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Name

ADX-RSI策略ADX-RSI-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

这是一个结合ADX和RSI指标的趋势跟踪策略。策略利用RSI判断超买超卖情况发出交易信号,同时用ADX判断市场趋势,过滤掉趋势不明显的交易,可以有效避免震荡市场的套牢。

策略原理

  1. 使用7周期RSI判断超买超卖
  • RSI低于30时视为超卖
  • RSI高于70时视为超买
  1. 使用ADX判断趋势
  • ADX高于30则认为趋势明显
  • ADX低于30则认为趋势不明显
  1. 入场规则
  • 当RSI低于30且ADX高于30时做多
  • 当RSI高于70且ADX高于30时做空
  1. 止盈止损
  • 有可选的止盈止损方式,可以选择收盘止盈止损或振荡止盈止损
  • 收盘止盈止损方式是以收盘价格为标准
  • 振荡止盈止损方式是以近期价格波动的最高最低点为标准

优势分析

  1. RSI指标可以有效判断超买超卖点,减少买点陷阱和卖点陷阱

  2. ADX指标可以过滤掉趋势不明显的情况,避免在震荡行情中被套牢

  3. 可选的止盈止损方式可以更好地控制风险

  4. 策略简单明了,容易理解实现,适合新手学习

  5. 策略参数优化空间大,可以通过调整RSI周期、超买超卖区间、ADX平滑周期等参数进行优化

风险分析

  1. RSI存在回调风险,超买超卖信号可能出现回调再反转

  2. ADX判断趋势存在滞后,可能错过趋势转折点

  3. 止盈止损点设定不合理可能带来亏损

  4. 策略较为朴素,存在超优化风险

  5. 需要进行参数优化以达到更好的效果

优化方向

  1. 可以优化RSI的参数,调整超买超卖区間,找到最佳参数组合

  2. 可以测试不同周期的ADX,找到判断趋势最佳的参数

  3. 可以测试不同的止盈止损方式,找到最适合策略的设置

  4. 可以加入趋势过滤指标,避免逆势交易

  5. 可以结合其它指标进行组合,使策略更具优势

总结

本策略整合了RSI和ADX两个经典指标的优势,可以有效发现趋势且避开震荡,是一种简单实用的趋势跟踪策略。策略优化空间较大,通过调整参数组合可以获得更好的效果。总体来说,该策略适合作为新手学习算法交易的入门策略,也可以作为模块集成到更复杂的策略系统中。

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Overview

This is a trend-following strategy that combines the ADX and RSI indicators. It uses RSI to identify overbought and oversold levels to generate trading signals, and ADX to determine the trend to filter out trades when the trend is unclear, thus avoiding whipsaws in range-bound markets.

Strategy Logic

  1. Use 7-period RSI to identify overbought and oversold levels
  • RSI below 30 is considered oversold
  • RSI above 70 is considered overbought
  1. Use ADX to determine the trend
  • ADX above 30 suggests a strong trend
  • ADX below 30 suggests no trend
  1. Entry rules
  • Long when RSI < 30 and ADX > 30
  • Short when RSI > 70 and ADX > 30
  1. Take profit and stop loss
  • Optional take profit and stop loss methods - close-based or swing-based
  • Close-based uses closing prices
  • Swing-based uses recent swing highs/lows

Advantage Analysis

  1. RSI effectively identifies overbought and oversold levels to avoid buying/selling traps

  2. ADX filters out range-bound markets to avoid whipsaws

  3. Optional take profit/stop loss methods help better control risks

  4. Simple and easy to understand, good for beginners to learn algorithm trading

  5. Much room for parameter optimization and refinement

Risk Analysis

  1. RSI overbought/oversold may have pullbacks and reversals

  2. ADX trend determination has lags, may miss trend turning points

  3. Improper stop loss placement may lead to losses

  4. Risk of over-optimization due to simplicity

  5. Parameter optimization needed for better performance

Optimization Directions

  1. Optimize RSI parameters and overbought/oversold levels

  2. Test different ADX periods to find the optimal setting

  3. Test different take profit/stop loss methods

  4. Add trend filter to avoid counter-trend trading

  5. Combine with other indicators for enhanced performance

Summary

This strategy combines the strengths of the classic RSI and ADX indicators to identify trends and avoid whipsaws. It has much room for optimization to achieve better performance. Overall, it serves well as a beginner's introductory algorithm trading strategy, and can also be incorporated into more complex trading systems.

[/trans]

Strategy Arguments

Argument Default Description
v_input_1 false Strategy Direction
v_input_2 false Use Swing Lo/Hi Stop Loss & Take Profit
v_input_3 20 Swing Lo/Hi Lookback
v_input_4 false SL Expander
v_input_5 false TP Expander
v_input_6 true Reverse Trades
v_input_7 30 OS
v_input_8 80 OB
v_input_9 14 ADX Smoothing
v_input_10 14 DI Length
v_input_11 30 adxlevel

Source (PineScript)

/*backtest
start: 2023-09-19 00:00:00
end: 2023-09-26 00:00:00
period: 15m
basePeriod: 5m
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/
// © tweakerID

// This is a strategy that uses the 7 Period RSI to buy when the indicator is shown as oversold (OS) and sells when 
// the index marks overbought (OB). It also uses the ADX to determine whether the trend is ranging or trending
// and filters out the trending trades. Seems to work better for automated trading when the logic is inversed (buying OB 
// and selling the OS) wihout stop loss.

//@version=4
strategy("ADX + RSI Strat", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100, initial_capital=100, commission_value=0.04, calc_on_every_tick=false)

direction = input(0, title = "Strategy Direction", type=input.integer, minval=-1, maxval=1)
strategy.risk.allow_entry_in(direction == 0 ? strategy.direction.all : (direction < 0 ? strategy.direction.short : strategy.direction.long))


//SL & TP Inputs
i_SL=input(false, title="Use Swing Lo/Hi Stop Loss & Take Profit")
i_SwingLookback=input(20, title="Swing Lo/Hi Lookback")
i_SLExpander=input(defval=0, step=.2, title="SL Expander")
i_TPExpander=input(defval=0, step=.2, title="TP Expander")
i_reverse=input(true, title="Reverse Trades")

//SL & TP Calculations
SwingLow=lowest(i_SwingLookback)
SwingHigh=highest(i_SwingLookback)
bought=strategy.position_size != strategy.position_size[1]
LSL=valuewhen(bought, SwingLow, 0)-((valuewhen(bought, atr(14), 0))*i_SLExpander)
SSL=valuewhen(bought, SwingHigh, 0)+((valuewhen(bought, atr(14), 0))*i_SLExpander)
lTP=strategy.position_avg_price + (strategy.position_avg_price-(valuewhen(bought, SwingLow, 0))+((valuewhen(bought, atr(14), 0))*i_TPExpander))
sTP=strategy.position_avg_price - (valuewhen(bought, SwingHigh, 0)-strategy.position_avg_price)-((valuewhen(bought, atr(14), 0))*i_TPExpander)
islong=strategy.position_size > 0
isshort=strategy.position_size < 0
SL= islong ? LSL : isshort ? SSL : na
TP= islong ? lTP : isshort ? sTP : na

//RSI Calculations
RSI=rsi(close, 7)
OS=input(30, step=5)
OB=input(80, step=5)

//ADX Calculations
adxlen = input(14, title="ADX Smoothing")
dilen = input(14, title="DI Length")
dirmov(len) =>
	up = change(high)
	down = -change(low)
	plusDM = na(up) ? na : (up > down and up > 0 ? up : 0)
	minusDM = na(down) ? na : (down > up and down > 0 ? down : 0)
	truerange = rma(tr, len)
	plus = fixnan(100 * rma(plusDM, len) / truerange)
	minus = fixnan(100 * rma(minusDM, len) / truerange)
	[plus, minus]
adx(dilen, adxlen) =>
	[plus, minus] = dirmov(dilen)
	sum = plus + minus
	adx = 100 * rma(abs(plus - minus) / (sum == 0 ? 1 : sum), adxlen)
sig = adx(dilen, adxlen)
adxlevel=input(30, step=5)


//Entry Logic
BUY = sig < adxlevel and (RSI < OS) 
SELL = sig < adxlevel and (RSI > OB) 

//Entries
strategy.entry("long", strategy.long, when=i_reverse?SELL:BUY)
strategy.entry("short", strategy.short, when=not i_reverse?SELL:BUY)
//Exits
if i_SL
    strategy.exit("longexit", "long", stop=SL, limit=TP)
    strategy.exit("shortexit", "short", stop=SL, limit=TP)

//Plots
plot(i_SL ? SL : na, color=color.red, style=plot.style_cross, title="SL")
plot(i_SL ? TP : na, color=color.green, style=plot.style_cross, title="TP")
plotshape(BUY ? 1 : na, style=shape.triangleup, location=location.belowbar, color=color.green, title="Bullish Setup")
plotshape(SELL ? 1 : na, style=shape.triangledown, location=location.abovebar, color=color.red, title="Bearish Setup")

Detail

https://www.fmz.com/strategy/427989

Last Modified

2023-09-27 16:27:39