Momentum Line Crossover EMA Nine Stock MACD Strategy

This strategy comprehensively utilizes the EMA indicator, Bollinger Bands indicator and MACD indicator. On the basis of the golden cross and death cross of the 9-day EMA and 30-day EMA, it determines the timing of…

Pine ScriptMomentum Trend following Breakout MACD EMA SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 73 lines
Parameters: 9
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Principle

Calculate the 3-day EMA, 9-day EMA and 30-day EMA.
Calculate the standard deviation within 20 days of the price and draw the Bollinger Bands with 1 and 2 times the standard deviation.
Calculate the 12-day, 26-day MACD and 9-day signal line.
When the 9-day EMA goes above the 30-day EMA, and the price exceeds the upper limit of the 1x standard deviation Bollinger Bands, a buy signal is issued.
When the 30-day EMA goes below the 9-day EMA, and the price is lower than the lower limit of the 1x standard deviation Bollinger Bands, a sell signal is issued.

Advantage Analysis

By combining moving average indicators and momentum indicators, this strategy can better grasp market trends and timing, with the following advantages:

The EMA indicator can respond quickly to price changes to determine market trends; the MACD indicator judges momentum to prevent false breakouts.
The combination of Bollinger Bands standard deviation indicators and EMAs can more accurately determine the timing of buying and selling.
The combination of multiple indicators can complement each other. Different indicators can verify the judgment in one breakthrough.

Risk and Optimization Analysis

This strategy also has some risks. Pay attention to the following points for optimization:

The combination of EMA moving averages can be adjusted and optimized. Different cycles can better capture trends.
The parameters of Bollinger Bands can be optimized by changing the multiplication of standard deviations to filter out false signals.
The parameters and combination of the MACD indicator can be optimized to improve the effect of judging momentum.

Summary

This strategy integrates the EMA indicator to determine the major trend, supplemented by the Bollinger Bands indicator which can accurately seize buy and sell points when momentum is relatively large; the MACD indicator supplements trend confirmation and can effectively filter out false signals. Through parameter optimization, the effect of this strategy can be further improved.

Parameters

Name	Default	What it controls
v_input_1	3	3 EMA
v_input_2	9	9 EMA
v_input_3	30	30 EMA
v_input_4	12	MACD Short
v_input_5	26	MACD Long
v_input_6	9	MACD Signal
v_input_int_1	20	length
v_input_7_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_float_1	2	StdDev

Source Pine Script

/*backtest
start: 2023-02-20 00:00:00
end: 2024-02-26 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("emabb_collab", shorttitle="emabb", overlay=true)

// Input parameters
ema3 = input(3, title="3 EMA")
ema9 = input(9, title="9 EMA")
ema30 = input(30, title="30 EMA")
macdShort = input(12, title="MACD Short")
macdLong = input(26, title="MACD Long")
macdSignal = input(9, title="MACD Signal")
length = input.int(20, minval=1)
src = input(close, title="Source")
mult = input.float(2.0, minval=0.001, maxval=50, title="StdDev")
basis = ta.sma(src, length)
dev1 = mult * ta.stdev(src, length)
upper1 = basis + dev1
lower1 = basis - dev1
dev2 = mult * 2 * ta.stdev(src, length)
upper2 = basis + dev2
lower2 = basis - dev2
plot(basis, "Basis", color=#FF6D00)
p1 = plot(upper1, "Upper1", color=#2962FF)
p2 = plot(lower1, "Lower1", color=#2962FF)
fill(p1, p2, title = "Background", color=color.rgb(33, 150, 243, 95))
plot(basis, "Basis", color=#FF6D00)
p3 = plot(upper2, "Upper2", color=#00FF8C)
p4 = plot(lower2, "Lower2", color=#00FF8C)
fill(p3, p4, title = "Background", color=color.rgb(0, 153, 140, 95))

// Calculate EMAs
ema3Value = ta.ema(close, ema3)
ema9Value = ta.ema(close, ema9)
ema30Value = ta.ema(close, ema30)


// Calculate MACD
[macdLine, signalLine, _] = ta.macd(close, macdShort, macdLong, macdSignal)


// Conditions for buy signal
buyCondition = ta.crossover(ema9Value, ema30Value)  and ta.stdev(close, 20) > ta.stdev(close, 20)[1]

//Conditions for sell signal
sellCondition = ta.crossover(ema30Value, ema9Value)  and ta.stdev(close, 20) < ta.stdev(close, 20)[1]

// Plot signals on the chart

plotshape(buyCondition, title='Buy Label', style=shape.triangleup, location=location.belowbar, size=size.normal, text='Buy', textcolor=color.new(color.white, 0), color=color.new(color.green, 0))
plotshape(sellCondition, title='sell Label', style=shape.triangledown, location=location.abovebar, size=size.normal, text='sell', textcolor=color.new(color.white, 0), color=color.new(color.red, 0))

// Plot EMAs
plot(ema3Value, title="3 EMA", color=color.orange)
plot(ema9Value, title="9 EMA", color=color.purple)
plot(ema30Value, title="30 EMA", color=color.red)


if buyCondition
    strategy.entry('Long', strategy.long)
if sellCondition
    strategy.entry('Short', strategy.short)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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