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strategies/多空指标策略Dual-Thrust-Indicator-Strategy.md
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Name

多空指标策略Dual-Thrust-Indicator-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

本策略的核心思想是结合多空指标与基础移动平均线,实现趋势跟踪与趋势反转交易。当价格与指标同向时,采取趋势跟踪;当价格与指标异向时,采取逆转交易。

策略原理

该策略主要基于三个自定义指标:

  1. 多空指标(Trend):计算价格与超买超卖通道的关系,判断多头和空头趋势,返回 1、0、-1 三种状态。

  2. 超买超卖通道(Tsl):参考ATR计算上轨和下轨,价格突破上轨视为超买,突破下轨视为超卖。

  3. 基础移动平均线(MA):计算20周期的收盘价简单移动平均线。

具体来说,策略根据多空指标值判断价格处于多头、震荡还是空头状态。当多空指标为1时,代表处于多头状态;当多空指标为-1时,代表空头状态。这时如果价格与指标方向一致,则采取趋势跟踪策略,在点位符合情况下做多做空。如果价格与指标方向相反,如指标显示多头而价格下跌突破下轨,则采取逆转策略,做空截获利润。

此外,价格突破移动平均线也作为辅助信号指导交易方向。价格上穿平均线时进多,下穿平均线时进空。

具体的多头交易策略如下:

  1. 多空指标 > 0,价格上涨突破上轨,属于趋势追踪情况,做多。

  2. 多空指标 < 0,价格下跌突破下轨,属于趋势反转情况,做空。

  3. 收盘价 > 开盘价 > 中枢点位,视为突破中枢做多机会,做多。

  4. 收盘价突破上轨,且收盘价 > 移动平均线,做多。

空头交易策略如下:

  1. 多空指标 < 0,价格下跌突破下轨,属于趋势追踪情况,做空。

  2. 多空指标 > 0,价格上涨突破上轨,属于趋势反转情况,做多。

  3. 开盘价 > 收盘价 < 中枢点位,视为突破中枢做空机会,做空。

  4. 收盘价突破下轨,且收盘价 < 移动平均线,做空。

平仓策略比较简单,通过价格重新突破超买超卖通道止损。

优势分析

该策略具有以下优势:

  1. 多空指标可以准确判断市场走势,是策略的核心指标。

  2. 超买超卖通道与指标配合,可以发现潜在的反转机会。

  3. 基础移动平均线可作为辅助过滤信号,避免假突破。

  4. 中枢点位结合多空指标,形成高概率交易点位。

  5. 兼具趋势跟踪与逆转交易能力,收益机会更丰富。

  6. 超买超卖通道止损清晰简洁,有利于风险控制。

风险分析

该策略也存在以下风险:

  1. 多空指标可能发出错误信号,需要组合其他指标过滤。

  2. 突破交易容易被套,需要严格止损。

  3. 移动平均线周期设置不当,可能错过趋势或产生假信号。

  4. 中枢点位需要回测验证概率可靠性。

  5. 超买超卖通道需要优化参数,以适应不同品种。

  6. 指标参数不匹配,可能导致交易频繁。

针对这些风险,可以采取以下措施:

  1. 结合其他指标如K线、成交量验证多空指标信号。

  2. 严格遵守超买超卖通道止损策略,快速止损。

  3. 测试不同移动平均线周期参数,找到最佳参数。

  4. 充分回测验证中枢点位策略概率。

  5. 优化通道参数,找到各品种最佳参数组合。

  6. 调整指标参数,使整体系统稳定运行。

优化方向

该策略还可以从以下几个方面进行优化:

  1. 增加机器学习算法,利用大数据训练多空指标。可以提高指标准确性,减少错误信号。

  2. 增加自适应通道,根据市场波动率自动调整通道参数。可以提高突破的准确率。

  3. 利用深度学习提取更多变化指标,组成指标集优化 entry 和 exit 策略。

  4. 增加高级止损算法,能够跟踪趋势止损。避免被反转止损。

  5. 进行参数优化和组合测试,提高整体策略稳定性。

  6. 添加资金管理模块,使风险控制更科学化。

总结

本策略通过多空指标判断市场结构,以及通道、移动平均线生成交易信号,实现了趋势跟踪与趋势反转的有机结合。具有指标效果好、交易机会丰富、止损清晰的优点。同时也存在一定的风险,需要进一步优化以提高稳定性。整体来说,该策略充分融合了趋势交易和反转交易的思想,值得进一步研究与应用。

||

Overview

The core idea of this strategy is to combine the dual thrust indicator with a basic moving average line to implement trend following and trend reversal trading. When the price is in the same direction as the indicator, follow the trend; when the price is in the opposite direction of the indicator, take reversal trades.

Strategy Logic

The strategy is mainly based on three custom indicators:

  1. Dual Thrust Indicator (Trend): Calculates the relationship between the price and the overbought/oversold channel to determine the bullish and bearish trends, returning 1, 0, -1 three states.

  2. Overbought/Oversold Channel (Tsl): Calculates the upper and lower rails with reference to ATR. Breaking through the upper rail is regarded as overbought, and breaking through the lower rail is regarded as oversold.

  3. Basic Moving Average Line (MA): Calculates the 20-period simple moving average of the closing price.

Specifically, the strategy judges whether the price is in bullish, sideways or bearish state according to the dual thrust indicator value. When the dual thrust indicator is 1, it means a bullish state; when the dual thrust indicator is -1, it means a bearish state. At this point, if the price is in the same direction as the indicator, a trend following strategy is adopted, going long or short at the right spot. If the price is in the opposite direction of the indicator, such as the indicator showing bullish while the price breaking through the lower rail, a reversal strategy is adopted by going short to capture profits.

In addition, the breaking through of price on moving average line also serves as an auxiliary signal to guide the trading direction. Go long when the price breaks through the average line upwards, and go short when the price breaks through the average line downwards.

The specific long trade strategies are as follows:

  1. Dual thrust indicator > 0, price rises to break through the upper rail, which belongs to trend following, go long.

  2. Dual thrust indicator < 0, price falls to break through the lower rail, which belongs to trend reversal, go short.

  3. Closing price > Opening price > Pivot level, regarded as breaking the pivot to go long, go long.

  4. Closing price breaks through upper rail, and closing price > Moving average line, go long.

The short trade strategies are as follows:

  1. Dual thrust indicator < 0, price falls to break through the lower rail, which belongs to trend following, go short.

  2. Dual thrust indicator > 0, price rises to break through the upper rail, which belongs to trend reversal, go long.

  3. Opening price > Closing price < Pivot level, regarded as breaking the pivot to go short, go short.

  4. Closing price breaks through lower rail, and closing price < Moving average line, go short.

The exit strategy is simple by stopping loss when price breaks through the overbought/oversold channel again.

Advantage Analysis

The strategy has the following advantages:

  1. The dual thrust indicator can accurately determine the market trend and is the core indicator of the strategy.

  2. The overbought/oversold channel combined with the indicator can discover potential reversal opportunities.

  3. The basic moving average line can serve as an auxiliary filtering signal to avoid false breakouts.

  4. The pivot point combined with the dual thrust indicator forms high-probability trading spots.

  5. It has both trend following and reversal trading capabilities for more profit opportunities.

  6. The overbought/oversold channel stop loss is simple and clear, which is beneficial for risk control.

Risk Analysis

The strategy also has the following risks:

  1. The dual thrust indicator may give out wrong signals, and needs to be filtered with other indicators.

  2. Breakout trading is prone to being trapped, so strict stop loss is required.

  3. Improper moving average period setting may miss trends or generate false signals.

  4. The pivot points need backtesting to verify probability reliability.

  5. The overbought/oversold channel needs optimization of parameters to adapt to different products.

  6. Mismatch of indicator parameters may lead to frequent trading.

To address these risks, the following measures can be taken:

  1. Combine other indicators like K-line, volume to verify dual thrust indicator signals.

  2. Strictly follow the overbought/oversold channel stop loss strategy for quick stop loss.

  3. Test different moving average period parameters to find the optimal one.

  4. Fully backtest the pivot point strategy probability.

  5. Optimize parameters to find the optimal combination for each product.

  6. Adjust indicator parameters to keep the overall system running smoothly.

Optimization Directions

The strategy can also be optimized in the following aspects:

  1. Increase machine learning algorithms to train the dual thrust indicator with big data. This can improve accuracy and reduce false signals.

  2. Add adaptive channels to automatically adjust channel parameters based on market volatility. This can improve breakout accuracy.

  3. Use deep learning to extract more changeable indicators to optimize entry and exit strategies.

  4. Add advanced stop loss algorithms that can track trends for stop loss, avoiding being stopped out by reversals.

  5. Conduct parameter optimization and combination tests to improve overall strategy stability.

  6. Add risk management modules for more scientific risk control.

Summary

The strategy combines trend following and trend reversal organically by judging market structure with the dual thrust indicator and generating trading signals with channels and moving average lines. It has the advantages of good indicator effectiveness, abundant trading opportunities, and clear stop loss. At the same time, it also has certain risks that need further optimization to improve stability. Overall, the strategy fully integrates the ideas of trend trading and reversal trading, and is worth further research and application.

[/trans]

Strategy Arguments

Argument Default Description
v_input_1 0 Pivot Type: Daily
v_input_2 true Plot PP
v_input_3 true Plot S1 and R1
v_input_4 true Plot S2 and R2
v_input_5 true Plot S3 and R3
v_input_6 true Plot S3 and R3
v_input_7 0800-1600 Trading Session
v_input_8 false Highlight Session?
v_input_9 false Show Labels?
v_input_10 2 Factor
v_input_11 10 Pd

Source (PineScript)

/*backtest
start: 2023-08-25 00:00:00
end: 2023-09-24 00:00:00
period: 2h
basePeriod: 15m
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/
// © amysojexson

//@version=3
strategy(title="Pivots strategy", overlay=true)

// Input settings
// Create a pull-down menu for the pivot type
pivotType = input(title="Pivot Type",
     options=["Daily", "Intraday", "Weekly"], defval="Daily")

// Make toggles for pivot level options
plotPP   = input(title="Plot PP", type=bool, defval=true)
plotS1R1 = input(title="Plot S1 and R1", type=bool, defval=true)
plotS2R2 = input(title="Plot S2 and R2", type=bool, defval=true)
plotS3R3 = input(title="Plot S3 and R3", type=bool, defval=true)
plotTCBC = input(title="Plot S3 and R3", type=bool, defval=true)
// Configure session options
sessRange = input(title="Trading Session",  defval="0800-1600")
showSess  = input(title="Highlight Session?", type=bool, defval=false)

// Enable or disable pivot labels
showLabels = input(title="Show Labels?", type=bool, defval=false)

// Step 2. Calculate indicator values
// Create a function to fetch daily and weekly data
GetData(res, data) =>
    security(syminfo.tickerid, res, data[1],
         lookahead=barmerge.lookahead_on)

// Fetch daily and weekly price data
dailyHigh  = GetData("D", high)
dailyLow   = GetData("D", low)
dailyClose = GetData("D", close)

weeklyHigh  = GetData("W", high)
weeklyLow   = GetData("W", low)
weeklyClose = GetData("W", close)

// Determine session pivot data
// First see how the price bar relates to
// the session time range
inSession = not na(time(timeframe.period, sessRange)[1])
sessStart = inSession and not inSession[1]
sessEnd   = not inSession and inSession[1]

// Determine session price data
sessHigh  = 0.0
sessLow   = 0.0
sessClose = 0.0

sessHigh := sessStart ? high :
     inSession ? max(high, sessHigh[1]) : na
sessLow := sessStart ? low :
     inSession ? min(low, sessLow[1]) : na
sessClose := sessEnd ? close[1] : na

// Compute high, low, close from previous intra-day session
highPrevSess  = 0.0
lowPrevSess   = 0.0
closePrevSess = 0.0

highPrevSess  := sessEnd ? fixnan(sessHigh) : highPrevSess[1]
lowPrevSess   := sessEnd ? fixnan(sessLow) : lowPrevSess[1]
closePrevSess := sessEnd ? fixnan(sessClose) : closePrevSess[1]

// Now figure out which kind of price data
// to use for the pivot calculation
theHigh = if (pivotType == "Daily")
    dailyHigh
else
    if (pivotType == "Intraday")
        highPrevSess
    else
        weeklyHigh

theLow = if (pivotType == "Daily")
    dailyLow
else
    if (pivotType == "Intraday")
        lowPrevSess
    else
        weeklyLow

theClose = if (pivotType == "Daily")
    dailyClose
else
    if (pivotType == "Intraday")
        closePrevSess
    else
        weeklyClose

// Finally calculate the pivot levels
pp = (theHigh + theLow + theClose) / 3
bc= (theHigh + theLow)/2
tc= (pp-bc)+pp

r1 = pp+(.382*(theHigh-theLow))
s1 = pp-(.382*(theHigh-theLow))
r2 = pp +(.618*(theHigh-theLow))
s2 = pp -(.618*(theHigh-theLow))
r3 = pp +(1*(theHigh-theLow))
s3 = pp -(1*(theHigh-theLow))

// Step 3. Output indicator data
// Plot the various pivot levels
plot(series=plotS3R3 ? r3 : na, title="R3",
     style=circles, linewidth=1, color=#0023FF)
plot(series=plotS2R2 ? r2 : na, title="R2",
     style=circles, linewidth=1, color=#1E90FF)
plot(series=plotS1R1 ? r1 : na, title="R1",
     style=circles, linewidth=1, color=#09E0F3)

plot(series=plotTCBC ? tc : na, title="TC",
     style=circles, linewidth=.75, color=#FF00D1)
plot(series=plotPP ? pp : na, title="PP",
     style=circles, linewidth=1, color=#000000)
plot(series=plotTCBC ? bc : na, title="BC",
     style=circles, linewidth=.75, color=#FF00D1)

plot(series=plotS1R1 ? s1 : na, title="S1",
     style=circles, linewidth=1, color=#09E0F3)
plot(series=plotS2R2 ? s2 : na, title="S2",
     style=circles, linewidth=1, color=#1E90FF)
plot(series=plotS3R3 ? s3 : na, title="S3",
     style=circles, linewidth=1, color=#0023FF)

// Display the pivot names on the chart, if applicable
newPivots = (showLabels == false) ? false :
     (pivotType == "Intraday") ? sessEnd :
     (pivotType == "Daily") ? dayofmonth != dayofmonth[1] :
     dayofweek == monday and dayofmonth != dayofmonth[1]

plotchar(series=newPivots and plotS3R3 ? r3 : na,
     char='', text="R3", offset=1,
     location=location.absolute,
     color=#0023FF, title="R3 label")

plotchar(series=newPivots and plotS2R2 ? r2 : na,
     char='', text="R2", offset=1,
     location=location.absolute,
     color=#1E90FF, title="R2 label")

plotchar(series=newPivots and plotS1R1 ? r1 : na,
     char='', text="R1", offset=1,
     location=location.absolute,
     color=#09E0F3, title="R1 label")

plotchar(series=newPivots and plotTCBC ? r1 : na,
     char='', text="TC", offset=1,
     location=location.absolute,
     color=#FF00D1, title="TC label")
     
plotchar(series=newPivots and plotTCBC ? r1 : na,
     char='', text="BC", offset=1,
     location=location.absolute,
     color=#FF00D1, title="BC label")

plotchar(series=newPivots and plotS1R1 ? s1 : na,
     char='', text="S1", offset=1,
     location=location.absolute,
     color=#09E0F3, title="S1 label")

plotchar(series=newPivots and plotS2R2 ? s2 : na,
     char='', text="S2", offset=1,
     location=location.absolute,
     color=#1E90FF, title="S2 label")

plotchar(series=newPivots and plotS3R3 ? s3 : na,
     char='', text="S3", offset=1,
     location=location.absolute,
     color=#0023FF, title="S3 label")

// Highlight the intra-day price data session on the chart
bgcolor(color=showSess and inSession and (pivotType == "Intraday") ?
     orange : na, transp=95)

// Step 4. Create indicator alerts
alertcondition(condition=cross(close, s3),
     title="Pivot S3 Cross",
     message="Prices crossed Pivot S3 level")

alertcondition(condition=cross(close, s2),
     title="Pivot S2 Cross",
     message="Prices crossed Pivot S2 level")

alertcondition(condition=cross(close, s1),
     title="Pivot S1 Cross",
     message="Prices crossed Pivot S1 level")
     
alertcondition(condition=cross(close, tc),
     title="Pivot TC Cross",
     message="Prices crossed Pivot TC level")

alertcondition(condition=cross(close, pp),
     title="Pivot PP Cross",
     message="Prices crossed the main Pivot Point level")
     
alertcondition(condition=cross(close, bc),
     title="Pivot BC Cross",
     message="Prices crossed Pivot BC level")

alertcondition(condition=cross(close, r1),
     title="Pivot R1 Cross",
     message="Prices crossed Pivot R1 level")

alertcondition(condition=cross(close, r2),
     title="Pivot R2 Cross",
     message="Prices crossed Pivot R2 level")

alertcondition(condition=cross(close, r3),
     title="Pivot R3 Cross",
     message="Prices crossed Pivot R3 level")
    
MA = sma(close, 20)
plot(MA, color=red)

Factor				= input(2, type=float)
Pd					= input(10, minval=1,maxval = 100)
Up					= hl2-(Factor*atr(Pd))
Dn					= hl2+(Factor*atr(Pd))
TrendUp				= 0.0
TrendUp				:= close[1]>TrendUp[1]? max(Up,TrendUp[1]) : Up
TrendDown			= 0.0
TrendDown			:= close[1]<TrendDown[1]? min(Dn,TrendDown[1]) : Dn
Trend				= 0.0
Trend 				:= close > TrendDown[1] ? 1: close< TrendUp[1]? -1: nz(Trend[1],1)
Tsl 				= Trend==1? TrendUp: TrendDown

plot(Tsl, color=blue)

if close>open
    if open<pp
        if close>pp
            if close>MA
                strategy.entry("long", true) 
if close<open
    if open>pp
        if close<pp
            if close<MA
                strategy.entry("short", false) 
                
strategy.close("long", when = open<Tsl)
strategy.close("short", when = open>Tsl)

Detail

https://www.fmz.com/strategy/427809

Last Modified

2023-09-25 17:34:46

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