# Where will Bitcoin transaction volume be in June 2016

Based on this, using curve fitting and extrapolation, I get 184,000 transactions per day:

with this Python 2.7 code:

```import datetime, time
def strdate(x): return datetime.datetime.strptime(x[0:10], '%d/%m/%Y')
def dateflt(x): return time.mktime(x.timetuple())/(24*60*60)
d2=[(dateflt(strdate(a)),float(b)) for [a,b] in d1]
startdate=d2[0][0]
(xv,yv)=([x-startdate for (x,y) in d2], [y for (x,y) in d2])
endpoint=dateflt(datetime.datetime(2016,06,07))-startdate
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
from scipy.signal import savgol_filter
dx=xv[1]-xv[0]
npoints=(xv[-1]-xv[0])/dx
xgrid=np.linspace(xv[0],xv[-1],npoints)
f=InterpolatedUnivariateSpline(xv,yv, k=1)
ygrid=f(xgrid)
ysav=savgol_filter(ygrid, 151, 1)
plt.plot(xgrid, ygrid, 'b', xgrid, ysav, 'r')
plt.savefig('bitcoin.png')
f2=InterpolatedUnivariateSpline(xgrid, ysav, k=1)
print f2(endpoint)```

On the other hand if I use a cubic spline in the Savitzky-Golay Filter i.e. say

`ysav=savgol_filter(ygrid, 151, 3)`

I get a different picture, and a result of 329,383 transactions.  So overall I guess it’s a coin toss: