BB Percentage Index Trend Fading Strategy

Pine ScriptTrend following Machine learning Breakout RSI SMA Bollinger Bands ATR

Author: ChaoZhang
Complexity: Intermediate · 81 lines
Parameters: 15
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is based on the BB percentage index combined with RSI and MFI indicators. It makes long and short decisions by detecting price breakouts of Bollinger Bands upper and lower rail, together with RSI oversold/overbought signals and MFI oversold/overbought signals. It is a typical trend fading trading strategy.

Strategy Logic

Calculate Bollinger Band Percentage (BB%). BB% represents the standard deviation of the price relative to the Bollinger middle band, which judges market direction through the Bollinger channel.
Incorporate RSI and MFI indicators to determine overbought and oversold conditions. RSI compares the average gain and average loss over a period of time to determine overbought and oversold levels. MFI compares up volume and down volume to determine overbought and oversold levels.
When the price breaks through the Bollinger lower rail upward, go long; when the price breaks through the Bollinger upper rail downward, go short. At the same time, use the oversold/overbought signals from RSI and MFI indicators for filtration.

Advantages

Trend fading trading avoids market trends and reduces return fluctuations.
The combination of multiple indicators filters signals and improves decision accuracy.
Parameterized settings are flexible for adjusting risk-return characteristics of the strategy.
Applicable to highly volatile instruments like commodities, forex, cryptocurrencies, etc.

Risks and Solutions

There is a high probability of false signals from Bollinger breakouts, requiring a combination of multiple indicators for filtration.
Breakout signal judgment requires appropriately relaxed criteria to avoid missing good opportunities.
Adjust parameter settings to control risks, such as position sizing, raising stop loss lines, etc.

Optimization Directions

Incorporate volatility-based stop loss mechanisms such as the ATR indicator.
Introduce machine learning models to assist in judging breakout signal quality.
Optimize instrument selection mechanisms to dynamically adjust participating instruments.
Incorporate more factors like sentiment indicators, news, etc. to improve the decision framework.

Conclusion

This strategy is mainly applied to high volatility non-trending instruments. It implements trend fading trading through Bollinger channel and indicator combinations. Risk-return characteristics can be controlled by adjusting parameters. Further improvements can be made by introducing more auxiliary indicators and models to optimize decision quality, thereby achieving better strategy performance.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	false	Short
v_input_3	100	Lot, %
v_input_4	1900	From Year
v_input_5	2100	To Year
v_input_6	true	From Month
v_input_7	12	To Month
v_input_8	true	From Day
v_input_9	31	To Day
v_input_10	14	length
v_input_11	2	mult
v_input_12	50	BB Period
v_input_13	true	Draw RSI?
v_input_14	false	Draw MFI?
v_input_15	true	Highlight Oversold/Overbought?

Source Pine Script

/*backtest
start: 2023-11-05 00:00:00
end: 2023-12-05 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=2
strategy(title = "BB%/MFI/RSI", shorttitle = "BB%/MFI/RSI", default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 100)

//Settings
needlong = input(true, defval = true, title = "Long")
needshort = input(false, defval = false, title = "Short")
capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Lot, %")
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")

source = hlc3
length = input(14, minval=1), mult = input(2.0, minval=0.001, maxval=50), bblength = input(50, minval=1, title="BB Period")
DrawRSI_f=input(true, title="Draw RSI?", type=bool)
DrawMFI_f=input(false, title="Draw MFI?", type=bool)
HighlightBreaches=input(true, title="Highlight Oversold/Overbought?", type=bool)

DrawMFI = (not DrawMFI_f) and (not DrawRSI_f) ? true : DrawMFI_f
DrawRSI = (DrawMFI_f and DrawRSI_f) ? false : DrawRSI_f
// RSI
rsi_s = DrawRSI ? rsi(source, length) : na
plot(DrawRSI ? rsi_s : na, color=maroon, linewidth=2)

// MFI
upper_s = DrawMFI ? sum(volume * (change(source) <= 0 ? 0 : source), length) : na
lower_s = DrawMFI ? sum(volume * (change(source) >= 0 ? 0 : source), length) : na
mf = DrawMFI ? rsi(upper_s, lower_s) : na
plot(DrawMFI ? mf : na, color=green, linewidth=2)

// Draw BB on indices
bb_s = DrawRSI ? rsi_s : DrawMFI ? mf : na
basis = sma(bb_s, length)
dev = mult * stdev(bb_s, bblength)
upper = basis + dev
lower = basis - dev
plot(basis, color=red)
p1 = plot(upper, color=blue)
p2 = plot(lower, color=blue)
fill(p1,p2, blue)

b_color = (bb_s > upper) ? red : (bb_s < lower) ? lime : na
bgcolor(HighlightBreaches ? b_color : na, transp = 0)

//Signals
up = bb_s < lower and close < open
dn = bb_s > upper and close > open
size = strategy.position_size
lp = size > 0 and close > open
sp = size < 0 and close < open
exit = (up == false and dn == false) and (lp or sp)

//Trading
lot = strategy.position_size == 0 ? strategy.equity / close * capital / 100 : lot[1]
if up
    if strategy.position_size < 0
        strategy.close_all()
        
    strategy.entry("Long", strategy.long, needlong == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))

if dn
    if strategy.position_size > 0
        strategy.close_all()
        
    strategy.entry("Short", strategy.short, needshort == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))
    
if time > timestamp(toyear, tomonth, today, 23, 59) or exit
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-06. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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