Bollinger Band Breakout Trading Strategy 435869

This strategy is based on the upper and lower rails of the Bollinger Bands to determine when the price breaks through the upper rail of the Bollinger Bands to go long and breaks through the lower rail to go short. It…

Pine ScriptBreakout Machine learning Momentum SMA Bollinger Bands

Author: ChaoZhang
Complexity: Starter · 54 lines
Parameters: 6
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

This strategy uses the middle / upper / lower rail of the Bollinger Bands to determine extreme price ranges. The middle rail is the simple moving average of closing prices over the past 25 periods. The upper and lower rails are one standard deviation above and below the middle rail. When the price breaks through the upper or lower rail, it indicates that there is a breakout and abnormal price behavior, which can be used to make trading decisions.

If the price is below the lower rail, go long. If the price is above the upper rail, go short. When going long, set the stop loss to the entry price multiplied by the stop loss factor and take profit to the entry price multiplied by the take profit factor.

The strategy also incorporates some auxiliary rules, such as allowing only one signal per 24 hours to avoid unnecessary trading.

Advantages of the Strategy

Using Bollinger Bands to determine abnormal price ranges belongs to trend tracking strategies that can capture price trends.
Stop loss and take profit parameters are set according to principles to control single loss.
Some auxiliary rules are added to avoid duplicate signals and unnecessary trading.

Risks of the Strategy

Bollinger Bands cannot fully represent price trends, and there may be false signals.
Improper timing of breakout signals can lead to losses.
The duration and momentum of trend or non-trend markets are difficult to predict, which may lead to unnecessary long positions.

Risk Management:

Adjust Bollinger Band parameters to optimize breakout signal timing.
Incorporate other indicators to determine the major trend.
Set stop loss and take profit range according to different products and market conditions.

Optimization Directions

Consider adaptive optimization of Bollinger Band parameters to make them fit better to current market conditions.
Incorporate other indicators to judge reliability of trend signals and avoid false signals.
Incorporate machine learning models to automatically identify optimal long and short timing.

Conclusion

In summary, this is a simple trend tracking strategy using Bollinger Bands to determine abnormal prices and track trends. There is room for improvement in parameter optimization, risk control and signal filtering, but the core idea is simple and clear, making it suitable as a beginner strategy for learning.

Parameters

Name	Default	What it controls
v_input_float_1	true	Leverage (x)
v_input_float_2	true	Risk Capital per Trade (%)
v_input_float_3	2	TP_Factor
v_input_bool_1	false	invertBuyLogic
v_input_int_1	25	lookbackDistance
v_input_float_4	2	devMult

Source Pine Script

/*backtest
start: 2023-11-18 00:00:00
end: 2023-12-18 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=5
strategy("I11L OIL Bot",overlay=true, initial_capital=1000000,default_qty_value=1000000,default_qty_type=strategy.cash,commission_type=strategy.commission.percent,commission_value=0.00)

leverage = input.float(1,"Leverage (x)",step=1)
SL_Factor = 1 - input.float(1,"Risk Capital per Trade (%)", minval=0.1, maxval=100, step=0.05) / 100 / leverage
TP_Factor = input.float(2, step=0.1)
invertBuyLogic = input.bool(false)
 
lookbackDistance = input.int(25)
devMult = input.float(2,step=0.1)

var lastSellHour = 0
var disableAdditionalBuysThisDay = false


if(time > lastSellHour + 1000 * 60 * 60 * 6)
    disableAdditionalBuysThisDay := false
if(strategy.position_size != strategy.position_size[1])
    disableAdditionalBuysThisDay := true
    lastSellHour := time

source = close

//Trade Logic
basis = ta.sma(source, lookbackDistance)
dev = devMult * ta.stdev(source, lookbackDistance)
upper = basis + dev
lower = basis - dev
isBuy = ta.crossunder(source, upper)
isBuyInverted = ta.crossover(source, lower)

plot(upper, color=color.white)
plot(lower, color=color.white)

strategy.initial_capital = 50000

if((invertBuyLogic ? isBuyInverted : isBuy) and not(disableAdditionalBuysThisDay))
    strategy.entry("Long", strategy.long, (strategy.initial_capital / close) * leverage)

if(strategy.position_size > 0)
    strategy.exit("SL Long", "Long", stop=strategy.position_avg_price * SL_Factor)
    strategy.close("Long",  when=close > strategy.position_avg_price * (1 + (1 - SL_Factor) * TP_Factor), comment="TP Long")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-19. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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