an initial list of nearly 13,400 Threshold Crossing Events (TCEs), 480 new host stars are identified from their flux time series as consistent with hosting transiting planets. Potential transit signals are subjected to further analysis using the pixel-level data, which allows background eclipsing binaries to be identified through small image position shifts during transit. We also re-evaluate Kepler Objects of Interest (KOI) 1-1609, which were identified early in the mission, using substantially more data to test for background false positives and to find additional multiple systems. Combining the new and previous KOI samples, we provide updated parameters for 2,738 Kepler planet candidates distributed across 2,017 host stars. From the combined Kepler planet candidates, 472 are new from the Q1-Q8 data examined in this study. The new Kepler planet candidates represent ~40% of the sample with Rp~1 Rearth and represent ~40% of the low equilibrium temperature (Teq less than 300 K) sample. We review the known biases in the current sample of Kepler planet candidates relevant to evaluating planet population statistics with the current Kepler planet candidate sample.