This strategy makes trading decisions based on the trend of MACD Histogram

This strategy makes trading decisions based on the trend of MACD Histogram. It utilizes the upward and downward trends of Histogram to generate buy and sell signals. When the Histogram continues to rise or fall for a…

Pine ScriptTrend following MACD EMA SMA

Author: ChaoZhang
Complexity: Intermediate · 68 lines
Parameters: 8
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy uses the fast line, slow line and Histogram of the MACD indicator. First calculate the fast EMA and slow EMA. Then subtract slow EMA from fast EMA to get MACD, and subtract Signal which is the moving average of MACD to get Histogram.

When the Histogram continues to rise for the set period, a buy signal is generated. This indicates that MACD is accelerating to break through its signal line upward, predicting that prices may rise.

When the Histogram continues to decline for the set period, a sell signal is generated. This indicates that MACD is accelerating to break through its signal line downward, predicting that prices may fall.

Advantage Analysis

The strategy has the following advantages:

Utilizing the trending characteristic of MACD Histogram, it can capture the turning points of price changes and enhance profitability.
Combining with the condition of consecutive rise or fall of Histogram, some noisy trades can be filtered out to reduce unnecessary losses.
Allowing customization of MACD parameters and Histogram trend period, it can be adjusted to suit different products and trading sessions.
The strategy logic is simple and clear, easy to understand and modify, and also convenient to combine with other indicators or strategies.

Risk Analysis

The strategy also has some risks:

Wrong signals may occur when prices are oscillating in range. Trend indicators need to be combined to filter.
After Histogram rises or falls, MACD line may fail to break through the signal line, unable to profitably exit. Stop loss should be set to control risk.
Trading cost and slippage are not considered. Actual profit may decrease in live trading.
Improper parameter settings (e.g. MACD period, Histogram trend period) may worsen strategy performance. Parameters need to be optimized for products and sessions.

These risks can be controlled and reduced through methods like combining with trend indicators, setting stop loss mechanism, optimizing parameters etc.

Optimization Directions

The strategy can be optimized in the following aspects:

Combine other indicators to determine overall trend direction, avoiding trading in oscillating ranges. E.g. 20-day line for medium-long term trend.
Add stop loss mechanism. E.g. stop loss when MACD re-breaks signal line downwards.
Optimize MACD parameters to suit products of different frequencies. E.g. shorten period parameters for high-frequency data.
Optimize minimum period of consecutive Histogram rise or fall, balancing signal frequency and reliability.
Try logic of trailing signals after breakout failure. I.e. trailing reverse signal after Histogram reversal.
Combine other indicators like volume or volatility indicators to gauge market heat and filter signals.

Conclusion

In conclusion, the MACD Histogram Trend strategy realizes judgment of price change turning points by capturing Histogram trend changes. Combining parameter optimization and combo indicators can effectively filter out wrong signals. As an important auxiliary judgment tool in quantitative trading, this strategy provides a simple and practical trading idea using MACD Histogram.

Parameters

Name	Default	What it controls
v_input_1	2017	Year
v_input_2	12	Fast Length
v_input_3	26	Slow Length
v_input_4	true	Trend of Histogram Number
v_input_5_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_6	9	Signal Smoothing
v_input_7	false	Simple MA(Oscillator)
v_input_8	false	Simple MA(Signal Line)

Source Pine Script

/*backtest
start: 2023-01-18 00:00:00
end: 2024-01-24 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
//study(title="Histogram Strategy by Sedkur", shorttitle="Histogram Strategy by Sedkur")
strategy (title="Histogram Trends Strategy by Sedkur", shorttitle="Histogram Trends Strategy by Sedkur")


/// Getting inputs
dyear = input(title="Year", type=input.integer, defval=2017, minval=1950, maxval=2500)
fast_length = input(title="Fast Length", type=input.integer, defval=12)
slow_length = input(title="Slow Length", type=input.integer, defval=26)
hist_length = input(title="Trend of Histogram Number", type=input.integer, defval=1, minval=1, maxval=100)
//buyh = input(title="Buy histogram value", type=input.float, defval=0.0, minval=-1000, maxval=1000, step=0.1)
//sellh = input(title="Sell histogram value", type=input.float, defval=0.0, minval=-1000, maxval=1000, step=0.1)
src = input(title="Source", type=input.source, defval=close)
signal_length = input(title="Signal Smoothing", type=input.integer, minval = 1, maxval = 50, defval = 9)
sma_source = input(title="Simple MA(Oscillator)", type=input.bool, defval=false)
sma_signal = input(title="Simple MA(Signal Line)", type=input.bool, defval=false)

// Plot colors
col_grow_above = #26A69A
col_grow_below = #FFCDD2
col_fall_above = #B2DFDB
col_fall_below = #EF5350
col_macd = #0094ff
col_signal = #ff6a00

/// Calculating
fast_ma = sma_source ? sma(src, fast_length) : ema(src, fast_length)
slow_ma = sma_source ? sma(src, slow_length) : ema(src, slow_length)
macd = fast_ma - slow_ma
signal = sma_signal ? sma(macd, signal_length) : ema(macd, signal_length)
hist = macd - signal

plot(hist, title="Histogram", style=plot.style_columns, color=(hist>=0 ? (hist[1] < hist ? col_grow_above : col_fall_above) : (hist[1] < hist ? col_grow_below : col_fall_below) ), transp=0 )
//plot(macd, title="MACD", color=col_macd, transp=0)
//plot(signal, title="Signal", color=col_signal, transp=0)

//bullish = hist[1] <= hist and buyh<=hist?true:false
//bearish = hist[1] >= hist and sellh>=hist?true:false
bull=0
bear=0


for i=0 to hist_length
    if (hist[i+1] <= hist[i])
        bull:=bull+1
bullish = bull==hist_length+1?true:false   

for j=0 to hist_length
    if (hist[j+1] >= hist[j])
        bear:=bear+1
bearish = bear==hist_length+1?true:false 



//bullish = hist[1] <= hist and hist[2] <= hist and hist[3] <= hist and hist[4] <= hist and hist[5] <= hist?true:false
//bearish = hist[1] >= hist and hist[2] >= hist and hist[3] >= hist and hist[4] >= hist and hist[5] >= hist?true:false

strategy.entry("buy", strategy.long, comment="buy", when = bullish and year>=dyear)
strategy.entry("sell", strategy.short, comment="sell", when = bearish and year>=dyear)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-25. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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