squeeze_pro#

API documentation for pandas_ta.momentum.squeeze_pro Python function.

squeeze_pro(high, low, close, bb_length=None, bb_std=None, kc_length=None, kc_scalar_wide=None, kc_scalar_normal=None, kc_scalar_narrow=None, mom_length=None, mom_smooth=None, use_tr=None, mamode=None, offset=None, **kwargs)[source]#

Squeeze PRO(SQZPRO)

This indicator is an extended version of “TTM Squeeze” from John Carter. The default is based on John Carter’s “TTM Squeeze” indicator, as discussed in his book “Mastering the Trade” (chapter 11). The Squeeze indicator attempts to capture the relationship between two studies: Bollinger Bands® and Keltner’s Channels. When the volatility increases, so does the distance between the bands, conversely, when the volatility declines, the distance also decreases. It finds sections of the Bollinger Bands® study which fall inside the Keltner’s Channels.

Sources:

https://usethinkscript.com/threads/john-carters-squeeze-pro-indicator-for-thinkorswim-free.4021/ https://www.tradingview.com/script/TAAt6eRX-Squeeze-PRO-Indicator-Makit0/

Calculation:

Default Inputs:: bb_length=20, bb_std=2, kc_length=20, kc_scalar_wide=2, kc_scalar_normal=1.5, kc_scalar_narrow=1, mom_length=12, mom_smooth=6, tr=True,

BB = Bollinger Bands KC = Keltner Channels MOM = Momentum SMA = Simple Moving Average EMA = Exponential Moving Average TR = True Range

RANGE = TR(high, low, close) if using_tr else high - low BB_LOW, BB_MID, BB_HIGH = BB(close, bb_length, std=bb_std) KC_LOW_WIDE, KC_MID_WIDE, KC_HIGH_WIDE = KC(high, low, close, kc_length, kc_scalar_wide, TR) KC_LOW_NORMAL, KC_MID_NORMAL, KC_HIGH_NORMAL = KC(high, low, close, kc_length, kc_scalar_normal, TR) KC_LOW_NARROW, KC_MID_NARROW, KC_HIGH_NARROW = KC(high, low, close, kc_length, kc_scalar_narrow, TR)

MOMO = MOM(close, mom_length) if mamode == “ema”:

SQZPRO = EMA(MOMO, mom_smooth)

else:: SQZPRO = EMA(momo, mom_smooth)

SQZPRO_ON_WIDE = (BB_LOW > KC_LOW_WIDE) and (BB_HIGH < KC_HIGH_WIDE) SQZPRO_ON_NORMAL = (BB_LOW > KC_LOW_NORMAL) and (BB_HIGH < KC_HIGH_NORMAL) SQZPRO_ON_NARROW = (BB_LOW > KC_LOW_NARROW) and (BB_HIGH < KC_HIGH_NARROW) SQZPRO_OFF_WIDE = (BB_LOW < KC_LOW_WIDE) and (BB_HIGH > KC_HIGH_WIDE) SQZPRO_NO = !SQZ_ON_WIDE and !SQZ_OFF_WIDE

Args:

high (pd.Series): Series of ‘high’s low (pd.Series): Series of ‘low’s close (pd.Series): Series of ‘close’s bb_length (int): Bollinger Bands period. Default: 20 bb_std (float): Bollinger Bands Std. Dev. Default: 2 kc_length (int): Keltner Channel period. Default: 20 kc_scalar_wide (float): Keltner Channel scalar for wider channel. Default: 2 kc_scalar_normal (float): Keltner Channel scalar for normal channel. Default: 1.5 kc_scalar_narrow (float): Keltner Channel scalar for narrow channel. Default: 1 mom_length (int): Momentum Period. Default: 12 mom_smooth (int): Smoothing Period of Momentum. Default: 6 mamode (str): Only “ema” or “sma”. Default: “sma” offset (int): How many periods to offset the result. Default: 0

Kwargs:

tr (value, optional): Use True Range for Keltner Channels. Default: True asint (value, optional): Use integers instead of bool. Default: True mamode (value, optional): Which MA to use. Default: “sma” detailed (value, optional): Return additional variations of SQZ for

visualization. Default: False

fillna (value, optional): pd.DataFrame.fillna(value) fill_method (value, optional): Type of fill method

Returns:

pd.DataFrame: SQZPRO, SQZPRO_ON_WIDE, SQZPRO_ON_NORMAL, SQZPRO_ON_NARROW, SQZPRO_OFF_WIDE, SQZPRO_NO columns by default. More: detailed columns if ‘detailed’ kwarg is True.