Alpha Trend Strategy with Trailing Stop Loss

The Alpha Trend Strategy with Trailing Stop Loss is an enhanced version of the Alpha Trend Strategy by incorporating a trailing stop loss mechanism, which can control risks more effectively and improve overall returns.

Pine ScriptTrend following RSI MACD SMA ATR

Author: ChaoZhang
Complexity: Intermediate · 98 lines
Parameters: 7
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy first uses the Alpha indicator to determine price trends. When the Alpha indicator goes up, it is a bullish signal. When the Alpha indicator goes down, it is a bearish signal. The strategy generates buy and sell signals based on the golden cross and dead cross of the Alpha indicator.

Meanwhile, a trailing stop loss mechanism is enabled. The trailing stop loss level defaults to 10% of the closing price of the day. When holding long positions, if the price falls below the stop loss level, the strategy will exit the position to stop loss. Similarly for short positions. This helps better lock in profits and reduce risks.

Advantage Analysis

The Alpha trend has stronger capabilities of determining price trends than simple moving averages and other indicators.
By enabling trailing stop loss, single-trade loss can be effectively controlled, lowering risks.
This strategy has strong risk control abilities. Even in unfavorable market conditions, losses can still be minimized.
With fewer reference inputs, this strategy is efficient to calculate, suitable for high frequency trading.

Risk Analysis

In sideways range-bound markets, the strategy may generate many unnecessary trading signals, increasing trading costs and slippage losses.
When enabling trailing stop loss, the stop loss percentage needs to set appropriately. An excessively high or low percentage will both be unfavorable for the strategy's profitability.
In violently fluctuating prices, the probability of stop loss being triggered will significantly rise, increasing the risk of being locked in positions.
When optimizing the stop loss parameters, various factors including the underlying's characteristics and trading frequency should be considered, not just pursuing maximum returns.

The above risks could be alleviated by adjusting the Alpha indicator parameters, setting DYNAMIC stop loss, shortening trading cycle lengths, etc.

Optimization Directions

Different indicator parameters can be tested to find more suitable Alpha indicator parameter combinations.
Attempt to set stop loss percentages dynamically based on ATR to better adapt to market fluctuations.
Combine with other indicators such as MACD, KD to filter out some false signals.
Parameters can be automatically optimized based on live trading and backtesting results, using machine learning techniques to improve the intelligence of parameter selection.

Conclusion

The Alpha Trend Strategy with Trailing Stop Loss combines trend determination and risk control. It can effectively identify price trends and lock in profits to reduce risks. Compared to simple trend tracking strategies, this strategy can obtain higher steady returns. With various aspects of optimization, it has the potential to achieve even better performance.

Parameters

Name	Default	What it controls
v_input_float_1	true	Multiplier
v_input_1	14	Common Period
v_input_2_close	0	src: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_3	false	Show Signals?
v_input_4	false	Change calculation (no volume data)?
v_input_bool_1	true	Enable Trailing Stop (%)
v_input_float_2	10	Trailing (%)

Source Pine Script

/*backtest
start: 2023-10-27 00:00:00
end: 2023-11-26 00:00:00
period: 1h
basePeriod: 15m
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/
// author © KivancOzbilgic
// developer © KivancOzbilgic
//@version=5

strategy("AlphaTrend Strategy", shorttitle='ATst', overlay=true, format=format.price, precision=2, margin_long=100, margin_short=100)
coeff = input.float(1, 'Multiplier', step=0.1)
AP = input(14, 'Common Period')
ATR = ta.sma(ta.tr, AP)
src = input(close)
showsignalsk = input(title='Show Signals?', defval=false)
novolumedata = input(title='Change calculation (no volume data)?', defval=false)
upT = low - ATR * coeff
downT = high + ATR * coeff
AlphaTrend = 0.0
AlphaTrend := (novolumedata ? ta.rsi(src, AP) >= 50 : ta.mfi(hlc3, AP) >= 50) ? upT < nz(AlphaTrend[1]) ? nz(AlphaTrend[1]) : upT : downT > nz(AlphaTrend[1]) ? nz(AlphaTrend[1]) : downT

color1 = AlphaTrend > AlphaTrend[1] ? #00E60F : AlphaTrend < AlphaTrend[1] ? #80000B : AlphaTrend[1] > AlphaTrend[3] ? #00E60F : #80000B
k1 = plot(AlphaTrend, color=color.new(#0022FC, 0), linewidth=3)
k2 = plot(AlphaTrend[2], color=color.new(#FC0400, 0), linewidth=3)

fill(k1, k2, color=color1)

buySignalk = ta.crossover(AlphaTrend, AlphaTrend[2])
sellSignalk = ta.crossunder(AlphaTrend, AlphaTrend[2])


K1 = ta.barssince(buySignalk)
K2 = ta.barssince(sellSignalk)
O1 = ta.barssince(buySignalk[1])
O2 = ta.barssince(sellSignalk[1])

plotshape(buySignalk and showsignalsk and O1 > K2 ? AlphaTrend[2] * 0.9999 : na, title='BUY', text='BUY', location=location.absolute, style=shape.labelup, size=size.tiny, color=color.new(#0022FC, 0), textcolor=color.new(color.white, 0))

plotshape(sellSignalk and showsignalsk and O2 > K1 ? AlphaTrend[2] * 1.0001 : na, title='SELL', text='SELL', location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.new(color.maroon, 0), textcolor=color.new(color.white, 0))


// //ENTER SOME SETUP TRADES FOR TSL EXAMPLE
// longCondition = ta.crossover(ta.sma(close, 10), ta.sma(close, 20))
// if longCondition
//     strategy.entry('My Long Entry Id', strategy.long)

// shortCondition = ta.crossunder(ta.sma(close, 10), ta.sma(close, 20))
// if shortCondition
//     strategy.entry('My Short Entry Id', strategy.short)



longCondition = buySignalk
if (longCondition)
    strategy.entry("Long", strategy.long)

shortCondition = sellSignalk
if (shortCondition)
    strategy.entry("Short", strategy.short)
    

enableTrailing = input.bool(title='Enable Trailing Stop (%)',defval = true)
//TRAILING STOP CODE
trailStop = input.float(title='Trailing (%)', minval=0.0, step=0.1, defval=10) * 0.01



longStopPrice = 0.0
shortStopPrice = 0.0
longStopPrice := if strategy.position_size > 0
    stopValue = close * (1 - trailStop)
    math.max(stopValue, longStopPrice[1])
else
    0
shortStopPrice := if strategy.position_size < 0
    stopValue = close * (1 + trailStop)
    math.min(stopValue, shortStopPrice[1])
else
    999999

//PLOT TSL LINES
plot(series=strategy.position_size > 0 ? longStopPrice : na, color=color.new(color.red, 0), style=plot.style_linebr, linewidth=1, title='Long Trail Stop', offset=1, title='Long Trail Stop')
plot(series=strategy.position_size < 0 ? shortStopPrice : na, color=color.new(color.red, 0), style=plot.style_linebr, linewidth=1, title='Short Trail Stop', offset=1, title='Short Trail Stop')

    
if enableTrailing
    //EXIT TRADE @ TSL
    if strategy.position_size > 0
        strategy.exit(id='Close Long', stop=longStopPrice)
    if strategy.position_size < 0
        strategy.exit(id='Close Short', stop=shortStopPrice)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-27. 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/Alpha-Trend-Strategy-with-Trailing-Stop-Loss.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.