squeeze-momentum-indicator-general.py

# import required libraries
import pandas as pd
import yfinance as yf
import numpy as np
import math

# get stock prices
df = yf.download('AAPL', start='2020-01-01', threads= False)

# parameter setup
length = 20
mult = 2
length_KC = 20
mult_KC = 1.5

# calculate BB
m_avg = df['Close'].rolling(window=length).mean()
m_std = df['Close'].rolling(window=length).std(ddof=0)
df['upper_BB'] = m_avg + mult * m_std
df['lower_BB'] = m_avg - mult * m_std

# calculate true range
df['tr0'] = abs(df["High"] - df["Low"])
df['tr1'] = abs(df["High"] - df["Close"].shift())
df['tr2'] = abs(df["Low"] - df["Close"].shift())
df['tr'] = df[['tr0', 'tr1', 'tr2']].max(axis=1)

# calculate KC
range_ma = df['tr'].rolling(window=length_KC).mean()
df['upper_KC'] = m_avg + range_ma * mult_KC
df['lower_KC'] = m_avg - range_ma * mult_KC

# calculate bar value
highest = df['High'].rolling(window = length_KC).max()
lowest = df['Low'].rolling(window = length_KC).min()
m1 = (highest + lowest)/2
df['value'] = (df['Close'] - (m1 + m_avg)/2)
fit_y = np.array(range(0,length_KC))
df['value'] = df['value'].rolling(window = length_KC).apply(lambda x: 
                          np.polyfit(fit_y, x, 1)[0] * (length_KC-1) + 
                          np.polyfit(fit_y, x, 1)[1], raw=True)

# check for 'squeeze'
df['squeeze_on'] = (df['lower_BB'] > df['lower_KC']) & (df['upper_BB'] < df['upper_KC'])
df['squeeze_off'] = (df['lower_BB'] < df['lower_KC']) & (df['upper_BB'] > df['upper_KC'])

# buying window for long position:
# 1. black cross becomes gray (the squeeze is released)
long_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True) 
# 2. bar value is positive => the bar is light green k
long_cond2 = df['value'][-1] > 0
enter_long = long_cond1 and long_cond2

# buying window for short position:
# 1. black cross becomes gray (the squeeze is released)
short_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True) 
# 2. bar value is negative => the bar is light red 
short_cond2 = df['value'][-1] < 0
enter_short = short_cond1 and short_cond2

df['squeeze_on'] Is calculated but in this snip remain unused till the last line

Awesome piece of code, thank you for sharing :)

By any chance would it be possible to convert it into a screener so filter at watchlist of stocks determining if each one is Squeeze on or off.