Trailing Stop Loss Strategy Based on Price Gaps

This strategy adopts the price gap principle to go long when price breaks recent lows, with stop loss and take profit orders to trail the lowest price for profit taking.

Pine ScriptMachine learning Breakout

Author: ChaoZhang
Complexity: Intermediate · 98 lines
Parameters: 10
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy adopts the price gap principle to go long when price breaks recent lows, with stop loss and take profit orders to trail the lowest price for profit taking.

Strategy Logic

It identifies gaps when price breaks below the lowest price in recent N hours, goes long based on configured percentage, with stop loss and take profit orders. Stop loss line and take profit line move according to price action. The logic is:

Calculate lowest price in recent N hours as binding price
Go long when realtime price is below binding price * buy percent
Set take profit based on entry price * sell percent
Set stop loss based on entry price - entry price * stop loss percent
Position size is percent of strategy equity
Trail stop loss line with lowest price
Close position when take profit or stop loss is triggered

Advantage Analysis

The advantages of this strategy:

Utilize price gap concept, improve winning rate
Automatic trailing stop loss to lock in most profits
Customizable stop loss and take profit percentage for different markets
Works well for instruments with obvious rebounds
Simple logic and easy to implement

Risk Analysis

There are also some risks:

Breakout of gaps may fail with lower lows
Improper stop loss or take profit settings may cause premature exit
Require periodic parameter tuning for market changes
Limited applicable instruments, may not work for some
Manual intervention needed from time to time

Optimization Directions

The strategy can be improved in the following aspects:

Add machine learning models for automatic parameter tuning
Add more types of stop loss/take profit, e.g. trailing stop loss, bracket orders
Optimize stop loss/take profit logic for smarter exits
Incorporate more indicators to filter out false signals
Expand to more instruments to improve universality

Conclusion

In conclusion, this is a simple and effective trailing stop loss strategy based on price gaps. It reduces false entries and locks in profits effectively. There is still much room for improvements in parameters tuning and signal filtering. It is worth further research and refinement.

Parameters

Name	Default	What it controls
v_input_1	3	(?Squeeze Settings)Buy, %
v_input_2	true	Sell, %
v_input_3	true	Stop Loss, %
v_input_4	true	Max Bars To Sell
v_input_5	2	maxBars
v_input_6_close	0	Bind: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_7	true	(?Backtesting Period)Fixed Range
v_input_8	0	rangeStart: 1 Week \| 12 Hours \| 24 Hours \| 48 Hours \| 6 Hours \| 2 Weeks \| 1 Month \| Maximum
v_input_9	timestamp(01 Aug 2021 00:00 +0000)	Backtesting Start
v_input_10	timestamp(01 Aug 2022 00:00 +0000)	Backtesting End

Source Pine Script

/*backtest
start: 2022-11-21 00:00:00
end: 2023-11-27 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4

strategy(title="Squeeze Backtest by Shaqi v1.0", overlay=true, pyramiding=0, currency="USD", process_orders_on_close=true, commission_type=strategy.commission.percent, commission_value=0.075, default_qty_type=strategy.percent_of_equity, default_qty_value=100, initial_capital=100, backtest_fill_limits_assumption=0)
strategy.risk.allow_entry_in(strategy.direction.long)

R0 = "6 Hours"
R1 = "12 Hours"
R2 = "24 Hours"
R3 = "48 Hours"
R4 = "1 Week"
R5 = "2 Weeks"
R6 = "1 Month"
R7 = "Maximum"


buyPercent = input( title="Buy, %",         type=input.float,   defval=3,       minval=0.01,                        step=0.01,  inline="Percents",  group="Squeeze Settings") * 0.01
sellPercent = input(title="Sell, %",        type=input.float,   defval=1,       minval=0.01,                        step=0.01,  inline="Percents",  group="Squeeze Settings") * 0.01
stopPercent = input(title="Stop Loss, %",   type=input.float,   defval=1,       minval=0.01,        maxval=100,     step=0.01,  inline="Percents",  group="Squeeze Settings") * 0.01
isMaxBars = input(  title="Max Bars To Sell",               type=input.bool,    defval=true ,                                   inline="MaxBars",   group="Squeeze Settings")
maxBars = input(    title="",       type=input.integer, defval=2,     minval=0,           maxval=1000, step=1,                  inline="MaxBars",   group="Squeeze Settings")
bind = input(       title="Bind",           type=input.source,  defval=close,                                                                       group="Squeeze Settings")
isRange = input(    title="Fixed Range",               type=input.bool,    defval=true,                                         inline="Range",     group="Backtesting Period")
rangeStart = input( title="",                       defval=R4,      options=[R0, R1, R2, R3, R4, R5, R6, R7],                   inline="Range",     group="Backtesting Period")
periodStart = input(title="Backtesting Start", type=input.time,    defval=timestamp("01 Aug 2021 00:00 +0000"),                                     group="Backtesting Period")
periodEnd = input(  title="Backtesting End",   type=input.time,    defval=timestamp("01 Aug 2022 00:00 +0000"),                                     group="Backtesting Period")

int startDate = na
int endDate = na
if isRange
    if rangeStart == R0
        startDate := timenow - 21600000
        endDate := timenow
    else if rangeStart == R1
        startDate := timenow - 43200000
        endDate := timenow
    else if rangeStart == R2
        startDate := timenow - 86400000
        endDate := timenow
    else if rangeStart == R3
        startDate := timenow - 172800000
        endDate := timenow
    else if rangeStart == R4
        startDate := timenow - 604800000
        endDate := timenow
    else if rangeStart == R5
        startDate := timenow - 1209600000
        endDate := timenow
    else if rangeStart == R6
        startDate := timenow - 2592000000
        endDate := timenow
    else if rangeStart == R7
        startDate := time
        endDate := timenow
else 
    startDate := periodStart
    endDate := periodEnd

afterStartDate = (time >= startDate)
beforeEndDate = (time <= endDate)
notInTrade = strategy.position_size == 0
inTrade = strategy.position_size > 0

barsFromEntry = barssince(strategy.position_size[0] > strategy.position_size[1])
entry = strategy.position_size[0] > strategy.position_size[1]
entryBar = barsFromEntry == 0
notEntryBar = barsFromEntry != 0
buyLimitPrice = bind - bind * buyPercent
buyLimitFilled = low <= buyLimitPrice
sellLimitPriceEntry = buyLimitPrice * (1 + sellPercent)
sellLimitPrice = strategy.position_avg_price * (1 + sellPercent)

stopLimitPriceEntry = buyLimitPrice - buyLimitPrice * stopPercent
stopLimitPrice = strategy.position_avg_price - strategy.position_avg_price * stopPercent

if afterStartDate and beforeEndDate and notInTrade
    strategy.entry("BUY", true, limit = buyLimitPrice)
    strategy.exit("INSTANT", limit = sellLimitPriceEntry, stop = stopLimitPriceEntry)
strategy.cancel("INSTANT", when = inTrade)
if isMaxBars
    strategy.close("BUY", when = barsFromEntry >= maxBars, comment = "Don't Sell")
strategy.exit("SELL", limit = sellLimitPrice, stop = stopLimitPrice)

showStop = stopPercent <= 0.03

plot(showStop ? stopLimitPrice : na, title="Stop Loss Limit Order", style=plot.style_linebr, color=color.red, linewidth=1)
plot(sellLimitPrice, title="Take Profit Limit Order", style=plot.style_linebr, color=color.purple, linewidth=1)
plot(strategy.position_avg_price, title="Buy Order Filled Price", style=plot.style_linebr, color=color.blue, linewidth=1)
plot(buyLimitPrice, title="Trailing Buy Limit Order", style=plot.style_stepline, color=color.new(color.blue, 30), offset=1)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-28. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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