DEMA Volatility Index Strategy

This strategy uses Double Exponential Moving Average (DEMA) to calculate price volatility, and further smoothes the volatility to detect trends in price fluctuations, going long when volatility rises and short when…

Pine ScriptVolatility Machine learning Mean reversion RSI MACD EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 65 lines
Parameters: 12
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

Calculate Double Exponential Moving Average (DEMA) of price, formula: DEMA = 2*EMA(price, N) - EMA(EMA(price, N), N)
Calculate price volatility relative to DEMA: Volatility = (price - DEMA) / price * 100%
Apply DEMA smoothing on volatility again to get trend signal of volatility
When smoothed volatility crosses above a level, go long. When it crosses below, go short.
Can set to trade only during specific time period.

Advantages

DEMA catches trend changes faster than simple moving averages.
Volatility reflects market sentiment, rise in volatility represents dominance of bulls, fall represents bears.
Smoothing volatility filters out short-term noise and captures major trend.
Trading in specific time periods avoids unnecessary slippage costs.
Stop loss and exit strategies control risk.

Risks

DEMA may lag during strong trends, missing best entry points.
Volatility index may give false signals, should combine with other indicators.
Should set stop loss to prevent magnified losses.
Missing opportunities outside trading time period.
Trading time period needs testing on historical data, improper time may reduce profits.

Risk Management

Optimize DEMA parameters, use smaller N values.
Combine other indicators like RSI, MACD for confirmation.
Set stop loss based on historical data and maximum tolerable loss.
Optimize trading time period selection.
Test optimal trading times separately for different products.

Enhancement Opportunities

Test different DEMA parameter combinations for best smoothing.
Try other moving averages like EMA, SMA.
Additional smoothing of volatility with different parameters.
Add other indicators for multi-factor verification.
Use machine learning to auto-optimize entry and exit parameters.
Test optimal parameters separately for different products.
Add stop loss and exit strategies to control risk.

Summary

This strategy captures trend changes in market sentiment by calculating smoothed DEMA volatility, going long when volatility rises and short when it falls. But DEMA lag and false signals are risks. Parameters should be optimized, strict stop loss implemented, and other indicators combined for confirmation. If used properly, this strategy can catch trend reversals and provide good investment returns.

Parameters

Name	Default	What it controls
v_input_1	-2	buyper
v_input_2	2	sellper
v_input_3	50	Dema Length
v_input_4	true	Band for OverBought
v_input_5	-1	Band for OverSold
v_input_6	21	DEMA to Calculate dema of DPD
v_input_7	2018	yearfrom
v_input_8	2019	yearuntil
v_input_9	6	monthfrom
v_input_10	12	monthuntil
v_input_11	true	dayfrom
v_input_12	31	dayuntil

Source Pine Script

/*backtest
start: 2022-10-17 00:00:00
end: 2023-10-23 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version= 2
strategy("DEMA of DPD Strategy ",shorttitle="DPD% DEMA " ,overlay=false)

buyper =input(-2)
sellper=input(2)

demalen = input(50,title="Dema Length")

e1= ema(close,demalen)
e2=ema(e1,demalen)
demaprice  =   2 * e1 - e2

price=close
demadifper =  ((price-demaprice)/price)*100


OverDemaPer = input(1, title="Band for OverBought")
UnderDemaPer= input(-1,title="Band for OverSold")

band1 = hline(OverDemaPer)
band0 = hline(UnderDemaPer)
zeroline=0
fill(band1, band0, color=green, transp=90)


demalen2 = input(21,title="DEMA to Calculate dema of DPD")
demaofdpd =ema(demadifper,demalen2)
demaofdpd2 =ema(demaofdpd,demalen2)
resultstrategy = 2*demaofdpd - demaofdpd2

plot(resultstrategy,color=blue)


yearfrom = input(2018)
yearuntil =input(2019)
monthfrom =input(6)
monthuntil =input(12)
dayfrom=input(1)
dayuntil=input(31)



if (  crossover(resultstrategy,buyper)  ) 
    strategy.entry("BUY", strategy.long, stop=close, oca_name="TREND",  comment="BUY")
    
else
    strategy.cancel(id="BUY")


if ( crossunder(resultstrategy,sellper) ) 

    strategy.entry("SELL", strategy.short,stop=close, oca_name="TREND",  comment="SELL")
else
    strategy.cancel(id="SELL")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-24. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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