JMA Crossing RSI Trading Strategy

This strategy generates trading signals by crossing of Jurik Moving Average (JMA) and RSI indicator. It goes long when JMA crosses above RSI and goes short when crossing below. The strategy attempts to filter false…

Pine ScriptBreakout Momentum Trend following RSI MACD

Author: ChaoZhang
Complexity: Intermediate · 95 lines
Parameters: 16
Backtest bar: 1d · swing
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Principles

The strategy mainly utilizes two types of indicators:

JMA indicator: A smoothed moving average using power multipliers, with lower lag and quicker in capturing price changes.
RSI indicator: A common strength indicator reflecting buying/selling momentum.

When JMA crosses above RSI, it indicates stronger short-term uptrend over long term trend and generates buy signal. When crossing below RSI, it prompts sell signal.

Upon signal, the strategy enters trade in corresponding direction. Exits when price reaches predetermined profit ratio or indicators cross reverse direction.

Advantages

Adjustable JMA parameters adaptable to different periods.
RSI filters false breakouts.
Dual indicator combination reduces false signals.
Built-in stop loss controls loss.
Customizable profit ratio for profit targeting.

Risks and Mitigations

Dual indicators combo may generate too few signals. Can tweak parameters for sensitivity.
JMA still has lag, may miss turning points. Can optimize with leading indicators.
Improper stop loss placement may be hit for greater loss. Should backtest for suitable placement.
Overreliance on indicators can produce false signals. Can add volume or volatility filters.

Enhancement Opportunities

Test JMA parameters to find optimal settings.
Try different RSI parameters for better performance.
Add trailing stop mechanism for adaptive stops.
Optimize entry position sizing like adding to winning trades.
Research additional filters like KD, MACD.

Summary

The strategy enables trend following with JMA and RSI crossovers and limits risk via stops. But false signals remain probable, requiring further optimization on parameters and filters. Stop loss also needs backtest validation. It provides a basic framework for dual indicator crossing system with room for improvements.

Parameters

Name	Default	What it controls
v_input_1	2019	Start Year
v_input_2	6	Start Month
v_input_3	true	Start Day
v_input_4	false	Start Hour
v_input_5	false	Start Minute
v_input_6	2019	End Year
v_input_7	12	End Month
v_input_8	true	End Day
v_input_9	false	End Hour
v_input_10	false	End Minute
v_input_11	14	Length
v_input_12	7	Length
v_input_13	50	Phase
v_input_14	2	Power
v_input_15	true	Highlight Movements ?
v_input_16	true	Use Initial Stop Loss?

Source Pine Script

/*backtest
start: 2023-01-01 00:00:00
end: 2023-03-15 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
// Stratégie marche le mieux sur du 2 jours
strategy("JMA(7,50,RSI) crossing RSI(14,close)", overlay=false, currency=currency.EUR, default_qty_type=strategy.cash, default_qty_value=5000)

// Strategy Tester Start Time
sYear = input(2019, title = "Start Year")
sMonth = input(06, title = "Start Month", minval = 01, maxval = 12)
sDay = input(01, title = "Start Day", minval = 01, maxval = 31)
sHour = input(00, title = "Start Hour", minval = 00, maxval = 23)
sMinute = input(00, title = "Start Minute", minval = 00, maxval = 59)
startTime = true

// Strategy Tester End Time
eYear = input(2019, title = "End Year")
eMonth = input(12, title = "End Month", minval = 01, maxval = 12)
eDay = input(01, title = "End Day", minval = 01, maxval = 31)
eHour = input(00, title = "End Hour", minval = 00, maxval = 23)
eMinute = input(00, title = "End Minute", minval = 00, maxval = 59)
endTime = true

// === RSI ===
src = close, len = input(14, minval=1, title="Length")
up = rma(max(change(src), 0), len)
down = rma(-min(change(src), 0), len)
rsi = down == 0 ? 100 : up == 0 ? 0 : 100 - (100 / (1 + up / down))
plot(rsi, color=color.purple)
band1 = hline(70)
band0 = hline(30)

// === JMA ===
_length = input(7, title="Length")
_phase = input(50, title="Phase")
_power = input(2, title="Power")
highlightMovements = input(true, title="Highlight Movements ?")

// srcJMA = input(rsi, title="Source")
srcJMA = rsi

phaseRatio = _phase < -100 ? 0.5 : _phase > 100 ? 2.5 : _phase / 100 + 1.5
beta = 0.45 * (_length - 1) / (0.45 * (_length - 1) + 2)
alpha = pow(beta, _power)
jma = 0.0
e0 = 0.0
e0 := (1 - alpha) * srcJMA + alpha * nz(e0[1])
e1 = 0.0
e1 := (srcJMA - e0) * (1 - beta) + beta * nz(e1[1])
e2 = 0.0
e2 := (e0 + phaseRatio * e1 - nz(jma[1])) * pow(1 - alpha, 2) + pow(alpha, 2) * nz(e2[1])
jma := e2 + nz(jma[1])
// === End of JMA def ===

jmaColor = highlightMovements ? (jma > jma[1] ? color.green : color.red) : #6d1e7f
plot(jma, title="JMA switch", linewidth=2, color=jmaColor, transp=0)

// === Inputs ===
// risk management
useStop = input(true, title = "Use Initial Stop Loss?")

goLong() => crossover(rsi, jma)
killLong() => crossunder(rsi, jma)

// ======= DEBUGGGGGGGG ============
long_price = 0.0
short_price = 0.0

if(startTime and endTime)
    if(goLong())
        long_price := close
    strategy.entry("Buy", strategy.long, when = goLong())
    strategy.close("Buy", when = killLong() and close > long_price)

// Shorting if using
goShort() => killLong()
killShort() => goLong()

if(startTime and endTime)
    if(goShort())
        short_price := close
    strategy.entry("Sell", strategy.short, when = goShort() and close < short_price)
    strategy.close("Sell", when = killShort())
// =========================

if (useStop)
    strategy.exit("XLS", from_entry ="Buy", stop = strategy.position_avg_price / 1.08)
    strategy.exit("XSS", from_entry ="Sell", stop = strategy.position_avg_price * 1.08)

Published by ChaoZhang on the FMZ strategy square · last modified 2023-09-18. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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