Squeeze Momentum Trading Strategy Based on LazyBear Indicator

This strategy is based on LazyBear's Squeeze Momentum Indicator, with added momentum filters, changed data source, and enhanced risk management system and customizable backtesting timeframe, aiming to catch price…

Pine ScriptMomentum Breakout Trend following SMA Bollinger Bands Keltner

Author: ChaoZhang
Complexity: Intermediate · 81 lines
Parameters: 9
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy uses Bollinger Bands and Keltner Channels to calculate price channels. Breakouts signal increased volatility. It incorporates LazyBear's Squeeze Momentum Indicator which uses linear regression to determine price momentum direction.

The strategy adds momentum filters, only trading when absolute momentum exceeds a threshold. On volatility squeeze (channel tightening) with momentum filter passed, it judges trend direction for long/short. It also sets stop loss, take profit and trailing stop to control risks.

Advantage Analysis

The strategy integrates multiple indicators for comprehensive judgment. It limits per trade loss with risk management mechanisms. It can timely judge post-squeeze price trends. Customizable parameters make it adaptable.

Risk Analysis

Main risks include: false breakouts causing wrong judgements; failure to reverse in time with improper parameter settings; stop loss breaches enlarging losses. These can be mitigated by optimizing parameters, adjusting risk management settings, selecting suitable products and trading sessions.

Optimization Directions

Consider combining other indicator filters like volume; fine-tune momentum threshold for higher precision; add drawdown stop loss for tighter risk control; test effectiveness across more products. These optimizations can make the strategy more robust and generalized.

Summary

The strategy judges price trends and volatility relatively comprehensively with high integration degree and improved risk control measures. It can be further enhanced through the optimization directions for strong adaptiveness in catching post-squeeze price outbreaks.

Parameters

Name	Default	What it controls
v_input_1	12	BB Length
v_input_2	2	BB MultFactor
v_input_3	16	KC Length
v_input_4	1.5	KC MultFactor
v_input_5	false	Filter for Momenutum value
v_input_6	20	Min for momentum
v_input_7	600	Stop Loss
v_input_8	1000	Take Profit
v_input_9	20	Trailing Stop

Source Pine Script

/*backtest
start: 2024-01-05 00:00:00
end: 2024-02-04 00:00:00
period: 2h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// @version=4
// Strategy based on LazyBear Squeeze Momentum Indicator
// © Bitduke
// All scripts: https://www.tradingview.com/u/Bitduke/#published-scripts

strategy(shorttitle="SMS", title="Squeeze Momentum Strategy", overlay=false )

length = input(12, title="BB Length")
mult = input(2.0, title="BB MultFactor")
lengthKC = input(16, title="KC Length")
mult_kc = input(1.5, title="KC MultFactor")


//FILTERS
useMomAverage = input(false, title="Filter for Momenutum value", type=input.bool)
MomentumMin = input(20, title="Min for momentum")

// Calculate BB
src = ohlc4

ma_1 = sma(src, length)
ma_2 = sma(src, lengthKC)
range_ma = sma(high - low, lengthKC)

dev = mult * stdev(src, length)

upper_bb = ma_1 + dev
lower_bb = ma_1 - dev

upper_kc = ma_2 + range_ma * mult_kc
lower_kc = ma_2 - range_ma * mult_kc

sqz_on = lower_bb > lower_kc and upper_bb < upper_kc
sqz_off = lower_bb < lower_kc and upper_bb > upper_kc
no_sqz = sqz_on == false and sqz_off == false

val = linreg(src - avg(avg(highest(hl2, lengthKC), lowest(low, lengthKC)), sma(hl2, lengthKC)), lengthKC, 0)

bcolor = iff(val > 0, iff(val > nz(val[1]), color.lime, color.green), iff(val < nz(val[1]), color.red, color.maroon))
scolor = no_sqz ? color.blue : sqz_on ? color.black : color.aqua
plot(val, color=bcolor, style=plot.style_histogram, linewidth=4)
plot(0, color=scolor, style=plot.style_cross, linewidth=2)

//LOGIC
//momentum filter
filterMom = useMomAverage ? abs(val) > MomentumMin / 100000 ? true : false : true

//standard condition
longCondition = scolor[1] != color.aqua and scolor == color.aqua and bcolor == color.lime and filterMom
exitLongCondition = bcolor == color.green
shortCondition = scolor[1] != color.aqua and scolor == color.aqua and bcolor == color.red and filterMom
exitShortCondition = bcolor == color.maroon

// Risk Management Sysyem
stop_loss = input(defval = 600, title="Stop Loss", minval = 0)
take_profit = input(defval = 1000, title="Take Profit", minval = 0)
trailing_stop = input(defval = 20, title="Trailing Stop", minval = 0)
// If the zero value is set for stop loss, take profit or trailing stop, then the function is disabled
s_loss = stop_loss >= 1 ? stop_loss : na
tk_profit = take_profit >= 1 ? take_profit : na
tr_stop = trailing_stop >= 1 ? trailing_stop : na


//STRATEGY
strategy.entry("SQ_Long", strategy.long, when=longCondition)
strategy.exit("Exit Long", from_entry = "SQ_Long", profit = take_profit, trail_points = trailing_stop, loss = s_loss)
strategy.close("SQ_Long", exitLongCondition)

strategy.entry("SQ_Short", strategy.short, when=shortCondition)
strategy.exit("Exit Short", from_entry = "SQ_Short", profit = take_profit, trail_points = trailing_stop, loss = s_loss )
strategy.close("SQ_Short", when=exitShortCondition)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-05. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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