Quantitative Breakthrough Uptrend Reference Strategy

Pine ScriptBreakout Trend following Volatility SMA Pivots / S&R

Author: ChaoZhang
Complexity: Intermediate · 105 lines
Parameters: 7
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is a long-term holding strategy based on determining the trend direction with simple moving average lines and forming breakthrough signals with resistance and support lines. By calculating the price Pivot High and Pivot Low points, plotting the resistance and support lines, going long when the price breaks through the resistance line, and closing positions when the price breaks through the support line. This strategy is suitable for stocks with obvious trends and can obtain a good risk-reward ratio.

Strategy Principle

Calculate the 20-day simple moving average line as the baseline for determining the trend
Calculate the Pivot High and Pivot Low points based on user input parameters
Plot the resistance and support lines based on the Pivot High and Pivot Low points
Go long when the closing price is higher than the resistance line
Close positions when the support line crosses below the resistance line

This strategy uses simple moving averages to determine the overall trend direction, and then uses key point breakthroughs to generate trading signals, which is a typical breakout strategy. By judging key points and trends, false breakouts can be effectively filtered out.

Advantage Analysis

The strategy has sufficient opportunities and is suitable for high volatility stocks, making it easy to capture trends
Good risk control for long positions, high risk-reward ratio
Use breakthrough signals to avoid the risk of false breakouts
Customizable parameters, high adaptability

Risk Analysis

Rely on parameter optimization, improper parameters will increase the probability of false breakouts
Delay in breakthrough signals, may miss some opportunities
Easy to be stopped out in volatile markets
Failure to adjust the support line in time may lead to losses

Risks can be reduced by optimizing parameters through live trading, and incorporating stop loss/take profit strategies.

Optimization Directions

Optimize moving average period parameters
Optimize resistance and support line parameters
Add stop loss/take profit strategies
Increase breakthrough confirmation mechanisms
Filter signals with trading volume and other indicators

Summary

Overall, this strategy is a typical breakout strategy that relies on parameter optimization and liquidity, suitable for trend traders. As a reference framework, it can be extended according to actual needs by adding mechanisms like stop loss/take profit, signal filtering to reduce risk and improve stability.

Parameters

Name	Default	What it controls
v_input_int_1	20	length
v_input_1_close	0	Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_int_2	false	Offset
v_input_int_3	46	LookbackLeft
v_input_int_4	32	LookbackRight
v_input_float_1	2	Take Profit
v_input_float_2	1.75	Stop Loss

Source Pine Script

/*backtest
start: 2023-02-14 00:00:00
end: 2024-02-20 00:00:00
period: 1d
basePeriod: 1h
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/
// © CheatCode1

//@version=5
strategy("Quantitative Trend Strategy- Uptrend long", 'Steady Uptrend Strategy', overlay=true, initial_capital = 1500, default_qty_value = 100, commission_type = strategy.commission.percent, commission_value = 0.01, default_qty_type = strategy.percent_of_equity)


length = input.int(20, minval=1)
src = input(close, title="Source")
basis = ta.sma(src, length)
offset = input.int(0, "Offset", minval = -500, maxval = 500)
plot(basis, "Basis", color=#FF6D00, offset = offset)

inp1 = input.int(46, 'LookbackLeft')
inp2 = input.int(32, 'LookbackRight')

l1 = ta.pivothigh(close, inp1, inp2)
S1 = ta.pivotlow(close, inp1, inp2)

// plot(l1, 'Pivothigh', color.red, 1)
// // plot(S1, 'Pivot Low', color.red)

l1V = ta.valuewhen(l1, close, 0)
S1V = ta.valuewhen(S1, close, 0)

Plotl1 = not na(l1) ? l1V : na
PlotS1 = not na(S1) ? S1V : na

plot(Plotl1, 'Resistance', color.green, 1, plot.style_stepline, true)
plot(PlotS1, 'Support', color.red, 1, plot.style_stepline, true)

Priceforlong = close > l1V ? true : na
Priceforshort = close < S1V ? true : na

plotshape(Priceforlong ? high : na, 'p', shape.arrowup, location.abovebar, color.green, size = size.small)
plotshape(Priceforshort ? low : na, 's', shape.arrowdown, location.belowbar, color.red, size = size.small)

vol = volume
volma = ta.sma(vol, 20)

Plotl1C = ta.valuewhen(na(Plotl1), l1V, 0)
PlotS1C = ta.valuewhen(na(PlotS1), S1V, 0)
//Strategy Execution
volc = volume > volma 

Lc1 = Priceforlong 

Sc1 = Priceforshort

sL = Plotl1 < PlotS1 ? close : na
sS = PlotS1 > Plotl1 ? close : na


if Lc1 
    strategy.entry('Long', strategy.long)
// if Sc1 and C2
//     strategy.entry('Short', strategy.short)

if Priceforshort
    strategy.cancel('Long')
if Priceforlong   
    strategy.cancel('Short')


// Stp1 = ta.crossover(k, d)
// Ltp1 = ta.crossunder(k, d)
// Ltp = d > 70  ? Ltp1 : na
// Stp = d < 30  ? Stp1 : na


if strategy.openprofit >= 0 and sL
    strategy.close('Long')
if strategy.openprofit >= 0 and sS
    strategy.close('Short')
takeP = input.float(2, title='Take Profit') / 100
stopL = input.float(1.75, title='Stop Loss') / 100


// // Pre Directionality

Stop_L = strategy.position_avg_price * (1 - stopL)

Stop_S = strategy.position_avg_price * (1 + stopL)

Take_S= strategy.position_avg_price * (1 - takeP)

Take_L = strategy.position_avg_price * (1 + takeP)
     
// sL = Plotl1 < PlotS1 ? close : na
// sS = PlotS1 < Plotl1 ? close : na
     
// //Post Excecution
if strategy.position_size > 0 and not (Lc1)
    strategy.exit("Close Long", stop = Stop_L, limit = Take_L)

if strategy.position_size < 0 and not (Sc1)
    strategy.exit("Close Short", stop = Stop_S, limit = Take_S)

Published by ChaoZhang on the FMZ strategy square · last modified 2024-02-21. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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