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yongghongg/squeeze-momentum-indicator-general.py

Last active September 1, 2024 12:07
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Revisions

  1. yongghongg revised this gist Jul 3, 2021. 1 changed file with 7 additions and 7 deletions.
    14 changes: 7 additions & 7 deletions squeeze-momentum-indicator-general.py
    View file
    Original file line number Diff line number Diff line change
    @@ -46,14 +46,14 @@

    # buying window for long position:
    # 1. black cross becomes gray (the squeeze is released)
    buy_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True)
    # 2. bar value is positive => the bar is light green
    buy_cond2 = df['value'][-1] > 0
    buy_long = buy_cond1 and buy_cond2
    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)
    buy_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True)
    short_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True)
    # 2. bar value is negative => the bar is light red
    buy_cond2 = df['value'][-1] < 0
    buy_short = buy_cond1 and buy_cond2
    short_cond2 = df['value'][-1] < 0
    enter_short = short_cond1 and short_cond2
  2. yongghongg revised this gist Jul 3, 2021. 1 changed file with 10 additions and 3 deletions.
    13 changes: 10 additions & 3 deletions squeeze-momentum-indicator-general.py
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    Original file line number Diff line number Diff line change
    @@ -45,8 +45,15 @@
    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
    # 1. black cross becomes gray (the squeeze is released)
    buy_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True)
    # 2. bar value is positive => the bar is light green
    buy_cond2 = df['value'][-1] >= 0
    buy = buy_cond1 and buy_cond2
    buy_cond2 = df['value'][-1] > 0
    buy_long = buy_cond1 and buy_cond2

    # buying window for short position:
    # 1. black cross becomes gray (the squeeze is released)
    buy_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True)
    # 2. bar value is negative => the bar is light red
    buy_cond2 = df['value'][-1] < 0
    buy_short = buy_cond1 and buy_cond2
  3. yongghongg revised this gist Jul 3, 2021. 1 changed file with 1 addition and 1 deletion.
    2 changes: 1 addition & 1 deletion squeeze-momentum-indicator-general.py
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    Original file line number Diff line number Diff line change
    @@ -4,7 +4,7 @@
    import numpy as np
    import math

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

    # parameter setup
  4. yongghongg created this gist Jul 3, 2021.
    52 changes: 52 additions & 0 deletions squeeze-momentum-indicator-general.py
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    Original file line number Diff line number Diff line change
    @@ -0,0 +1,52 @@
    # import required libraries
    import pandas as pd
    import yfinance as yf
    import numpy as np
    import math

    # calculation
    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
    buy_cond1 = (df['squeeze_off'][-2] == False) & (df['squeeze_off'][-1] == True)
    # 2. bar value is positive => the bar is light green
    buy_cond2 = df['value'][-1] >= 0
    buy = buy_cond1 and buy_cond2