Adaptive Volatility Breakout Trading Strategy

Pine ScriptBreakout Volatility Machine learning SMA

Author: ChaoZhang
Complexity: Intermediate · 156 lines
Parameters: 11
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy identifies market trends based on price breakthrough points and uses adaptive indicators to determine the overall trend in order to capture short-term price reversal opportunities. It generates buy/sell signals when prices break out of the baseline channel. The strategy is suitable for trading highly volatile cryptocurrencies.

Strategy Logic

Identify extreme price points as channel boundaries. When prices hit new highs or lows, set those points as channel boundaries.
Calculate the adaptive volatility MA indicator to determine the overall trend direction. Larger MA values indicate the market is currently in a volatile stage.
Generate buy signals when prices break above the channel top, and sell signals when prices break below the channel bottom.
Set stop loss points. Long position stop loss points are set at 1% below the entry price.

Advantage Analysis

The price channel is adaptive and can accurately determine trend reversal points.
The volatility indicator judges the overall trend and avoids missing the big picture in volatile markets.
As a reversal strategy, it is suitable to capture short-term price bounces.

Risk Analysis

In a sustained downtrend, multiple stop loss points may be triggered resulting in large losses.
Frequent buy and sell trades in ranging markets increase transaction costs.
Requires manual determination of entry times. Fully automated trading has overfitting risks.

Optimization Directions

Optimize MA parameters to better determine overall trends.
Incorporate volume indicators to avoid reversal signals in volume exhaustion scenarios.
Add machine learning models to enable dynamic parameter optimization.

Summary

The overall logic of this strategy is clear and has some practical value. However, trading risks should still be controlled to prevent large losses in certain market conditions. Next steps include optimizing multiple dimensions such as the overall framework, indicator parameters, and risk control to make the strategy parameters and trading signals more reliable.

Parameters

Name	Default	What it controls
v_input_1	13	Start Date
v_input_2	9	Start Month
v_input_3	2021	Start Year
v_input_4	true	filter Bill Williams Fractals:
v_input_5	true	Filter fractals using extreme method:
v_input_6	2	Extreme Window:
v_input_7	true	ShowSwingsHL
v_input_8	true	showchannel
v_input_9	true	showZigZag
v_input_10	2	VolumeMA
v_input_11	0.01	Long Stop Loss (%)

Source Pine Script

/*backtest
start: 2023-11-03 00:00:00
end: 2023-12-03 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// @version = 4
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © TradingGroundhog



//  ||---   Cash & Date:
cash_amout = 10000
pyramid_val = 1
cash_given_per_lot = cash_amout/pyramid_val
startDate = input(title="Start Date",defval=13)
startMonth = input(title="Start Month",defval=9)
startYear = input(title="Start Year",defval=2021)
afterStartDate = (time >= timestamp(syminfo.timezone,startYear, startMonth, startDate, 0, 0))
//  ||------------------------------------------------------------------------------------------------------



//  ||---   Strategy:
strategy(title="TradingGroundhog - Strategy & Fractal V1 - Short term", overlay=true, max_bars_back = 4000, max_labels_count=500, commission_type=strategy.commission.percent, commission_value=0.00,default_qty_type=strategy.cash, default_qty_value= cash_given_per_lot, pyramiding=pyramid_val)
//  ||------------------------------------------------------------------------------------------------------



//  ||---   Fractal Recognition:
filterBW = input(true, title="filter Bill Williams Fractals:")
filterFractals = input(true, title="Filter fractals using extreme method:")
length = input(2, title="Extreme Window:")
regulartopfractal = high[4] < high[3] and high[3] < high[2] and high[2] > high[1] and high[1] > high[0]
regularbotfractal = low[4] > low[3] and low[3] > low[2] and low[2] < low[1] and low[1] < low[0]
billwtopfractal = filterBW ? false : (high[4] < high[2] and high[3] < high[2] and high[2] > high[1] and high[2] > high[0] ? true : false)
billwbotfractal = filterBW ? false : (low[4] > low[2] and low[3] > low[2] and low[2] < low[1] and low[2] < low[0] ? true : false)
ftop = filterBW ? regulartopfractal : regulartopfractal or billwtopfractal
fbot = filterBW ? regularbotfractal : regularbotfractal or billwbotfractal
topf = ftop ? high[2] >= highest(high, length) ? true : false : false
botf = fbot ? low[2] <= lowest(low, length) ? true : false : false
filteredtopf = filterFractals ? topf : ftop
filteredbotf = filterFractals ? botf : fbot
//  ||------------------------------------------------------------------------------------------------------



//  ||---   V1 : Added Swing High/Low Option
ShowSwingsHL = input(true)
highswings = filteredtopf == false ? na : valuewhen(filteredtopf == true, high[2], 2) < valuewhen(filteredtopf == true, high[2], 1) and valuewhen(filteredtopf == true, high[2], 1) > valuewhen(filteredtopf == true, high[2], 0)
lowswings = filteredbotf == false ? na : valuewhen(filteredbotf == true, low[2], 2) > valuewhen(filteredbotf == true, low[2], 1) and valuewhen(filteredbotf == true, low[2], 1) < valuewhen(filteredbotf == true, low[2], 0)
//---------------------------------------------------------------------------------------------------------



//  ||---   V2 : Plot Lines based on the fractals.
showchannel = input(true)
//---------------------------------------------------------------------------------------------------------



//  ||---   ZigZag:
showZigZag = input(true)
//----------------------------------------------------------------------------------------------------------



//  ||---   Fractal computation:
istop = filteredtopf ? true : false
isbot = filteredbotf ? true : false
topcount = barssince(istop)
botcount = barssince(isbot)
vamp = input(title="VolumeMA",  defval=2)
vam = sma(volume, vamp)
fractalup = 0.0
fractaldown = 0.0
up = high[3]>high[4] and high[4]>high[5] and high[2]<high[3] and high[1]<high[2] and volume[3]>vam[3]
down = low[3]<low[4] and low[4]<low[5] and low[2]>low[3] and low[1]>low[2] and volume[3]>vam[3]
fractalup :=  up ? high[3] : fractalup[1] 
fractaldown := down ? low[3] : fractaldown[1]
//----------------------------------------------------------------------------------------------------------



//  ||---   Fractal save:
fractaldown_save = array.new_float(0)
for i = 0 to 4000
    if array.size(fractaldown_save) < 3
        if array.size(fractaldown_save) == 0
            array.push(fractaldown_save, fractaldown[i])
        else 
            if fractaldown[i] != array.get(fractaldown_save, array.size(fractaldown_save)-1)
                array.push(fractaldown_save, fractaldown[i])
if array.size(fractaldown_save) < 3
    array.push(fractaldown_save, fractaldown)
    array.push(fractaldown_save, fractaldown)
fractalup_save = array.new_float(0)
for i = 0 to 4000
    if array.size(fractalup_save) < 3
        if array.size(fractalup_save) == 0
            array.push(fractalup_save, fractalup[i])
        else 
            if fractalup[i] != array.get(fractalup_save, array.size(fractalup_save)-1)
                array.push(fractalup_save, fractalup[i])
if array.size(fractalup_save) < 3
    array.push(fractalup_save, fractalup)
    array.push(fractalup_save, fractalup)
Bottom_1 = array.get(fractaldown_save,  0)
Bottom_2 = array.get(fractaldown_save,  1)
Bottom_3 = array.get(fractaldown_save,  2)
Top_1 = array.get(fractalup_save, 0)
Top_2 = array.get(fractalup_save, 1)
Top_3 = array.get(fractalup_save, 2)
//----------------------------------------------------------------------------------------------------------



//  ||---   Fractal Buy Sell Signal:
bool Signal_Test = false
bool Signal_Test_OUT_TEMP = false
var Signal_Test_TEMP = false
longLossPerc = input(title="Long Stop Loss (%)", minval=0.0, step=0.1, defval=0.01) * 0.01
if filteredbotf and open < Bottom_1 and (Bottom_1 - open) / Bottom_1 >= longLossPerc
    Signal_Test := true
if filteredtopf and open > Top_1
    Signal_Test_TEMP := true
if filteredtopf and Signal_Test_TEMP
    Signal_Test_TEMP := false
    Signal_Test_OUT_TEMP := true
//----------------------------------------------------------------------------------------------------------



//  ||---   Plotting:
//plotshape(filteredtopf, style=shape.triangledown, location=location.abovebar, color=color.red, text="•", offset=0)
//plotshape(filteredbotf, style=shape.triangleup, location=location.belowbar, color=color.lime, text="•", offset=0)
//plotshape(ShowSwingsHL ? highswings : na, style=shape.triangledown, location=location.abovebar, color=color.maroon, text="H", offset=0)
//plotshape(ShowSwingsHL ? lowswings : na, style=shape.triangleup, location=location.belowbar, color=color.green, text="L", offset=0)
plot(showchannel ? (filteredtopf ? high[2] : na) : na, color=color.black, offset=0)
plot(showchannel ? (filteredbotf ? low[2] : na) : na, color=color.black, offset=0)
plot(showchannel ? (highswings ? high[2] : na) : na, color=color.black, offset=-2)
plot(showchannel ? (lowswings ? low[2] : na) : na, color=color.black, offset=-2)
plotshape(Signal_Test, style=shape.flag, location=location.belowbar, color=color.yellow, offset=0)
plotshape(Signal_Test_OUT_TEMP, style=shape.flag, location=location.abovebar, color=color.white, offset=0)
//----------------------------------------------------------------------------------------------------------



//  ||---   Buy And Sell:
strategy.entry(id="Long", long=true, when = Signal_Test and afterStartDate)
strategy.close_all(when = Signal_Test_OUT_TEMP and afterStartDate)
//----------------------------------------------------------------------------------------------------------

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-04. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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