Support and Resistance Trend lines Calculator for Financial Analysis

# Support and Resistance Trend lines Calculator for Financial Analysis

## Note

This library can calculate and plot trend lines for any time series, not only for its primary intended purpose of financial analysis.

Changelog »

==> Check out this article on Programmatic Identification of Support/Resistance Trend lines with Python or alternatively here for details on how the library and its features are implemented and work.

# Quick Start

## Calculation Only

The calc_support_resistance function will calculate all support and resistance information including local extrema, average and their trend lines using several different methods:

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)
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


The get_extrema function will calculate all of the local minima and local maxima without performing the full trend line calculation.

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

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


## Plotting Calculations

The plot_support_resistance function will calculate and plot the average and top 2 support and resistance lines, along with marking extrema used with a maximum history length, and otherwise identical arguments to the calculation function.

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

fig = 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

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

fig = 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

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 ## Installation

Install trendln using pip:

$pip install trendln --upgrade --no-cache-dir  Install trendln using conda: $ conda install -c GregoryMorse trendln


Installation sanity check:

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


## Requirements

• Python >= 2.7, 3.4+
• numpy >= 1.15
• findiff >= 0.7.0 (if using default numerical differentiation method)
• scikit-image >= 0.14.0 (if using image-based Hough line transform or its probabilistic variant)
• pandas >= 0.23.1 (if using date plotting function, or using naive minima/maxima methods)
• matplotlib >= 2.2.4 (if using any plotting function)

## Support

Any questions, issues or ideas can kindly be submitted for review.

Gregory Morse gregory.morse@live.com

## Project details

This version 0.1.10 0.1.8

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