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拉盖尔RSI交易策略Laguerre-RSI-Trading-Strategy.md

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Name

拉盖尔RSI交易策略Laguerre-RSI-Trading-Strategy

Author

ChaoZhang

Strategy Description

IMG

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概述

拉盖尔RSI交易策略是基于John EHLERS的拉盖尔滤波器的RSI指标。该策略通过调整α系数来增加或减少RSI指标的滞后性和平滑性,从而过滤掉RSI指标的噪音,发出更清晰的买卖信号。

策略原理

该策略的核心指标是拉盖尔RSI。它的计算公式如下:

L0 = (1-γ)Src + γL0[1] L1 = -γL0 + L0[1] + γL1[1]
L2 = -γL1 + L1[1] + γL2[1] L3 = -γL2 + L2[1] + γL3[1]

这里γ=1-α,α是可以调整的系数,Src代表价格。L0到L3是包含递推关系的4个指标。在此基础上可以计算当前上涨积分cu和下跌积分cd:

cu = (L0>L1 ? L0-L1 : 0) + (L1>L2 ? L1-L2 : 0) + (L2>L3 ? L2-L3 : 0) cd = (L0<L1 ? L1-L0 : 0) + (L1<L2 ? L2-L1 : 0) + (L2<L3 ? L3-L2 : 0)

然后利用cu和cd可以计算出拉盖尔RSI:

LaRSI = cu / (cu + cd)

这里通过递归滤波器的结构,拉盖尔RSI指标在保持RSI指标趋势识别能力的同时,过滤了大量的随机性噪音,能够产生更清晰平滑的交易信号。

具体交易规则: 当拉盖尔RSI指标上穿20时做多;当拉盖尔RSI指标下穿80时做空。

优势分析

拉盖尔RSI策略的主要优势有:

  1. 通过拉盖尔滤波器结构有效过滤RSI指标噪音,使得交易信号更清晰可靠

  2. α系数的调整使得策略的参数可以灵活优化,适应更广泛的市场环境

  3. 保留了RSI指标的长期有效性,同时通过滤波器实现动量识别,整合趋势和超买超卖

  4. 策略规则简单直观,容易实施,在多种市场环境下表现较好

风险分析

该策略主要存在以下风险:

  1. α系数不当设置可能导致过度滞后或过滤过度,错过价格变化

  2. 大幅震荡市场中可能出现频繁交易亏损

  3. 长期持续牛市可能错过部分上涨机会

优化方向

该策略可以从以下几个方面进行优化:

  1. 利用机器学习算法优化α系数的设置

  2. 增加止损机制,降低亏损风险

  3. 结合其他指标判断过滤误报信号

  4. 增加 quantitative easing 模式,在特定阶段锁定盈利

总结

拉盖尔RSI策略通过滤波机制有效识别超买超卖情况,在发出交易信号的同时避免被噪音干扰。该策略简单实用,参数优化空间大,能够适应各种市场环境,是一种值得推荐的交易策略。

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Overview

The Laguerre RSI trading strategy is based on John EHLERS’ Laguerre filter applied to the RSI indicator. By adjusting the α coefficient, this strategy increases or decreases the lag and smoothness of the RSI to filter out noise and generate clearer trading signals.

Strategy Logic

The core indicator of this strategy is Laguerre RSI. Its calculation formula is as follows:

L0 = (1-γ)Src + γL0[1] L1 = -γL0 + L0[1] + γL1[1] L2 = -γL1 + L1[1] + γL2[1] L3 = -γL2 + L2[1] + γL3[1]

Here γ=1-α, α is an adjustable coefficient, Src represents price. L0 to L3 are 4 indicators containing recursive relationships. On this basis, the current up integral cu and down integral cd can be calculated:

cu = (L0>L1 ? L0-L1 : 0) + (L1>L2 ? L1-L2 : 0) + (L2>L3 ? L2-L3 : 0) cd = (L0<L1 ? L1-L0 : 0) + (L1<L2 ? L2-L1 : 0) + (L2<L3 ? L3-L2 : 0)

Then Laguerre RSI can be calculated using cu and cd:

LaRSI = cu / (cu + cd)

Through the recursive filter structure, Laguerre RSI retains the trend identification capability of RSI while filtering out a lot of random noise, generating clearer and smoother trading signals.

The specific trading rules are: Go long when Laguerre RSI crosses above 20, and go short when Laguerre RSI crosses below 80.

Advantage Analysis

The main advantages of Laguerre RSI strategy are:

  1. Effectively filter the noise of RSI through Laguerre filter structure, making trading signals clearer and more reliable

  2. The adjustable α coefficient makes the strategy parameters flexible for optimization to adapt to more market environments

  3. Retains the long-term validity of RSI while enabling momentum identification through filtering, integrating trend and overbought/oversold

  4. Simple and intuitive strategy rules that are easy to implement and perform well in various market environments

Risk Analysis

The main risks of this strategy are:

  1. Improper settings of α may lead to excessive lag or over-filtering, thus missing price changes

  2. Frequent trading losses may occur in volatile markets

  3. Some upside opportunities may be missed in long-term sustained bull markets

Optimization Directions

This strategy can be optimized from the following aspects:

  1. Use machine learning algorithms to optimize the α coefficient setting

  2. Add stop loss mechanisms to reduce loss risk

  3. Combine other indicators to filter false signals

  4. Increase the quantitative easing model to lock in profits at specific stages

Conclusion

The Laguerre RSI strategy effectively identifies overbought and oversold situations through filtering mechanisms while avoiding interference from noise when issuing trading signals. This simple and practical strategy has large optimization space for parameters and can adapt to various market environments. It is a recommended trading strategy.

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Strategy Arguments

Argument Default Description
v_input_1_close 0 Source: close
v_input_2 0.2 Alpha
v_input_3 false Change Color ?
v_input_4 true === Date Backtesting ===
v_input_5 true From Day
v_input_6 true From Month
v_input_7 2020 From Year
v_input_8 true To Day
v_input_9 true To Month
v_input_10 9999 To Year

Source (PineScript)

/*backtest
start: 2022-12-12 00:00:00
end: 2023-12-18 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © mertriver1
// Developer: John EHLERS
//@version=3
// Author:Kıvanç Özbilgiç
strategy("Laguerre RSI", shorttitle="LaRSI", overlay=false)
src = input(title="Source", defval=close)
alpha = input(title="Alpha", type=float, minval=0, maxval=1, step=0.1, defval=0.2)
colorchange = input(title="Change Color ?", type=bool, defval=false)

Date1      = input(true, title = "=== Date Backtesting ===")
FromDay1   = input(defval = 1, title = "From Day", minval = 1, maxval = 31)
FromMonth1 = input(defval = 1, title = "From Month", minval = 1, maxval = 12)
FromYear1  = input(defval = 2020, title = "From Year", minval = 2017)

ToDay1     = input(defval = 1, title = "To Day", minval = 1, maxval = 31)
ToMonth1   = input(defval = 1, title = "To Month", minval = 1, maxval = 12)
ToYear1    = input(defval = 9999, title = "To Year", minval = 2017)

start1     = timestamp(FromYear1, FromMonth1, FromDay1, 00, 00) 
finish1    = timestamp(ToYear1, ToMonth1, ToDay1, 23, 59)        
window1()  => time >= start1 and time <= finish1 ? true : false

gamma=1-alpha
L0 = 0.0
L0 := (1-gamma) * src + gamma * nz(L0[1])
L1 = 0.0
L1 := -gamma * L0 + nz(L0[1]) + gamma * nz(L1[1])

L2 = 0.0
L2 := -gamma * L1 + nz(L1[1]) + gamma * nz(L2[1])

L3 = 0.0
L3 := -gamma * L2 + nz(L2[1]) + gamma * nz(L3[1])

cu= (L0>L1 ? L0-L1 : 0) + (L1>L2 ? L1-L2 : 0) + (L2>L3 ? L2-L3 : 0)

cd= (L0<L1 ? L1-L0 : 0) + (L1<L2 ? L2-L1 : 0) + (L2<L3 ? L3-L2 : 0)

temp= cu+cd==0 ? -1 : cu+cd
LaRSI=temp==-1 ? 0 : cu/temp

Color = colorchange ? (LaRSI > LaRSI[1] ? green : red) : blue
plot(100*LaRSI, title="LaRSI", linewidth=2, color=Color, transp=0)
plot(20,linewidth=1, color=maroon, transp=0)
plot(80,linewidth=1, color=maroon, transp=0)

strategy.entry("Long",   true, when = window1() and crossover(cu, cd))
strategy.entry("Short", false, when = window1() and crossunder(cu, cd))

Detail

https://www.fmz.com/strategy/435868

Last Modified

2023-12-19 14:04:46