Support Resistance Algorithm - Technical analysis

Question

I have an intra-day chart and I am trying to figure out how to calculate support and resistance levels, anyone knows an algorithm for doing that, or a good starting point?

Answer 1

The best way I have found to get SR levels is with clustering. Maxima and Minima is calculated and then those values are flattened (like a scatter plot where x is the maxima and minima values and y is always 1). You then cluster these values using Sklearn.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.cluster import AgglomerativeClustering

# Calculate VERY simple waves
mx = df.High_15T.rolling( 100 ).max().rename('waves')
mn = df.Low_15T.rolling( 100 ).min().rename('waves')

mx_waves = pd.concat([mx,pd.Series(np.zeros(len(mx))+1)],axis = 1)
mn_waves = pd.concat([mn,pd.Series(np.zeros(len(mn))+-1)],axis = 1)    

mx_waves.drop_duplicates('waves',inplace = True)
mn_waves.drop_duplicates('waves',inplace = True)

W = mx_waves.append(mn_waves).sort_index()
W = W[ W[0] != W[0].shift() ].dropna()

# Find Support/Resistance with clustering

# Create [x,y] array where y is always 1
X = np.concatenate((W.waves.values.reshape(-1,1),
                    (np.zeros(len(W))+1).reshape(-1,1)), axis = 1 )

# Pick n_clusters, I chose the sqrt of the df + 2
n = round(len(W)**(1/2)) + 2
cluster = AgglomerativeClustering(n_clusters=n,
          affinity='euclidean', linkage='ward')
cluster.fit_predict(X)
W['clusters'] = cluster.labels_

# I chose to get the index of the max wave for each cluster
W2 = W.loc[W.groupby('clusters')['waves'].idxmax()]

# Plotit
fig, axis = plt.subplots()
for row in W2.itertuples():

    axis.axhline( y = row.waves, 
            color = 'green', ls = 'dashed' )

axis.plot( W.index.values, W.waves.values )
plt.show()