Bollinger Band Reversal Based Quantitative Strategy

Pine ScriptMean reversion Breakout Momentum RSI MACD SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 72 lines
Parameters: 7
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The strategy is named "Bollinger Band Reversal Based Quantitative Strategy". It utilizes the upper and lower rails of the Bollinger Bands to determine entries and exits. When the price is near the lower rail of the bands and shows signs of a downward breakthrough, it indicates the price may be reversing, so go long. When the price rises to the upper rail, it indicates the price may reverse downwards, so go short.

Strategy Logic

The strategy uses the RSI indicator to determine long entries. Specifically, it checks if the closing price of the most recent bar is lower than the lowest price of the previous 6 bars, meantime the Bollinger Band Width (BBW) is greater than a threshold, and the Bollinger Band Ratio (BBR) is within a range. If these criteria are met, it indicates the price may be reversing, so go long.

The exit is simple. When RSI goes above 70, indicating the price is overheated, close the long position.

Advantage Analysis

The biggest advantage of this strategy is utilizing the upper and lower rails of Bollinger Bands to determine entries. When BB reverses direction, go long or short to catch short-term reversal opportunities. Compared to simple RSI strategies, this strategy has more prudent criteria for entries, thus avoiding wrong trades.

Also, the strategy is sensitive to parameters. By tuning BBW and BBR, it can be optimized for different products and achieve better results.

Risk Analysis

The main risk is that BB does not perfectly predict price reversals. If the timing is inappropriate, it easily leads to missing best entries or floating losses.

Also, short-term fluctuations may trigger frequent entries and exits, increasing costs from commissions and slippages. If the reversing momentum is not enough, it risks taking losses from exits.

Optimization Directions

The strategy can be improved in the following aspects:

Optimize parameters. Test and tune BBW, BBR and other parameters more finely for different products.
Add stop loss mechanisms, such as trailing stop loss and time stop loss, to limit maximum losses.
Incorporate other indicators, like KDJ and MACD, to make entries more reliable.
Improve exit logic. The current exit is simple. Can optimize with trailing profit taking or exits based on volatility.

Conclusion

This strategy utilizes the characteristics of Bollinger Bands to determine potential reversal points for entries and exits. Compared to single indicators like RSI, it has more accurate timing. With parameter tuning, stop losses and take profits, it can be more reliable. But BB's prediction is not perfect, so there are still some randomness in performance.

Parameters

Name	Default	What it controls
v_input_1	15	bbw3
v_input_2	0.45	bbr3level
v_input_3	0.448	bbrlower
v_input_4	0.456	bbrhigher
v_input_5	7	sincelowestmin
v_input_6	57	sincelowestmax
v_input_7	20	length

Source Pine Script

/*backtest
start: 2022-11-15 00:00:00
end: 2023-11-21 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4

//study(title = "Bolinger strategy", overlay=true)
strategy("Bolinger strategy",currency="SEK",default_qty_value=10000,default_qty_type=strategy.cash,max_bars_back=50)
len = 5
src = close
up = rma(max(change(src), 0), len)
down = rma(-min(change(src), 0), len)
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))


bbw3level = input(15, title="bbw3")
bbr3level = input(0.45, title="bbr3level")
bbrlower = input(0.4480, title="bbrlower")
bbrhigher = input(0.4560, title="bbrhigher")
sincelowestmin = input(7, title="sincelowestmin")
sincelowestmax = input(57, title="sincelowestmax")


length = input(20, minval=1)
mult = 20
src3 = close[3]
basis3 = sma(src3, length)
dev3 = mult * stdev(src3, length)
upper3 = basis3 + dev3
lower3 = basis3 - dev3
bbr3 = (src3 - lower3)/(upper3 - lower3)
bbw3 = (upper3-lower3)/basis3*100


basis = sma(src, length)
dev = mult * stdev(src, length)
upper = basis + dev
lower = basis - dev
bbr = (src - lower)/(upper - lower)
bbw = (upper-lower)/basis*100

criteriamet = 0
crossUnderB0 = crossunder(bbr,0)

since_x_under = barssince(crossUnderB0)


sincelowest = barssince(close[6] > close[3] and close[5] > close[3] and close[4] > close[3] and close[2] > close[3] and close[1] > close[3] and close > close[3] and bbw3 > bbw3level and bbr3 < bbr3level) //  and bbr3 < 0 

if sincelowest > sincelowestmin and sincelowest < sincelowestmax and bbr > bbrlower and bbr < bbrhigher
	criteriamet := 1
else
	criteriamet := 0	
//plot (criteriamet)

//exit 
exitmet = 0
if rsi > 70
	exitmet := 1
else
	exitmet := 0

if criteriamet == 1
	strategy.entry("long", strategy.long)
if exitmet == 1
	strategy.close("long")

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-22. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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