squeeze#

API documentation for pandas_ta.momentum.squeeze Python function.

squeeze(high, low, close, bb_length=None, bb_std=None, kc_length=None, kc_scalar=None, mom_length=None, mom_smooth=None, use_tr=None, mamode=None, offset=None, **kwargs)[source]#

Squeeze (SQZ)

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://tradestation.tradingappstore.com/products/TTMSqueeze https://www.tradingview.com/scripts/lazybear/ https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/T-U/TTM-Squeeze

Calculation:

Default Inputs:: bb_length=20, bb_std=2, kc_length=20, kc_scalar=1.5, mom_length=12, mom_smooth=12, tr=True, lazybear=False,

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, KC_MID, KC_HIGH = KC(high, low, close, kc_length, kc_scalar, TR)

if lazybear:

HH = high.rolling(kc_length).max() LL = low.rolling(kc_length).min() AVG = 0.25 * (HH + LL) + 0.5 * KC_MID SQZ = linreg(close - AVG, kc_length)

else:

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

SQZ = EMA(MOMO, mom_smooth)

else:: SQZ = EMA(momo, mom_smooth)

SQZ_ON = (BB_LOW > KC_LOW) and (BB_HIGH < KC_HIGH) SQZ_OFF = (BB_LOW < KC_LOW) and (BB_HIGH > KC_HIGH) NO_SQZ = !SQZ_ON and !SQZ_OFF

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 (float): Keltner Channel scalar. Default: 1.5 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” lazybear (value, optional): Use LazyBear’s TradingView implementation.

Default: False

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: SQZ, SQZ_ON, SQZ_OFF, NO_SQZ columns by default. More: detailed columns if ‘detailed’ kwarg is True.