Reversal Trading Strategy Based on Multi Period RSI

This article explains in detail a quantitative trading strategy that utilizes multi-period RSI indicators to identify reversal points. It analyzes multiple RSI indicators simultaneously to spot market turning points…

Pine ScriptMean reversion Multi-timeframe RSI SMA

Author: ChaoZhang
Complexity: Intermediate · 77 lines
Parameters: 11
Backtest bar: 45m · intraday
Tested on: BTC/USDT · futures

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I. Strategy Logic

The strategy employs 3 groups of RSI indicators with different parameters:

Calculate RSI values for period 2, 7, and 14 respectively.
When RSI-2 is below 10, RSI-7 below 20, and RSI-14 below 30, a bottom is identified.
When RSI-2 is above 90, RSI-7 above 80, and RSI-14 above 70, a top is identified.
Generate buy/sell signals based on RSI unanimity.
Preset adjustable parameters for indicator consensus, controlling signal frequency.

By analyzing RSI indicators across periods collectively, reversal point accuracy can be improved.

II. Advantages of the Strategy

The biggest advantage is using multiple timeframe RSI analysis improves identification of key points and filters out false signals.

Another advantage is the flexibility to adjust consensus parameters and adapt to different market environments.

Lastly, the RSI combinations also provide more tuning options.

III. Potential Risks

However, the following risks exist:

Firstly, RSI itself has inherent lag in identifying reversals.

Secondly, multiple indicators introduce signal ambiguity requiring clear rules.

Finally, reversal trades have a failure rate requiring psychological preparation.

IV. Summary

In summary, this article has explained a quantitative strategy of identifying reversals based on multi-period RSI analysis. It improves recognition of market turning points by judging RSI unanimity. But risks like lagging and wrong signals need to be managed. Overall it provides a flexible RSI strategy optimization approach.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	true	Short
v_input_3	true	leverage
v_input_4	3	Indicators
v_input_5	3	accuracy
v_input_6	1900	From Year
v_input_7	2100	To Year
v_input_8	true	From Month
v_input_9	12	To Month
v_input_10	true	From day
v_input_11	31	To day

Source Pine Script

/*backtest
start: 2023-09-06 00:00:00
end: 2023-09-13 00:00:00
period: 45m
basePeriod: 5m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=2
strategy(title = "Noro's Triple RSI Top/Bottom v1.1", shorttitle = "3RSI Top/Bottom 1.1", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0)

//Settings
needlong = input(true, defval = true, title = "Long")
needshort = input(true, defval = true, title = "Short")
leverage = input(1, defval = 1, minval = 1, maxval = 100, title = "leverage")
indi = input(3, defval = 3, minval = 1, maxval = 3, title = "Indicators")
accuracy = input(3, defval = 3, minval = 1, maxval = 10, title = "accuracy")
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-2
fastup = rma(max(change(close), 0), 2)
fastdown = rma(-min(change(close), 0), 2)
fastrsi = fastdown == 0 ? 100 : fastup == 0 ? 0 : 100 - (100 / (1 + fastup / fastdown))

//RSI-7
middleup = rma(max(change(close), 0), 7)
middledown = rma(-min(change(close), 0), 7)
middlersi = middledown == 0 ? 100 : middleup == 0 ? 0 : 100 - (100 / (1 + middleup / middledown))

//RSI-14
slowup = rma(max(change(close), 0), 14)
slowdown = rma(-min(change(close), 0), 14)
slowrsi = slowdown == 0 ? 100 : slowup == 0 ? 0 : 100 - (100 / (1 + slowup / slowdown))

//Body
body = abs(close - open)
abody = sma(body, 10)

//Signals
acc = 10 - accuracy
signalup1 = fastrsi < (5 + acc) ? 1 : 0
signalup2 = middlersi < (10 + acc * 2) ? 1 : 0
signalup3 = slowrsi < (15 + acc * 3) ? 1 : 0

signaldn1 = fastrsi > (95 - acc) ? 1 : 0
signaldn2 = middlersi > (90 - acc * 2) ? 1 : 0
signaldn3 = slowrsi > (85 - acc * 3) ? 1 : 0

up = signalup1 + signalup2 + signalup3 >= indi
dn = signaldn1 + signaldn2 + signaldn3 >= indi
exit = ((strategy.position_size > 0 and close > open) or (strategy.position_size < 0 and close < open)) and body > abody / 3

//Trading
lot = strategy.position_size == 0 ? strategy.equity / close * leverage : lot[1]

if up
    if strategy.position_size < 0
        strategy.close_all()
        
    strategy.entry("Bottom", strategy.long, needlong == false ? 0 : lot)

if dn
    if strategy.position_size > 0
        strategy.close_all()
        
    strategy.entry("Top", strategy.short, needshort == false ? 0 : lot)
    
if time > timestamp(toyear, tomonth, today, 23, 59) or exit
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-14. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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