Trend Breakout Long Shadow Strategy

This strategy judges the current trend direction by calculating the ratio of bullish/bearish shadow length, and identifies trend with ATR indicator. It opens reverse position on breakout points and sets stop loss and…

Pine ScriptBreakout Trend following Mean reversion ATR

Author: ChaoZhang
Complexity: Intermediate · 68 lines
Parameters: 7
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Strategy Logic

The strategy mainly judges the current trend by calculating bullish/bearish shadow ratio. Long bearish indicates downward trend while long bullish indicates upward trend.

The specific logic is:

Calculate bearish shadow: close - low
Calculate bullish shadow: high - open
Take the max of bearish and bullish shadow as shadow length
Calculate candle body length: high - low
Calculate ratio between shadow and body length
If ratio > 0.5 and bearish > bullish, judge downward trend and long position
If ratio > 0.5 and bullish > bearish, judge upward trend and short position
Validate breakout with candle length > 0.75 * ATR
Set stop loss and take profit after entry, with 2:1 ratio

The above is the basic trading logic, identifying reverse breakout points with trend detection and optimizing profit with stop loss/take profit.

Advantages

Shadow ratio accurately judges the trend
ATR filters out false breakout signals
Stop loss and take profit manages risk
2:1 risk-reward ratio meets quant trading standard
Suitable for short-term trading on high volatility stocks
Simple and clear logic, easy to understand

Risks

Price volatility may hit stop loss and increase loss
Performance relies heavily on parameter tuning
Trend reversal may lead to loss
Expanding stop loss/take profit may increase loss probability
Failed breakout can lead to large loss

Risks can be managed by reasonable stop loss, parameter optimization, and timely position exit.

Enhancement

The strategy can be optimized in the following ways:

Optimize shadow ratio parameter for best value
Optimize ATR parameter for best candle length
Optimize stop loss/take profit coefficients for optimal risk-reward
Add position sizing like gradual position increase
Add trailing stop loss for profit protection
Add other indicators to filter signals
Optimize backtest time period and test different market stages

With multi-faceted testing and optimization, the strategy performance can be maximized.

Overall, this strategy profits from short-term price swings through trend identification and risk management. When optimized, it can become a robust short-term breakout strategy for quant trading.

Parameters

Name	Default	What it controls
v_input_1	2020	Backtest Start Year
v_input_2	true	Backtest Start Month
v_input_3	true	Backtest Start Day
v_input_4	2025	Backtest End Year
v_input_5	true	Backtest End Month
v_input_6	true	Backtest End Day
v_input_7	0.8	N - % offset for NSL and (2N)TP

Source Pine Script

/*backtest
start: 2022-11-08 00:00:00
end: 2023-11-14 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/
// © ondrej17

//@version=4
strategy("longWickstrategy", overlay=true )
 
// Inputs
st_yr_inp = input(defval=2020, title='Backtest Start Year')
st_mn_inp = input(defval=01, title='Backtest Start Month')
st_dy_inp = input(defval=01, title='Backtest Start Day')
en_yr_inp = input(defval=2025, title='Backtest End Year')
en_mn_inp = input(defval=01, title='Backtest End Month')
en_dy_inp = input(defval=01, title='Backtest End Day')
sltp_inp = input(defval=0.8, title='N - % offset for N*SL and (2N)*TP')/100
 
// Dates
start = timestamp(st_yr_inp, st_mn_inp, st_dy_inp,00,00)
end = timestamp(en_yr_inp, en_mn_inp, en_dy_inp,00,00)
canTrade = time >= start and time <= end
// Indicators Setup


 
// Strategy Calcuations
lowerWick = (open > close) ? close-low : open - low
upperWick = (open > close) ? high-open : high-close
wickLength = max(lowerWick,upperWick)
candleLength = high-low
wickToCandleRatio = wickLength / candleLength
entryFilterCandleLength = candleLength > 0.75*atr(48)


// Entries and Exits
 
longCondition = entryFilterCandleLength and wickToCandleRatio > 0.5 and lowerWick > upperWick and canTrade and strategy.position_size == 0
shortCondition = entryFilterCandleLength and wickToCandleRatio > 0.5 and lowerWick < upperWick and canTrade and strategy.position_size == 0

strategy.entry("pendingLong", strategy.long, limit=low+wickLength/2, when = longCondition)
strategy.entry("pendingShort", strategy.short, limit=high-wickLength/2, when = shortCondition)

longStop = strategy.position_size > 0 ? strategy.position_avg_price*(1-sltp_inp) : na
longTP = strategy.position_size > 0 ? strategy.position_avg_price*(1+2*sltp_inp) : na
shortStop = strategy.position_size < 0 ? strategy.position_avg_price*(1+sltp_inp) : na
shortTP = strategy.position_size < 0 ? strategy.position_avg_price*(1-2*sltp_inp) : na

strategy.exit("longSLTP","pendingLong", stop=longStop, limit = longTP)
strategy.exit("shortSLTP","pendingShort", stop=shortStop, limit = shortTP)  
 

plot(longStop, color=color.red, style=plot.style_linebr, linewidth=2)
plot(shortStop, color=color.red, style=plot.style_linebr, linewidth=2)
plot(longTP, color=color.green, style=plot.style_linebr, linewidth=2)
plot(shortTP, color=color.green, style=plot.style_linebr, linewidth=2)

plotLongCondition = longCondition ? high+abs(open-close) : na
plot(plotLongCondition, style=plot.style_circles, linewidth=4, color=color.green)
plotShortCondition = shortCondition ? high+abs(open-close) : na
plot(plotShortCondition, style=plot.style_circles, linewidth=4, color=color.red)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-15. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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