RSI Rising Crypto Trending Strategy

The RSI Rising Crypto Trending Strategy is a trend trading strategy designed for longer timeframes (4h+) in crypto and stock markets. It utilizes RSI to identify rising and falling trends combined with Bollinger…

Pine ScriptTrend following Machine learning Mean reversion RSI MACD EMA SMA Bollinger Bands

Author: ChaoZhang
Complexity: Intermediate · 67 lines
Parameters: 9
Backtest bar: 2h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

The RSI Rising Crypto Trending Strategy is a trend trading strategy designed for longer timeframes (4h+) in crypto and stock markets.

It utilizes RSI to identify rising and falling trends combined with Bollinger Bands and ROC to avoid trading in sideways markets. From tests, it appears to work better trading crypto against crypto rather than against fiat.

Strategy Logic

The strategy uses the following indicators:

RSI - To identify rising/falling trends
Bollinger Bands - To identify sideways markets
ROC - To confirm trend direction

The specific trading rules are:

Entry Rules

Long entry: RSI rising AND not sideways market per BB and ROC Short entry: RSI falling AND not sideways market per BB and ROC

Exit Rules

Exit when opposite signal is triggered

Advantage Analysis

Captures trend turning points early using RSI
Avoids getting trapped in sideways markets using BB
ROC confirms trend direction for more robust signals
Good for longer timeframe trades and capturing trends
Better for crypto/crypto pairs to avoid fiat exposure

Risk Analysis

No stop loss so high risk of large losses
Poor BB and ROC parameters could lead to missed trades or bad signals
Purely technical so misses major black swan events

Increase stop loss, optimize BB/ROC parameters, and incorporate fundamental analysis.

Enhancement Opportunities

Some ways this strategy could be improved:

Add stop loss for risk management and setting maximum loss per trade.
Optimize BB and ROC parameters through backtesting to find best settings.
Incorporate additional indicators like MACD, KD for multi-indicator signal reliability.
Build a liquidity model to pause trading during volatility spikes to avoid traps.
Use machine learning to automatically optimize parameters and signal weighting.
Incorporate on-chain data like exchange liquidity and fund flows for greater adaptability.

Summary

The RSI Rising Crypto Trend Strategy captures longer timeframe crypto trends using RSI plus BB and ROC. The advantage is quickly catching trend reversals and avoiding traps. The weaknesses are no stop loss and parameter dependency. Enhancements like stop loss, optimization, machine learning can make it more robust.

Parameters

Name	Default	What it controls
v_input_1	true	From Day
v_input_2	true	From Month
v_input_3	2010	From Year
v_input_4	31	To Day
v_input_5	12	To Month
v_input_6	2021	To Year
v_input_7	19	periods
v_input_8	14	RSI Length
v_input_9_low	0	Source: low \| high \| close \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4

Source Pine Script

/*backtest
start: 2023-09-16 00:00:00
end: 2023-10-16 00:00:00
period: 2h
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/
// © exlux99

//@version=4
strategy(title = "RSI Rising", overlay = true, initial_capital = 100, default_qty_type= strategy.percent_of_equity, default_qty_value = 100, slippage=0,commission_type=strategy.commission.percent,commission_value=0.03)

/////////////////////
source          = close
bb_length       = 20
bb_mult         = 1.0
basis           = sma(source, bb_length)
dev             = bb_mult * stdev(source, bb_length)
upperx           = basis + dev
lowerx           = basis - dev
bbr             = (source - lowerx)/(upperx - lowerx)
bbr_len         = 21
bbr_std         = stdev(bbr, bbr_len)
bbr_std_thresh  = 0.1
is_sideways     = (bbr > 0.0 and bbr < 1.0) and bbr_std <= bbr_std_thresh


////////////////
fromDay = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
fromMonth = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
fromYear = input(defval = 2010, title = "From Year", minval = 1970)
 //monday and session 
// To Date Inputs
toDay = input(defval = 31, title = "To Day", minval = 1, maxval = 31)
toMonth = input(defval = 12, title = "To Month", minval = 1, maxval = 12)
toYear = input(defval = 2021, title = "To Year", minval = 1970)

startDate = timestamp(fromYear, fromMonth, fromDay, 00, 00)
finishDate = timestamp(toYear, toMonth, toDay, 00, 00)
time_cond = true


sourcex = close
length = 2
pcntChange = 1

roc = 100 * (sourcex - sourcex[length])/sourcex[length]
emaroc = ema(roc, length/2)
isMoving() => emaroc > (pcntChange / 2) or emaroc < (0 - (pcntChange / 2))


periods = input(19)
smooth = input(14, title="RSI Length" )
src = input(low, title="Source" )


rsiClose = rsi(ema(src, periods), smooth)
long=rising(rsiClose,2) and not is_sideways and isMoving()
short=not rising(rsiClose,2) and not is_sideways and isMoving()


if(time_cond)
    strategy.entry('long',1,when=long)
    strategy.entry('short',0,when=short)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-10-17. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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