momentum breakout trading strategy 433512

The momentum breakout strategy mainly judges potential reversal signals by identifying bullish engulfing patterns or bearish engulfing patterns to enter the market. After identifying the signal, it quickly tracks the…

Pine ScriptBreakout Momentum Arbitrage

Author: ChaoZhang
Complexity: Intermediate · 111 lines
Parameters: 15
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

This article introduces a momentum breakout trading strategy based on candlestick patterns. The strategy identifies market trends and entry opportunities by recognizing candlestick formations.

Strategy Overview

Strategy Principle

The core logic of the momentum breakout strategy is based on identifying engulfing patterns, including bullish engulfs and bearish engulfs.

A bullish engulfing pattern forms when the current period's closing price is higher than the opening price, and the previous period's closing price is lower than the previous period's opening price. This pattern often signals a reversal in market sentiment from bearish to bullish, making it a good opportunity to chase the uptrend.

A bearish engulfing pattern forms when the current period's closing price is lower than the opening price, and the previous period's closing price is higher than the previous period's opening price. This also signals a change in market sentiment, providing an opportunity to short the market.

After identifying an engulfing pattern, the momentum breakout strategy quickly establishes a position with excess leverage to track the potential reversal trend. It also dynamically adjusts the stop loss and take profit to control risk while locking in profits.

Advantages

Quickly identify market reversal opportunities
Balanced risk-reward ratio with proper stop loss and take profit
Adjustable leverage catering to different risk appetites
High efficiency with automated trading

Risks

Engulfing patterns not fully ensuring reversal
Probability of failed breakout and sideways price action
Risk of liquidation from excess leverage
Requiring sufficient capital to support adequate position sizing

Improvements

The strategy can be optimized in the following ways:

Incorporate other indicators to filter signals
Adjust leverage to limit risk
Scale in to lower cost basis
Optimize stop loss and take profit to lock in profits

Summary

The momentum breakout strategy is a typical mean-reversion strategy. By capturing key candlestick signals, it quickly judges and tracks market trend reversals. Although risks exist, the strategy can be effectively enhanced through multiple optimization techniques to control the risk-reward ratio. It suits aggressive investors seeking arbitrage-like returns.

Parameters

Name	Default	What it controls
v_input_1	2021	syear
v_input_2	true	smonth
v_input_3	true	sday
v_input_4	2022	fyear
v_input_5	12	fmonth
v_input_6	31	fday
v_input_7	true	longs
v_input_8	true	shorts
v_input_9	2.5	rr
v_input_10	true	position_risk_percent
v_input_string_1	0	signal_bar_check: 3 \| 2 \| 1
v_input_11	80	margin_req
v_input_12	0.2	sl_increase_factor
v_input_13	false	tp_decrease_factor
v_input_14	true	check_for_volume

Source Pine Script

/*backtest
start: 2022-11-27 00:00:00
end: 2023-11-09 05:20:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy(title = "MomGulfing", shorttitle = "MomGulfing", overlay = true, initial_capital=10000, pyramiding=3, calc_on_order_fills=false, calc_on_every_tick=false, currency="USD", default_qty_type=strategy.cash, default_qty_value=1000, commission_type=strategy.commission.percent, commission_value=0.04)

syear = input(2021)
smonth = input(1)
sday = input(1)
fyear = input(2022)
fmonth = input(12)
fday = input(31)
start = timestamp(syear, smonth, sday, 01, 00)
finish = timestamp(fyear, fmonth, fday, 23, 59)
date = time >= start and time <= finish ? true : false

longs = input(true)
shorts = input(true)
rr = input(2.5)
position_risk_percent = input(1)/100
signal_bar_check = input.string(defval="3", options=["1", "2", "3"])
margin_req = input(80)
sl_increase_factor = input(0.2)
tp_decrease_factor = input(0.0)
check_for_volume = input(true)
var long_sl = 0.0
var long_tp = 0.0
var short_sl = 0.0
var short_tp = 0.0
var long_lev = 0.0
var short_lev = 0.0

initial_capital = strategy.equity
position_risk = initial_capital * position_risk_percent

bullishEngulfing_st = close[1] < open[1] and close > open and high[1] < close and (check_for_volume ? volume[1]<volume : true)
bullishEngulfing_nd = close[2] < open[2] and close[1] > open[1] and close > open and high[2] > close[1] and high[2] < close and (check_for_volume ? volume[2]<volume : true)
bullishEngulfing_rd = close[3] < open[3] and close[2] > open[2] and close[1] > open[1] and close > open and high[3] > close[2] and high[3] > close[1] and high[3] < close and (check_for_volume ? volume[3]<volume : true)
bullishEngulfing = signal_bar_check == "1" ? bullishEngulfing_st : signal_bar_check == "2" ? bullishEngulfing_st or bullishEngulfing_nd : bullishEngulfing_st or bullishEngulfing_nd or bullishEngulfing_rd
long_stop_level = bullishEngulfing_st ? math.min(low[1], low) : bullishEngulfing_nd ? math.min(low[2], low[1], low) : bullishEngulfing_rd ? math.min(low[3], low[2], low[1], low) : na
rr_amount_long = close-long_stop_level
long_exit_level = close + rr*rr_amount_long
long_leverage = math.floor(margin_req/math.floor((rr_amount_long/close)*100))

bearishEngulfing_st = close[1] > open[1] and close < open and low[1] > close and (check_for_volume ? volume[1]<volume : true)
bearishEngulfing_nd = close[2] > open[2] and close[1] < open[1] and close < open and low[2] < close[1] and low[2] > close and (check_for_volume ? volume[2]<volume : true)
bearishEngulfing_rd = close[3] > open[3] and close[2] < open[2] and close[1] < open[1] and close < open and low[3] < close[2] and low[3] < close[1] and low[3] > close and (check_for_volume ? volume[3]<volume : true)
bearishEngulfing = signal_bar_check == "1" ? bearishEngulfing_st : signal_bar_check == "2" ? bearishEngulfing_st or bearishEngulfing_nd : bearishEngulfing_st or bearishEngulfing_nd or bearishEngulfing_rd
short_stop_level = bearishEngulfing_st ? math.max(high[1], high) : bearishEngulfing_nd ? math.max(high[2], high[1], high) : bearishEngulfing_rd ? math.max(high[3], high[2], high[1], high) : na
rr_amount_short = short_stop_level-close
short_exit_level = close - rr*rr_amount_short
short_leverage = math.floor(margin_req/math.floor((rr_amount_short/short_stop_level)*100))

long = longs and date and bullishEngulfing
short = shorts and date and bearishEngulfing
bgcolor(long[1] ? color.new(color.teal, 80) : (short[1] ? color.new(color.purple, 80) : na))

if long and strategy.position_size <= 0
    long_lev := long_leverage

if short and strategy.position_size >= 0
    short_lev := short_leverage

long_pos_size = long_lev * position_risk
long_pos_qty = long_pos_size/close
short_pos_size = short_lev * position_risk
short_pos_qty = short_pos_size/close

if long
    if strategy.position_size <= 0
        long_sl := long_stop_level
        long_tp := long_exit_level

    else if strategy.position_size > 0
        long_sl := long_stop_level + sl_increase_factor*rr_amount_long
        long_tp := long_exit_level - tp_decrease_factor*rr_amount_long

    strategy.entry("L"+str.tostring(long_lev)+"X", strategy.long, qty=long_pos_qty)
    label_text = str.tostring(long_lev)+"X\nSL:"+str.tostring(long_sl)+"\nTP:"+str.tostring(long_tp)
    label.new(bar_index+1, na, text=label_text, color=color.green, style=label.style_label_up, xloc=xloc.bar_index, yloc=yloc.belowbar)

else if short
    if strategy.position_size >= 0
        short_sl := short_stop_level
        short_tp := short_exit_level

    else if strategy.position_size < 0
        short_sl := short_stop_level - sl_increase_factor*rr_amount_short
        short_tp := short_exit_level + tp_decrease_factor*rr_amount_short

    strategy.entry("S"+str.tostring(short_lev)+"X", strategy.short, qty=short_pos_qty)
    label_text = str.tostring(short_lev)+"X\nSL:"+str.tostring(short_sl)+"\nTP:"+str.tostring(short_tp)
    label.new(bar_index+1, na, text=label_text, color=color.red, style=label.style_label_down, xloc=xloc.bar_index, yloc=yloc.abovebar)

if (strategy.position_size > 0)
    strategy.exit(id="L TP/SL", stop=long_sl, limit=long_tp)

if (strategy.position_size < 0)
    strategy.exit(id="S TP/SL", stop=short_sl, limit=short_tp)

sl_level = strategy.position_size > 0 ? long_sl : strategy.position_size < 0 ? short_sl : na
plot(sl_level, color=color.red, style=plot.style_linebr)

tp_level = strategy.position_size > 0 ? long_tp : strategy.position_size < 0 ? short_tp : na
plot(tp_level, color=color.green, style=plot.style_linebr)

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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