SupportResistance

A python script that estimates the support and resistance lines of a stock's prices or a period

Prerequisites

Have Python3 and pip installed on PC. (Install from https://python.org)
Have git installed. (Install from https://git-scm.com/downloads)

Setup

git clone the repository

git clone https://github.com/lil-zohee/SupportResistance.git

Move in the new directory

cd SupportResistance/

Install all necessary modules

pip3 install -r requirements.txt

Now you can run the program

python3 support_resistance.py

In the python script, I tried to predict the support and resistance for AAPL stock from the dates of January 1, 2019 to April 1, 2019. However, you can change that in lines 51 and 52 of the program.

symbol = 'AAPL'
df = web.DataReader(symbol, 'yahoo', '2019-01-01', '2019-04-01')

How it works

The closing prices of the Bank Of America stock from January 1, 2019 to April 1, 2019 look like this.

Step 1: Smoothen the graph

We can use the savgol_filter function from the scipy.signal module, to make the graph smoother. I have created a simple algorithm to determine how much we should smoothen a graph. All the closing prices of a stock are stored in a pd.Series object. To determine the level of smoothness we want in our graph, we have to find the difference in months of the 2 dates. Then we multiply it with 2 and add it with 3.

month_diff = series.shape[0] // 30
if month_diff == 0: # If we have 0 months worth of prices, edit the variable to = 1
    month_diff = 1
smooth = int(2 * month_diff + 3) # Level of smoothness in our graph

Now lets smoothen our graph

pts = savgol_filter(series, smooth, 3) # 3 is the order of the polynomials

Here's the result of plotting pts.

Here's what the two graphs look like in comparison.

Step 2: Finding the local maximum and minimum points

To find the local maximum and local minimum, I have created a function, which takes up lines 21 - 49 in the support_resistance.py. Essentially, this function loops through the given pts from indexes 1 to -1. If the point is less than the point behind it and less than the point ahead of it, then it is a local minimum. Similarly, if the point in greater than the point behind it and greater than the point ahead of it, it is a local maximum. However, if we simply do this, then the algorithm will detect many local minimums and maximums. Therefore, I have used the Pythagorean theorem to determine the distance between the previous point and current point, as well as the distance between the current point and next point. I made sure to only consider a point as a local maximum or minimum if the distance between it and the next point was greater than half of the distance between it and the previous point. Here is the result of plotting the local maximum and minimum points.

Step 3: Line of best fit between local minimum and maximum

To find the line of best fit between the local minimum and maximum points, I used the sklean.linear_model.LinearRegression model. I have created a function on lines 14 - 19 in the support_resistance.py.

def regression_ceof(pts):
    """
    Uses LinearRegression model to determine
    the best fit slope and y-intercept of
    the given points.
    """
    return slope, y_int

local_min_slope, local_min_int = regression_ceof(local_min)
local_max_slope, local_max_int = regression_ceof(local_max)

Step 4: Creating support and resistance lines

Now that we have the slope and y-intercept of the local minimum and maximum, we can easily draw the support and resistance lines using the linear equation y = mx + b. m is the slope and b is the y-intercept.

support = (local_min_slope * np.array(series.index)) + local_min_int
resistance = (local_max_slope * np.array(series.index)) + local_max_int

After plotting the support and resistance lines, we get this.

This is what it looks like on the normal graph.

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