index = TI.index
TI.index = range(len(TI))
Turbulence = pd.DataFrame(index = TI.index)
Turbulence['TI'] = np.NaN
for i in xrange(20,len(TI)):
mean = TI.ix[:i-1].mean().values
cov = np.matrix(np.cov(TI.ix[:i-1].T))
rets = TI.ix[i].values
d = rets - mean
Turbulence.ix[i,'TI'] = d.dot(cov.I).dot(d.T)
Turbulence.index = index
Turbulence = Turbulence.dropna()
Turbulence['MA'] = efa(Turbulence.TI,1,30)
Turbulence.MA.ix[:29] = np.NaN
def get_weight(interval, q, method = 'continuous'):
if not q:
return np.NaN
pc = sum(interval <= q)/np.float64(len(interval))
if method == 'continuous':
if pc <= 0.2:
return 1.0
elif pc <= 0.4:
return 0.8
elif pc <= 0.6:
return 0.6
elif pc <= 0.8:
return 0.4
import numpy as np import pandas as pd import scipy as stats import foundation as fd from functions import efa fxData=fd.getFxRates() fxData = fxData[fxData.Currency!='USD'] fxData = fxData.reset_index() fxData = fxData[['Date','Currency', 'SPOT']].set_index(['Currency','Date']) fxData = fxData.groupby(level = 0).apply(lambda x: x.fillna(method = 'ffill',limit = 30 )) emafxData = fxData.groupby(level = 0).SPOT.apply(lambda x: efa(x,0.97,300)) fxData = fxData.groupby(level = 0).SPOT.values signal = fxData - emafxData print signal
