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Lazy Bear Squeeze Momentum Strategy

Author: ChaoZhang, Date: 2024-02-02 17:42:58
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Overview

The Lazy Bear Squeeze Momentum strategy is a quantitative trading strategy that combines Bollinger Bands, Keltner Channels and a momentum indicator. It utilizes Bollinger Bands and Keltner Channels to determine if the market is currently in a squeeze, then uses a momentum indicator to generate trading signals.

The main advantage of this strategy is being able to automatically identify the start of trending moves and determine entry timing with the momentum indicator. However, there are also certain risks that need to be addressed through optimization across different products.

Strategy Logic

The Lazy Bear Squeeze Momentum strategy makes judgements based on the following three indicators:

Bollinger Bands: Includes middle band, upper band and lower band
Keltner Channels: Includes middle line, upper line and lower line
Momentum Indicator: Current price minus price n days ago

When the Bollinger upper band is below the Keltner upper line and the Bollinger lower band is above the Keltner lower line, we determine the market is in a squeeze. This usually implies a trending move is about to start.

To pinpoint entry timing, we use the momentum indicator to gauge the speed of price changes. A buy signal is generated when momentum crosses above its moving average, and a sell signal when momentum crosses below its moving average.

Advantage Analysis

The main advantages of the Lazy Bear Squeeze Momentum strategy:

Automatically identify early entries into new trends
Combination of indicators prevents false signals
Captures both trend and mean-reversion
Customizable parameters for optimization
Robust across different products

Risk Analysis

There are also certain risks to the Lazy Bear Squeeze Momentum strategy:

Probability of false signals from Bollinger & Keltner
Momentum instability, missing best entries
Poor performance without optimization
High correlation to product selection

To mitigate risks, recommendations include: optimizing lengths for Bollinger & Keltner, adjusting stop loss, selecting liquid products, verifying signals with other indicators.

Optimization Directions

The main directions to further enhance performance:

Test combinations of parameters across products and timeframes
Optimize lengths for Bollinger Bands and Keltner Channels
Optimize length of momentum indicator
Different stop loss/take profit for longs and shorts
Additional indicators for signal verification

Through rigorous testing and optimization, the strategy’s edge and profitability can be greatly improved.

Conclusion

The Lazy Bear Squeeze Momentum strategy has strong signal generation through a multi-indicator approach, and can effectively identify new trend starts. But it also carries risks that necessitate optimization across trading instruments. With continual testing and enhancement, it can become a robust algorithmic trading system.

/*backtest
start: 2024-01-31 00:00:00
end: 2024-02-01 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © mtahreemalam original strategy by LazyBear

strategy(title = 'SQM Strategy, TP & SL',
         shorttitle = 'Squeeze.M Strat',
         overlay = true,
         pyramiding = 0,
         default_qty_type = strategy.percent_of_equity,
         default_qty_value = 100,
         initial_capital = 1000,
         commission_type=strategy.commission.percent, 
         commission_value=0.0,
         process_orders_on_close=true,
         use_bar_magnifier=true)
//Strategy logic
strategy_logic = input.string("Cross above 0", "Strategy Logic", options = ["LazyBear", "Cross above 0"])

// Date Range
testPeriodSwitch = input(false, "Custom Backtesting Date Range",group="Backtesting Date Range")
i_startTime = input(defval = timestamp("01 Jan 2022 00:01 +0000"), title = "Backtesting Start Time",group="Backtesting Date Range")
i_endTime = input(defval = timestamp("31 Dec 2022 23:59 +0000"), title = "Backtesting End Time",group="Backtesting Date Range")
timeCond = true
isPeriod = testPeriodSwitch == true ? timeCond : true

//// Stoploss and Take Profit Parameters
// Enable Long Strategy
enable_long_strategy = input.bool(true, title='Enable Long Strategy', group='SL/TP For Long Strategy', inline='1')
long_stoploss_value = input.float(defval=5, title='Stoploss %', minval=0.1, group='SL/TP For Long Strategy', inline='2')
long_stoploss_percentage = close * (long_stoploss_value / 100) / syminfo.mintick
long_takeprofit_value = input.float(defval=5, title='Take Profit %', minval=0.1, group='SL/TP For Long Strategy', inline='2')
long_takeprofit_percentage = close * (long_takeprofit_value / 100) / syminfo.mintick

// Enable Short Strategy
enable_short_strategy = input.bool(true, title='Enable Short Strategy', group='SL/TP For Short Strategy', inline='3')
short_stoploss_value = input.float(defval=5, title='Stoploss %', minval=0.1, group='SL/TP For Short Strategy', inline='4')
short_stoploss_percentage = close * (short_stoploss_value / 100) / syminfo.mintick
short_takeprofit_value = input.float(defval=5, title='Take Profit %', minval=0.1, group='SL/TP For Short Strategy', inline='4')
short_takeprofit_percentage = close * (short_takeprofit_value / 100) / syminfo.mintick

//// Inputs
//SQUEEZE MOMENTUM STRATEGY
length = input(20, title='BB Length', group = "Squeeze Momentum Settings")
mult = input(2.0, title='BB MultFactor', group = "Squeeze Momentum Settings")
source = close
lengthKC = input(20, title='KC Length', group = "Squeeze Momentum Settings")
multKC = input(1.5, title='KC MultFactor', group = "Squeeze Momentum Settings")
useTrueRange = input(true, title='Use TrueRange (KC)', group = "Squeeze Momentum Settings")
signalPeriod=input(5, title="Signal Length", group = "Squeeze Momentum Settings")
show_labels_sqm = input(title='Show Buy/Sell SQM Labels', defval=true, group = "Squeeze Momentum Settings")
h0 = hline(0)

// Defining MA
ma = ta.sma(source, length)

// Calculate BB
basis = ma
dev = mult * ta.stdev(source, length)
upperBB = basis + dev
lowerBB = basis - dev

// Calculate KC
range_1 = useTrueRange ? ta.tr : high - low
rangema = ta.sma(range_1, lengthKC)
upperKC = ma + rangema * multKC
lowerKC = ma - rangema * multKC


// SqzON | SqzOFF | noSqz
sqzOn = lowerBB > lowerKC and upperBB < upperKC
sqzOff = lowerBB < lowerKC and upperBB > upperKC
noSqz = sqzOn == false and sqzOff == false

// Momentum
val = ta.linreg(source - math.avg(math.avg(ta.highest(high, lengthKC), ta.lowest(low, lengthKC)), ta.sma(close, lengthKC)), lengthKC, 0)

red_line = ta.sma(val,signalPeriod)
blue_line = val

// lqm = if val > 0
//         if val > nz(val[1])
        // long_sqm_custom
        // if val < nz(val[1])
        // short_sqm_custom
// Plots
//plot(val, style = plot.style_line, title = "blue line", color= color.blue, linewidth=2)
//plot(ta.sma(val,SignalPeriod), style = plot.style_line, title = "red line",color = color.red, linewidth=2)

//plot(val, color=blue, linewidth=2)
//plot(0, color=color.gray, style=plot.style_cross, linewidth=2)
//plot(red_line, color=red, linewidth=2)

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

////SQM Long Short Conditions
//Lazy Bear Buy Sell Condition
// long_sqm_lazy = (blue_line>red_line)
// short_sqm_lazy = (blue_line<red_line)

long_sqm_lazy = ta.crossover(blue_line,red_line)
short_sqm_lazy = ta.crossunder(blue_line,red_line)


//Custom Buy Sell Condition
dir_sqm = val < 0 ? -1 : 1
long_sqm_custom = dir_sqm == 1 //and dir_sqm[1] == -1
short_sqm_custom = dir_sqm == -1 //and dir_sqm[1] == 1

long_sqm = strategy_logic == "LazyBear" ? long_sqm_lazy : long_sqm_custom 
short_sqm = strategy_logic == "LazyBear" ? short_sqm_lazy : short_sqm_custom 


// Plot Stoploss & Take Profit Levels
long_stoploss_price = strategy.position_avg_price * (1 - long_stoploss_value / 100)
long_takeprofit_price = strategy.position_avg_price * (1 + long_takeprofit_value / 100)
short_stoploss_price = strategy.position_avg_price * (1 + short_stoploss_value / 100)
short_takeprofit_price = strategy.position_avg_price * (1 - short_takeprofit_value / 100)
plot(enable_long_strategy and not enable_short_strategy ? long_stoploss_percentage : na, color=color.red, style=plot.style_linebr, linewidth=2, title='Long SL Level')
plot(enable_long_strategy and not enable_short_strategy ? long_takeprofit_percentage : na, color=color.green, style=plot.style_linebr, linewidth=2, title='Long TP Level')
plot(enable_short_strategy and not enable_long_strategy ? short_stoploss_price : na, color=color.red, style=plot.style_linebr, linewidth=2, title='Short SL Level')
plot(enable_short_strategy and not enable_long_strategy ? short_takeprofit_price : na, color=color.green, style=plot.style_linebr, linewidth=2, title='Short TP Level')


// Long Strategy
if long_sqm and enable_long_strategy == true
    strategy.entry('Long', strategy.long)
    strategy.exit('Long  SL/TP', from_entry='Long', loss=long_stoploss_percentage, profit=long_takeprofit_percentage)
    strategy.close('Long', comment = "L. CL")

// Short Strategy
if short_sqm and enable_short_strategy == true 
    strategy.entry('Short', strategy.short)
    strategy.exit('Short SL/TP', from_entry='Short', loss=short_stoploss_percentage, profit=short_takeprofit_percentage)
    strategy.close('Short', comment = "S.Cl")

plot_sqm_long = long_sqm and not long_sqm[1]
plot_sqm_short = short_sqm and not short_sqm[1]

plotshape(plot_sqm_long and show_labels_sqm, title='Buy', style=shape.labelup, location=location.belowbar, size=size.normal, text='Buy', textcolor=color.new(color.white, 0), color=color.new(color.green, 0))
plotshape(plot_sqm_short and show_labels_sqm, title='Sell', style=shape.labeldown, location=location.abovebar, size=size.normal, text='Sell', textcolor=color.new(color.white, 0), color=color.new(color.red, 0))

// Date Range EXIT
if (not isPeriod) 
    strategy.cancel_all()
    strategy.close_all()

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