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Dual Moving Average Crossover MACD Trend Following Strategy

Author: ChaoZhang, Date: 2024-01-23 11:22:02
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Overview

This strategy is an automated trading strategy based on the dual moving average crossover of the MACD technical indicator. It utilizes the signal line crossover of MACD to determine the trend direction for trend following.

Strategy Logic

The strategy first calculates the three lines of the MACD indicator: fast line, slow line and histogram. The fast line is a faster moving average over a shorter period while the slow line is a slower moving average over a longer period. The histogram is the difference between the fast and slow lines. When the fast line crosses above the slow line, it is a golden cross signal indicating a buy signal. When the fast line crosses below the slow line, it is a death cross signal indicating a sell signal.

The strategy utilizes this logic to go long on golden crosses and close position on death crosses; or go short on death crosses and close position on golden crosses to automatically follow the trend. Meanwhile, the strategy also judges if the absolute MACD line is positive or negative to avoid false signals and ensure truly capturing trend reversal points.

Advantages of the Strategy

Utilizes dual moving average crossover to determine trend direction accurately and capture trend reversal
MACD technical indicator reduces false signals and improves signal quality
Flexibility to go long or short or only long/short
Adjustable parameters cater to different market environments

Risks of the Strategy

Dual moving average crossover has lagging effect, may miss partial profits at the beginning of reversals
MACD indicator prone to false signals during market consolidation
Parameters need proper adjustment to avoid overly sensitive or inert

Risk Mitigations:

Combine with other indicators to filter signals
Tune parameters to lower trading frequency
Only adopt the strategy when trend is obvious

Enhancement Areas

The strategy can be enhanced from the following aspects:

Incorporate other indicators like KDJ, Bollinger Bands etc to confirm signals and filter false signals
Improve entry mechanism, e.g. add breakout filter to avoid premature or late entries
Optimize parameter settings, adjust fast and slow line periods based on different timeframes and market regimes
Add stop loss to control single trade loss
Expand to other products like forex, crypto currencies etc

Summary

The dual moving average crossover MACD trend following strategy utilizes MACD indicator to determine trend direction combined with signal line crossovers to effectively filter signals and capture trend reversals for automated trend following. The advantages lie in accurate trend judgment, flexible parameter tuning catering to market environments. Risk management is important to avoid false signals. Further optimizations with additional technical indicators and parameter tuning can improve strategy performance.

/*backtest
start: 2023-01-16 00:00:00
end: 2024-01-22 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © DeMindSET

//@version=4
strategy("MACD Trend Follow Strategy", overlay=false)
// Getting inputs
LSB = input(title="Long/Short", defval="Long only", options=["Long only", "Short only" , "Both"]) 
fast_length = input(title="Fast Length", type=input.integer, defval=12)
slow_length = input(title="Slow Length", type=input.integer, defval=26)
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)
//
Bull= macd > signal
Bear= macd < signal
ConBull=macd>0
ConBear=macd<0
//
Green= Bull and ConBull
Red= Bear and ConBear
Yellow= Bull and ConBear
Blue= Bear and ConBull
//
bcolor = Green ? color.green : Red ? color.red : Yellow ? color.yellow : Blue ? color.blue : na
barcolor(color=bcolor)
// === INPUT BACKTEST RANGE ===
FromYear  = input(defval = 2019, title = "From Year", minval = 1920)
FromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
FromDay   = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
ToYear    = input(defval = 9999, title = "To Year", minval = 2009)
ToMonth   = input(defval = 12, title = "To Month", minval = 1, maxval = 12)
ToDay     = input(defval = 31, title = "To Day", minval = 1, maxval = 31)

// === FUNCTION EXAMPLE ===
start     = timestamp(FromYear, FromMonth, FromDay, 00, 00)  // backtest start window
finish    = timestamp(ToYear, ToMonth, ToDay, 23, 59)        // backtest finish window
window()  => true // create function "within window of time"

if LSB == "Long only" and Green
    strategy.entry("L",true)
if LSB == "Long only" and Red
    strategy.close("L",qty_percent=100,comment="TP Long")
if LSB == "Both" and Green
    strategy.entry("L",true)
if LSB == "Both" and Red
    strategy.entry("S",false)
if LSB == "Short only" and Red
    strategy.entry("S",false)
if LSB == "Short only" and Green
    strategy.close("S",qty_percent=100,comment="TP Short")

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