ángulo de la línea de tendencia usando el ejemplo de código de Python

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Ejemplo: python stock de la línea de tendencia

import trendln
# this will serve as an example for security or index closing prices, or low and high pricesimport yfinance as yf # requires yfinance - pip install yfinance
tick = yf.Ticker('^GSPC')# S&P500
hist = tick.history(period="max", rounding=True)
mins, maxs = calc_support_resistance(hist[-1000:].Close)
minimaIdxs, pmin, mintrend, minwindows = calc_support_resistance((hist[-1000:].Low,None))#support only
mins, maxs = calc_support_resistance((hist[-1000:].Low, hist[-1000:].High))(minimaIdxs, pmin, mintrend, minwindows),(maximaIdxs, pmax, maxtrend, maxwindows)= mins, maxs

(minimaIdxs, pmin, mintrend, minwindows),(maximaIdxs, pmax, maxtrend, maxwindows)= 
	calc_support_resistance(# list/numpy ndarray/pandas Series of data as bool/int/float and if not a list also unsigned# or 2-tuple (support, resistance) where support and resistance are 1-dimensional array-like or one or the other is None# can calculate only support, only resistance, both for different data, or both for identical data
	h,# METHOD_NAIVE - any local minima or maxima only for a single interval (currently requires pandas)# METHOD_NAIVECONSEC - any local minima or maxima including those for consecutive constant intervals (currently requires pandas)# METHOD_NUMDIFF (default) - numerical differentiation determined local minima or maxima (requires findiff)
	extmethod = METHOD_NUMDIFF,# METHOD_NCUBED - simple exhuastive 3 point search (slowest)# METHOD_NSQUREDLOGN (default) - 2 point sorted slope search (fast)# METHOD_HOUGHPOINTS - Hough line transform optimized for points# METHOD_HOUGHLINES - image-based Hough line transform (requires scikit-image)# METHOD_PROBHOUGH - image-based Probabilistic Hough line transform (requires scikit-image)
	method=METHOD_NSQUREDLOGN,# window size when searching for trend lines prior to merging together
	window=125,# maximum percentage slope standard error
	errpct =0.005,# for all METHOD_*HOUGH*, the smallest unit increment for discretization e.g. cents/pennies 0.01
	hough_scale=0.01# only for METHOD_PROBHOUGH, number of iterations to run
	hough_prob_iter=10,# sort by area under wrong side of curve, otherwise sort by slope standard error
	sortError=False,# accuracy if using METHOD_NUMDIFF for example 5-point stencil is accuracy=3
	accuracy=1)# if h is a 2-tuple with one value as None, then a 2-tuple is not returned, but the appropriate tuple instead# minimaIdxs - sorted list of indexes to the local minima# pmin - [slope, intercept] of average best fit line through all local minima points# mintrend - sorted list containing (points, result) for local minima trend lines# points - list of indexes to points in trend line# result - (slope, intercept, SSR, slopeErr, interceptErr, areaAvg)# slope - slope of best fit trend line# intercept - y-intercept of best fit trend line# SSR - sum of squares due to regression# slopeErr - standard error of slope# interceptErr - standard error of intercept# areaAvg - Reimann sum area of difference between best fit trend line#   and actual data points averaged per time unit# minwindows - list of windows each containing mintrend for that window# maximaIdxs - sorted list of indexes to the local maxima# pmax - [slope, intercept] of average best fit line through all local maxima points# maxtrend - sorted list containing (points, result) for local maxima trend lines#see for mintrend above# maxwindows - list of windows each containing maxtrend for that window

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