Trend Following Strategy with 3 EMAs DMI and MACD

Pine ScriptTrend following Machine learning Mean reversion RSI MACD EMA ADX / DMI

Author: ChaoZhang
Complexity: Starter · 63 lines
Parameters: 4
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This is a trend following strategy that uses a combination of 3 exponential moving averages (EMA), a trend indicator (DMI), and a moving average convergence indicator (MACD) to determine trend direction and generate buy and sell signals. Key components include EMA golden cross signals, DMI for trend strength and MACD for momentum confirmation.

Strategy Logic

The core logic relies on 3 EMAs - 34, 89 and 200 - calculated on the M5 period to identify the overall trend. The 34-period EMA provides near-term direction, while the 89 and 200 EMA define mid- to long-term trends.

When the buy signal is triggered:

Closing price crosses above34 EMA
+DI(Bullish trend movement)> 17
ADX(Trend Strength)> -DI

When a sell signal is generated:

Closing price crosses below34 EMA
-DI(Bearish trend movement)> 17
ADX > +DI

There is also a MACD indicator to provide additional confirmation before entering the market.

Advantage

This strategy has several key advantages:

Catching trend shifts early with short-term EMA golden crosses
Use multiple EMAs to determine trend strength under different time frames
DMIFilters help avoid false signals by checking strong trending movements
MACDProvide momentum confirmation to improve the quality and probability of trading opportunities
The combination of indicators improves the accuracy and timing of entry signals

Risk

Main risks to consider:

Relying only on the EMA golden cross is susceptible to misleading and misalignment
Multiple confirmations may cause a lag at the point of signal generation
Easily affected by sudden trend reversals

Mitigation Method:

Use appropriate stop-loss and position management
Optimize EMA parameters based on current market conditions
Observe the price action for visual confirmation

Optimization Direction

Further improvement of the strategy:

Add indicators like RSI to determine overbought and oversold zones
Combine volume analysis to generate stronger signals
Optimize indicators and parameters based on different assets and timeframes
Use machine learning technology to continuously learn from new market data

Summary

Overall, this is a powerful trend following system that uses a combination of simple but practical indicators to follow the trend. The three-EMA configuration determines the trend under multiple time frames, and DMI and MACD checks improve entry timing and profit probability. Paired with proper optimization and risk management, it can be an effective tool for trend traders.

Parameters

Name	Default	What it controls
v_input_1	14	DI Length
v_input_2	12	Fast Length
v_input_3	26	Slow Length
v_input_4	9	Signal Length

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=5
strategy("2 EMA di+ Buy Sell, strategy ", overlay=true)

// Define the EMA calculation function
ema(src, length) =>
    ta.ema(src, length)

// Calculate and plot EMA on M5
ema34_M5 = ema(close, 34)
ema89_M5 = ema(close, 89)
ema200_M5 = ema(close, 200)

// Plot EMAs
plot(ema34_M5, color=color.green, title="EMA 34 M5", linewidth=2)
plot(ema89_M5, color=color.blue, title="EMA 89 M5", linewidth=2)
plot(ema200_M5, color=color.black, title="EMA 200 M5", linewidth=2)

// Define DMI parameters
len = input(14, title="DI Length")
up = ta.change(high)
down = -ta.change(low)
plusDM = na(up) ? na : (up > down and up > 0 ? up : 0)
minusDM = na(down) ? na : (down > up and down > 0 ? down : 0)
trur = ta.rma(ta.tr, len)
plusDI = 100 * ta.rma(plusDM, len) / trur
minusDI = 100 * ta.rma(minusDM, len) / trur

// Calculate ADX
adxValue = 100 * ta.rma(math.abs(plusDI - minusDI) / (plusDI + minusDI == 0 ? 1 : plusDI + minusDI), len)

// Define MACD parameters
fastLength = input(12, title="Fast Length")
slowLength = input(26, title="Slow Length")
signalLength = input(9, title="Signal Length")

// Calculate MACD
[macdLine, signalLine, _] = ta.macd(close, fastLength, slowLength, signalLength)

// Create buy/sell conditions
buyCondition = close > ema34_M5 and plusDI > 17 and adxValue > minusDI 
sellCondition = close < ema34_M5 and minusDI > 17 and adxValue > plusDI 

// Strategy logic
strategy.entry("Buy", strategy.long, when = buyCondition)
strategy.entry("Sell", strategy.short, when = sellCondition)

// Create alerts for buy/sell signals
alertcondition(buyCondition, title="Buy Signal", message="Buy Signal")
alertcondition(sellCondition, title="Sell Signal", message="Sell Signal")

// Plot buy/sell arrows on the price chart
bgcolor(buyCondition ? color.new(color.green, 90) : sellCondition ? color.new(color.red, 90) : na)

plotarrow(buyCondition ? 1 : sellCondition ? -1 : na, colorup=color.new(color.green, 0), colordown=color.new(color.red, 0), offset=-1)

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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