Dual Moving Average Golden Cross Quantitative Strategy

Pine ScriptTrend following Breakout Volatility MACD SMA Volume

Author: ChaoZhang
Complexity: Starter · 51 lines
Parameters: 6
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy judges price trends and trading opportunities by calculating dual moving average crossovers. When the fast MA crosses above the slow MA, it is considered a golden cross to go long. When the fast MA crosses below the slow MA, it is considered a death cross to go short. At the same time, combine the Volume Indicator to judge the reliability of crossovers to avoid false signals.

Strategy Principle

The core principles of this strategy are:

Calculate two groups of moving averages with different parameters, one group responds quickly to price changes and the other reacts relatively slowly. When the fast MA crosses above the slow MA, it signals an upward price trend. When the fast MA crosses below the slow MA, it signals a downward price trend.
When the MAs cross, check the change of the Volume Indicator. If the Volume Indicator also breaks through significantly, the crossover signal is reliable. If there is no corresponding breakout in Volume, it may be a false signal.
Enter long or short positions based on the crossover direction and volume judgment. Set take profit criteria to close positions when reaching a certain profit level.

Specifically, the strategy judges price trends using 7-period dual MAs crossover. It uses Volume indicators to check signal reliability. When getting reliable signals, it goes long or short per the SIGNAL. It sets profit targets to take profits.

Advantage Analysis

The main advantages of this strategy are:

Combining dual MAs to determine trend direction and Volume filter to avoid false signals and being trapped.
Only taking positions when Volume confirms, increasing success rate.
Having profit taking mechanism to take profits in time and avoid giving back profits.

Risk Analysis

The main risks of this strategy are:

MA crossover delay may miss best opportunity. Can optimize parameters to make MAs more sensitive.
Hard to judge when Volume diverges. Can add more indicators for confirmation.
Improper stop profit setting may lead to over trading or holding winners too long. Need to test and optimize stop profit parameters.

Optimization Directions

The strategy can be optimized from the following aspects:

Optimize MA periods to make it more responsive to catch price changes in time.
Add more indicators such as MACD, KD for signal confirmation to avoid Volume false signals.
Incorporate more profit taking mechanics like trail stop, percentage stop, volatility stop for dynamic profit taking.
Add stop loss mechanism to control single trade loss amount.
Optimize position sizing adaptable to different market environments.

Conclusion

In conclusion, the core idea of this strategy is using dual MA crossover for trend and Volume filter for signal reliability. It is stable and easy to implement. Further optimizations on parameters, signal filtering, profit taking and stop loss can make it more reliable and intelligent for practical trading.

Parameters

Name	Default	What it controls
v_input_1	0.001	Decision Threshold
v_input_2	7	Double HullMA Cross
v_input_3	34	MA 1
v_input_4	144	MA 2
v_input_5	377	MA 3
v_input_6	4200	TP ($)

Source Pine Script

/*backtest
start: 2022-12-19 00:00:00
end: 2023-12-25 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
strategy("ZendicatoR", overlay=true, calc_on_order_fills= true, calc_on_every_tick=true, default_qty_type=strategy.percent_of_equity, default_qty_value=15, pyramiding=0)
dt = input(defval=0.0010, title="Decision Threshold", type=float, step=0.0001)
keh=input(title="Double HullMA Cross",defval=7, minval=1)
che1=input(title="MA 1",defval=34,minval=1)
che2=input(title="MA 2",defval=144,minval=1)
che3=input(title="MA 3",defval=377,minval=1)
amnt=input(title="TP ($)",defval=4200,minval=1)
wma1=wma(close,che1)
wma2=wma(close,che2)
wma3=wma(close,che3)
sma1=sma(close,11)
tms=10000000000000
A=request.security(syminfo.tickerid, 'D', close)*tms
B=request.security(syminfo.tickerid, 'D', close[1])*tms
C=A>B?green:red
D=wma2>wma3?green:red
plot(wma1,style=line,color=C,linewidth=4)
p1=plot(wma2,style=line,color=D)
p2=plot(wma3,style=line,color=D)
fill(p1, p2, color=D, transp=75)
n2ma=2*wma(close,round(keh/2))
nma=wma(close,keh)
diff=n2ma-nma,sqn=round(sqrt(keh))
n2ma1=2*wma(close[2],round(keh/2))
nma1=wma(close[2],keh)
diff1=n2ma1-nma1,sqn1=round(sqrt(keh))
n1=wma(diff,sqn)*tms
n2=wma(diff1,sqn)*tms
Q=n1>n2?blue:yellow
plot(sma1,style=line,color=Q,linewidth=4)
closelong = A*tms<B*tms and n2*tms>n1*tms and strategy.openprofit>amnt
if (closelong)
    strategy.close("Long")
closeshort = A*tms>B*tms and n1*tms>n2*tms and strategy.openprofit>amnt
if (closeshort)
    strategy.close("Short") 
longCondition = A*tms>B*tms and n1*tms>n2*tms
if (longCondition)
    strategy.entry("Long",strategy.long)
shortCondition = A*tms<B*tms and n1*tms<n2*tms
if (shortCondition)
    strategy.entry("Short",strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-26. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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