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Using PlanB's model on blockstream.info data
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README.md

Bitcoin Stock to Flow

Medium blogger PlanB created a model which got some international traction. The model shows that the Bitcoin price seems to follow a power law, based on its scarcity which can be expressed as the ratio between its stock and flow.

I collect data on a granularity of 1 day from coinmetric.io and calculate the ratio based on the real number of bitcoins that were mined for all of Bitcoin's history until the day of data gathering and extrapolate it into the future based on mining 144 blocks per day. The actual data is kept in a SQLite database which is added to as needed. The data is used by a Python script to calculate the R squared value and the root mean sqared error of the logarithm of the stock to flow ratio and the logarithm of the bitcoin price in US dollars. The data points that are included in the analysis are all dates, prices and total number of bitcoins where the price is non-zero. The flow is calculated as the added number of coins that lead to the total at a specific time during 365 days.

The SQLite database is pretty simple and has the following schema:

CREATE TABLE btc ( date int PRIMARY KEY, price float, coins float );

The date is the integer of the epoch time divided by 86400. This is to enforce a daily granularity and prevend introduction of spurious data.

The script generates 4 files:

  • gpvars.txt: this file cointains a number of values that are read in the gnuplot script like the R2 and RMSE values, the slope and intercept of the regression line etc.
  • sftime.csv: a comma seperated file with the data points for the two timeline charts
  • sfdata.csv: a comma seperated file to generate the points and regression line of the s2f and price logarithm values
  • bashvars.sh: a few prices of the latest data point with which the HTML file containing the graphs can be adjusted.

These scripts are used daily to generate three charts that are presented on this web page.

Tech details

The python script is using python 3.6 and the following extra modules:

  • numpy
  • requests
  • sklearn.linear_model
  • sklearn.metrics

I installed it all in a virtualenv

Gnuplot is version 5.0 (running on Ubuntu Xenial). I tried it with gnuplot 4.6 on EL7 but the results weren't quite stellar...

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