Eltrut Turtle Reversal Strategy

This strategy uses cross-period technical indicators to identify trend direction, combined with trend filtering and volatility filtering, to achieve low-risk trend tracking trading.

Pine ScriptBreakout Mean reversion Machine learning SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 122 lines
Parameters: 8
Backtest bar: 4h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy uses cross-period technical indicators to identify trend direction, combined with trend filtering and volatility filtering, to achieve low-risk trend tracking trading.

Strategy Logic

Use high-low point breakthroughs to determine buy and sell signals. Go short when the price breaks through the 7-period high, and go long when it breaks through the 7-period low.
The Trendflex indicator determines the main trend direction. This indicator combines double smoothing techniques and can effectively identify trend middle sections. A value above 1 indicates an upward trend, while a value below -1 indicates a downward trend. Here we require Trendflex > 1 for longs and < -1 for shorts, thereby filtering out consolidation states.
Use Bollinger Bands to identify oscillation ranges. Avoid longs and shorts when the close price is within the band.
Use moving stop loss and take profit to manage positions.

Advantage Analysis

Cross-period indicators combined with double smoothing techniques can effectively identify trend directions and avoid oscillating markets.
Considering both trend direction and volatility patterns makes trading signals more reliable.
Reasonable stop loss and take profit settings lock in profits and prevent losses from expanding.
The strategy is relatively simple and easy to implement.

Risk Analysis

Breakthrough signals may have false breakouts, resulting in wrong trades. More filtering conditions can be considered.
Fixed cycle parameters cannot adapt to market changes. Dynamic optimization of parameters can be considered.
Lack of price stops fails to prevent huge losses from extreme market conditions.
Fixed take profit and stop loss points cannot be adjusted intelligently according to market volatility.

Optimization Directions

Consider adding more trend judgment indicators to form a strategy combination to improve judgment accuracy.
Add oscillation identification modules to pause trading when oscillation is severe to reduce risk.
Introduce machine learning algorithms to achieve dynamic parameter optimization.
Add price stop loss modules to stop loss when losses reach a certain threshold.
Calculate take profit and stop loss ratios based on market volatility to achieve intelligent adjustment of take profit and stop loss.

Summary

Overall, this strategy is relatively stable and reliable, while also having room for improvement. The core idea is to determine the trend direction across cycles, and then filter using trend strength indicators and volatility indicators to generate high-quality signals. This simple and practical strategy is very suitable for tracking medium and long term trends. By introducing more conditional judgments and dynamic parameter optimization, the strategy effect can be further improved.

Parameters

Name	Default	What it controls
v_input_1	2016	Backtest Start Year
v_input_2	true	Backtest Start Month
v_input_3	true	Backtest Start Day
v_input_4	2030	Backtest Stop Year
v_input_5	12	Backtest Stop Month
v_input_6	30	Backtest Stop Day
v_input_7	3	Stop Loss %
v_input_8	9	Take Profit %

Source Pine Script

/*backtest
start: 2023-08-27 00:00:00
end: 2023-09-26 00:00:00
period: 4h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy("Eltrut", shorttitle="Eltrut Strat", overlay=true, pyramiding=0, default_qty_type= strategy.percent_of_equity,calc_on_order_fills=false, slippage=25,commission_type=strategy.commission.percent,commission_value=0.075)

testStartYear = input(2016, "Backtest Start Year")
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)

testStopYear = input(2030, "Backtest Stop Year")
testStopMonth = input(12, "Backtest Stop Month")
testStopDay = input(30, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)


// R E F L E X / T R E N D F L E X

f_supersmoother(_src,_len)=>
    pi = 2 * asin(1)
    _a = exp(-sqrt(2) * pi / _len)
    _c2 = 2 * _a * cos(sqrt(2) * pi / _len)
    _c3 = -_a * _a
    _c1 = 1 - _c2 - _c3
    _out = 0.0
    _out := _c1 * _src + _c2 * nz(_out[1],nz(_src[1],_src)) + _c3 * nz(_out[2],nz(_src[2],nz(_src[1],_src)))

f_IQIFM(_src1,_max)=>
    _src = _src1 < 0.001 ? _src1 * 10000 : _src1
    _imult = 0.635, _qmult = 0.338 , _inphase = 0.0, _quad = 0.0
    _re = 0.0, _im = 0.0, _deltaphase = 0.0, _instper = 0.0, _per = 0.0, _v4 = 0.0
    _v1 = _src - nz(_src[7])
    _inphase := 1.25 * (nz(_v1[4]) - _imult * _v1[2]) + _imult * nz(_inphase[3])
    _quad := _v1[2] - _qmult * _v1 + _qmult * nz(_quad[2])
    _re := 0.2 * (_inphase * _inphase[1] + _quad * _quad[1]) + 0.8 * nz(_re[1])
    _im := 0.2 * (_inphase * _quad[1] - _inphase[1] * _quad) + 0.8 * nz(_im[1])
    if _re != 0.0
        _deltaphase := atan(_im / _re)
    for i = 0 to _max
        _v4 := _v4 + _deltaphase[i]
        if _v4 > 4 * asin(1) and _instper == 0.0
            _instper := i
    if _instper == 0.0
        _instper := nz(_instper[1])
    _per := 0.25 * _instper + 0.75 * nz(_per[1])
    _per

f_flex(_src1, _fixed_len, _reflex) =>
    _src = _src1
    _len = _fixed_len 
    _ss1 = f_supersmoother(_src, _len)
    _ss = _ss1
    _slope = (_ss[_len] - _ss) / _len
    _sum = 0.0
    for _i = 1 to _len
        _c1 = _reflex ? _ss + _i * _slope - _ss[_i] : _ss - _ss[_i]
        _sum := _sum + _c1
    _sum := _sum / _len
    _ms = 0.0
    _ms := 0.04 * pow(_sum,2) + 0.96 * nz(_ms[1])
    _flex1 = _ms != 0 ? _sum / sqrt(nz(_ms)) : 0.0
    _flex = _flex1
    _flex

rflx = f_flex(close, 20, true)  
trndflx = f_flex(close, 20, false)   

// S I G N A L
hi7 = highest(7)
lo7 = lowest(7)
long_cond = crossunder(close, lo7[1])
short_cond = crossover(close, hi7[1])

// F I L T E R S

long_filter1 = trndflx < 1
short_filter1 = trndflx > -1

basis = sma(close, 35)
dev = 3 * stdev(close, 35)
long_filter2 = close > basis - dev
short_filter2 = close < basis + dev

// S T R A T E G Y

long = long_cond and long_filter1 and long_filter2
short = short_cond and short_filter1 and short_filter2

if( true)
    strategy.entry("Long", strategy.long, when = long)
    strategy.entry("Long", strategy.long, when = short)


// User Options to Change Inputs (%)
stopPer = input(3, title='Stop Loss %', type=input.float) / 100
takePer = input(9, title='Take Profit %', type=input.float) / 100

// Determine where you've entered and in what direction
longStop = strategy.position_avg_price * (1 - stopPer)
shortStop = strategy.position_avg_price * (1 + stopPer)
shortTake = strategy.position_avg_price * (1 - takePer)
longTake = strategy.position_avg_price * (1 + takePer)

if strategy.position_size > 0 
    strategy.exit(id="Exit Long", stop=longStop, limit=longTake)
if strategy.position_size < 0 
    strategy.exit(id="Exit Short", stop=shortStop, limit=shortTake)


// P L O T 

plotshape(long, color = #1e90ff, text = "", style=shape.triangleup, location=location.belowbar, size=size.tiny)
plotshape(short, color = #ff69b4, text = "", style=shape.triangledown, location=location.abovebar, size=size.tiny)

alertcondition(long, "Long", "Enter Long")
alertcondition(short, "Short", "Enter S")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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