ADX Filtered SuperTrend Pivot Trading Strategy

Pine ScriptTrend following Breakout Mean reversion EMA SMA ATR Supertrend ADX / DMI Pivots / S&R

Author: ChaoZhang
Complexity: Intermediate · 95 lines
Parameters: 6
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy combines SuperTrend pivot points and the ADX indicator for high-frequency trading. The SuperTrend lines dynamically calculate the latest support and resistance levels to determine price trends and generate trading signals. The ADX indicator measures trend strength and acts as a filter, only taking trades when the trend is strong enough.

Strategy Logic

Calculate pivot support and resistance lines. Take the closing price and add/subtract an ATR range above and below. Breaks of these lines signal trend reversions.
ADX determines trend strength. High ADX values indicate a strong trend.
Combine both for trade signals. Go long/short only on pivot breaks and high ADX.

Advantage Analysis

Advantages of this strategy:

Dynamic SuperTrend lines quickly identify breakouts.
ADX filter avoids false signals during range-bound markets.
Good risk-reward ratio and drawdown control.

Risk Analysis

Risks of this strategy:

Gap moves can invalidate SuperTrend lines.
Poor ADX threshold setting impacts performance.
High trading frequency increases transaction costs.

Solutions:

Optimize parameters to allow wider breakout ranges.
Test for better ADX values.
Reduce trade frequency.

Optimization Directions

Areas for improvement:

Optimize ATR multiplier for more robust lines.
Test different ADX parameters.
Add stop-loss to limit losses.

Conclusion

This strategy combines the strengths of SuperTrend and ADX to identify high-probability trend reversal points, filtered by ADX for quality. With parameter tuning and mechanisms adjustments, it can become a steady profit-generating high-frequency strategy.

Parameters

Name	Default	What it controls
v_input_1_close	0	EMA Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_2	2	Pivot Point Period
v_input_3	5	ATR Factor
v_input_4	20	ATR Period
v_input_5_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_6	false	Offset

Source Pine Script

/*backtest
start: 2023-02-12 00:00:00
end: 2024-02-18 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy("STPP20 + ADX", overlay = true)

///////////////////////////
// SuperTrend + Pivot Point
//////////////////////////

src =  input(close, title="EMA Source")
PPprd = input(defval = 2, title="Pivot Point Period", minval = 1, maxval = 50)
AtrFactor=input(defval = 5, title = "ATR Factor", minval = 1, step = 0.1)
AtrPd=input(defval = 20, title = "ATR Period", minval=1)

float ph = na
float pl = na
ph := pivothigh(PPprd, PPprd)
pl := pivotlow(PPprd, PPprd)

float center = na
center := center[1]
float lastpp = ph ? ph : pl ? pl : na
if lastpp
    if na(center)
        center := lastpp
    else
        center := (center * 2 + lastpp) / 3

Up = center - (AtrFactor * atr(AtrPd))
Dn = center + (AtrFactor * atr(AtrPd))

float TUp = na
float TDown = na
Trend = 0
TUp := close[1] > TUp[1] ? max(Up, TUp[1]) : Up
TDown := close[1] < TDown[1] ? min(Dn, TDown[1]) : Dn
Trend := close > TDown[1] ? 1: close < TUp[1]? -1: nz(Trend[1], 1)
Trailingsl = Trend == 1 ? TUp : TDown

// Lines
linecolor = Trend == 1 and nz(Trend[1]) == 1 ? color.lime : Trend == -1 and nz(Trend[1]) == -1 ? color.red : na
plot(Trailingsl, color = linecolor ,  linewidth = 2, title = "PP SuperTrend")

bsignalSSPP = close > Trailingsl
ssignalSSPP = close < Trailingsl


///////
// ADX
//////

lenADX = 14
th = 25
TrueRange = max(max(high-low, abs(high-nz(close[1]))), abs(low-nz(close[1])))
DirectionalMovementPlus = high-nz(high[1]) > nz(low[1])-low ? max(high-nz(high[1]), 0): 0
DirectionalMovementMinus = nz(low[1])-low > high-nz(high[1]) ? max(nz(low[1])-low, 0): 0
SmoothedTrueRange = 0.0
SmoothedTrueRange := nz(SmoothedTrueRange[1]) - (nz(SmoothedTrueRange[1])/lenADX) + TrueRange
SmoothedDirectionalMovementPlus = 0.0
SmoothedDirectionalMovementPlus := nz(SmoothedDirectionalMovementPlus[1]) - (nz(SmoothedDirectionalMovementPlus[1])/lenADX) + DirectionalMovementPlus
SmoothedDirectionalMovementMinus = 0.0
SmoothedDirectionalMovementMinus := nz(SmoothedDirectionalMovementMinus[1]) - (nz(SmoothedDirectionalMovementMinus[1])/lenADX) + DirectionalMovementMinus
DIPlus = SmoothedDirectionalMovementPlus / SmoothedTrueRange * 100
DIMinus = SmoothedDirectionalMovementMinus / SmoothedTrueRange * 100
DX = abs(DIPlus-DIMinus) / (DIPlus+DIMinus)*100
ADX = sma(DX, lenADX)


//////
// MA
/////

lenMA = 21
srcMA = input(close, title="Source")
offsetMA = input(title="Offset", type=input.integer, defval=0, minval=-500, maxval=500)
outMA = sma(srcMA, lenMA)


// Buy - Sell Entries
buy = bsignalSSPP and outMA < close and ADX > th
sell = ssignalSSPP 

if (buy)
    // .order // Tuned version
    strategy.entry("Buy", strategy.long)


if (sell) and (strategy.position_size > 0)
    strategy.order("Sell", false, when = sell)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-19. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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