The Dual RSI Mean Reversion Strategy

Pine ScriptMean reversion Trend following Volatility RSI SMA Heikin Ashi

Author: ChaoZhang
Complexity: Intermediate · 83 lines
Parameters: 15
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The two-way RSI moving average recovery strategy is a trend following strategy that uses the RSI indicator in two different time periods to identify overbought and oversold conditions. This strategy aims to profit by going long after oversold and short after overbought. This strategy uses Moving Average Convergence and Divergence, RSI indicator, and position opening color filters to identify trading opportunities.

Strategy Logic

This strategy uses two RSI indicators with different time periods - one on the 5-minute chart and one on the 1-hour chart. For the RSI indicator, oversold levels are considered below 30 and overbought levels are above 70.

It tracks the RSI value and looks for situations where the RSI remains in the oversold or overbought zone for a certain period, indicating an expanded oversold or overbought state.

In addition, it uses the smoothed moving average of convergence and divergence to check the red or green candlestick of a certain period to confirm the trend direction before entering the transaction. Open position color filter helps avoid false signals.

When the conditions of RSI and smoothed moving average of convergence and divergence are met, this strategy will go long after oversold and short after overbought, betting that the price will return to the moving average.

Close positions at the end of each day to avoid overnight holding.

Advantage Analysis

Use multiple timeframes to identify overbought and oversold conditions
Smoothed Moving Average Convergence and Divergence filters out noise and identifies trend direction
Open position color filter to avoid false signals
Match clear opening and closing rules based on two indicators
Close positions before each day to control risks

Risk Analysis

If the strong trend continues, the RSI may experience volatility after signaling overbought and oversold signals
Market gaps may trigger stop loss
The lagging of the smoothed moving average of convergence and divergence may delay the opening of positions and miss the market.
Possible gains from closing positions before the end of each day and giving up overnight positions

Optimization Direction

Add additional filters like volume or volatility to confirm signals
Optimize RSI period and overbought/oversold level parameters
Consider dynamic position control based on volatility
Test take-profit and stop-loss exit instead of closing the position before the end of each day
Test effects and adjust parameters across different varieties

Summary

The two-way RSI moving average reversal strategy uses a rule-based approach to trade momentum. By combining two time frames, overbought and oversold indicators, candlestick pattern analysis, and entry filters, it aims to identify high-probability moving average reversal opportunities. Strict risk management and prudent position control help manage drawdowns while making profits. Further optimization and robustness testing will assist in successfully deploying this strategy across various markets.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	true	Short
v_input_3	100	Lot, %
v_input_4	14	RSI period
v_input_5	30	RSI limit
v_input_6	3	RSI signals
v_input_7	true	Use Open Color Filter
v_input_8	2	Open Color, Bars
v_input_9	true	Show indicator RSI
v_input_10	2018	From Year
v_input_11	2100	To Year
v_input_12	true	From Month
v_input_13	12	To Month
v_input_14	true	From Day
v_input_15	31	To Day

Source Pine Script

/*backtest
start: 2023-09-01 00:00:00
end: 2023-09-30 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Gidra
//2018

//@version=2
strategy(title = "Gidra's Vchain Strategy v0.1", shorttitle = "Gidra's Vchain Strategy v0.1", overlay = false, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 100)

//Settings
needlong = input(true, defval = true, title = "Long")
needshort = input(true, defval = true, title = "Short")
capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Lot, %")
rsiperiod = input(14, defval = 14, minval = 2, maxval = 100, title = "RSI period")
rsilimit = input(30, defval = 30, minval = 1, maxval = 50, title = "RSI limit")
rsibars = input(3, defval = 3, minval = 1, maxval = 20, title = "RSI signals")
useocf = input(true, defval = true, title = "Use Open Color Filter")
openbars = input(2, defval = 2, minval = 1, maxval = 20, title = "Open Color, Bars")
showrsi = input(true, defval = true, title = "Show indicator RSI")
fromyear = input(2018, defval = 2018, 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")


//Heikin Ashi Open/Close Price
o=open
c=close
h=high
l=low
haclose = (o+h+l+c)/4
haopen = na(haopen[1]) ? (o + c)/2 : (haopen[1] + haclose[1]) / 2
hahigh = max (h, max(haopen,haclose))
halow = min (l, min(haopen,haclose))
col=haopen>haclose ? red : lime
plotcandle(haopen, hahigh, halow, haclose, title="heikin", color=col)

//RSI
uprsi = rma(max(change(close), 0), rsiperiod)
dnrsi = rma(-min(change(close), 0), rsiperiod)
rsi = dnrsi == 0 ? 100 : uprsi == 0 ? 0 : 100 - (100 / (1 + uprsi / dnrsi))
uplimit = 100 - rsilimit
dnlimit = rsilimit
rsidn = rsi < dnlimit ? 1 : 0
rsiup = rsi > uplimit ? 1 : 0

//RSI condition
rsidnok = highest(rsidn, rsibars) == 1? 1 : 0
rsiupok = highest(rsiup, rsibars) == 1? 1 : 0

//Color Filter
bar = haclose > haopen ? 1 : haclose < haopen ? -1 : 0
gbar = bar == 1 ? 1 : 0
rbar = bar == -1 ? 1 : 0
openrbarok = sma(gbar, openbars) == 1 or useocf == false
opengbarok = sma(rbar, openbars) == 1 or useocf == false

//Signals
up = openrbarok and rsidnok
dn = opengbarok and rsiupok

lot = strategy.position_size == 0 ? strategy.equity / close * capital / 100 : lot[1]

//Indicator RSI
colbg = showrsi == false ? na : rsi > uplimit ? red : rsi < dnlimit ? lime : na
bgcolor(colbg, transp = 20)

//Trading
if up
    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
    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 2023-10-23. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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