Dynamic RSI Oscillation Trading Strategy

This strategy combines dynamic support/resistance levels and the relative strength index (RSI) indicator. It sets overbought/oversold thresholds for RSI and generates buy/sell signals when price breaks the dynamic…

Pine ScriptMachine learning Breakout Mean reversion RSI Pivots / S&R

Author: ChaoZhang
Complexity: Intermediate · 58 lines
Parameters: 15
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Principles

1. Dynamic Support/Resistance

Use the security function to get closing price as dynamic support/resistance levels. Trading signals are generated when price breaks through these dynamic levels.

2. RSI Indicator

Calculate the average gain and average loss over a certain period to generate RSI values and determine if RSI reaches the overbought/oversold area.

3. Trading Signals

When price breaks the dynamic levels, if RSI is not in the overbought/oversold area, buy/sell signals are generated. Otherwise, the breakout signals are ignored.

4. Exit Signals

Close positions when price falls back to the dynamic level, or when RSI returns to normal range.

Advantage Analysis

Use dynamic support/resistance to determine trend direction for higher winning rate.
RSI filters out false breakouts and avoids false entries.
Combining trend and indicator makes the strategy adaptable to different market conditions.
Simple and clear rules make it easy to implement.

Risks and Solutions

Multiple tests of dynamic levels may generate false signals. Widen the breakout range to filter.
Solo RSI may cause misjudgement. Add other indicators for combo filtering.
Frequent trading in range-bound market leads to higher costs. Widen RSI's normal range to lower frequency.
Improper parameter settings lead to missing or false signals. Optimize parameters based on different assets.

Optimization Directions

Use machine learning to auto optimize RSI parameters.
Add stop loss/profit taking strategy to lock in profits and reduce losses.
Incorporate more indicators for combo filtering to improve stability.
Add volatility indicator to lower position size when volatility is low.
Optimize position sizing algorithm to dynamically adjust positions for different market environments.

Summary

This strategy combines trend judgment and indicator filtering to effectively identify trend reversal around key levels while controlling risks. Further optimizations on parameter tuning, stop loss/profit taking, more indicators etc. can improve its stability and adaptability to generate steady profits in a wider range of markets.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	true	Short
v_input_3	100	Capital, %
v_input_4	W	timeframe 1
v_input_5_ohlc4	0	Source: ohlc4 \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| close
v_input_6	true	antipila
v_input_7	true	color filter
v_input_8	7	RSI Period
v_input_9	30	RSI Limit
v_input_10	1900	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: 2022-10-26 00:00:00
end: 2023-11-01 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=2
strategy(title = "Noro's Levels+RSI Strategy v1.0", shorttitle = "Levels+RSI str 1.0", overlay = true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 3)

//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 = "Capital, %")
tf = input('W', title = "timeframe 1")
src = input(ohlc4, "Source")
ap = input(true, defval = true, title = "antipila")
cf = input(true, defval = true, title = "color filter")
rsiperiod = input(7, defval = 7, minval = 2, maxval = 100, title = "RSI Period")
rsilimit = input(30, defval = 30, minval = 1, maxval = 50, 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")

//Level
level = request.security(syminfo.tickerid, tf, src[1])
plot(level, linewidth = 3, color = silver)

//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))

//Level Signals
ls = close > level and ap == false ? true : low > level ? true : false
up1 = strategy.position_size == 0 and ls and (close < open or cf == false)
exit1 = close < level and ap == false ? true : high < level ? true : false 

//RSI Signal

up2 = rsi < rsilimit and (close < open or cf == false)
exit2 = rsi > rsilimit and ls == false

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

if up1 or up2 
    strategy.entry("Long", strategy.long, needlong == false ? 0 : lot)
    
if  (exit1 and rsi > rsilimit) or exit2
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2023-11-02. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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