RSI Based Quantitative Trading Strategy

The strategy is named "Dual Timeframe RSI Reversal". It is a quantitative trading strategy based on Relative Strength Index (RSI). The strategy uses two RSI with different periods to generate buy and sell signals…

Pine ScriptMean reversion Trend following Volatility RSI EMA

Author: ChaoZhang
Complexity: Starter · 47 lines
Parameters: 6
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy constructs trading signals by comparing a fast period RSI (default 55 days) and a slow period RSI (default 126 days). When the fast RSI crosses above the slow RSI, a buy signal is generated. When the fast RSI falls below the slow RSI, a sell signal is triggered. By comparing the relative strength between two different timeframes, it detects opportunities when short-term and long-term trends reversing.

After entering a position, profit target and stop loss will be set. Default profit target is 0.9 times the entry price. Default stop loss is 3% below the entry price. Positions will also be closed if a reverse signal is triggered.

Advantages

Catch short-term price reversals by comparing dual RSIs
Filter out false signals using dual confirmation
Limit loss on single bet with stop loss

Risks

Frequent reverse signals during high volatility
Stop loss too tight, easily knocked out by small fluctuations
Miss major reversals with poorly configured parameters

Enhancement

Test more RSI parameter combinations to find optimum
Add other indicators to filter false signals
Dynamic stop loss and take profit ratio for better profitability

Summary

The "Dual Timeframe RSI Reversal" strategy generates trading signals by comparing crossovers between fast and slow RSI periods. It aims to capture short-term price reversals. Profit and stop loss rules are set to limit risks. This is a typical strategy of using multi-timeframe indicator comparisons to trade reversals. Enhancement areas include parameter tuning and risk control rules.

Parameters

Name	Default	What it controls
v_input_1	55	Short length
v_input_2	126	Long length
v_input_3	55	Overbought
v_input_4	45	Oversold
v_input_5	0.9	Take profit level %
v_input_6	3	Stoploss level %

Source Pine Script

/*backtest
start: 2022-11-29 00:00:00
end: 2023-12-05 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=2
strategy(title="Relative Strength Index", shorttitle="RSI")
slen    = input(55, title="Short length")
llen    = input(126, title="Long length")
sup     = ema(max(change(close), 0), slen)
sdown   = ema(-min(change(close), 0), slen)
rsi1    = sdown == 0 ? 100 : sup == 0 ? 0 : 100 - (100 / (1 + sup / sdown))
lup     = ema(max(change(close), 0), llen)
ldown   = ema(-min(change(close), 0), llen)
rsi2    = ldown == 0 ? 100 : lup == 0 ? 0 : 100 - (100 / (1 + lup / ldown))
ob      = input(55, title="Overbought")
os      = input(45, title="Oversold")
tp      = input(.9, title="Take profit level %")*.01
sl      = input(3, title="Stoploss level %")*.01
mid     = avg(ob,os)
plot    (mid, color=#4f4f4f, transp=0)
hline   (ob, color=#4f4f4f)
hline   (os, color=#4f4f4f)
long    = crossover(rsi1,rsi2)
short   = crossunder(rsi1,rsi2)
vall    = valuewhen(long,close,0)
lexit1  = high>=(vall*tp)+vall
lexit2  = low<=vall-(vall*sl)
vals    = valuewhen(short,close,0)
sexit1  = low<=vals - (vals*tp)
sexit2  = high>=vals + (vals*sl)
bgcolor (color=long?lime:na,transp=50)
bgcolor (color=short?red:na, transp=50)
strategy.entry("Long", strategy.long, when=long)
strategy.close("Long", when=lexit1)
strategy.close("Long", when=lexit2)
strategy.close("Long", when=short)
strategy.entry("Short", strategy.short, when=short)
strategy.close("Short", when=sexit1)
strategy.close("Short", when=sexit2)
strategy.close("Short", when=long)
plot    (rsi1, color=orange, transp=0,linewidth=1, title="Short period RSI")
plot    (rsi2, color=aqua  , transp=0,linewidth=1, title="Long period RSI")

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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