DPD RSI BB Quantitative Strategy

The DPD-RSI-BB quantitative strategy combines three indicators - DPD, RSI and Bollinger Bands for stock trading. It uses DPD to determine the trend, RSI to judge overbought and oversold levels, and Bollinger Bands to…

Pine ScriptMean reversion Trend following RSI EMA SMA Bollinger Bands Pivots / S&R

Author: ChaoZhang
Complexity: Intermediate · 91 lines
Parameters: 15
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy consists of the following main components:

DPD indicator to determine the trend

It builds the DEMA line using double EMA averages and calculates the price differential percentage against DEMA as a trend determination indicator. A low difference percentage is used as a bullish signal.
RSI indicator to judge overbought and oversold conditions

It calculates the RSI value over a certain period. RSI above the upper limit is judged as an overbought zone and RSI below the lower limit is judged as an oversold zone.
Bollinger Bands to identify support and resistance

It calculates the middle band, upper band and lower band over a certain period. Price approaching the upper band signals a bearish outlook, while price approaching the lower band signals a bullish outlook.
Comprehensive judgment

When the DPD price differential percentage is lower than the threshold, RSI is lower than the oversold zone's lower limit, and price is lower than the Bollinger upper band, a bullish signal is generated. When RSI is higher than the overbought zone's upper limit, DPD differential percentage is higher than the threshold, and price is higher than the Bollinger upper band, a bearish signal is generated.

Advantage Analysis

The strategy has the following advantages:

Comprehensive judgment using multiple indicators avoids false signals from a single indicator.
Using the RSI indicator to judge overbought and oversold conditions allows setting stop loss and take profit points beforehand.
The DPD indicator can better determine price trends, while Bollinger Bands can identify support and resistance levels.
Flexible parameter settings allow optimizations for different stocks.

Risks and Optimization

The strategy also has some risks:

The combination of multiple indicators makes the strategy rather complex with difficulty in parameter tuning.
Indicators like DPD and RSI have certain lags, which may miss the best entry timing.
Parameters need to be optimized to suit different cycles and stock characteristics.

The following aspects can be optimized:

Adjust indicator parameters to optimize entry and exit points.
Add stop loss mechanisms to strictly control per trade loss.
Test on different stocks and cycle parameters to evaluate strategy performance.

Conclusion

The DPD-RSI-BB strategy combines multiple indicators for judgments to avoid false signals from a single indicator. Through parameter optimization, it can become a relatively strong stock trading strategy. But due to its complexity, it may still fail to completely hedge against market risks and should be used with caution.

Parameters

Name	Default	What it controls
v_input_1	-1	buyper
v_input_2	false	sellper
v_input_3	50	Dema Length
v_input_4	6	lengthrsi
v_input_5	20	overSold
v_input_6	60	overBought
v_input_7	50	lengthbb
v_input_8	1.5	multlow
v_input_9	1.5	multup
v_input_10	2018	yearfrom
v_input_11	2039	yearuntil
v_input_12	6	monthfrom
v_input_13	12	monthuntil
v_input_14	true	dayfrom
v_input_15	31	dayuntil

Source Pine Script

/*backtest
start: 2023-11-14 00:00:00
end: 2023-11-21 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version= 2
strategy("DPD+RSI+BB ",overlay=true)
price=close




//############### DPD  #################


buyper =input(-1,step=0.1)
sellper=input(0,step=0.1)
demalen = input(50,title="Dema Length")
e1= ema(close,demalen)
e2=ema(e1,demalen)
demaprice  =   2 * e1 - e2
demadifper =  ((price-demaprice)/price)*100


//############## DPD #####################

//############# RSI ####################


lengthrsi = input(6)
overSold = input( 20 )
overBought = input( 60 )

vrsi = rsi(price, lengthrsi)

//########## RSI #######################

//############### BB #################

lengthbb = input(50, minval=1)
multlow = input(1.5, minval=0.001, maxval=50,step=0.1)
multup = input(1.5,minval=0.001,maxval=50,step=0.1)

basisup = sma(close, lengthbb)
basislow = sma(close, lengthbb)

devup = multup * stdev(close, lengthbb)

devlow = multlow*stdev(close,lengthbb)

upperbb = basisup + devup
lowerbb = basislow - devlow

p1 = plot(upperbb, color=blue)
p2 = plot(lowerbb, color=blue)
fill(p1, p2)



//########### BB ###################




yearfrom = input(2018)
yearuntil =input(2039)
monthfrom =input(6)
monthuntil =input(12)
dayfrom=input(1)
dayuntil=input(31)



if (  (demadifper<buyper) and crossover(vrsi,overSold) and  (price < upperbb) and   year >= yearfrom and year <= yearuntil and month>=monthfrom and month <=monthuntil and dayofmonth>=dayfrom and dayofmonth < dayuntil) 
    strategy.entry("BUY", strategy.long, stop=close, oca_name="TREND",  comment="BUY")
    
else
    strategy.cancel(id="BUY")


if (   price>upperbb and vrsi>overBought and demadifper>sellper   and  year >= yearfrom and year <= yearuntil and month>=monthfrom and month <=monthuntil and dayofmonth>=dayfrom and dayofmonth < dayuntil ) 

    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 2023-11-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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