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April 20, 2017 07:20
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Python implementation of smoothed z-score algorithm from http://stackoverflow.com/a/22640362/6029703
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#!/usr/bin/env python | |
# Implementation of algorithm from http://stackoverflow.com/a/22640362/6029703 | |
import numpy as np | |
import pylab | |
def thresholding_algo(y, lag, threshold, influence): | |
signals = np.zeros(len(y)) | |
filteredY = np.array(y) | |
avgFilter = [0]*len(y) | |
stdFilter = [0]*len(y) | |
avgFilter[lag - 1] = np.mean(y[0:lag]) | |
stdFilter[lag - 1] = np.std(y[0:lag]) | |
for i in range(lag, len(y) - 1): | |
if abs(y[i] - avgFilter[i-1]) > threshold * stdFilter [i-1]: | |
if y[i] > avgFilter[i-1]: | |
signals[i] = 1 | |
else: | |
signals[i] = -1 | |
filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i-1] | |
avgFilter[i] = np.mean(filteredY[(i-lag):i]) | |
stdFilter[i] = np.std(filteredY[(i-lag):i]) | |
else: | |
signals[i] = 0 | |
filteredY[i] = y[i] | |
avgFilter[i] = np.mean(filteredY[(i-lag):i]) | |
stdFilter[i] = np.std(filteredY[(i-lag):i]) | |
return dict(signals = np.asarray(signals), | |
avgFilter = np.asarray(avgFilter), | |
stdFilter = np.asarray(stdFilter)) | |
# Data | |
y = np.array([1,1,1.1,1,0.9,1,1,1.1,1,0.9,1,1.1,1,1,0.9,1,1,1.1,1,1,1,1,1.1,0.9,1,1.1,1,1,0.9, | |
1,1.1,1,1,1.1,1,0.8,0.9,1,1.2,0.9,1,1,1.1,1.2,1,1.5,1,3,2,5,3,2,1,1,1,0.9,1,1,3, | |
2.6,4,3,3.2,2,1,1,0.8,4,4,2,2.5,1,1,1]) | |
# Settings: lag = 30, threshold = 5, influence = 0 | |
lag = 30 | |
threshold = 5 | |
influence = 0 | |
# Run algo with settings from above | |
result = thresholding_algo(y, lag=lag, threshold=threshold, influence=influence) | |
# Plot result | |
pylab.subplot(211) | |
pylab.plot(np.arange(1, len(y)+1), y) | |
pylab.plot(np.arange(1, len(y)+1), | |
result["avgFilter"], color="cyan", lw=2) | |
pylab.plot(np.arange(1, len(y)+1), | |
result["avgFilter"] + threshold * result["stdFilter"], color="green", lw=2) | |
pylab.plot(np.arange(1, len(y)+1), | |
result["avgFilter"] - threshold * result["stdFilter"], color="green", lw=2) | |
pylab.subplot(212) | |
pylab.step(np.arange(1, len(y)+1), result["signals"], color="red", lw=2) | |
pylab.ylim(-1.5, 1.5) | |
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Very nice - Im trying to implement it with a Dataframe containing a pandas Series of Close prices.
df.Close.to_numpy() --> seems to work fine with thresholding_algo
but It seems it have issue with the plotting part, any tips on how to that part working ?