Dual Moving Average and Supertrend Strategy

This strategy generates trading signals based on the crossover of 21-day and 55-day moving averages, and uses the supertrend indicator to filter out false signals. It is a trend-following strategy.

Pine ScriptTrend following Machine learning Tools & templates EMA SMA ATR Supertrend

Author: ChaoZhang
Complexity: Intermediate · 103 lines
Parameters: 10
Backtest bar: 1d · swing
Tested on: BTC/USDT · spot

Jump to code View in vault Original on FMZ

Overview

This strategy generates trading signals based on the crossover of 21-day and 55-day moving averages, and uses the supertrend indicator to filter out false signals. It is a trend-following strategy.

Strategy Logic

The code first defines the 21-day EMA (EMA1) and 55-day EMA (EMA2). A buy signal is generated when EMA1 crosses above EMA2. A sell signal is generated when EMA1 crosses below EMA2.

To filter false signals, the supertrend indicator is added. The supertrend calculates trend direction based on ATR and recent high-low prices. In the code, above the supertrend line is uptrend and below is downtrend.

So a buy signal is only generated when EMA1 crosses above EMA2 during an uptrend. A sell signal is only generated when EMA1 crosses below EMA2 during a downtrend. The supertrend filters out false signals during trend transitions.

In addition, the 200-day and 233-day moving averages are added to determine the long-term trend. Signals are only generated when long-term and short-term trends align.

Advantages

Dual moving averages combined with supertrend can effectively identify trends and filter false signals.
Adjustable moving average parameters can adapt the strategy to different market conditions.
Long-term moving averages prevent risks from conflicting trends.
Clear rules easy for algorithmic trading.
Visual buy/sell signals make trading decisions clear.

Risks

Moving averages may generate false signals around turning points. Potential turns need to be identified.
Inappropriate parameter settings may cause missed trends or excessive false signals. Parameters need to be adjusted for different markets.
High trading frequency leads to higher transaction costs. Costs need to be monitored.
Supertrend parameters need to be optimized to balance filtering effectiveness and lagging.
Long-term averages may lag in generating signals. Timing trend changes is critical.

Enhancements

Test different moving average combinations to find optimal parameters.
Optimize supertrend parameters to balance filtering and lagging.
Add other indicators like volume to further validate signals.
Incorporate sentiment and news analytics to identify potential turning points.
Use machine learning to dynamically optimize parameters.

Conclusion

This strategy combines the strengths of dual moving averages and supertrend in identifying trends and filtering false signals. It can be continuously improved through parameter optimization and additional validation. Despite certain risks, they can be managed through risk control techniques. The strategy is suitable for algorithmic trading.

Parameters

Name	Default	What it controls
v_input_1	21	len1
v_input_2	55	len2
v_input_3	200	len3
v_input_4	233	len4
v_input_5	10	ATR Period
v_input_6_hl2	0	Source: hl2 \| high \| low \| open \| close \| hlc3 \| hlcc4 \| ohlc4
v_input_7	3	ATR Multiplier
v_input_8	true	Change ATR Calculation Method ?
v_input_9	true	Show Buy/Sell Signals ?
v_input_10	true	Highlighter On/Off ?

Source Pine Script

/*backtest
start: 2022-09-21 00:00:00
end: 2023-09-27 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"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/
// © bhavikmota

//@version=4
strategy("EMA & Supertrend", overlay = true)

//length = input(9, minval=1)
//ema1 = ema(close, length)
//ema2 = ema(ema1, length)
//ema3 = ema(ema2, length)

//shortest = ema(close, 20)
//short = ema(close, 50)
//longer = ema(close, 100)
//longest = ema(close, 200)


//for Ema1
len1 = input(21, minval=1)
//src1 = input(close)
ema1 = ema(close,len1)
plot(ema1, color=color.red, linewidth=1)

//for Ema2
len2 = input(55, minval=1)
//src2 = input(close)
ema2 = ema(close,len2)
plot(ema2, color=color.green, linewidth=1)

//for Ema3
len3 = input(200, minval=1)
//src3 = input(close)
ema3 = ema(close,len3)
plot(ema3, color=color.blue, linewidth=1)

//for Ema4
len4 = input(233, minval=1)
//src4 = input(close)
ema4 = ema(close,len4)
plot(ema4, color=color.black, linewidth=1)


Periods = input(title="ATR Period", type=input.integer, defval=10)
src = input(hl2, title="Source")
Multiplier = input(title="ATR Multiplier", type=input.float, step=0.1, defval=3.0)
changeATR= input(title="Change ATR Calculation Method ?", type=input.bool, defval=true)
showsignals = input(title="Show Buy/Sell Signals ?", type=input.bool, defval=true)
highlighting = input(title="Highlighter On/Off ?", type=input.bool, defval=true)
atr2 = sma(tr, Periods)
atr= changeATR ? atr(Periods) : atr2
up=src-(Multiplier*atr)
up1 = nz(up[1],up)
up := close[1] > up1 ? max(up,up1) : up
dn=src+(Multiplier*atr)
dn1 = nz(dn[1], dn)
dn := close[1] < dn1 ? min(dn, dn1) : dn
trend = 1
trend := nz(trend[1], trend)
trend := trend == -1 and close > dn1 ? 1 : trend == 1 and close < up1 ? -1 : trend
upPlot = plot(trend == 1 ? up : na, title="Up Trend", style=plot.style_linebr, linewidth=2, color=color.green)
buySignal = trend == 1 and trend[1] == -1
plotshape(buySignal ? up : na, title="UpTrend Begins", location=location.absolute, style=shape.circle, size=size.tiny, color=color.green, transp=0)
plotshape(buySignal and showsignals ? up : na, title="Buy", text="Buy", location=location.absolute, style=shape.labelup, size=size.tiny, color=color.green, textcolor=color.white, transp=0)
dnPlot = plot(trend == 1 ? na : dn, title="Down Trend", style=plot.style_linebr, linewidth=2, color=color.red)
sellSignal = trend == -1 and trend[1] == 1
plotshape(sellSignal ? dn : na, title="DownTrend Begins", location=location.absolute, style=shape.circle, size=size.tiny, color=color.red, transp=0)
plotshape(sellSignal and showsignals ? dn : na, title="Sell", text="Sell", location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.red, textcolor=color.white, transp=0)
mPlot = plot(ohlc4, title="", style=plot.style_circles, linewidth=0)
longFillColor = highlighting ? (trend == 1 ? color.green : color.white) : color.white
shortFillColor = highlighting ? (trend == -1 ? color.red : color.white) : color.white
fill(mPlot, upPlot, title="UpTrend Highligter", color=longFillColor)
fill(mPlot, dnPlot, title="DownTrend Highligter", color=shortFillColor)
alertcondition(buySignal, title="SuperTrend Buy", message="SuperTrend Buy!")
alertcondition(sellSignal, title="SuperTrend Sell", message="SuperTrend Sell!")
changeCond = trend != trend[1]
alertcondition(changeCond, title="SuperTrend Direction Change", message="SuperTrend has changed direction!")


//Trading logic

Enterlong = crossover(ema1,ema2) or (close>ema1 and close>ema2 and ema1>ema2) and close>ema4// positive ema crossover
Exitlong = crossunder(close,ema2) // candle closes below supertrend

Entershort = crossunder(ema1,ema2) or (close<ema1 and close<ema2 and ema2<ema1) and close<ema4// negative ema crossover
Exitshort = crossover(close,ema2) // candle closes above supertrend

//Execution Logic - Placing Order

start = timestamp(2008,1,1,0,0)

if time>= start
    strategy.entry("long", strategy.long, when=Enterlong)
    strategy.close("long",when=Exitlong)
//strategy.entry("short",strategy.short,100,when=Entershort)
//strategy.close("short",when=Exitshort)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-28. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

Build this with AgenKit

A spec is not a trading system. Paste this into your coding agent and AgenKit runs a specialist team — architecture, test-first build, risk gates, review — with paper trading on by default.

in Claude Code, Cursor, Codex & 4 more

/agenkit build a production-grade trading bot with a backtest engine, risk controls and paper trading by default from strategies/Dual-Moving-Average-and-Supertrend-Strategy.md

npx agenkit install engineering-kit in your project
Paste the command into your agent and approve each phase
Get tests, backtest, risk controls and a paper-trading switch

Get AgenKit How /agenkit works

Understand it first

Before you run this, read the lesson behind the idea and how to backtest it honestly. Specs like this one are the survivors of an unknown number of trials; deflate their results accordingly.