Three EMA Stochastic RSI Crossover Golden Cross Strategy

The Three EMA Stochastic RSI Crossover Golden Cross strategy is a trend following strategy. It combines the Three EMA indicator and the Stochastic Relative Strength Index to determine entry signals based on the…

Pine ScriptTrend following Machine learning Breakout RSI MACD EMA SMA ATR Stochastic

Author: ChaoZhang
Complexity: Intermediate · 92 lines
Parameters: 17
Backtest bar: 1h · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

Strategy Logic

The signal determination of this strategy is based on the following logic:

Three EMA to determine the trend: 8-day EMA on top, 14-day EMA in the middle and 50-day EMA at the bottom form an uptrend; the reverse forms a downtrend.
Stochastic RSI to determine crossover: when the K line crosses above the D line from below, a golden cross signal is generated, indicating a strong uptrend.
Only go long; short is not considered for now.

When the Three EMA shows an uptrend and the Stochastic RSI generates a golden cross, go long. Set stop loss and take profit based on this to lock in profits.

Advantage Analysis

The main advantages of this strategy with dual indicator determination are:

Three EMA filters out short term noise and locks in medium-long term trends.
Stochastic RSI golden cross confirms strong uptrend.
ATR smart stop loss and take profit locks in profits.
Simple and clear strategy logic, easy to understand and implement.

Risk Analysis

The main risks of this strategy are:

Vulnerable to consolidation. Frequent open/close orders bring trading risk when Three EMA generates multiple crosses in sideways markets. This can be solved by optimizing EMA parameters or adding other filters.
No shorting opportunities. Only longs miss bottom rebound chances. Consider adding MACD to find shorts in downtrends.

Optimization Directions

The main optimization directions include:

Optimize EMA parameters to improve trend determination.
Add MACD etc to determine downtrends and increase short chances.
Add volatility indicators like ATR to improve stops and limits.
Incorporate volume to avoid false breakouts.
Utilize machine learning etc for parameter optimization.

Conclusion

In summary, the Three EMA Stochastic RSI Crossover strategy effectively filters out consolidation and locks in trends by combining dual indicator determination, making it a simple and practical trend following strategy. Further improvements on parameters, filters, technologies will lead to better strategy performance.

Parameters

Name	Default	What it controls
v_input_1	true	FromMonth
v_input_2	true	FromDay
v_input_3	2020	FromYear
v_input_4	true	ToMonth
v_input_5	true	ToDay
v_input_6	9999	ToYear
v_input_7	3	smoothK
v_input_8	3	smoothD
v_input_9	14	lengthRSI
v_input_10	14	lengthStoch
v_input_11_close	0	RSI Source: close \| high \| low \| open \| hl2 \| hlc3 \| hlcc4 \| ohlc4
v_input_12	14	ATR Length
v_input_13	8	EMA 1
v_input_14	14	EMA 2
v_input_15	50	EMA 3
v_input_16	2	Profitfactor
v_input_17	3	Stopfactor

Source Pine Script

/*backtest
start: 2024-01-01 00:00:00
end: 2024-01-25 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4

strategy(title="Stoch RSI Crossover Strat + EMA", shorttitle="Stoch RSI Cross + EMA Strat", overlay = true)

// Time Range
FromMonth=input(defval=1,title="FromMonth",minval=1,maxval=12)
FromDay=input(defval=1,title="FromDay",minval=1,maxval=31)
FromYear=input(defval=2020,title="FromYear",minval=2017)
ToMonth=input(defval=1,title="ToMonth",minval=1,maxval=12)
ToDay=input(defval=1,title="ToDay",minval=1,maxval=31)
ToYear=input(defval=9999,title="ToYear",minval=2017)
start=timestamp(FromYear,FromMonth,FromDay,00,00)
finish=timestamp(ToYear,ToMonth,ToDay,23,59)
window()=>true

// See if this bar's time happened on/after start date
afterStartDate = time >= start and time<=finish?true:false

//STOCH RSI
smoothK = input(3, minval=1)
smoothD = input(3, minval=1)
lengthRSI = input(14, minval=1)
lengthStoch = input(14, minval=1)
src = input(close, title="RSI Source")

rsi1 = rsi(src, lengthRSI)
k = sma(stoch(rsi1, rsi1, rsi1, lengthStoch), smoothK)
d = sma(k, smoothD)

//ATR
lengthATR = input(title="ATR Length", defval=14, minval=1)
atr = atr(lengthATR)

//MULTI EMA
emasrc = close, 
len1 = input(8, minval=1, title="EMA 1")
len2 = input(14, minval=1, title="EMA 2")
len3 = input(50, minval=1, title="EMA 3")

ema1 = ema(emasrc, len1)
ema2 = ema(emasrc, len2)
ema3 = ema(emasrc, len3)

col1 = color.lime
col2 = color.blue
col3 = color.orange

//EMA Plots
//plot(ema1, title="EMA 1", linewidth=1, color=col1)
//plot(ema2, title="EMA 2", linewidth=1, color=col2)
//plot(ema3, title="EMA 3", linewidth=1, color=col3)

crossup = k[0] > d[0] and k[1] <= d[1]
emapos = ema1 > ema2 and ema2 > ema3 and close > ema1
barbuy = crossup and emapos

//plotshape(crossup, style=shape.triangleup, location=location.belowbar, color=color.white)
plotshape(barbuy, style=shape.triangleup, location=location.belowbar, color=color.green)

longloss = sma(open, 1)
//plot(longloss, color=color.red)

//Buy and Sell Factors
profitfactor = input(title="Profitfactor", type=input.float, step=0.1, defval=2)
stopfactor = input(title="Stopfactor", type=input.float, step=0.1, defval=3)
bought = strategy.position_size[1] < strategy.position_size
longcondition = barbuy

if (longcondition) and (afterStartDate) and strategy.opentrades < 1
    strategy.entry("Long", strategy.long)

if (afterStartDate) and strategy.opentrades > 0
    barsbought = barssince(bought)
    profit_level = strategy.position_avg_price + (atr*profitfactor)
    stop_level = strategy.position_avg_price - (atr*stopfactor)
    strategy.exit("Take Profit/ Stop Loss", "Long", stop=stop_level[barsbought], limit=profit_level[barsbought])

Published by ChaoZhang on the FMZ strategy square · last modified 2024-01-26. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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