pred = 1200
channel = Channel(cur.loc[:end, 'aud'].values, std_ratio = 2)
wave = Wave(cur.loc[:end, 'aud'].values)
plt.plot(cur.loc[:end, 'aud'].values)
plt.figure()
plt.plot(channel.flattened)
plt.plot(np.zeros(channel.flattened.shape[0]), color='black')
plt.plot(np.zeros(channel.flattened.shape[0]) + channel.channel_deviation * 2, color='black')
plt.plot(np.zeros(channel.flattened.shape[0]) - channel.channel_deviation * 2, color='black')
plt.plot(wave.channel_wave)
plt.figure()
plt.plot(cur.loc[:pred, 'aud'].values)
plt.plot(cur.loc[:end, 'aud'].values)
plt.plot(channel.c1(), color='black')
plt.plot(channel.c3(), color='black')
plt.plot(channel.c5(), color='black')
plt.plot(wave.wave)
###############################################################################
# Better Autocorrelations
###############################################################################
if 0:
def serial_corr(wave, lag=1):
n = wave.shape[0]
y1 = wave[lag:]
y2 = wave[:n-lag]
corr = np.corrcoef(y1, y2, ddof=0)[0, 1]
return corr
fit of wave ?
'''
for i in range(100, 1000, 10):
start = 0
end = i
instrument = 'EUR_USD'
inst = ratios.loc[start:end, instrument].astype(float)
channel = Channel(inst.values)
wave = Wave(inst.values)
plt.figure()
plt.plot(channel.flattened)
plt.plot(channel.c5() - channel.line)
plt.plot(channel.c1() - channel.line)
plt.plot(wave.wave - wave.linregress)
plt.show()
plt.figure()
plt.plot(channel.flattened)
plt.plot(channel.c5() - channel.line)
plt.plot(channel.c1() - channel.line)
plt.plot(wave.wave - wave.linregress, 'o')
plt.show()
print(end)
print('FIT: {}'.format(waves_fit.loc[end].values))
print('FREQ: {}'.format(waves_freq.loc[end].values))