def exp_smoothing(df):
'''
Function to calculate exponential smoothing
Pandas functions are used to calculate moving average and standard deviation
Upper and lower thresholds are calculated by
Upper threshold = moving average + standard deviation
Lower threshold = moving average - standard deviation
'''
#Calculate exp weighted moving average
ExpWeightedMovingAvg = ewma(df, span=N)
ExpWeightedMovingAvg.columns = ['Exp Weighted Moving Avg']
#Calculate exp weighted moving std
ExpWeightedMovingStd = ewmstd(df, span=N)
ExpWeightedMovingStd.columns = ['Std Deviation']
s1 = df['Original data']
s2 = ExpWeightedMovingAvg['Exp Weighted Moving Avg']
s3 = ExpWeightedMovingStd['Std Deviation']
s4 = s2.add(s3, fill_value=0)
s5 = s2.sub(s3, fill_value=0)
df2 = DataFrame(s1, columns=['Original data'])
df2['Exp Weighted Moving Avg'] = s2
df2['Std Deviation'] = s3
df2['Upper Threshold'] = s4
df2['Lower Threshold'] = s5
return df2
def exp_smoothing(df):
'''
Function to calculate exponential smoothing
Pandas functions are used to calculate moving average and standard deviation
Upper and lower thresholds are calculated by
Upper threshold = moving average + standard deviation
Lower threshold = moving average - standard deviation
'''
#Calculate exp weighted moving average
ExpWeightedMovingAvg = ewma(df,span=N)
ExpWeightedMovingAvg.columns=['Exp Weighted Moving Avg']
#Calculate exp weighted moving std
ExpWeightedMovingStd = ewmstd(df,span=N)
ExpWeightedMovingStd.columns=['Std Deviation']
s1=df['Original data']
s2=ExpWeightedMovingAvg['Exp Weighted Moving Avg']
s3=ExpWeightedMovingStd['Std Deviation']
s4=s2.add(s3, fill_value=0)
s5=s2.sub(s3, fill_value=0)
df2=DataFrame(s1,columns=['Original data'])
df2['Exp Weighted Moving Avg']=s2
df2['Std Deviation']=s3
df2['Upper Threshold']=s4
df2['Lower Threshold']=s5
return df2
def stddev_from_moving_average(timeseries):
"""
A timeseries is anomalous if the absolute value of the average of the latest
three datapoint minus the moving average is greater than three standard
deviations of the moving average. This is better for finding anomalies with
respect to the short term trends.
"""
# print('算法计算: stddev_from_moving_average')
series = pandas.Series([x[1] for x in timeseries])
expAverage = moments.ewma(series, com=50)
stdDev = moments.ewmstd(series, com=50)
return abs(series.iget(-1) - expAverage.iget(-1)) > 3 * stdDev.iget(-1)
def test_rolling_std_neg_sqrt(self):
# unit test from Bottleneck
# Test move_nanstd for neg sqrt.
a = np.array([
0.0011448196318903589, 0.00028718669878572767,
0.00028718669878572767, 0.00028718669878572767,
0.00028718669878572767
])
b = mom.rolling_std(a, window=3)
self.assert_(np.isfinite(b[2:]).all())
b = mom.ewmstd(a, span=3)
self.assert_(np.isfinite(b[2:]).all())
def test_rolling_std_neg_sqrt(self):
# unit test from Bottleneck
# Test move_nanstd for neg sqrt.
a = np.array([0.0011448196318903589,
0.00028718669878572767,
0.00028718669878572767,
0.00028718669878572767,
0.00028718669878572767])
b = mom.rolling_std(a, window=3)
self.assertTrue(np.isfinite(b[2:]).all())
b = mom.ewmstd(a, span=3)
self.assertTrue(np.isfinite(b[2:]).all())
def auto_envelope(s, nema=22, nsmooth=100, ndev=2.7):
sema = ema(s.close, nema)
mdiff = s[['high', 'low']].sub(sema, axis=0).abs().max(axis=1)
csize = moments.ewmstd(mdiff, nsmooth) * ndev
return DataFrame(dict(ema=sema, lenv=sema - csize, henv=sema + csize))
def auto_envelope(s, nema=22, nsmooth=100, ndev=2.7):
sema = ema(s.close, nema)
mdiff = s[['high','low']].sub(sema, axis=0).abs().max(axis=1)
csize = moments.ewmstd(mdiff, nsmooth)*ndev
return DataFrame(dict(ema=sema, lenv=sema - csize, henv=sema + csize))
