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  2. Ehlers-Smoothed 스토카스틱 RSI 전략

Ehlers-Smoothed 스토카스틱 RSI 전략

저자:차오장날짜: 2024-01-26 15:58:48
태그:

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전반적인 설명

전략 논리

이점 분석

위험 분석

최적화 방향

이 전략의 주요 최적화 방향은 다음과 같습니다.

  1. 매개 변수 설정을 최적화합니다. 광범위한 백테스팅을 통해 스토카스틱 RSI의 길이와 평평화 상수와 같은 매개 변수를 최적화합니다.

  3. 다른 지표 또는 패턴과 결합합니다. 여러 필터 조건을 추가하고 위험을 더 줄이기 위해 변동성 지표, 이동 평균 등과 결합하는 것을 고려하십시오.

요약

/*backtest
start: 2023-12-01 00:00:00
end: 2023-12-31 23:59:59
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=3
strategy("ES Stoch RSI Strategy [krypt]", overlay=true, calc_on_order_fills=true, calc_on_every_tick=true, initial_capital=10000, currency='USD')

//Backtest Range
FromMonth = input(defval = 06, title = "From Month", minval = 1)
FromDay   = input(defval = 1, title = "From Day", minval = 1)
FromYear  = input(defval = 2018, title = "From Year", minval = 2014)
ToMonth   = input(defval = 7, title = "To Month", minval = 1)
ToDay     = input(defval = 30, title = "To Day", minval = 1)
ToYear    = input(defval = 2018, title = "To Year", minval = 2014)

PI = 3.14159265359

drop1st(src) =>
    x = na
    x := na(src[1]) ? na : src

xlowest(src, len) =>
    x = src
    for i = 1 to len - 1
        v = src[i]
        if (na(v))
            break
        x := min(x, v)
    x

xhighest(src, len) =>
    x = src
    for i = 1 to len - 1
        v = src[i]
        if (na(v))
            break
        x := max(x, v)
    x

xstoch(c, h, l, len) =>
    xlow = xlowest(l, len)
    xhigh = xhighest(h, len) 
    100 * (c - xlow) / (xhigh - xlow)

Stochastic(c, h, l, length) =>
    rawsig = xstoch(c, h, l, length)
    min(max(rawsig, 0.0), 100.0)

xrma(src, len) =>
    sum = na
    sum := (src + (len - 1) * nz(sum[1], src)) / len

xrsi(src, len) =>
    msig = nz(change(src, 1), 0.0)
    up = xrma(max(msig, 0.0), len)
    dn = xrma(max(-msig, 0.0), len)
    rs = up / dn
    100.0 - 100.0 / (1.0 + rs)

EhlersSuperSmoother(src, lower) =>
	a1 = exp(-PI * sqrt(2) / lower)
	coeff2 = 2 * a1 * cos(sqrt(2) * PI / lower)
	coeff3 = -pow(a1, 2)
	coeff1 = (1 - coeff2 - coeff3) / 2
	filt = na
	filt := nz(coeff1 * (src + nz(src[1], src)) + coeff2 * filt[1] + coeff3 * filt[2], src)

smoothK = input(7, minval=1, title="K")
smoothD = input(2, minval=1, title="D")
lengthRSI = input(10, minval=1, title="RSI Length")
lengthStoch = input(3, minval=1, title="Stochastic Length")
showsignals = input(true, title="Buy/Sell Signals")
src = input(close,  title="Source")

ob = 80
os = 20
midpoint = 50

price = log(drop1st(src))
rsi1 = xrsi(price, lengthRSI)
rawsig = Stochastic(rsi1, rsi1, rsi1, lengthStoch)
sig = EhlersSuperSmoother(rawsig, smoothK)
ma = sma(sig, smoothD)

plot(sig, color=#0094ff, title="K", transp=0)
plot(ma, color=#ff6a00, title="D", transp=0)
lineOB = hline(ob, title="Upper Band", color=#c0c0c0)
lineOS = hline(os, title="Lower Band", color=#c0c0c0)
fill(lineOB, lineOS, color=purple, title="Background")

// Buy/Sell Signals

// use curvature information to filter out some false positives
mm1 = change(change(ma, 1), 1)
mm2 = change(change(ma, 2), 2)
ms1 = change(change(sig, 1), 1)
ms2 = change(change(sig, 2), 2)

sellsignals = showsignals and (mm1 + ms1 < 0 and mm2 + ms2 < 0) and crossunder(sig, ma) and sig[1] > ob
buysignals = showsignals and (mm1 + ms1 > 0 and mm2 + ms2 > 0) and crossover(sig, ma) and sig[1] < os

ploff = 4
plot(buysignals ? sig[1] - ploff : na, style=circles, color=#008fff, linewidth=3, title="Buy Signal", transp=0)
plot(sellsignals ? sig[1] + ploff : na, style=circles, color=#ff0000, linewidth=3, title="Sell Signal", transp=0)

longCondition = buysignals
if (longCondition)
    strategy.entry("L", strategy.long, comment="Long", when=(buysignals))

shortCondition = sellsignals
if (shortCondition)
    strategy.entry("S", strategy.short, comment="Short", when=(sellsignals))
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