Double Moving Average Crossover Trading Strategy

The double moving average crossover trading strategy generates trading signals by calculating two moving averages with different parameter settings and trading when the moving averages cross over. This simple and…

Pine ScriptTrend following Machine learning Breakout EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 120 lines
Parameters: 20
Backtest bar: 15m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The core logic of this strategy is:

Input parameters: faster MA period maLen1, slower MA period maLen2, MA type maTypeChoice
Calculate faster MA maValue1 and slower MA maValue2 based on input parameters
Define buy and sell conditions by comparing the two MAs:
- Buy when maValue1 crosses above maValue2
- Sell when maValue1 crosses below maValue2
Execute trades at buy and sell signals
Visualize MAs in different colors based on their relationship
Send buy and sell alert signals

Advantages

Utilizes dual MA crossover system, avoids false signals from single MA
Customizable MA periods suit different trading horizons
Simple and straightforward logic, easy to understand and implement
Customizable signal alerts for timely execution
Visualized MA trends form intuitive trading indicator
Optimizable parameters to find best combination
Applicable for backtesting and live trading

Risks

MA crosses may generate false signals, need extra trend and pattern confirmation
Whipsaws around MA crossover incur unnecessary trading costs
Improper parameters lead to over-trading or sparse trading
Extreme events cause huge price swings, unable to limit losses
Long-term trend breaks invalidate short-term indicators
Requires frequent monitoring, not fully automatable

Risk Management:

Add trend filter to avoid trading against trend
Add pattern filter to confirm signal validity
Optimize parameters for reasonable trading frequency
Set stop loss/take profit to limit losses
Robustness test across long time frames
Price and time filters to avoid false breakouts

Optimization Directions

Test different MA parameters to find optimum
Test different MA types for most accurate signals
Add trend filter to avoid counter-trend trades
Add volatility filter to identify proper exit points
Add price/time filter to reduce false signals
Implement slippage control for real trading
Robustness test across instruments and timeframes
Integrate auto stop loss/take profit
Explore machine learning to improve strategy

Conclusion

The dual moving average crossover is a classic technical indicator strategy. It generates signals from MA crosses and can produce good backtest results through optimization. However, risks like false signals remain, requiring additional filters. Real trading also needs execution details like slippage control. Overall, the strategy suits medium-term trading as a simple and intuitive choice. With continuous improvements and robustness validation, this strategy can achieve stable returns in live trading.

Parameters

Name	Default	What it controls
v_input_1	0	MA Type: EMA \| WMA \| SMA
v_input_2_close	0	MA Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_3	15	MA Length
v_input_4	95	MA Length
v_input_5	true	(?Strategy Tester)Strategy Tester [ON/OFF]
v_input_6	2200-1200	Daily trading time session (in Exchange GMT)
v_input_7	2200-2201	Start Time
v_input_8	2500	Max daily loss
v_input_9	12000	Max daily loss
v_input_10	true	Contract size
v_input_11	true	First trade on session start [ON/OFF]
v_input_12	false	Fixed TP/SL PIPS [ON/OFF]
v_input_13	10	TP
v_input_14	10	SL
v_input_15	true	(?Date Range)From Day
v_input_16	true	From Month
v_input_17	2020	From Year
v_input_18	true	Thru Day
v_input_19	true	Thru Month
v_input_20	2112	Thru Year

Source Pine Script

/*backtest
start: 2023-10-05 00:00:00
end: 2023-10-05 22:00:00
period: 15m
basePeriod: 5m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
// © sehweijun
//study( title="Arch1tect's New Toy", shorttitle="Arch1tect's New Toy", overlay=true, resolution="")
// strategy( title="Arch1tect's New Toy (Strategy Tester Version)", shorttitle="Arch1tect's New Toy (Strategy Tester Version)", overlay=true, initial_capital = 100000, commission_value=0.07, commission_type=strategy.commission.cash_per_contract)

maTypeChoice = input( "EMA", title="MA Type", options=["EMA", "WMA", "SMA"] )
maSrc = input( close, title="MA Source" )
maLen1 = input( 15, minval=1, title="MA Length" )
maLen2 = input( 95, minval=1, title="MA Length" )

maValue1 = if ( maTypeChoice == "EMA" )
    ema( maSrc, maLen1 )
else if ( maTypeChoice == "WMA" )
    wma( maSrc, maLen1 )
else if ( maTypeChoice == "SMA" )
    sma( maSrc, maLen1 )
else
    0
    
maValue2 = if ( maTypeChoice == "EMA" )
    ema( maSrc, maLen2 )
else if ( maTypeChoice == "WMA" )
    wma( maSrc, maLen2 )
else if ( maTypeChoice == "SMA" )
    sma( maSrc, maLen2 )
else
    0

buySignal = crossover( maValue1, maValue2 )
sellSignal = crossunder( maValue1, maValue2 )

mainMAColour = ( maValue1 > maValue2 ) ? color.green : color.red 

plot( maValue1, title="Arch1tect's New Toy", color=mainMAColour, offset=0, linewidth=4 )
//plot( maValue2, title="Arch1tect's Filter", color=color.black, offset=0, linewidth=2 )

var color buyCandleColour = #00ff0a
var color sellCandleColour = #ff1100

barcolor( buySignal ? buyCandleColour : sellSignal ? sellCandleColour : na, title="Signal Bar Colour" )
bgcolor( color=buySignal ? buyCandleColour : sellSignal ? sellCandleColour : na, transp=85, title="Signal Background Colour")

alertcondition( buySignal or sellSignal, title="Signal change!", message="Signal change!")
alertcondition( buySignal, title="Buy signal!", message="Buy signal!")
alertcondition( sellSignal, title="Sell signal!", message="Sell signal!")

// Strategy Tester
stratTesterOn    = input( title="Strategy Tester [ON/OFF]", group="Strategy Tester", type=input.bool, defval=true)
entryTime        = input( "2200-1200", title = "Daily trading time session (in Exchange GMT)", group="Strategy Tester", type = input.session )
startTime        = input( "2200-2201", title = "Start Time", group="Strategy Tester", type = input.session )
maxDailyLoss     = input( 2500, title = "Max daily loss", group="Strategy Tester", type = input.integer )
maxTotalDrawdown = input( 12000, title = "Max daily loss", group="Strategy Tester", type = input.integer )
contractSize     = input( 1, title = "Contract size", group="Strategy Tester", type = input.integer )

tradeOnStartSess = input( title="First trade on session start [ON/OFF]", group="Strategy Tester", type=input.bool, defval=true)

fixedTPSL        = input( title="Fixed TP/SL PIPS [ON/OFF]", group="Strategy Tester", type=input.bool, defval=false)
fixedTPValue     = input ( 10.00, minval=0.01, type=input.float, title="TP", group="Strategy Tester" )
fixedSLValue     = input ( 10.00, minval=0.01, type=input.float, title="SL", group="Strategy Tester" )

fromDay          = input(defval = 1,    title = "From Day", group="Date Range", type = input.integer, minval = 1, maxval = 31)
fromMonth        = input(defval = 1,    title = "From Month", group="Date Range", type = input.integer, minval = 1, maxval = 12)
fromYear         = input(defval = 2020, title = "From Year", group="Date Range", type = input.integer, minval = 1970)
thruDay          = input(defval = 1,    title = "Thru Day", group="Date Range", type = input.integer, minval = 1, maxval = 31)
thruMonth        = input(defval = 1,    title = "Thru Month", group="Date Range", type = input.integer, minval = 1, maxval = 12)
thruYear         = input(defval = 2112, title = "Thru Year", group="Date Range", type = input.integer, minval = 1970)

start     = timestamp(fromYear, fromMonth, fromDay, 00, 00)        // backtest start window
finish    = timestamp(thruYear, thruMonth, thruDay, 23, 59)        // backtest finish window
window()  => time >= start and time <= finish ? true : false       // create function "within window of time"

// strategy.risk.max_intraday_loss( maxDailyLoss, strategy.cash )
// strategy.risk.max_drawdown( maxTotalDrawdown, strategy.cash )

isTime(_position) =>
    range = time( timeframe.period, _position + ':1234567' )
bgcolor( color=isTime( entryTime ) and stratTesterOn and window() ? color.yellow : na, title="Daily trading time session (in Exchange GMT)", transp=75 )

if ( stratTesterOn and window() )
    if ( buySignal and isTime( entryTime ) )
        if ( not fixedTPSL )
            strategy.close_all()
            strategy.entry( "Buy", strategy.long, contractSize )
        
        if ( fixedTPSL and strategy.position_size == 0 )
            strategy.entry( "Buy", strategy.long, contractSize )
            strategy.exit( "TP/SL", "Buy", stop=close[0]-fixedSLValue, limit=close[0]+fixedTPValue )
        
    if ( sellSignal and isTime( entryTime ))
        if ( not fixedTPSL )
            strategy.close_all()
            strategy.entry( "Sell", strategy.short, contractSize )
        
        if ( fixedTPSL and strategy.position_size == 0  )
            strategy.entry( "Sell", strategy.short, contractSize )
            strategy.exit( "TP/SL", "Sell", stop=close[0]+fixedSLValue, limit=close[0]-fixedTPValue )
    
    if ( isTime( startTime ) and tradeOnStartSess and strategy.position_size == 0 )
        if ( maValue1 > maValue2 )
            strategy.entry( "Buy", strategy.long, contractSize )
            
            if ( fixedTPSL )
                strategy.exit( "TP/SL", "Buy", stop=close[0]-fixedSLValue, limit=close[0]+fixedTPValue )
        else
            strategy.entry( "Sell", strategy.short, contractSize ) 
            
            if ( fixedTPSL )
                strategy.exit( "TP/SL", "Sell", stop=close[0]+fixedSLValue, limit=close[0]-fixedTPValue )
    
    strategy.close_all( when=not isTime( entryTime ) )

plot( strategy.equity )

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-13. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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