Stochastic Momentum Index and RSI Based Quant Trading Strategy

Pine ScriptMomentum Breakout Mean reversion RSI EMA SMA Stochastic

Author: ChaoZhang
Complexity: Intermediate · 104 lines
Parameters: 21
Backtest bar: 5m · intraday
Tested on: BTC/USDT · futures

Jump to code View in vault Original on FMZ

Overview

This strategy is mainly based on two indicators - Stochastic Momentum Index (SMI) and Relative Strength Index (RSI). It also incorporates color filter and candle body filter as auxiliary judgement conditions. Trading signals are generated based on the buy and sell signals from SMI and RSI, combined with filter conditions. This strategy can effectively discover short-term trading opportunities in the market.

Strategy Logic

This strategy relies on SMI and RSI indicators for judgement. SMI mainly judges whether a stock is overbought or oversold, while RSI determines the relative strength of a stock. When both indicators give buy signals at the same time, a buy action will be triggered. The specific logic is as follows:

When SMI is oversold (below lower limit), it is considered as a buy signal
When RSI is below threshold, it is considered as a buy signal
When both SMI oversold and RSI below corresponding threshold occur, a buy signal is triggered
Sell signal logic is similar

In addition, this strategy has a dual signals mode. This mode requires both SMI and RSI signals to trigger any trades. This can effectively reduce false signals.

Moreover, color filter and candle body filter are incorporated. These filters require relatively large candle body and last candle close higher than open. This can further avoid trading false breakouts.

Advantages

Utilize SMI for overbought/oversold and RSI for relative strength, dual confirmation can reduce false signals
Dual signal mode can greatly decrease ineffective trades
Color and body filters can filter out false breakouts effectively
Strategy logic is simple and clean
Most parameters are customizable

Risks and Optimization

SMI and RSI may produce more false signals when used alone, need careful examination
In dual signal mode, good trading opportunities may be missed if parameters not set properly
Can test strategy profitability under different periodic parameters to find optimum parameter combination
Can evaluate threshold parameters through simulation or backtesting
Can consider incorporating more filters to optimize the strategy

Summary

This strategy integrates the signals from both SMI and RSI indicators and generates trading orders through dual confirmation. Color filter and candle body filter are also implemented to filter out false breakouts. The strategy has simple and clean logic flow, and most parameters are customizable. Better return can be achieved by tuning the parameters accordingly.

Parameters

Name	Default	What it controls
v_input_1	true	Long
v_input_2	true	Short
v_input_3	false	Use Martingale
v_input_4	100	Capital, %
v_input_5	true	Use SMI Strategy
v_input_6	true	Use RSI Strategy
v_input_7	true	Use Color-Filter
v_input_8	true	Use Body-Filter
v_input_9	2	SMI Percent K Length
v_input_10	2	SMI Percent D Length
v_input_11	50	SMI Limit
v_input_12	2	RSI Period
v_input_13	10	RSI Limit
v_input_14	false	SMI+RSI Mode
v_input_15	false	Show background
v_input_16	2018	From Year
v_input_17	2100	To Year
v_input_18	true	From Month
v_input_19	12	To Month
v_input_20	true	From day
v_input_21	31	To day

Source Pine Script

/*backtest
start: 2023-12-04 00:00:00
end: 2023-12-06 19:00:00
period: 5m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//Noro
//2018

//@version=2
strategy(title = "Noro's Stochastic Strategy v1.3", shorttitle = "Stochastic str 1.3", overlay = false, default_qty_type = strategy.percent_of_equity, default_qty_value = 100, pyramiding = 0)

//Settings 
needlong = input(true, defval = true, title = "Long")
needshort = input(true, defval = true, title = "Short")
usemar = input(false, defval = false, title = "Use Martingale")
capital = input(100, defval = 100, minval = 1, maxval = 10000, title = "Capital, %")
usesmi = input(true, defval = true, title = "Use SMI Strategy")
usersi = input(true, defval = true, title = "Use RSI Strategy")
usecol = input(true, defval = true, title = "Use Color-Filter")
usebod = input(true, defval = true, title = "Use Body-Filter")
a = input(2, defval = 2, minval = 2, maxval = 50, title = "SMI Percent K Length")
b = input(2, defval = 2, minval = 2, maxval = 50, title = "SMI Percent D Length")
limitsmi = input(50, defval = 50, minval = 1, maxval = 100, title = "SMI Limit")
periodrsi = input(2, defval = 2, minval = 2, maxval = 50, title = "RSI Period")
limitrsi = input(10, defval = 10, minval = 1, maxval = 50, title = "RSI Limit")
double = input(false, defval = false, title = "SMI+RSI Mode")
showbg = input(false, defval = false, title = "Show background")
fromyear = input(2018, defval = 2018, minval = 1900, maxval = 2100, title = "From Year")
toyear = input(2100, defval = 2100, minval = 1900, maxval = 2100, title = "To Year")
frommonth = input(01, defval = 01, minval = 01, maxval = 12, title = "From Month")
tomonth = input(12, defval = 12, minval = 01, maxval = 12, title = "To Month")
fromday = input(01, defval = 01, minval = 01, maxval = 31, title = "From day")
today = input(31, defval = 31, minval = 01, maxval = 31, title = "To day")

//Fast RSI
fastup = rma(max(change(close), 0), periodrsi)
fastdown = rma(-min(change(close), 0), periodrsi)
fastrsi = fastdown == 0 ? 100 : fastup == 0 ? 0 : 100 - (100 / (1 + fastup / fastdown))

//Stochastic Momentum Index
ll = lowest (low, a)
hh = highest (high, a)
diff = hh - ll
rdiff = close - (hh+ll)/2
//avgrel = ema(ema(rdiff,b),b)
//avgdiff = ema(ema(diff,b),b)
avgrel = sma(sma(rdiff,b),b)
avgdiff = sma(sma(diff,b),b)
SMI = avgdiff != 0 ? (avgrel/(avgdiff/2)*100) : 0
SMIsignal = ema(SMI,b)

//Lines
plot(SMI, color = blue, linewidth = 3, title = "Stochastic Momentum Index")
plot(SMIsignal, color = red, linewidth = 3, title = "SMI Signal Line")
plot(limitsmi, color = black, title = "Over Bought")
plot(-1 * limitsmi, color = black, title = "Over Sold")
plot(0, color = blue, title = "Zero Line")

//Color-Filter
gb = close > open or usecol == false
rb = close < open or usecol == false

//Body Filter
nbody = abs(close - open)
abody = sma(nbody, 10)
body = nbody > abody / 3 or usebod == false

//Signals
up1 = SMI < -1 * limitsmi and rb and body and usesmi
dn1 = SMI > limitsmi and gb and body and usesmi
up2 = fastrsi < limitrsi and rb and body and usersi
dn2 = fastrsi > 100 - limitrsi and gb and body and usersi
exit = ((strategy.position_size > 0 and close > open) or (strategy.position_size < 0 and close < open)) and body

//Background
redb = (SMI > limitsmi and usesmi) or (fastrsi > 100 - limitrsi and usersi)
limeb = (SMI < -1 * limitsmi and usesmi) or (fastrsi < limitrsi and usersi)
col = showbg == false ? na : redb ? red : limeb ? lime : na
bgcolor(col, transp = 50)

//Trading
profit = exit ? ((strategy.position_size > 0 and close > strategy.position_avg_price) or (strategy.position_size < 0 and close < strategy.position_avg_price)) ? 1 : -1 : profit[1]
mult = usemar ? exit ? profit == -1 ? mult[1] * 2 : 1 : mult[1] : 1
lot = strategy.position_size == 0 ? strategy.equity / close * capital / 100 * mult : lot[1]

signalup = ((up1 or up2) and double == false) or (up1 and up2 and double)
if signalup
    if strategy.position_size < 0
        strategy.close_all()
        
    strategy.entry("long", strategy.long, needlong == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))

signaldn = ((dn1 or dn2) and double == false) or (dn1 and dn2 and double)
if signaldn
    if strategy.position_size > 0
        strategy.close_all()
        
    strategy.entry("Short", strategy.short, needshort == false ? 0 : lot, when=(time > timestamp(fromyear, frommonth, fromday, 00, 00) and time < timestamp(toyear, tomonth, today, 23, 59)))
    
if time > timestamp(toyear, tomonth, today, 23, 59) or exit
    strategy.close_all()

Published by ChaoZhang on the FMZ strategy square · last modified 2023-12-12. Collected in the Quant Trading Vault. Unvalidated research: re-test with your own data and costs before trusting it.

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