Hello all, On TradingView there is a popular coder by the name of Lazy Bear. He made a script in Pine Script that he calls the “Squeeze Momentum Indicator [LazyBear]”. The code for that is open source so I thought it would be fun to try and replicate it in python. This is what I came up with. It is not a perfect 1:1 because my linear regression does not return a list. Instead of checking the values in a list from linear regression against each other I look at the strength of the linear regression. We see in the code that I define that strength as a value greater then 2 or less than -2 to trade. Super excited to see what you guys think of this. I haven't even run a back test with this code trading yet. Hope you enjoy, open to any suggestions and criticisms.
Link to google doc from Lazy Bear with all of his indicators: Master List of all my indicators - Google Docs
Link to him on TradingView: Trader LazyBear — Trading Ideas & Charts — TradingView
Link to this indicator specifically: Squeeze Momentum Indicator [LazyBear] by LazyBear — TradingView
# region imports
from AlgorithmImports import *
import numpy as np
from sklearn.linear_model import LinearRegression
# endregion
class AdaptableFluorescentPinkBee(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 1, 1) # Set Start Date
self.SetEndDate(2023,1,1)
self.SetCash(100000) # Set Strategy Cash
self.futureSP500 = self.AddFuture(Futures.Indices.SP500EMini, extendedMarketHours= True)
self.futureSP500.SetFilter(timedelta(0), timedelta(182))
self.symbol = self.futureSP500.Symbol
seeder = FuncSecuritySeeder(self.GetLastKnownPrices)
self.SetSecurityInitializer(lambda security: seeder.SeedSecurity(security))
self.bb = self.BB(self.symbol, 20, 2)
self.kch = self.KCH(self.symbol, 20, 1.5)
#self.sma = SimpleMovingAverage(name, period)
self.RegisterIndicator(self.symbol, self.kch, Resolution.Minute)
self.RegisterIndicator(self.symbol, self.bb, Resolution.Minute)
self.LongcontractSymbol = None
self.ShortcontractSymbol = None
self.prices_window = RollingWindow[QuoteBar](20)
self.prices = []
self.lins = RollingWindow[float](20)
self.pylins = []
def OnData(self, data: Slice):
if not(data.ContainsKey(self.symbol) and data[self.symbol] is not None):
return
if not self.kch.IsReady or not self.bb.IsReady:
return
future_invested = [x.Key for x in self.Portfolio if x.Value.Invested and x.Value.Type==SecurityType.Future]
if future_invested:
if self.Time + timedelta(1) > future_invested[0].ID.Date:
self.Liquidate(future_invested[0], "Future too close to expiration.")
KCmiddle_band = self.kch.MiddleBand.Current.Value
KCupper_band = self.kch.UpperBand.Current.Value
KClower_band = self.kch.LowerBand.Current.Value
BBmiddle_band = self.bb.MiddleBand.Current.Value
BBupper_band = self.bb.UpperBand.Current.Value
BBlower_band = self.bb.LowerBand.Current.Value
########################################################################################
# Add the current data to the rolling window
self.prices_window.Add(data[self.symbol])
# Ensure the rolling window is fully populated before calculating entropy
if not self.prices_window.IsReady:
return
self.prices = [bar.Close for bar in self.prices_window]
avg1 = (max(self.prices) + min(self.prices) + BBmiddle_band) / 3
avg2 = self.futureSP500.Price - ((avg1 + 20) / 2)
self.lins.Add(avg2)
if not self.lins.IsReady:
return
self.pylins = [num for num in self.lins]
########################################################################################
sqzOn = (BBlower_band > KClower_band) and (BBupper_band < KCupper_band)
sqzOff = (BBlower_band < KClower_band) and (BBupper_band > KCupper_band)
noSqz = (sqzOn == False) and (sqzOff == False)
val = self.LinReg()
self.Log(val)
#bcolor = ("lime" if val[-1] > 0 and val[-1] > val[-2] else "green") if val[-1] > 0 else ("red" if val[-1] < val[-2] else "maroon")
if val > 0:
if val < 2:
bcolor = "green"
elif val > 2:
bcolor = "lime"
else:
if val > -2:
bcolor = "maroon"
elif val < -2:
bcolor = "red"
scolor = 'blue' if noSqz else ('black' if sqzOn else 'gray')
if bcolor == "lime" and scolor == "grey":
"long"
elif bcolor == "red" and scolor == "grey":
"short"
self.Log(bcolor)
self.Log(scolor)
def LinReg(self):
# A is the design matrix
A = range(20 + 1)
# response
Y = self.pylins
# features
X = np.column_stack([np.ones(len(A)), A])
# data preparation
length = min(len(X), len(Y))
X = X[-length:]
Y = Y[-length:]
A = A[-length:]
# fit the linear regression
reg = LinearRegression().fit(X, Y)
# run linear regression y = ax + b
b = reg.intercept_
a = reg.coef_[1]
# store slopes for symbols
slopes = a/b
return slopes
Rishab Maheshwari
Here is how I made the Squeeze Oscillator
.ekz.
Joseph Matteo Scorsone
Someone did this and shared it not too long ago
Joseph Matteo Scorsone
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