ATR Channel Mean Reversion Quantitative Trading Strategy

This is a long-only strategy that identifies entry signals when prices break below the lower band of the ATR channel, and takes profit when prices reach the middle band (EMA) or upper band of the ATR channel. It also…

Pine ScriptMean reversion Trend following EMA ATR

Author: ChaoZhang
Complexity: Starter · 52 lines
Parameters: 5
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

When the price breaks below the lower ATR band, it signals an anomaly drop. The strategy will go long at the next candle's open. The stop loss is set at entry price minus ATR stop loss multiplier times ATR. Take profit is at the middle band (EMA) or upper ATR band. If current bar's close is lower than previous bar's low, then use previous bar's low as take profit.

Specifically, the key logic includes:

Calculate ATR and middle band (EMA)
Define time filters
Identify long signal when price < lower ATR band
Enter long at next bar's open
Record entry price
Calculate stop loss price
Take profit when price > middle band (EMA) or upper ATR band
Stop out when price < stop loss price

Advantage Analysis

The advantages of this strategy:

Uses ATR channel for reliable entry and exit signals
Only long after anomaly drop avoids chasing highs
Strict stop loss controls risk
Suitable for quick short-term trades
Simple logic easy to implement and optimize

Risk Analysis

There are some risks:

High trading frequency leads to higher transaction costs and slippage
Consecutive stop loss triggers may happen
Inappropriate parameter optimization impacts performance
Large price swings may result in oversized stop loss

These risks can be reduced by adjusting ATR period, stop loss multiplier etc. Choosing brokers with low trading fees is also important.

Optimization Directions

The strategy can be improved by:

Adding other filter indicators to avoid missing best entry signals
Optimizing ATR period
Considering re-entry mechanism
Adaptive stop loss size
Adding trend filter to avoid counter-trend trades

Conclusion

In summary, this is a simple and practical mean reversion strategy based on ATR channel. It has clear entry rules, strict stop loss, and reasonable take profit. There is also room for parameter tuning. If traders can choose the right symbol and control risk with stop loss, this strategy can achieve good results.

Parameters

Name	Default	What it controls
v_input_1	1.5	SL Multiplier
v_input_2_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_int_1	10	ATR & MA PERIOD
v_input_3	timestamp(01 Jan 1995 13:30 +0000)	(?Time Filter)Start Filter
v_input_4	timestamp(1 Jan 2099 19:30 +0000)	End Filter

Source Pine Script

/*backtest
start: 2022-12-04 00:00:00
end: 2023-12-10 00:00:00
period: 1d
basePeriod: 1h
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/
// © Bcullen175

//@version=5
strategy("ATR Mean Reversion", overlay=true, initial_capital=100000,default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=6E-5) // Brokers rate (ICmarkets = 6E-5)
SLx = input(1.5, "SL Multiplier", tooltip = "Multiplies ATR to widen stop on volatile assests, Higher values reduce risk:reward but increase winrate, Values below 1.2 are not reccomended")
src = input(close, title="Source")
period = input.int(10, "ATR & MA PERIOD")
plot(open+ta.atr(period))
plot(open-ta.atr(period))
plot((ta.ema(src, period)), title = "Mean", color=color.white)

i_startTime     = input(title="Start Filter", defval=timestamp("01 Jan 1995 13:30 +0000"), group="Time Filter", tooltip="Start date & time to begin searching for setups")
i_endTime       = input(title="End Filter", defval=timestamp("1 Jan 2099 19:30 +0000"), group="Time Filter", tooltip="End date & time to stop searching for setups")

// Check filter(s)
f_dateFilter = true

atr = ta.atr(period)

// Check buy/sell conditions
var float buyPrice = 0
buyCondition    = low < (open-ta.atr(period)) and strategy.position_size == 0 and f_dateFilter
sellCondition   = (high > (ta.ema(close, period)) and strategy.position_size > 0 and close < low[1]) or high > (open+ta.atr(period))
stopDistance    = strategy.position_size > 0 ? ((buyPrice - atr)/buyPrice) : na
stopPrice       = strategy.position_size > 0 ? (buyPrice - SLx*atr): na
stopCondition   = strategy.position_size > 0 and low < stopPrice

// Enter positions
if buyCondition
    strategy.entry(id="Long", direction=strategy.long)

if buyCondition[1]
    buyPrice := open

// Exit positions
if sellCondition or stopCondition
    strategy.close(id="Long", comment="Exit" + (stopCondition ? "SL=true" : ""))
    buyPrice := na

// Draw pretty colors
plot(buyPrice, color=color.lime, style=plot.style_linebr)
plot(stopPrice, color=color.red, style=plot.style_linebr, offset=-1)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-11. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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