Arah utama optimasi untuk strategi ini adalah:
Mengoptimalkan parameter pengaturan. Mengoptimalkan parameter seperti panjang dan kelancaran konstanta dari Stochastic RSI melalui backtesting ekstensif.
Bergabung dengan indikator atau pola lain. Pertimbangkan untuk menggabungkan dengan indikator volatilitas, moving average, dll untuk menambahkan beberapa kondisi filter dan mengurangi risiko lebih lanjut.
Sesuaikan ukuran posisi berdasarkan analisis jangka waktu yang lebih tinggi Sesuaikan secara dinamis ukuran posisi setiap perdagangan berdasarkan hasil analisis tren dari jangka waktu yang lebih tinggi.
/*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))