资源加载中... loading...
  1. Square
  2. Price Volatility Breakout Strategy Based on Double Moving Averages

Price Volatility Breakout Strategy Based on Double Moving Averages

Author: ChaoZhang, Date: 2023-12-08 16:44:22
Tags:

img

Overview

The core idea of this strategy is to use price volatility to judge market trends. When volatility rises, it means the market is forming a new trend. And when volatility declines, it means the current trend is ending. The strategy calculates the percentage change of price and then filters it with double moving averages to get an indicator reflecting price volatility. It generates buy signals when the indicator crosses above its signal line, and sells signals when crossing below.

Strategy Logic

The strategy first calculates the percentage change of price:

i=(src/nz(src[1], src))*100

Then it filters i with a 35-period moving average to get the preliminary volatility indicator pmol2. Pmol2 is filtered again with a 20-period moving average to get the final indicator pmol. Finally, a 10-period moving average of pmol is used as the signal line pmols. Buy when pmol crosses over pmols and sell when crossing below.

Advantage Analysis

  • The double MA filtering extracts volatility well and filters out noise.
  • Calculating percentage change amplifies price movements, making trend changes more visible.
  • Profit model is clear: buy at trend start, sell at trend end.

Risk Analysis

  • Double filtering causes some lag.
  • Percentage change calculation is sensitive to price amplitude.
  • Need timely exits at bull-bear transitions.

Optimization Directions

  • Optimize MA parameters to improve trend catching.
  • Try different price change calculation methods.
  • Add filters to avoid wrong signals.

Summary

This strategy uses percentage change and double MA filtering to extract price volatility and judge trend changes. It belongs to the relatively mature technical indicator strategies. The strategy has good trend catching capability but medium turning point recognition capability. Can optimize via parameter tuning and adding auxiliary conditions.

/*backtest
start: 2022-12-01 00:00:00
end: 2023-12-07 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
strategy("Strategy for DPMO", overlay=true)

src=input(close, title="Source")
length1=input(35, title="First Smoothing")
length2=input(20, title="Second Smoothing")
siglength=input(10, title="Signal Smoothing")
ebc=input(false, title="Enable Bar Colors")

upSign = '↑' // indicates the indicator shows uptrend
downSign = '↓' // incicates the indicator showing downtrend
exitSign ='x' //indicates the indicator uptrend/downtrend ending

calc_csf(src, length) => 
	sm = 2.0/length
	csf=(src - nz(csf[1])) * sm + nz(csf[1])
	csf
i=(src/nz(src[1], src))*100
pmol2=calc_csf(i-100, length1)
pmol=calc_csf( 10 * pmol2, length2)
pmols=ema(pmol, siglength)
d=pmol-pmols
hc=d>0?d>d[1]?lime:green:d<d[1]?red:orange

buyDPMO = hc==lime and hc[1]!=lime
closeBuyDPMO = hc==green and hc[1]!=green
sellDPMO = hc==red and hc[1]!=red
closeSellDPMO = hc==orange and hc[1]!=orange

plotshape(buyDPMO, color=lime, style=shape.labelup, textcolor=#000000, text="DPMO", location=location.belowbar, transp=0)
plotshape(closeBuyDPMO, color=green, style=shape.labelup, textcolor=#ffffff,  text="X", location=location.belowbar, transp=0)
plotshape(sellDPMO, color=red, style=shape.labeldown, textcolor=#000000, text="DPMO", location=location.abovebar, transp=0)
plotshape(closeSellDPMO, color=orange, style=shape.labeldown, textcolor=#ffffff,  text="X", location=location.abovebar, transp=0)
barcolor(ebc?hc:na)


strategy.entry("Long", strategy.long, when=buyDPMO)
strategy.close("Long", when=closeBuyDPMO or sellDPMO)   
strategy.entry("Short", strategy.short, when=sellDPMO)
strategy.close("Short", when=closeSellDPMO or buyDPMO)
More

2019 FMZ - All rights reserved