Squeeze momentum breakout strategy (LazyBear-inspired).
Timeframe
5m
Direction
Long Only
Stoploss
-5.0%
Trailing Stop
Yes
ROI
0m: 18.0%, 180m: 9.0%, 540m: 4.0%
Interface Version
N/A
Startup Candles
30
Indicators
5
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
"""
SqueezeMomentumStrategy — Volatility squeeze + momentum breakout
================================================================
Logic:
Detects "squeeze" when Bollinger Bands are inside Keltner Channels
(low volatility compression). Enters when squeeze releases upward
with positive momentum.
Entry : Squeeze just released (BB outside KC) AND
momentum histogram positive and rising AND
RSI > 50
Exit : Momentum turns negative OR RSI > 75
Stop : 5%
"""
import talib.abstract as ta
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy
from pandas import DataFrame
class SqueezeMomentumStrategy(IStrategy):
"""Squeeze momentum breakout strategy (LazyBear-inspired)."""
timeframe = "5m"
minimal_roi = {"0": 0.18, "180": 0.09, "540": 0.04}
stoploss = -0.05
trailing_stop = True
trailing_stop_positive = 0.025
trailing_stop_positive_offset = 0.05
trailing_only_offset_is_reached = True
can_short = False
startup_candle_count = 30
bb_period = IntParameter(15, 25, default=20, space="buy")
kc_period = IntParameter(15, 25, default=20, space="buy")
kc_mult = DecimalParameter(1.0, 2.0, default=1.5, space="buy")
mom_period = IntParameter(10, 20, default=12, space="buy")
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Bollinger Bands
bb = ta.BBANDS(dataframe, timeperiod=self.bb_period.value, nbdevup=2.0, nbdevdn=2.0)
dataframe["bb_upper"] = bb["upperband"]
dataframe["bb_lower"] = bb["lowerband"]
dataframe["bb_mid"] = bb["middleband"]
# Keltner Channels (EMA ± mult * ATR)
dataframe["kc_mid"] = ta.EMA(dataframe, timeperiod=self.kc_period.value)
dataframe["atr"] = ta.ATR(dataframe, timeperiod=self.kc_period.value)
dataframe["kc_upper"] = dataframe["kc_mid"] + self.kc_mult.value * dataframe["atr"]
dataframe["kc_lower"] = dataframe["kc_mid"] - self.kc_mult.value * dataframe["atr"]
# Squeeze: BB inside KC = low volatility
dataframe["squeeze_on"] = (
(dataframe["bb_upper"] < dataframe["kc_upper"])
& (dataframe["bb_lower"] > dataframe["kc_lower"])
).astype(int)
dataframe["squeeze_prev"] = dataframe["squeeze_on"].shift(1)
# Squeeze just released = was on, now off
dataframe["squeeze_release"] = (
(dataframe["squeeze_on"] == 0) & (dataframe["squeeze_prev"] == 1)
).astype(int)
# Momentum: delta of midpoint of high/low and BB mid
highest_high = dataframe["high"].rolling(self.mom_period.value).max()
lowest_low = dataframe["low"].rolling(self.mom_period.value).min()
mid_hl = (highest_high + lowest_low) / 2
dataframe["momentum"] = dataframe["close"] - (mid_hl + dataframe["bb_mid"]) / 2
dataframe["momentum_prev"] = dataframe["momentum"].shift(1)
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe["squeeze_release"] == 1) # squeeze just fired
& (dataframe["momentum"] > 0) # momentum is positive
& (dataframe["momentum"] > dataframe["momentum_prev"]) # and rising
& (dataframe["rsi"] > 50) # RSI confirms upside
& (dataframe["volume"] > 0)
),
"enter_long",
] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe["momentum"] < 0)
| (dataframe["rsi"] > 75)
),
"exit_long",
] = 1
return dataframe