Python - Calculate current 1 standard deviation from a linear regression line

Question

I managed to get a linear regression line for time series data, mainly thanks to stackoverflow. Thus, I have the following graphs / lines drawn from python:

I got this regression line with the following code originally importing price / time series data from a csv file:

f4 = open('C:\Users\cost9\OneDrive\Documents\PYTHON\TEST-ASSURANCE FILES\LINEAR REGRESSION MULTI TREND IDENTIFICATION\ES_1H.CSV')    
ES_1H = pd.read_csv(f4)
ES_1H.rename(columns={'Date/Time': 'Date'}, inplace=True)
ES_1H['Date'] = ES_1H['Date'].reset_index()
ES_1H.Date.values.astype('M8[D]')
ES_1H_Last_300_Periods = ES_1H[-300:]
x = ES_1H_Last_300_Periods['Date']
y = ES_1H_Last_300_Periods['Close']
x = sm.add_constant(x)
ES_1H_LR = pd.ols(y = ES_1H_Last_300_Periods['Close'], x = ES_1H_Last_300_Periods['Date'])
plt.scatter(y = ES_1H_LR.y_fitted.values, x = ES_1H_Last_300_Periods['Date'])

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edit: , :

plt.scatter(y = ES_1D_LR.y_fitted.values, x = ES_1D_Last_30_Periods['Date'])
plt.scatter(y = ES_1D_Last_30_Periods.Close, x = ES_1D_Last_30_Periods.Date)
plt.scatter(y = ES_1D_LR.y_fitted.values - np.std(ES_1D_LR.y_fitted.values), x = ES_1D_Last_30_Periods.Date)
plt.scatter(y = ES_1D_LR.y_fitted.values + np.std(ES_1D_LR.y_fitted.values), x = ES_1D_Last_30_Periods.Date)
plt.show()

Answer 1

IIUC :

In [185]: x = np.arange(100)

In [186]: y = x*0.6

In [187]: plt.scatter(x, y, c='b')
Out[187]: <matplotlib.collections.PathCollection at 0xc512390>

In [188]: plt.scatter(x, y - np.std(y), c='y')
Out[188]: <matplotlib.collections.PathCollection at 0xc683940>

In [189]: plt.scatter(x, y + np.std(y), c='y')
Out[189]: <matplotlib.collections.PathCollection at 0xc69a550>

:

Answer 2

. .

import matplotlib.pyplot as plt
import numpy as np

:

plt.plot(time, price)
plt.plot(time, predicted_price)
plt.show()

predicted_price:

sq_dis = (price - predicted_price) ** 2
limit = (sq_dis.mean() + sq_dis.std()) * 0.3 # < - adjust window here
filter = np.abs(sq_dis) < limit
plt.plot(time, price)
plt.plot(time, predicted_price)
plt.plot(time[filter], price[filter])
plt.show()