Multi timeframe Super Trend Tracking Strategy

This strategy uses the ATR indicator to build a multi-timeframe dynamic trend channel to track trends. It generates signals when prices break through the channel by continuously adjusting the channel to capture…

Pine ScriptTrend following Multi-timeframe Mean reversion SMA ATR Supertrend

Author: ChaoZhang
Complexity: Intermediate · 53 lines
Parameters: 11
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy uses the ATR indicator to build an uptrend channel and a downtrend channel. Specifically, the uptrend channel line is the closing price minus N times the ATR indicator; the downtrend channel line is the closing price plus N times the ATR indicator. The N value can be adjusted through parameters.

When the price breaks through the uptrend channel, a buy signal is generated; when the price breaks through the downtrend channel, a sell signal is generated. The channel dynamically adjusts based on the latest prices to track trends.

In addition, the strategy also defines a trend variable to determine whether the current is in an uptrend or a downtrend. The trend variable works with the channel lines to avoid generating wrong signals.

Advantages

Uses dynamic channels to track trends and trade along with trends
Avoids chasing highs and selling lows, reducing the risk of market reversal
Adjustable channel parameters, high adaptability
More flexible multi-timeframe settings

Risks

Overly aggressive tracking may increase loss risk
Improper channel parameter settings lead to fewer or more incorrect signals
Requires strong programming skills to adjust parameters

Improvement:

Appropriately reduce ATR multiplier to lower tracking magnitude
Optimize parameters to find the best combination
Add stop loss strategy to reduce per trade loss

Optimization Directions

Add other indicators for filter for more reliable signals
Add stop loss strategy to reduce risk
Conduct parameter optimization to find optimum
Optimize entry and exit timing to improve profit rate

Summary

Overall this is a decent trend tracking strategy. It dynamically adjusts to trade along with trends and avoids chasing highs and selling lows. With parameter optimization and proper improvements, the strategy advantages can be further enhanced and risks reduced to achieve better results.

Parameters

Name	Default	What it controls
v_input_1	10	ATRtimeframe
v_input_2_hl2	0	Price data source: hl2 \| high \| low \| open \| close \| hlc3 \| hlcc4 \| ohlc4
v_input_float_1	3	ATR Multiplier
v_input_3	true	Change ATR calculation method
v_input_4	false	Show buy/sell signals
v_input_int_1	9	From Month
v_input_int_2	true	From Day
v_input_int_3	2018	From Year
v_input_int_4	true	To Month
v_input_int_5	true	To Day
v_input_int_6	9999	To Year

Source Pine Script

/*backtest
start: 2023-01-08 00:00:00
end: 2024-01-14 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy('Supertrend simplified optimized version', overlay=true)
Periods = input(title='ATRtimeframe', defval=10)
src = input(hl2, title='Price data source')
Multiplier = input.float(title='ATR Multiplier', step=0.1, defval=3.0)
changeATR = input(title='Change ATR calculation method', defval=true,tooltip = 'Default is art otherwise sma(ta.tr,ATR period)')
showsignals = input(title='Show buy/sell signals', defval=false)
atr2 = ta.sma(ta.tr, Periods)
atr = changeATR ? ta.atr(Periods) : atr2
up = src - Multiplier * atr
up1 = nz(up[1], up)
up := close[1] > up1 ? math.max(up, up1) : up
dn = src + Multiplier * atr
dn1 = nz(dn[1], dn)
dn := close[1] < dn1 ? math.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='Uptrend', style=plot.style_linebr, linewidth=2, color=color.new(color.green, 0))
buySignal = trend == 1 and trend[1] == -1
plotshape(buySignal and showsignals ? up : na, title='Buy some', text='Buy some', location=location.absolute, style=shape.labelup, size=size.tiny, color=color.new(color.green, 0), textcolor=color.new(color.white, 0))
dnPlot = plot(trend == 1 ? na : dn, title='Downtrend', style=plot.style_linebr, linewidth=2, color=color.new(color.red, 0))
sellSignal = trend == -1 and trend[1] == 1
plotshape(sellSignal and showsignals ? dn : na, title='Selling Points', text='Selling Points', location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.new(color.red, 0), textcolor=color.new(color.white, 0))
FromMonth = input.int(defval=9, title='From Month', minval=1, maxval=12)
FromDay = input.int(defval=1, title='From Day', minval=1, maxval=31)
FromYear = input.int(defval=2018, title='From Year', minval=999)
ToMonth = input.int(defval=1, title='To Month', minval=1, maxval=12)
ToDay = input.int(defval=1, title='To Day', minval=1, maxval=31)
ToYear = input.int(defval=9999, title='To Year', minval=999)
start = timestamp(FromYear, FromMonth, FromDay, 00, 00)
finish = timestamp(ToYear, ToMonth, ToDay, 23, 59)
window() =>
    time >= start and time <= finish ? true : false
longCondition = buySignal
if longCondition and window()
    strategy.entry('BUY', strategy.long, comment = 'buy')
shortCondition = sellSignal
if shortCondition and window()
    strategy.close('BUY',comment = 'sell')
buy1 = ta.barssince(buySignal)
sell1 = ta.barssince(sellSignal)
color1 = buy1[1] < sell1[1] ? color.green : buy1[1] > sell1[1] ? color.red : na

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-15. 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/Multi-timeframe-Super-Trend-Tracking-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.