MACD Trend Following Strategy 432361

This strategy implements trend following trading on the MACD indicator. It identifies trend direction using MACD and with dynamic stop loss to lock in profits.

Pine ScriptTrend following Mean reversion RSI MACD EMA SMA Bollinger Bands ATR

Author: ChaoZhang
Complexity: Intermediate · 70 lines
Parameters: 11
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

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Overview

This strategy implements trend following trading on the MACD indicator. It identifies trend direction using MACD and with dynamic stop loss to lock in profits.

Strategy Logic

Calculate Fast MA, Slow MA and MACD indicator. Fast MA uses 12-period EMA, Slow MA uses 26-period EMA. MACD is the difference between Fast and Slow MA.
Set buy and sell lines. Generate buy signal when MACD crosses over buy line, generate sell signal when MACD crosses below sell line.
Set dynamic stop loss after opening position. The initial stop loss is set at 95% of entry price, and will trail upwards as price runs.
Close position when stop loss is triggered or reverse signal appears.

Advantage Analysis

Using MACD to identify trend direction can effectively track trends.
The dynamic stop loss can continuously lock in profits and avoid enlarged losses.
The strategy logic is simple and clear, easy to understand and automate for quant trading.

Risk Analysis

MACD has lagging effect, may miss short term trend reversal.
Stop loss set too loose may miss some profits, too tight may stop out early.
Parameter tuning issue. MACD parameters and buy/sell lines need constant testing and optimization.
Works better in trending market, may have losses in choppy market.

Optimization Directions

Optimize MACD parameters to find best combination.
Test different stop loss methods, like fixed points, ATR-based etc.
Add filter conditions using other indicators to avoid false signals, e.g. Bollinger Bands, RSI etc.
Combine with trend identification tools, dynamically adjust strategy parameters based on trend vs range-bound market.

Summary

The strategy has clear logic of using MACD to track trends and dynamic stop loss to lock in profits. It can effectively track trending markets. But MACD has lagging issue, and stop loss points need to be optimized. Next step is to further test parameters, optimize stop loss mechanism, add filters with other indicators, to make it robust across different market conditions, improving stability.

Parameters

Name	Default	What it controls
v_input_1	12	Fast moving average
v_input_2	26	Slow moving average
v_input_3	12	Fast Length
v_input_4	26	Slow Length
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)
v_input_9	-0.0002	Enter Long
v_input_10	0.0001	Close Long
v_input_11	0.05	Stop Loss %

Source Pine Script

/*backtest
start: 2023-10-16 00:00:00
end: 2023-11-15 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy(shorttitle = "EURUSD MACD", title = "EURUSD MACD")
fastMA = input(title="Fast moving average",  defval = 12, minval = 7)
slowMA = input(title="Slow moving average",  defval = 26, minval = 7)
lastColor = yellow
[currMacd,_,_] = macd(close[0], fastMA, slowMA, 9)
[prevMacd,_,_] = macd(close[1], fastMA, slowMA, 9)
plotColor = currMacd > 0 ? currMacd > prevMacd ? lime : green : currMacd < prevMacd ? maroon : red
plot(currMacd, style = histogram, color = plotColor, linewidth = 3)
plot(0, title = "Zero line", linewidth = 1, color = gray)

//MACD
// Getting inputs
fast_length = input(title="Fast Length",  defval=12)
slow_length = input(title="Slow Length",  defval=26)
src = input(title="Source",  defval=close)
signal_length = input(title="Signal Smoothing",  minval = 1, maxval = 50, defval =9)
sma_source = input(title="Simple MA(Oscillator)", type=bool, defval=false)
sma_signal = input(title="Simple MA(Signal Line)", type=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=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)
///END OF MACD

//Long and Close Long Lines
linebuy = input(title="Enter Long", type=float, defval=-0.0002)
linesell = input(title="Close Long", type=float, defval=0.0001)

//Plot Long and Close Long Lines
plot(linebuy,color=green),plot(linesell,color=red)


//Stop Loss Input
sl_inp = input(0.05, title='Stop Loss %', type=float)/100


//Order Conditions
longCond = crossover(currMacd, linebuy)
exitLong = crossover(currMacd, linesell)
stop_level = strategy.position_avg_price * (1 - sl_inp)


//Order Entries
strategy.entry("long", strategy.long,  when=longCond==true)
strategy.close("long", when=exitLong==true)
strategy.exit("Stop Loss", stop=stop_level)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-16. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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