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

minimaIdxs, maximaIdxs = trendln.get_extrema(hist[-1000:].Close)
maximaIdxs = trendln.get_extrema((None, hist[-1000:].High)) #maxima only
minimaIdxs, maximaIdxs = trendln.get_extrema((hist[-1000:].Low, hist[-1000:].High))

minimaIdxs, maximaIdxs = trendln.get_extrema(
	h,
	extmethod=METHOD_NUMDIFF,
	accuracy=1)
# parameters and results are as per defined for calc_support_resistance

fig = trendln.plot_support_resistance(hist[-1000:].Close) # requires matplotlib - pip install matplotlib
plt.savefig('suppres.svg', format='svg')
plt.show()
plt.clf() #clear figure

fig = trendln.plot_support_resistance(
	hist, #as per h for calc_support_resistance
	xformatter = None, #x-axis data formatter turning numeric indexes to display output
	  # e.g. ticker.FuncFormatter(func) otherwise just display numeric indexes
	numbest = 2, #number of best support and best resistance lines to display
	fromwindows = True, #draw numbest best from each window, otherwise draw numbest across whole range
	pctbound = 0.1, # bound trend line based on this maximum percentage of the data range above the high or below the low
	extmethod = METHOD_NUMDIFF,
	method=METHOD_NSQUREDLOGN,
	window=125,
	errpct = 0.005,
	hough_prob_iter=10,
	sortError=False,
	accuracy=1)
# other parameters as per calc_support_resistance
# fig - returns matplotlib.pyplot.gcf() or the current figure

idx = hist[-1000:].index
fig = trendln.plot_sup_res_date((hist[-1000:].Low, hist[-1000:].High), idx) #requires pandas
plt.savefig('suppres.svg', format='svg')
plt.show()
plt.clf() #clear figure

fig = trendln.plot_sup_res_date( #automatic date formatter based on US trading calendar
	hist, #as per h for calc_support_resistance
	idx, #date index from pandas
	numbest = 2,
	fromwindows = True,
	pctbound = 0.1,
	extmethod = METHOD_NUMDIFF,
	method=METHOD_NSQUREDLOGN,
	window=125,
	errpct = 0.005,
	hough_scale=0.01,
	hough_prob_iter=10,
	sortError=False,
	accuracy=1)
# other parameters as per plot_support_resistance

trendln.plot_sup_res_learn('.', hist)

trendln.plot_sup_res_learn( #draw learning figures, included for reference material only
	curdir, #base output directory for png and svg images, will be saved in 'data' subfolder
	hist) #pandas DataFrame containing Close and date index

$ pip install trendln --upgrade --no-cache-dir

$ conda install -c GregoryMorse trendln

import trendln
#requires yfinance library install, not a package requirement, but used to assist with sanity check
#pip install yfinance
directory = '.' # a 'data' folder will be created here if not existing to store images
trendln.test_sup_res(directory) #simple tests that all methods are executing correct, assertion or other error indicates problem