Gradient Trailing Stop Loss Strategy

Pine ScriptMachine learning Trend following Volatility ATR Supertrend

Author: ChaoZhang
Complexity: Intermediate · 47 lines
Parameters: 8
Backtest bar: 10m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The Gradient Trailing Stop Loss strategy dynamically adjusts the stop loss line to balance risk control and profit taking. It uses the Average True Range (ATR) to calculate the stop loss line and effectively tracks price trends, protecting profits while reducing unnecessary stop outs. This strategy works well for stocks with strong trends and can generate steady returns.

Principles

The strategy uses the Average True Range (ATR) as the basis for dynamic stop loss. ATR effectively reflects the volatility of a stock. The strategy first takes the ATR period as input, typically 10 days. Then the ATR value is calculated. As the price rises, the stop loss line also moves up to trail the price. When the price drops, the stop loss line remains unchanged to lock in profits. Also, the strategy allows fine tuning the stop loss distance from the price using a "factor" parameter.

Specifically, the strategy calculates the current ATR, then multiplies it by the "factor" to get the stop loss distance. If the price is above the stop loss price, a long position is opened. If the price is below, a short position is opened. Thus, the stop loss line closely follows the price, achieving a gradient trailing effect.

Advantages

Dynamic trailing stop loss adjusts stop distance based on market conditions
ATR calculates stop distance based on market volatility
Simple and easy to automate trading
Customizable ATR period and stop loss factor for different assets
Balances between stopping loss and profit taking
Reduces unnecessary stop outs

Risks

Choosing proper ATR parameters is crucial
Stop loss too close may increase unnecessary stop outs
Stop loss too far may fail to control risks
Strategy itself cannot determine market trends
Need to evaluate ATR period and factor settings

Enhancements

Add filters like moving averages to reduce false signals
Auto-optimize ATR period and stop loss factor via machine learning
Incorporate profit taking strategy to lock in profits
Combine with other indicators to verify buy/sell signals
Research better ATR calculation or dynamic ATR period
Explore other dynamic trailing stop algorithms
Further optimize the stop loss effect

Conclusion

The Gradient Trailing Stop Loss strategy effectively balances risk and profit by dynamically adjusting the stop loss distance. With simple logic and high configurability, it is suitable for algorithmic trading. Proper parameter tuning and indicator combinations still rely on human expertise. Further optimizations can make this strategy even more profitable.

Parameters

Name	Default	What it controls
v_input_1	2020	Start Year
v_input_2	true	Start Month
v_input_3	true	Start Day
v_input_4	9999	End Year
v_input_5	12	End Month
v_input_6	31	End Day
v_input_7	10	ATR Length
v_input_float_1	3	Factor

Source Pine Script

/*backtest
start: 2023-10-17 00:00:00
end: 2023-10-24 00:00:00
period: 10m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("Supertrend Strategy, by Ho.J.", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=15)

// 백테스팅 시작일과 종료일 입력
startYear = input(2020, title="Start Year")
startMonth = input(1, title="Start Month")
startDay = input(1, title="Start Day")

endYear = input(9999, title="End Year")
endMonth = input(12, title="End Month")
endDay = input(31, title="End Day")

// 백테스팅 시간 범위 확인
backtestingTimeBool = (year >= startYear and month >= startMonth and dayofmonth >= startDay) and (year <= endYear and month <= endMonth and dayofmonth <= endDay)

atrPeriod = input(10, "ATR Length")
factor = input.float(3.0, "Factor", step = 0.01)

[_, direction] = ta.supertrend(factor, atrPeriod)

var bool longCondition = false
var bool shortCondition = false

if backtestingTimeBool
    prevDirection = direction[1]
    if direction < 0
        longCondition := false
        shortCondition := true
    else if direction > 0
        longCondition := true
        shortCondition := false

if longCondition
    strategy.entry("My Long Entry Id", strategy.long)

if shortCondition
    strategy.entry("My Short Entry Id", strategy.short)

plot(strategy.equity, title="equity", color=color.rgb(255, 255, 255), linewidth=2, style=plot.style_area)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-25. 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/Gradient-Trailing-Stop-Loss-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.