Oscillation Breakthrough Strategy

This strategy is based on the classic idea of Larry Connors, using double moving average system to capture the medium-term oscillation of the market and take profit when it is overbought or oversold.

Pine ScriptBreakout Mean reversion Trend following RSI SMA Volume

Author: ChaoZhang
Complexity: Intermediate · 80 lines
Parameters: 15
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is based on the classic idea of Larry Connors, using double moving average system to capture the medium-term oscillation of the market and take profit when it is overbought or oversold.

Strategy Logic

Use 2-period RSI to determine if the price is in oversold region.
Use long period moving average (200 periods) to determine the major trend direction. Only consider opening position when price is above the long MA.
When price is above long MA and RSI is below oversold line, open long position at market price.
When price breaks through short period MA (5 periods) upwards, close long position at market price to take profit.

In addition, the strategy provides the following configurable options:

RSI parameters: period length, overbought/oversold levels.
MA parameters: long and short period.
RSI MA filter: add RSI MA to avoid RSI fluctuation.
Stop loss: configurable to add stop loss or not.

Advantage Analysis

The double MA system can effectively track medium-long term trends.
RSI avoids missing the best entry timing during violent fluctuation.
Flexible configuration suitable for parameter optimization.
Breakthrough strategy, not likely to miss signals.

Risk Analysis

Double MA strategy is sensitive to parameters, requiring optimization to achieve best performance.
No stop loss brings risk of expanding losses. Cautious position sizing is needed.
False breakout risks losses in oscillating market. Consider optimizing MA periods or adding other filters.
Backtest overfitting risk. Requires validation across markets and time periods.

Optimization Directions

Test and optimize combinations of RSI and MA parameters to find optimum.
Test additional entry filters like volume spike to reduce false signals.
Add trailing stop loss to control single trade loss. Assess impact on overall profitability.
Evaluate impact of different holding periods to find optimal.
Test robustness in longer timeframes like daily.

Summary

This strategy combines double MA trend tracking and RSI overbought/oversold to form a typical breakout system. With parameter optimization, strict risk management and robustness validation, it can become a powerful quantitative trading tool. But traders should beware of backtest overfitting and keep improving the strategy to adapt to changing market conditions.

Parameters

Name	Default	What it controls
v_input_1	2	RSI Lenght
v_input_2	10	OverBought Level for RSI
v_input_3	5	Short MA Length
v_input_4	200	Long MA Length
v_input_5	true	RSI Moving Average Filter
v_input_6	4	RSI Moving Average Length
v_input_7	30	OverBought Level for the Moving Average of the RSI
v_input_8	false	Apply Stop Loss
v_input_9	10	% Stop Loss
v_input_10	2009	Backtest Start Year
v_input_11	true	Backtest Start Month
v_input_12	2	Backtest Start Day
v_input_13	2020	Backtest Stop Year
v_input_14	12	Backtest Stop Month
v_input_15	30	Backtest Stop Day

Source Pine Script

/*backtest
start: 2023-09-26 00:00:00
end: 2023-10-26 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("RSI Strategy", overlay=true, default_qty_type = strategy.percent_of_equity, default_qty_value = 100)

//Starter Parameters

length = input(title="RSI Lenght", defval=2)
overBoughtRSI = input(title="OverBought Level for RSI",  defval=10)
shortLength = input(title="Short MA Length",  defval=5)
longLength = input(title="Long MA Length",  defval=200)

RuleMRSI=input(title="RSI Moving Average Filter", defval= true)
lengthmrsi=input(title="RSI Moving Average Length",  defval=4)
overBoughtMRSI=input(title="OverBought Level for the Moving Average of the RSI",  defval=30)

Rulestop=input(title="Apply Stop Loss", defval=false)
stop_percentual=input(title="% Stop Loss",  defval=10)

//RSI

vrsi = rsi(close, length)

//Moving Averages

longma = sma(close,longLength)
shortma = sma(close,shortLength)
mrsi=sma(vrsi,lengthmrsi)

//Stop Loss

stop_level = strategy.position_avg_price*((100-stop_percentual)/100)

//Backtest Period
testStartYear = input(2009, "Backtest Start Year")
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(2, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)

testStopYear = input(2020, "Backtest Stop Year")
testStopMonth = input(12, "Backtest Stop Month")
testStopDay = input(30, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay,0,0)

testPeriod() => true
    
//Strategy

if testPeriod() and (not na(vrsi))
    if  (RuleMRSI==false) and (Rulestop==false)
        if (vrsi<overBoughtRSI) and (close>longma)
            strategy.entry("RsiLE", strategy.long , comment="Open")
        if (close>shortma)
            strategy.close_all()

    if (RuleMRSI==true) and (Rulestop==false)
        if (vrsi<overBoughtRSI) and (close>longma) and (mrsi<overBoughtMRSI)
            strategy.entry("RsiLE", strategy.long , comment="Open")
        if (close>shortma)
            strategy.close_all()

    if (RuleMRSI==false) and (Rulestop==true)
        if (vrsi<overBoughtRSI) and (close>longma)
            strategy.entry("RsiLE", strategy.long , comment="Open")
            strategy.exit("RsiLE", stop = stop_level)
        if (close>shortma)
            strategy.close_all()

    if (RuleMRSI==true) and (Rulestop==true)
        if (vrsi<overBoughtRSI) and (close>longma) and (mrsi<overBoughtMRSI)
            strategy.entry("RsiLE", strategy.long , comment="Open")
            strategy.exit("RsiLE", stop = stop_level)
        if (close>shortma)
            strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-27. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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