Moving Average Trailing Stop Strategy

This strategy generates buy signals when fast EMA buy line crosses over slow SMA buy line, and uses ATR dynamic trailing stop for risk control. It aims to outperform buy and hold strategy with limited trades.

Pine ScriptMachine learning Trend following Volatility EMA SMA ATR

Author: ChaoZhang
Complexity: Intermediate · 81 lines
Parameters: 14
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Calculate fast EMA and slow SMA buy lines, generate buy signal when fast line crosses over slow line with certain buy strength.
Calculate fast EMA and slow SMA sell lines, generate sell signal when fast line crosses below slow line.
Use N day ATR average multiplied by coefficient as dynamic trailing stop for risk control.
Start strategy in backtest period for buy and sell execution.
Optimize parameters for each stock to find best values.

The strategy combines the advantages of MA crossing for signals and ATR trailing stop for risk control. Parameter optimization adapts to each product's characteristics, aiming for excess returns over buy and hold with precise trades.

Advantage Analysis

Fast EMA and slow SMA crossovers identify trends and generate signals.
ATR stop adjusts based on market volatility, effectively controlling risks.
Optimizing for each stock improves profitability.
Simple logic and rules, easy to implement and verify.
Complete backtest functionality to validate strategy.
Seeks steady outperformance over buy and hold.

Risk Analysis

Optimized parameters may not work for future, periodic re-optimization may be needed.
EMA and SMA crosses may generate incorrect or lagging signals.
ATR stop may be too aggressive, can loosen stop loss range.
Low trade frequency may miss good opportunities.
Need to consider impact of trading costs.

Optimization Directions

Continue testing different parameter combinations for optimum values.
Try introducing other indicators for signal filtering.
Optimize ATR period to balance stop loss sensitivity.
Evaluate effect of relaxing stop loss range.
Consider machine learning for automated parameter optimization.
Study effect of increasing trade frequency.

Summary

This moving average trailing stop strategy combines the strengths of MA crossovers for signals and ATR stops for risk control. Parameter optimization adapts it to each stock's characteristics. Although optimized parameters have no guarantee, the overall logic is simple and practical for outperforming buy and hold. Further improvements and verification are worthwhile, as the strategy has good inspirational value.

Parameters

Name	Default	What it controls
v_input_1	2005	Start Year
v_input_2	true	Start Month
v_input_3	true	Start Day
v_input_4	2050	Stop Year
v_input_5	12	Stop Month
v_input_6	31	Stop Day
v_input_7	true	Background
v_input_8	12	Fast EMA Buy
v_input_9	54	Slow SMA Buy
v_input_10	52	Minimum Buy Strength
v_input_11	18	Fast EMA Sell
v_input_12	55	Slow SMA Sell
v_input_13	100	Minimum Sell Strength
v_input_14	8	Trailing Stop (#ATR)

Source Pine Script

/*backtest
start: 2023-01-01 00:00:00
end: 2023-09-18 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
//created by XPloRR 04-03-2018

strategy("XPloRR MA-Trailing-Stop Strategy",overlay=true, initial_capital=1000,default_qty_type=strategy.percent_of_equity,default_qty_value=100)

testStartYear = input(2005, "Start Year")
testStartMonth = input(1, "Start Month")
testStartDay = input(1, "Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)

testStopYear = input(2050, "Stop Year")
testStopMonth = input(12, "Stop Month")
testStopDay = input(31, "Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)

testPeriodBackground = input(title="Background", type=bool, defval=true)
testPeriodBackgroundColor = testPeriodBackground and (time >= testPeriodStart) and (time <= testPeriodStop) ? #00FF00 : na
bgcolor(testPeriodBackgroundColor, transp=97)

ema1Period = input(12, "Fast EMA Buy")
sma1Period = input(54, "Slow SMA Buy")
strength1 = input(52, "Minimum Buy Strength")

ema2Period = input(18, "Fast EMA Sell")
sma2Period = input(55, "Slow SMA Sell")
strength2 = input(100, "Minimum Sell Strength")

delta = input(8, "Trailing Stop (#ATR)")

testPeriod() => true

ema1val=ema(close,ema1Period)
sma1val=sma(close,sma1Period)
ema1strength=10000*(ema1val-ema1val[1])/ema1val[1]

ema2val=ema(close,ema2Period)
sma2val=sma(close,sma2Period)
ema2strength=10000*(ema2val-ema2val[1])/ema2val[1]

plot(ema1val,color=blue,linewidth=1)
plot(sma1val,color=orange,linewidth=1)
plot(ema2val,color=navy,linewidth=1)
plot(sma2val,color=red,linewidth=1)

long=crossover(ema1val,sma1val) and (ema1strength > strength1) 
short=crossunder(ema2val,sma2val) and (ema2strength < -strength2)

stopval=ema(close,6)
atr=sma((high-low),15)

inlong=0
buy=0
stop=0
if testPeriod()
    if (inlong[1])
        inlong:=inlong[1]
        buy:=close
        stop:=iff((stopval>(stop[1]+delta*atr)),stopval-delta*atr,stop[1])
    if (long) and (not inlong[1])
        strategy.entry("buy",strategy.long)
        inlong:=close
        buy:=close
        stop:=stopval-delta*atr
plot(buy,color=iff(close<inlong,red,lime),style=columns,transp=90,linewidth=1)
plot(stop,color=iff((short or (stopval<stop)) and (close<inlong),red,lime),style=columns,transp=60,linewidth=1)
if testPeriod()
    if (short or (stopval<stop)) and (inlong[1])
        strategy.close("buy")
        inlong:=0
        stop:=0
        buy:=0

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-19. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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