Multi filter Bollinger Band Trading Strategy

Pine ScriptMachine learning Breakout Mean reversion RSI SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 77 lines
Parameters: 15
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The Multi-filter Bollinger Band Trading Strategy is a quantitative trading strategy that combines the Bollinger Band indicator, moving average indicator, RSI indicator and K-line graphical features for multi-conditional screening to generate trading signals when conditions are met. It is a typical trend-following strategy that profits by capturing mid-to-long term price trend fluctuations.

Strategy Principle

Indicator Calculation

The strategy mainly uses three indicators: Bollinger Bands, moving average and RSI. Among them, the middle rail of Bollinger Bands is the n-day simple moving average of price, and the upper and lower rails are the middle rails +2 standard deviations and the middle rails -2 standard deviations respectively. The RSI indicator is a value between 0 and 100 calculated based on the rise/fall range over a certain period of time.

Trading Signals

The strategy generates trading signals through the following three main conditions:

(1) Bollinger lower-band breakout & K-line body contradiction. When the closing price breaks through the lower band upwards and the color of the K-line body contradicts the current trend direction, go long.

(2) Bollinger upper-band breakout & K-line body contradiction. When the closing price breaks through the upper band downwards and the color of the K-line body contradicts the current trend direction, go short.

(3) K-line body reversal. If the position direction is consistent with the K-line body color reversal, close the position.

In addition, the strategy also sets moving average filters, K-line body filters, RSI filters and other auxiliary conditions to strictly control entry.

Advantage Analysis

Multiple strict conditions control can reduce the risk of false breakouts
Trend tracking method reduces trading frequency
RSI indicator assists in avoiding reversal traps

Risk Analysis

Improper Bollinger parameter settings may result in few signals
Failed breakouts can cause greater losses
Lower trading frequency may miss some trading opportunities

Risks can be reduced by adjusting Bollinger parameters and strictly controlling stops.

Optimization Directions

Test strategy performance under different parameters to find optimal parameters
Add machine learning algorithms to automatically optimize parameters
Add more factors and filters to improve strategy stability

Summary

Overall, this strategy is a typical mid-to-long term trend following strategy. By multi-conditional screening and strictly controlling entry and exit timing with a trend trading approach, it can reduce unnecessary trading and capture mid-to-long term market trends. There is still great room for optimizing this strategy by adjusting parameters, adding more auxiliary tools and so on, to further enhance the stability and profitability of the strategy.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	false	Short
v_input_3	20	Bollinger Length
v_input_4	true	Bollinger Mult
v_input_5_ohlc4	0	Bollinger Source: ohlc4 \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| close
v_input_6	true	Use body-filter
v_input_7	true	Use color-filter
v_input_8	true	Use RSI-filter
v_input_9	1900	From Year
v_input_10	2100	To Year
v_input_11	true	From Month
v_input_12	12	To Month
v_input_13	true	From day
v_input_14	31	To day
v_input_15	true	Show Bollinger Bands

Source Pine Script

/*backtest
start: 2023-12-17 00:00:00
end: 2024-01-16 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=3
strategy("Noro's Bollinger Strategy v1.4", shorttitle = "Bollinger str 1.4", overlay = true )

//Settings
needlong = input(true, defval = true, title = "Long")
needshort = input(false, defval = false, title = "Short")

length = input(20, defval = 20, minval = 1, maxval = 1000, title = "Bollinger Length")
mult = input(1, defval = 1, minval = 0.001, maxval = 50, title = "Bollinger Mult")
source = input(ohlc4, defval = ohlc4, title = "Bollinger Source")

usebf = input(true, defval = true, title = "Use body-filter")
usecf = input(true, defval = true, title = "Use color-filter")
userf = input(true, defval = true, title = "Use RSI-filter")

fromyear = input(1900, defval = 1900, minval = 1900, maxval = 2100, title = "From Year")
toyear = input(2100, defval = 2100, minval = 1900, maxval = 2100, title = "To Year")
frommonth = input(01, defval = 01, minval = 01, maxval = 12, title = "From Month")
tomonth = input(12, defval = 12, minval = 01, maxval = 12, title = "To Month")
fromday = input(01, defval = 01, minval = 01, maxval = 31, title = "From day")
today = input(31, defval = 31, minval = 01, maxval = 31, title = "To day")
showbands = input(true, defval = true, title = "Show Bollinger Bands")

//Bollinger Bands
basis = sma(source, length)
dev = mult * stdev(source, length)
upper = basis + dev
lower = basis - dev

//Lines
col = showbands ? black : na 
plot(upper, linewidth = 1, color = col)
plot(basis, linewidth = 1, color = col)
plot(lower, linewidth = 1, color = col)

//Body filter
nbody = abs(close - open)
abody = sma(nbody, 10)
body = nbody > abody / 2 or usebf == false

//Color filter
bar = close > open ? 1 : close < open ? -1 : 0 
gb = bar == 1 or usecf == false
rb = bar == -1 or usecf == false

//RSI Filter
fastup = rma(max(change(close), 0), 7)
fastdown = rma(-min(change(close), 0), 7)
rsi = fastdown == 0 ? 100 : fastup == 0 ? 0 : 100 - (100 / (1 + fastup / fastdown))
ursi = rsi > 70 or userf == false
drsi = rsi < 30 or userf == false

//Signals
up = close <= lower and rb and body and drsi and (close < strategy.position_avg_price or strategy.position_size == 0)
dn = close >= upper and gb and body and ursi and (close > strategy.position_avg_price or strategy.position_size == 0)
exit = ((strategy.position_size > 0 and close > open) or (strategy.position_size < 0 and close < open)) and body

//Trading
if up
    strategy.entry("Long", strategy.long, needlong == false ? 0 : na)

if dn
    strategy.entry("Short", strategy.short, needshort == false ? 0 : na)
    
if  exit
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-17. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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