Converting linreg function from pinescript to Python?

External question — Stack Overflow Stack Exchange Author: YoYoYo Original post: https://stackoverflow.com/questions/67719509 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I am trying to convert a TradingView indicator into Python (also using pandas to store its result). This is the indicator public code I want to convert into a python indicator: https://www.tradingview.com/script/sU9molfV/ And I am stuck creating that pine script linereg default function. This is the fragment of the pinescript indicator I have troubles with: lrc = linreg(src, length, 0) lrc1 = linreg(src,length,1) lrs = (lrc-lrc1) TSF = linreg(src, length, 0)+lrs This is its documentation: Linear regression curve. A line that best fits the prices specified over a user-defined time period. It is calculated using the least squares method. The result of this function is calculated using the formula: linreg = intercept + slope * (length - 1 - offset), where length is the y argument, offset is the z argument, intercept and slope are the values calculated with the least squares method on source series (x argument). linreg(source, length, offset) → series[float] Source: https://www.tradingview.com/pine-script-reference/#fun_linreg I have found this mql4 code and tried to follow it step by step in order to convert it and finally to create a function linreg in Python in order to use it further for building that pine script indicator: https://www.mql5.com/en/code/8016 And this is my code so far: # calculate linear regression: # https://www.mql5.com/en/code/8016 barsToCount = 14 # sumy+=Close[i]; df['sumy'] = df['Close'].rolling(window=barsToCount).mean() # sumxy+=Close[i]*i; tmp = [] sumxy_lst = [] for window in df['Close'].rolling(window=barsToCount): for index in range(len(window)): tmp.append(window[index] * index) sumxy_lst.append(sum(tmp)) del tmp[:] df.loc[:,'sumxy'] = sumxy_lst # sumx+=i; sumx = 0 for i in range(barsToCount): sumx += i # sumx2+=i*i; sumx2 = 0 for i in range(barsToCount): sumx2 += i * i # c=sumx2*barsToCount-sumx*sumx; c = sumx2*barsToCount - sumx*sumx # Line equation: # b=(sumxy*barsToCount-sumx*sumy)/c; df['b'] = ((df['sumxy']*barsToCount)-(sumx*df['sumy']))/c # a=(sumy-sumx*b)/barsToCount; df['a'] = (df['sumy']-sumx*df['b'])/barsToCount # Linear regression line in buffer: df['LR_line'] = 0.0 for x in range(barsToCount): # LR_line[x]=a+b*x; df['LR_line'].iloc[x] = df['a'].iloc[x] + df['b'].iloc[x] * x # print(x, df['a'].iloc[x], df['b'].iloc[x], df['b'].iloc[x]*x) print(df.tail(50)) print(list(df)) It doesn't work. Any idea how to create a similar pine script linereg function into python, please? Thank you in advance!

External answer — Stack Overflow Stack Exchange Author: jukeboX Original post: https://stackoverflow.com/a/68889027 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I used talib to calculate the slope and intercept on the closing prices, then realised talib offers the full calc also. The result looks to be same as TradingView (just eyeballing). Did the following in jupyterlab: import pandas as pd import numpy as np import talib as tl from pandas_datareader import data %run "../../plt_setup.py" asset = data.DataReader('^AXJO', 'yahoo', start='1/1/2015') n = 270 (asset .assign(linreg = tl.LINEARREG(asset.Close, n)) [['Close', 'linreg']] .dropna() .loc['2019-01-01':] .plot() );

External answer — Stack Overflow Stack Exchange Author: Slyly Sly Original post: https://stackoverflow.com/a/75833487 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I was soaring with this question for a very long time, as a result I made such a function, it calculates linear regression as on the TradingView function ta.linreg() in PineScript: import numpy as np def np_shift(array: np.ndarray, offset: int = 1, fill_value=np.nan): result = np.empty_like(array) if offset > 0: result[:offset] = fill_value result[offset:] = array[:-offset] elif offset < 0: result[offset:] = fill_value result[:offset] = array[-offset:] else: result[:] = array return result def Linreg(source: np.ndarray, length: int, offset: int = 0): size = len(source) linear = np.zeros(size) for i in range(length, size): sumX = 0.0 sumY = 0.0 sumXSqr = 0.0 sumXY = 0.0 for z in range(length): val = source[i-z] per = z + 1.0 sumX += per sumY += val sumXSqr += per * per sumXY += val * per slope = (length * sumXY - sumX * sumY) / (length * sumXSqr - sumX * sumX) average = sumY / length intercept = average - slope * sumX / length + slope linear[i] = intercept if offset != 0: linear = np_shift(linear, offset) return linear

External answer — Stack Overflow Stack Exchange Author: Tony_Tong Original post: https://stackoverflow.com/a/77056392 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. import talib def linreg(data, period, offset): intercept = talib.LINEARREG_INTERCEPT(data, period) slope = talib.LINEARREG_SLOPE(data, period) return intercept + slope * (period - 1 - offset)