Contrarian Breakout Trading Strategy

Author: ChaoZhang, Date: 2023-10-25 12:09:40



The contrarian breakout trading strategy is a strategy that takes contrarian signals based on consecutive price rises or falls to go long when the short condition is met or go short when the long condition is met. It aims to produce a profit by taking the opposing trade when an asset produces only losses with a given strategy.

Strategy Logic

The strategy is mainly implemented through the following parts:

  1. Set the consecutive periods for price rises and falls, i.e. consecutiveBarsUp and consecutiveBarsDown. When the current period trend reaches the set length, a trading signal is triggered.

  2. Calculate the rise and fall of the current price relative to the previous period price. Calculate the length of the current consecutive rise or fall periods ups and dns based on the rise and fall.

  3. Set the backtesting time range to limit the strategy to operate only within the backtesting time through time_cond.

  4. Set the daily trading time to limit trading signals to be issued only within the set time frame through timetobuy.

  5. When the consecutive rising cycle reaches the set length, issue a long signal through strategy.long. When the consecutive falling cycle reaches the set length, issue a short signal through strategy.short.

  6. Stop loss and take profit prices can be set. Set short-term stops for long positions and long-term stops for short positions. Set long-term take profits for long positions and short-term take profits for short positions.

  7. Trade signal messages can be set during sending.

  8. Issue long or short signals when conditions are met based on the above parameters and price levels.

Advantage Analysis

This contrarian breakout strategy has the following advantages:

  1. Captures price reversal points. Contrarian operation can obtain good profits. Operations in the opposite direction when the price forms a trend can profit at price reversals.

  2. Flexible configurable parameters. Parameters such as consecutive periods can be adjusted, stop loss and take profit levels can be set, trading time frame can be limited. Parameters can be optimized according to market conditions.

  3. Stop loss and take profit can control risks. Setting stop loss and take profit in advance helps control trading risks after going long or short.

  4. Trade messages facilitate automated trading. Setting trade signal messages facilitates integration with automated trading systems.

  5. Backtesting time range facilitates strategy testing. Adding backtesting time range settings allows easy observation of strategy performance under different market conditions.

Risk Analysis

The strategy also has some risks to note:

  1. Avoid significant news events. Price trends are unpredictable during major announcements, causing simultaneous long and short signals and losses. Major economic releases should be avoided.

  2. Less effective when reversals are unclear. Less effective when trends are ambiguous, contrarian trading should be used with caution.

  3. Backtesting data overfitting risk. Optimization should avoid over-reliance on backtesting data, which does not represent future trends. Parameters should be adjusted appropriately during live trading.

  4. High trading frequency risks overtrading. Short cycle settings may result in excessive trading frequency and risks for long-term steady gains.

  5. Stop loss and take profit strategies can be optimized to reduce risks. The fixed stops can be further improved to trailing stops.

Optimization Directions

The strategy can be further optimized in the following aspects:

  1. Add trend detection to avoid random reversals in non-trending markets, using volatility, channels etc to gauge trends and capture reversals.

  2. Optimize stops and takes to adjust based on market volatility, using percentage-based, ATR or other adaptive methods.

  3. Add volume analysis to avoid false signals from price patterns alone.

  4. Portfolio diversification across multiple products to reduce single asset risk.

  5. Parameter optimization and machine learning. Collect more historical data and use machine learning to auto-optimize parameters for more robust strategies.


The contrarian breakout strategy provides good trading signal by capturing price reversals through contrarian operations. The advantages include flexible configuration, risk control and suitability for automated trading. But risks exist and continuous improvements in parameters and logic are needed for long-term steady profits.

start: 2023-10-17 00:00:00
end: 2023-10-24 00:00:00
period: 45m
basePeriod: 5m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]


// Strategy
strategy("Up/Down Strategy - Contrarian", overlay=true, initial_capital = 10000, default_qty_value = 10000, default_qty_type =

consecutiveBarsUp = input(1, title='Consecutive Bars Up')
consecutiveBarsDown = input(1, title='Consecutive Bars Down')

price = close

ups = 0.0
ups := price > price[1] ? nz(ups[1]) + 1 : 0

dns = 0.0
dns := price < price[1] ? nz(dns[1]) + 1 : 0

// Strategy Backtesting
startDate  = input(timestamp("2021-01-01T00:00:00"), type = input.time, title='Backtesting Start Date')
finishDate = input(timestamp("2021-12-31T00:00:00"), type = input.time, title='Backtesting End Date')

time_cond  = true

//Time Restriction Settings
startendtime = input("", title='Time Frame To Enter Trades')
enableclose = input(false, title='Enable Close Trade At End Of Time Frame')
timetobuy = true
timetoclose = true

// Stop Loss & Take Profit Tick Based
enablesltp = input(false, title='Enable Take Profit & Stop Loss')
stopTick = input(5.0, title='Stop Loss Ticks', type=input.float) / 100
takeTick = input(10.0, title='Take Profit Ticks', type=input.float) / 100

longStop = strategy.position_avg_price - stopTick
shortStop = strategy.position_avg_price + stopTick
shortTake = strategy.position_avg_price - takeTick
longTake = strategy.position_avg_price + takeTick

plot(strategy.position_size > 0 and enablesltp ? longStop : na, style=plot.style_linebr,, linewidth=1, title="Long Fixed SL")
plot(strategy.position_size < 0 and enablesltp ? shortStop : na, style=plot.style_linebr,, linewidth=1, title="Short Fixed SL")
plot(strategy.position_size > 0 and enablesltp ? longTake : na, style=plot.style_linebr,, linewidth=1, title="Long Take Profit")
plot(strategy.position_size < 0 and enablesltp ? shortTake : na, style=plot.style_linebr,, linewidth=1, title="Short Take Profit")

// Alert messages
message_enterlong  = input("", title="Long Entry message")
message_entershort = input("", title="Short Entry message")
message_closelong = input("", title="Close Long message")
message_closeshort = input("", title="Close Short message")

// Strategy Execution
if (dns >= consecutiveBarsDown) and time_cond and timetobuy
    strategy.entry("Long", strategy.long, stop = high + syminfo.mintick, alert_message = message_enterlong)
if (ups >= consecutiveBarsUp) and time_cond and timetobuy
    strategy.entry("Short", strategy.short, stop = low + syminfo.mintick, alert_message = message_entershort)
if strategy.position_size < 0 and timetoclose and enableclose
    strategy.close_all(alert_message = message_closelong)
if strategy.position_size > 0 and timetoclose and enableclose
    strategy.close_all(alert_message = message_closeshort)
if strategy.position_size < 0 and enablesltp and time_cond
    strategy.exit(id="Close Long", stop=longStop, limit=longTake, alert_message = message_closelong)
if strategy.position_size > 0 and enablesltp and time_cond
    strategy.exit(id="Close Short", stop=shortStop, limit=shortTake, alert_message = message_closeshort)