RSI and Bollinger Bands Quantitative Trading Strategy

Pine ScriptBreakout Mean reversion Volatility RSI MACD SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 60 lines
Parameters: 16
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

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Overview

This strategy combines Relative Strength Index (RSI) and Bollinger Bands to identify trading opportunities, belonging to mean reversion strategies in quantitative trading. It buys when RSI is below a threshold and closes position when price breaks above the middle band of Bollinger Bands. There is no shorting opportunity.

Strategy Logic

Use RSI indicator to judge if the market is oversold. RSI below 30 is considered an oversold signal.
Use Bollinger Bands to determine if price starts rebounding upward. When price bounces from the lower band and breaks above the middle band, the long direction ends.
Combine the RSI oversold signal and Bollinger Bands breakout signal to set buy entry points. Buy when both signals trigger and close position when price breaks above the middle band to take profit.

Advantage Analysis

The strategy combines mean reversion indicator RSI and channel indicator Bollinger Bands to locate entry points more precisely.
The RSI indicator can filter out many false breakouts and reduce unnecessary trades.
The Bollinger Bands acts as a stop loss to control the risk of each trade.

Risk Analysis

RSI indicator may give wrong signals, leading to missing buy opportunities.
Improper parameter settings of Bollinger Bands can result in stop loss being too loose or strict.
Choosing improper trading instruments, such as trading small cap stocks with higher liquidity risk.

Optimization Direction

Test different parameter combinations like RSI period, Bollinger period and multiplier to find optimum parameters.
Incorporate other indicators like KD, MACD to set more strict buy conditions to filter signals.
Set stop loss based on volatility for different trading instruments, such as using volatility stop loss.

Conclusion

This strategy utilizes the logic of buying at RSI lows and selling at Bollinger highs, belonging to mean reversion strategies. Compared to using single indicators like RSI or Bollinger Bands, this strategy can locate entry and exit points more precisely, thus achieving better results. Next steps could be improving through parameter optimization, signal filtering, stop loss strategies etc.

Parameters

Name	Default	What it controls
v_input_1	100	Lot, %
v_input_2	true	Use RSI
v_input_3	true	Use BB
v_input_4	true	Show BB Overlay
v_input_5	20	BB period
v_input_6_ohlc4	0	BB source: ohlc4 \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| close
v_input_7	2	BB Mult
v_input_8	7	RSI period
v_input_9_close	0	RSI source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_10	30	RSI Limit
v_input_11	1900	From Year
v_input_12	2100	To Year
v_input_13	true	From Month
v_input_14	12	To Month
v_input_15	true	From Day
v_input_16	31	To Day

Source Pine Script

/*backtest
start: 2023-12-08 00:00:00
end: 2024-01-07 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=3
strategy(title = "Noro's BB + RSI Strategy v1.0", shorttitle = "BB+RSI str 1.0", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 5)

//Settings
capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Lot, %")
rsiuse = input(true, defval = true, title = "Use RSI")
bbuse = input(true, defval = true, title = "Use BB")
showbb = input(true, defval = true, title = "Show BB Overlay")
bbperiod = input(20, defval = 20, minval = 2, maxval = 1000, title = "BB period")
bbsource = input(ohlc4, title = "BB source")
bbmult = input(2, defval = 2, minval = 1, maxval = 100, title = "BB Mult")
rsiperiod = input(7, defval = 7, minval = 2, maxval = 1000, title = "RSI period")
rsisource = input(close, title = "RSI source")
rsilimit = input(30, defval = 30, minval = 1, maxval = 49, title = "RSI Limit")
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")

//RSI
rsi = rsi(rsisource, rsiperiod)

//BB
basis = sma(bbsource, bbperiod)
dev = bbmult * stdev(bbsource, bbperiod)
upper = basis + dev
lower = basis - dev

//Overlay
col = showbb ? blue : na
plot(upper, color = col)
plot(basis, color = col)
plot(lower, color = col)

//Signals
up = (rsi < rsilimit or rsiuse == false) and (low < lower or bbuse == false)
cl = close > open

//Trading
lot = 0.0 
lot := strategy.position_size == 0 ? strategy.equity / close * capital / 100 : lot[1]
if up
    strategy.entry("Long", strategy.long, lot)
if cl
    strategy.entry("Close", strategy.short, 0)
if time > timestamp(toyear, tomonth, today, 23, 59)
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-08. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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