Cumulative RSI Breakout Strategy

This strategy utilizes the Cumulative RSI indicator to identify trends and makes buy and sell decisions when the cumulative RSI value breaks through key threshold levels. It can effectively filter market noise and…

Pine ScriptBreakout Trend following RSI EMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 51 lines
Parameters: 12
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The strategy is primarily based on the Cumulative RSI indicator for trading decisions. The Cumulative RSI indicator is the accumulation of RSI values. By setting the cumlen parameter, the RSI values over the past cumlen days are added up to derive the Cumulative RSI indicator. This indicator can filter out short-term market noise.

When the Cumulative RSI indicator crosses above the Bollinger Band upper rail, a long position will be opened. When the Cumulative RSI crosses below the Bollinger Band lower rail, the open position will be closed. The Bollinger Band rails are dynamically calculated based on historical data over many years.

Additionally, a trend filter option is added. Long trades will only be opened when the price is above the 100-day Moving Average, meaning it is in an upward trend channel. This filter avoids erroneous trades during market fluctuations.

Advantages

Effectively filter noise and capture mid-to-long-term trends using Cumulative RSI
Avoid unreasonable trades with the trend filter
Use dynamic reference levels instead of fixed values for decision making
Highly configurable parameters for adjustments based on different markets
Outstanding backtest results over 10 years, significantly outperforming buy and hold

Risks and Improvements

Decisions based solely on one indicator, can add other indicators or filters
Fixed high leverage ratio, can adjust based on drawdowns
Only long trades, can look into shorting opportunities
Optimize parameters combinations which vary significantly across markets
Enrich exit conditions with stop loss, moving stop loss etc.
Consider combining with other strategies for synergistic effects

Summary

The Cumulative RSI breakout strategy has smooth logic flow and accurately identifies mid-to-long term trends by filtering with Cumulative RSI and adding trend judgment. The backtest results are exceptional over the past decade. There is still room for improvements in areas like parameter tuning, adding indicators, enriching exit conditions to make the strategy more robust. The novel concept is worth further exploration and application.

Parameters

Name	Default	What it controls
v_input_int_1	3	RSI Length
v_input_1	3	RSI Cumulation Length
v_input_2	94	Oversold Level
v_input_3	20	Overbought Level
v_input_4	false	Only Trade When Price is Above EMA?
v_input_5	100	EMA Length
v_input_int_2	true	Start Date
v_input_int_3	true	Start Month
v_input_int_4	2010	Start Year
v_input_int_5	true	End Date
v_input_int_6	true	End Month
v_input_int_7	2099	End Year

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"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// @version=5
// Author = TradeAutomation


strategy(title="Cumulative RSI Strategy", shorttitle="CRSI Strategy", process_orders_on_close=true, overlay=true, commission_type=strategy.commission.cash_per_contract, commission_value=.0035, slippage = 1, margin_long = 75, initial_capital = 25000, default_qty_type=strategy.percent_of_equity, default_qty_value=110)


// Cumulative RSI Indicator Calculations //
rlen  = input.int(title="RSI Length", defval=3, minval=1)
cumlen = input(3, "RSI Cumulation Length")
rsi = ta.rsi(close, rlen)
cumRSI = math.sum(rsi, cumlen)
ob = (100*cumlen*input(94, "Oversold Level")*.01)
os = (100*cumlen*input(20, "Overbought Level")*.01)


// Operational Function //
TrendFilterInput = input(false, "Only Trade When Price is Above EMA?")
ema = ta.ema(close, input(100, "EMA Length"))
TrendisLong = (close>ema)
plot(ema)


// Backtest Timeframe Inputs // 
startDate = input.int(title="Start Date", defval=1, minval=1, maxval=31)
startMonth = input.int(title="Start Month", defval=1, minval=1, maxval=12)
startYear = input.int(title="Start Year", defval=2010, minval=1950, maxval=2100)
endDate = input.int(title="End Date", defval=1, minval=1, maxval=31)
endMonth = input.int(title="End Month", defval=1, minval=1, maxval=12)
endYear = input.int(title="End Year", defval=2099, minval=1950, maxval=2100)
InDateRange = (time >= timestamp(syminfo.timezone, startYear, startMonth, startDate, 0, 0)) and (time < timestamp(syminfo.timezone, endYear, endMonth, endDate, 0, 0))


// Buy and Sell Functions //
if (InDateRange and TrendFilterInput==true)
    strategy.entry("Long", strategy.long, when = ta.crossover(cumRSI, os) and TrendisLong, comment="Buy", alert_message="buy")
    strategy.close("Long", when = ta.crossover(cumRSI, ob) , comment="Sell", alert_message="Sell")
if (InDateRange and TrendFilterInput==false)
    strategy.entry("Long", strategy.long, when = ta.crossover(cumRSI, os), comment="Buy", alert_message="buy")
    strategy.close("Long", when = ta.crossover(cumRSI, ob), comment="Sell", alert_message="sell")
if (not InDateRange)
    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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