Name
埃尔斯-平滑化随机相对强弱指数策略Ehlers-Smoothed-Stochastic-RSI-Strategy
Author
ChaoZhang
Strategy Description
该策略的主要思想是利用埃尔斯(Ehlers)超平滑(SuperSmoother)筛选器对随机相对强弱指数(Stochastic RSI)指标进行加工处理,从而过滤掉许多假信号,获得更加可靠的交易信号。基本原理是先计算随机相对强弱指数,然后使用埃尔斯超平滑筛选器对其进行平滑化处理,最后与其自身的移动平均线进行交叉做多做空。
该策略首先计算日志收盘价的 RSI 指标,然后基于 RSI 指标计算 Stochastic 指标,这是典型的相对强弱指数指标。为了过滤假信号,使用埃尔斯超平滑筛选器对 Stochastic RSI 进行加工,最后 Stochastic RSI 线与其自身的移动平均线进行黄金交叉做多,死叉做空。所以该策略的关键点有:1)计算 Stochastic RSI 指标;2)使用埃尔斯超平滑筛选器;3)与移动平均线形成交易信号。
该策略最大的优势在于使用埃尔斯超平滑筛选器,可以有效过滤掉许多假信号,使得交易信号更加可靠。另外,Stochastic RSI 指标本身就具有很好的突破性和趋势跟踪能力。所以该策略可以正确识别趋势,在合适的时候建仓,在合适的时候平仓。
该策略的主要风险在于市场大幅震荡时,容易产生错误信号。当价格在较窄范围内大幅波动时,Stochastic RSI 指标会产生许多上冲和下冲的假信号,这时埃尔斯超平滑筛选器的效果也会打折扣。此外,在某些剧烈行情中,指标的滞后性也可能带来一定的风险。
为了降低这些风险,可以适当调整参数,如调大 Stochastic 指标周期,调小平滑度等,从而进一步过滤假信号。另外,也可以考虑与其它指标或形态进行组合,形成多重过滤条件,避免错误信号带来的风险。
该策略主要可以从以下几个方面进行优化:
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优化参数设置。可以对 Stochastic RSI 指标的长度、平滑常数等参数进行细致测试,找到最佳的参数组合。
-
增加止损机制。可以设定移动止损或挂单止损来锁定利润,降低回撤。
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与其它指标或形态组合。可以考虑与波动率指标、移动平均线等进行组合,形成多重过滤条件,进一步减少风险。
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根据大周期分析结果调整仓位。可以根据更高时间周期的趋势分析结果,动态调整每次交易的头寸规模。
本策略首先计算 Stochastic RSI 指标,然后使用埃尔斯超平滑筛选器对其进行加工处理,最后与自身的移动平均线形成交易信号,实现对趋势的正确判断。策略优势在于指标和过滤器的组合使用,可以有效过滤假信号,获得高概率的交易机会。风险主要来自参数设置不当和止损机制缺失。通过参数优化、止损添加及组合优化等手段,可以进一步提高策略的稳定性和盈利水平。
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The main idea of this strategy is to use the Ehlers SuperSmoother filter to process the Stochastic RSI indicator, filtering out many false signals and obtaining more reliable trading signals. The basic principle is to first calculate the Stochastic RSI indicator, then use the Ehlers SuperSmoother filter to smooth it, and finally go long or short based on the crossover between it and its own moving average line.
This strategy first calculates the RSI indicator of the logarithmic closing price, then calculates the Stochastic indicator based on the RSI indicator, which is a typical relative strength index indicator. To filter out false signals, the Ehlers SuperSmoother filter is used to process the Stochastic RSI. Finally, go long when the Stochastic RSI line has a golden cross with its own moving average line, and go short when there is a death cross. So the key points of this strategy are: 1) Calculate the Stochastic RSI indicator; 2) Use the Ehlers SuperSmoother filter; 3) Form trading signals with moving average crossover.
The biggest advantage of this strategy is the use of the Ehlers SuperSmoother filter, which can effectively filter out many false signals and make trading signals more reliable. In addition, the Stochastic RSI indicator itself has very good breakthrough and trend tracking capabilities. Therefore, this strategy can correctly identify trends, establish positions at the right time, and close positions at the right time.
The main risk of this strategy is that it is prone to wrong signals when the market fluctuates sharply. When the price fluctuates sharply within a narrow range, the Stochastic RSI indicator will generate many false up and down signals. At this time, the effect of the Ehlers SuperSmoother filter will also be compromised. In addition, the lag of indicators may also bring some risks in some violent market conditions.
To reduce these risks, parameters can be adjusted appropriately, such as increasing the cycle of the Stochastic indicator, reducing the degree of smoothness, etc., to further filter out false signals. In addition, consider combining other indicators or patterns to form multiple filter conditions and avoid the risks of wrong signals.
The main optimization directions for this strategy are:
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Optimize parameter settings. Optimize parameters like length and smoothing constants of Stochastic RSI through extensive backtesting.
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Add stop loss mechanisms. Set trailing stop or pending orders to lock in profits and reduce drawdowns.
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Combine with other indicators or patterns. Consider combining with volatility indicators, moving averages etc. to add multiple filter conditions and further reduce risks.
-
Adjust position sizing based on analysis of higher timeframes. Dynamically adjust the position size of each trade based on trend analysis results from higher timeframes.
This strategy first calculates the Stochastic RSI indicator, then processes it with the Ehlers SuperSmoother filter, and finally generates trading signals with the moving average crossover to determine the trend correctly. The advantage lies in the combination of indicator and filter, which can effectively filter out false signals and obtain high probability trading opportunities. The main risks come from inappropriate parameter settings and lack of stop loss mechanisms. Methods like parameter optimization, adding stop loss, and strategy optimization can further improve the stability and profitability.
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Strategy Arguments
| Argument | Default | Description |
|---|---|---|
| v_input_1 | 6 | From Month |
| v_input_2 | true | From Day |
| v_input_3 | 2018 | From Year |
| v_input_4 | 7 | To Month |
| v_input_5 | 30 | To Day |
| v_input_6 | 2018 | To Year |
| v_input_7 | 7 | K |
| v_input_8 | 2 | D |
| v_input_9 | 10 | RSI Length |
| v_input_10 | 3 | Stochastic Length |
| v_input_11 | true | Buy/Sell Signals |
| v_input_12_close | 0 | Source: close |
Source (PineScript)
/*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))
Detail
https://www.fmz.com/strategy/440099
Last Modified
2024-01-26 15:58:48