Bollinger Bands ATR Trailing Stop Strategy

Pine ScriptMachine learning Breakout Mean reversion EMA SMA Bollinger Bands ATR OBV

Author: ChaoZhang
Complexity: Intermediate · 158 lines
Parameters: 21
Backtest bar: 1m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy combines the Bollinger Bands indicator and the Average True Range (ATR) indicator to form a breakout trading strategy with a trailing stop loss function. Trading signals are generated when prices break through the Bollinger bands of specified standard deviations. At the same time, the ATR indicator is used to calculate stop loss and take profit to control the risk/reward ratio. In addition, the strategy also has features like time filter and parameter optimization.

Strategy Logic

Step 1, Calculate the middle band, upper band and lower band. The middle band is the simple moving average (SMA) of price, and the upper and lower bands are multiples of price standard deviation. When price breaks out upwards from the lower band, go long. When price breaks downwards from upper band, go short.

Step 2, Calculate the ATR indicator. The ATR indicator reflects the average volatility of price. According to the ATR value, set the stop loss for long positions and short positions. At the same time, set the take profit position based on ATR value to control risk/reward ratio.

Step 3, Use time filter to trade only in specified time period to avoid drastic fluctuations from major news events.

Step 4, Trailing stop mechanism. Keep adjusting stop loss based on latest ATR position to lock in more profits.

Advantage Analysis

Bollinger bands itself reflects price equilibrium more effectively than single moving average;
ATR stop loss controls risk/reward ratio of each trade;
Trailing stop adjusts automatically based on market volatility to lock in profits;
Abundant strategy parameters enable high customizability.

Risk Analysis

Multiple small losses may occur when market consolidates;
Failed breakout reversal with Bollinger bands crossover;
Higher risks associated with overnight sessions and major news events.

Counter measures:

Strictly follow risk management principles, control loss per trade;
Optimize parameters to improve win rate;
Apply time filter to avoid high risk periods.

Optimization Directions

Test different parameter combinations;
Add timing indicator like OBV;
Incorporate machine learning model.

Conclusion

This strategy combines Bollinger bands to determine trend equilibrium and breakout directions, ATR to calculate stop loss and take profit to control risk/reward ratio, and trailing stop to lock in profits. Its advantages lie in high customizability, controllable risks, and suitability for short-term intraday trading. Further improvements on win rate and profitability can be achieved through parameter optimization and machine learning.

Parameters

Name	Default	What it controls
v_input_2	timestamp(1 Jan 2099 00:00 +0000)	End Date
v_input_3	0300-1700	Time Session To Analyze
v_input_string_1	0	Basis MA Type: SMA \| EMA \| SMMA (RMA) \| WMA \| VWMA
v_input_4_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_float_1	2	StdDev1
v_input_float_2	3	StdDev2
v_input_int_2	false	Offset
v_input_5	false	Show Cross the Bands?
v_input_6	true	Show second deviation entry point?
v_input_string_2	0	Smoothing: RMA \| SMA \| EMA \| WMA
v_input_float_3	true	Multiplier
v_input_7_high	0	src1: high \| close \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_8_low	0	src2: low \| high \| close \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_bool_1	false	show ATR lines ?
v_input_float_4	1.5	TP/SL
v_input_float_5	10	candle/pip
v_input_float_6	1.5	distance to midline/pip
v_input_bool_2	true	use trailing stop loss?
v_input_1	timestamp(01 Aug 2023 00:00 +0000)	(?Time Filter)Start Date
v_input_int_1	20	(?Bollinger Band)length
v_input_int_3	5	(?ATR)Length

Source Pine Script

/*backtest
start: 2023-12-26 00:00:00
end: 2024-01-02 00:00:00
period: 1m
basePeriod: 1m
exchanges: [{"eid":"Futures_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/
// © sadeq_haddadi

//@version=5

strategy('Bollinger Bands + ATR / trail- V2', overlay=true ) // Interactive Brokers rate)



//date and time
startDate   = input(title="Start Date", defval=timestamp("01 Aug 2023 00:00 +0000"), tooltip="Date & time to begin analysis",group = 'Time Filter')
endDate     = input(title="End Date", defval=timestamp("1 Jan 2099 00:00 +0000"), tooltip="Date & time to stop analysis")
timeSession = input(title="Time Session To Analyze", defval="0300-1700", tooltip="Time session to analyze")
inSession(sess) => true

// indicators 

length = input.int(20, minval=1,group = 'Bollinger Band')
maType = input.string("SMA", "Basis MA Type", options = ["SMA", "EMA", "SMMA (RMA)", "WMA", "VWMA"])
src = input(close, title="Source")
mult1 = input.float(2.0, minval=0.001, maxval=50, title="StdDev1")
mult2 = input.float(3.0, minval=0.001, maxval=50, title="StdDev2")

ma(source, length, _type) =>
    switch _type
        "SMA" => ta.sma(source, length)
        "EMA" => ta.ema(source, length)
        "SMMA (RMA)" => ta.rma(source, length)
        "WMA" => ta.wma(source, length)
        "VWMA" => ta.vwma(source, length)

basis = ma(src, length, maType)
dev1 = mult1 * ta.stdev(src, length)
dev2 = mult2 * ta.stdev(src, length)
upper1 = basis + dev1
lower1 = basis - dev1
upper2 = basis + dev2
lower2 = basis - dev2
offset = input.int(0, "Offset", minval = -500, maxval = 500)
plot(basis, "Basis", color=#2962FF, offset = offset,linewidth=2)
p1 = plot(upper1, "Upper", color=color.new(color.white,50), offset = offset,linewidth=2)
p2 = plot(lower1, "Lower", color=color.new(color.white,50), offset = offset,linewidth=2)
p3 = plot(upper2, "Upper", color=color.new(color.white,80), offset = offset,linewidth=1)
p4 = plot(lower2, "Lower", color=color.new(color.white,80), offset = offset,linewidth=1)

fill(p1, p2, title = "Background", color=color.rgb(33, 150, 243, 95))
fill(p3, p4, title = "Background", color=color.rgb(33, 150, 243, 95))

show_crosses = input(false, "Show Cross the Bands?")

plotshape(show_crosses and ta.crossover(close, upper2)  ? src : na, "S", style = shape.triangledown, location =location.abovebar, color = color.yellow, size = size.tiny)
plotshape(show_crosses and ta.crossunder(low, lower2) ? src : na ,"L", style = shape.triangleup, location =  location.belowbar, color = color.purple, size = size.tiny)

second_entry = input(true, "Show second deviation entry point?")

//atr

length_ATR = input.int(title="Length", defval=5, minval=1,group = 'ATR')
smoothing = input.string(title="Smoothing", defval="RMA", options=["RMA", "SMA", "EMA", "WMA"])
m = input.float(1, "Multiplier")
src1 = input(high)
src2 = input(low)
pline = input.bool(title = 'show ATR lines ?', defval=false)



ma_function(source, length_ATR) =>
	if smoothing == "RMA"
		ta.rma(source, length_ATR)
	else
		if smoothing == "SMA"
			ta.sma(source, length_ATR)
		else
			if smoothing == "EMA"
				ta.ema(source, length_ATR)
			else
				ta.wma(source, length_ATR)
				
a = ma_function(ta.tr(true), length_ATR) * m
x = ma_function(ta.tr(true), length_ATR) * m + src1
x2 = src2 - ma_function(ta.tr(true), length_ATR) * m

PP1 = plot(pline ? x :na , title = "ATR Short Stop Loss", color= color.new(color.red,20) )
PP2 = plot(pline ? x2:na , title = "ATR Long Stop Loss",  color=color.new(color.green,20) )

Tp_to_Sl = input.float(1.5, "TP/SL")
candle_size =  input.float(10, "candle/pip")
distance_source =  input.float(1.5, "distance to midline/pip")
//strategy

buyCondition = low[2] < lower1 and  ta.crossover(close[1], lower1)  and strategy.position_size == 0 and (close[1] - open[1]) < candle_size * 0.0001 and close > open and ( basis - close) > distance_source * 0.0001

sellCondition = high[2] > upper1 and ta.crossunder(close[1], upper1)  and strategy.position_size == 0 and (open[1] - close[1]) < candle_size * 0.0001 and close < open  and (close - basis) > distance_source * 0.0001
//
buyCondition2 = low[2] < lower2 and  ta.crossover(close[1], lower2)  and (close[1] - open[1]) < candle_size * 0.0001 and close > open and ( basis - close) > distance_source * 0.0001
sellCondition2 = high[2] > upper2 and ta.crossunder(close[1], upper2)   and (open[1] - close[1]) < candle_size * 0.0001 and close < open  and (close - basis) > distance_source * 0.0001

plotshape(second_entry and  sellCondition2 ? src : na, "S", style = shape.triangledown, location =location.abovebar, color = color.rgb(241, 153, 177), size = size.tiny)
plotshape(second_entry and buyCondition2 ? src : na ,"L", style = shape.triangleup, location =  location.belowbar, color = color.rgb(177, 230, 168), size = size.tiny)
//
since_buy  =ta.barssince(buyCondition)
since_sell =ta.barssince(sellCondition)
entry_price = ta.valuewhen(buyCondition or sellCondition, src, 0)

sl_long = ta.valuewhen(buyCondition, x2[1], 0)
sl_short = ta.valuewhen(sellCondition, x[1], 0)
buyprofit = entry_price + (Tp_to_Sl*( entry_price - sl_long))
sellprofit= entry_price + (Tp_to_Sl*( entry_price - sl_short))

//alert_massage = "new strategy position is {{strategy.position_size}}"
//prof = ta.crossover(high,upper1)
//buyexit=ta.valuewhen(prof,upper1,0)

if buyCondition and inSession(timeSession)

    strategy.entry( id = "long", direction = strategy.long , alert_message='Open Long Position' )

if sellCondition and inSession(timeSession)
   
    strategy.entry(id= "short", direction = strategy.short, alert_message='Open Short Position')

//trail-stop loss
use_trailing = input.bool(title = 'use trailing stop loss?', defval=true)
pricestop_long=0.00
pricestop_short=100000.00
if (strategy.position_size > 0)
   
    if use_trailing == false
        pricestop_long := sl_long
    else
        pricestop_long := math.max (x2, pricestop_long[1]) //trail - long

if (strategy.position_size < 0)
   
    if use_trailing == false
        pricestop_short := sl_short
    else
        pricestop_short := math.min (x, pricestop_short[1])  // trail - short 

if strategy.position_size > 0 
   
    strategy.exit(id = 'close', limit =  buyprofit , stop = pricestop_long  )

if strategy.position_size < 0 

    strategy.exit(id = 'close', limit = sellprofit  , stop = pricestop_short  )

alertcondition(buyCondition or sellCondition, 'Enter_position')

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-03. 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/Bollinger-Bands-ATR-Trailing-Stop-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.