Name

动量突破优化趋势跟踪策略Momentum-Breakout-Optimization

Author

ChaoZhang

Strategy Description

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概述

动量突破优化策略是基于动量指标实现交易信号生成和止损止盈设置的趋势跟踪策略。该策略通过计算价格与移动均线的交叉情况判断市场趋势方向,结合ATR和LinReg通道构建动态止损机制。同时,策略还利用CMO指标识别超买超卖情况,实现更好的入场。

策略原理

1. 计算价格的ZLEMA移动平均线作为判断趋势方向的技术指标
1. 根据ATR计算长仓止损价位和短仓止损价位
1. 计算CMO指标判断超买超卖区间,与移动平均线结合作为进场信号
1. 根据ATR和移动平均线的突破构建三组交易信号
- 移动均线和止损价位的交叉信号
- 价格和止损价位的交叉信号
- 价格和移动平均线的交叉信号
1. 通过参数设置控制不同类型信号的启用
1. 设置风险系数和头寸控制进行风险管理

整个策略通过多种指标的组合应用实现稳定的趋势跟踪和自动止损,既保证了足够的交易机会,又控制了交易风险。

优势分析

多种指标组合使用

策略运用了移动平均线、ATR、CMO等多种指标的组合,指标之间可以形成有效的互补,识别趋势方向和超买超卖区域的判断更加准确可靠。

动态 trailing stop

基于ATR的动态止损机制,可以根据市场波动度灵活调整止损位置,有效控制单笔损失。

完善的风险管理

策略提供了头寸控制和风险系数设置,可以预先定义最大损失资金比例,避免资金大幅波动。

丰富的交易信号

策略总共提供三套交易信号,可以通过选择启用不同类型的信号组合,获得更好的回测结果。

风险分析

交易频率过高

启用全部信号组合时,可能会出现交易频率过高的情况。可以通过只选择部分信号组合使用来避免。

指标参数敏感

多种指标组合使用使得参数选择更加复杂,对参数设置更加敏感,需要精心测试参数的最优组合。

突破信号回撤率大

仅基于价格和止损价位交叉的交易信号,其止损范围较大,可能导致较大的单笔损失和回撤。可以选择移动均线信号与其组合使用。

优化方向

测试不同参数组合

优化移动平均线类型、长度参数;优化ATR周期参数;优化CMO参数。找到参数的最优匹配。

优化信号使用策略

测试只使用移动平均线信号、止损价位信号以及组合信号,分析最佳使用策略。

测试在不同品种的表现

在股指、外汇、商品品种进行回测,分析策略对市场类型的适应性。

总结

本策略综合运用多种指标识别趋势方向、构建止损机制、发现超买超卖机会。通过参数优化、信号组合选择等方法进行调整,可以获得较好的回撤指标。整体来说,该策略体系完整,可靠性较高,值得进一步在实盘中测试优化。

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Overview

The Momentum Breakout Optimization strategy is a trend following strategy that generates trading signals and sets stop loss/take profit based on momentum indicators. It judges the market trend direction by calculating the crossovers between price and moving average, and builds a dynamic stop loss mechanism using ATR and LinReg Channel. Meanwhile, the strategy also identifies overbought/oversold levels using the CMO indicator for better entry prices.

Strategy Logic

1. Calculate the ZLEMA moving average of price as the technical indicator for trend direction
1. Calculate long stop loss and short stop loss based on ATR
1. Calculate the CMO indicator to identify overbought/oversold zones, combined with moving average as entry signals
1. Generate 3 sets of trading signals based on ATR, moving average and price breakouts
- Crossovers between moving average and stop loss levels
- Crossovers between price and stop loss levels
- Crossovers between price and moving average
1. Enable/Disable different signal combinations through parameter settings
1. Set risk percentage and position sizing for risk management

The overall strategy combines multiple indicators for steady trend following and automated stop loss, ensuring adequate trading opportunities while controlling trading risks.

Advantage Analysis

Combination of Multiple Indicators

The strategy utilizes a combination of indicators including moving average, ATR, CMO etc. The indicators complement each other and provide more reliable judgements on trend direction and overbought/oversold zones.

Dynamic Trailing Stop

The ATR-based dynamic stop loss can flexibly adjust stop loss levels based on market volatility, effectively controlling single trade loss.

Comprehensive Risk Management

The strategy provides position sizing and risk percentage settings, which defines the maximum percentage of capital at risk to prevent severe fund fluctuations.

Abundant Trading Signals

The strategy offers 3 sets of trading signals. By enabling different signal combinations, better backtest results can be obtained.

Risk Analysis

High Trading Frequency

There could be overly frequent trading when all signal combinations are enabled. This can be avoided by only using some of the signals.

Sensitive to Parameter Settings

The multiparameter model makes parameter optimization more complex and sensitive. The optimal parameter combination requires extensive testing.

Higher Drawdown for Breakout Signals

For the pure price/stop loss breakout signals, the stop loss range is wider, which may lead to larger single trade loss and drawdown. Combining with moving average signals is recommended.

Optimization Directions

Test Different Parameter Combinations

Optimize parameters like moving average type/length, ATR period, CMO period to find the optimal match.

Optimize Signal Usage Strategies

Test the performance of using only moving average signals, stop loss signals, or combination signals to find the best usage strategy.

Test Performance Across Different Products

Backtest the strategy across index, forex, commodity products to analyze adaptivity across different market types.

Conclusion

This strategy integrates multiple indicators for trend identification, stop loss construction, overbought/oversold detection. By tuning parameters and signal combinations, satisfactory risk metrics can be achieved. The overall system is comprehensive and reliable for further live testing and optimization.

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Strategy Arguments

Argument	Default	Description
v_input_1_hl2	0	Source: hl2
v_input_2	10	ATR Length
v_input_3	3	ATR Multiplier
v_input_4	0	Moving Average Type: ZLEMA
v_input_5	10	Moving Average Length
v_input_6	true	Change ATR Calculation Method ?
v_input_7	true	Show Moving Average?
v_input_8	true	Show Crossing Signals?
v_input_9	false	Show Price/Pmax Crossing Signals?
v_input_10	true	Highlighter On/Off ?
v_input_11	true	Use Position Sizing?
v_input_12	0.5	Risk %
v_input_13	true	Start Date
v_input_14	true	Start Month
v_input_15	2019	Start Year
v_input_16	true	End Date
v_input_17	12	End Month
v_input_18	2021	End Year

Source (PineScript)

/*backtest
start: 2024-01-09 00:00:00
end: 2024-01-16 00:00:00
period: 5m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © KivancOzbilgic
//developer: @KivancOzbilgic
//author: @KivancOzbilgic

strategy(title="Profit Maximizer PMax", overlay=true,
     pyramiding=0, initial_capital=1000,
     commission_type=strategy.commission.cash_per_order,
     commission_value=0.025, slippage=2)


src = input(hl2, title="Source")
Periods = input(title="ATR Length", type=input.integer, defval=10)
Multiplier = input(title="ATR Multiplier", type=input.float, step=0.1, defval=3.0)
mav = input(title="Moving Average Type", defval="ZLEMA", options=["SMA", "EMA", "WMA", "TMA", "VAR", "WWMA", "ZLEMA", "TSF"])
length =input(10, "Moving Average Length", minval=1)
changeATR= input(title="Change ATR Calculation Method ?", type=input.bool, defval=true)
showsupport = input(title="Show Moving Average?", type=input.bool, defval=true)
showsignalsk = input(title="Show Crossing Signals?", type=input.bool, defval=true)
showsignalsc = input(title="Show Price/Pmax Crossing Signals?", type=input.bool, defval=false)
highlighting = input(title="Highlighter On/Off ?", type=input.bool, defval=true)

usePosSize = input(title="Use Position Sizing?", type=input.bool, defval=true)
riskPerc   = input(title="Risk %", type=input.float, defval=0.5, step=0.25)

// Make input options that configure backtest date range
startDate = input(title="Start Date", type=input.integer,
     defval=1, minval=1, maxval=31)
startMonth = input(title="Start Month", type=input.integer,
     defval=1, minval=1, maxval=12)
startYear = input(title="Start Year", type=input.integer,
     defval=2019, minval=1800, maxval=2100)

endDate = input(title="End Date", type=input.integer,
     defval=1, minval=1, maxval=31)
endMonth = input(title="End Month", type=input.integer,
     defval=12, minval=1, maxval=12)
endYear = input(title="End Year", type=input.integer,
     defval=2021, minval=1800, maxval=2100)
     
// Look if the close time of the current bar
// falls inside the date range
inDateRange = true

atr2 = sma(tr, Periods)
atr= changeATR ? atr(Periods) : atr2
valpha=2/(length+1)
vud1=src>src[1] ? src-src[1] : 0
vdd1=src<src[1] ? src[1]-src : 0
vUD=sum(vud1,9)
vDD=sum(vdd1,9)
vCMO=nz((vUD-vDD)/(vUD+vDD))
VAR=0.0
VAR:=nz(valpha*abs(vCMO)*src)+(1-valpha*abs(vCMO))*nz(VAR[1])
wwalpha = 1/ length
WWMA = 0.0
WWMA := wwalpha*src + (1-wwalpha)*nz(WWMA[1])
zxLag = length/2==round(length/2) ? length/2 : (length - 1) / 2
zxEMAData = (src + (src - src[zxLag]))
ZLEMA = ema(zxEMAData, length)
lrc = linreg(src, length, 0)
lrc1 = linreg(src,length,1)
lrs = (lrc-lrc1)
TSF = linreg(src, length, 0)+lrs
getMA(src, length) =>
    ma = 0.0
    if mav == "SMA"
        ma := sma(src, length)
        ma

    if mav == "EMA"
        ma := ema(src, length)
        ma

    if mav == "WMA"
        ma := wma(src, length)
        ma

    if mav == "TMA"
        ma := sma(sma(src, ceil(length / 2)), floor(length / 2) + 1)
        ma

    if mav == "VAR"
        ma := VAR
        ma

    if mav == "WWMA"
        ma := WWMA
        ma

    if mav == "ZLEMA"
        ma := ZLEMA
        ma

    if mav == "TSF"
        ma := TSF
        ma
    ma
    
MAvg=getMA(src, length)
longStop = MAvg - Multiplier*atr
longStopPrev = nz(longStop[1], longStop)
longStop := MAvg > longStopPrev ? max(longStop, longStopPrev) : longStop
shortStop = MAvg + Multiplier*atr
shortStopPrev = nz(shortStop[1], shortStop)
shortStop := MAvg < shortStopPrev ? min(shortStop, shortStopPrev) : shortStop
dir = 1
dir := nz(dir[1], dir)
dir := dir == -1 and MAvg > shortStopPrev ? 1 : dir == 1 and MAvg < longStopPrev ? -1 : dir
PMax = dir==1 ? longStop: shortStop
plot(showsupport ? MAvg : na, color=#0585E1, linewidth=2, title="Moving Avg Line")
pALL=plot(PMax, color=color.red, linewidth=2, title="PMax", transp=0)

alertcondition(cross(MAvg, PMax), title="Cross Alert", message="PMax - Moving Avg Crossing!")
alertcondition(crossover(MAvg, PMax), title="Crossover Alarm", message="Moving Avg BUY SIGNAL!")
alertcondition(crossunder(MAvg, PMax), title="Crossunder Alarm", message="Moving Avg SELL SIGNAL!")
alertcondition(cross(src, PMax), title="Price Cross Alert", message="PMax - Price Crossing!")
alertcondition(crossover(src, PMax), title="Price Crossover Alarm", message="PRICE OVER PMax - BUY SIGNAL!")
alertcondition(crossunder(src, PMax), title="Price Crossunder Alarm", message="PRICE UNDER PMax - SELL SIGNAL!")


// Calculate position size
riskEquity  = (riskPerc / 100) * strategy.equity
atrCurrency = (atr(20) * syminfo.pointvalue)
posSize     = usePosSize ? floor(riskEquity / atrCurrency) : 1

//Long
buySignalk = crossover(MAvg, PMax)
plotshape(buySignalk and showsignalsk ? PMax*0.995 : na, title="Buy", text="BuyL", location=location.absolute, style=shape.labelup, size=size.tiny, color=color.green, textcolor=color.white, transp=0)


if(buySignalk and showsignalsk and inDateRange)
    strategy.entry(id="buySignalk", long=true, qty=posSize)
    
sellSignallk = crossunder(MAvg, PMax)
plotshape(sellSignallk and showsignalsk ? PMax*1.005 : na, title="Sell", text="SellL", location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.red, textcolor=color.white, transp=0)

if(sellSignallk and showsignalsk and inDateRange)
    strategy.order(id="sellSignallk", long=false, qty=strategy.position_size)
    
//Short
buySignalc = crossover(src, PMax)
plotshape(buySignalc and showsignalsc ? PMax*0.995 : na, title="Buy", text="BuyS", location=location.absolute, style=shape.labelup, size=size.tiny, color=#0F18BF, textcolor=color.white, transp=0)

if(buySignalc and showsignalsc and inDateRange)
    strategy.entry(id="BuyS", long=false, qty=posSize)

sellSignallc = crossunder(src, PMax)
plotshape(sellSignallc and showsignalsc ? PMax*1.005 : na, title="Sell", text="SellS", location=location.absolute, style=shape.labeldown, size=size.tiny, color=#0F18BF, textcolor=color.white, transp=0)

if(sellSignallc and showsignalsc and inDateRange)
    strategy.order(id="SellS", long=true, qty=abs(strategy.position_size))

mPlot = plot(ohlc4, title="", style=plot.style_circles, linewidth=0,display=display.none)

longFillColor = highlighting ? (MAvg>PMax ? color.green : na) : na
shortFillColor = highlighting ? (MAvg<PMax ? color.red : na) : na

fill(mPlot, pALL, title="UpTrend Highligter", color=longFillColor)
fill(mPlot, pALL, title="DownTrend Highligter", color=shortFillColor)

// Exit open market position when date range ends
if (not inDateRange)
    strategy.close_all()

Detail

https://www.fmz.com/strategy/439094

Last Modified

2024-01-17 16:44:30

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动量突破优化趋势跟踪策略Momentum-Breakout-Optimization.md

动量突破优化趋势跟踪策略Momentum-Breakout-Optimization.md

概述

策略原理

优势分析

多种指标组合使用

动态 trailing stop

完善的风险管理

丰富的交易信号

风险分析

交易频率过高

指标参数敏感

突破信号回撤率大

优化方向

测试不同参数组合

优化信号使用策略

测试在不同品种的表现

总结

Overview

Strategy Logic

Advantage Analysis

Combination of Multiple Indicators

Dynamic Trailing Stop

Comprehensive Risk Management

Abundant Trading Signals

Risk Analysis

High Trading Frequency

Sensitive to Parameter Settings

Higher Drawdown for Breakout Signals

Optimization Directions

Test Different Parameter Combinations

Optimize Signal Usage Strategies

Test Performance Across Different Products

Conclusion

Files

动量突破优化趋势跟踪策略Momentum-Breakout-Optimization.md

Latest commit

History

动量突破优化趋势跟踪策略Momentum-Breakout-Optimization.md

File metadata and controls

概述

策略原理

优势分析

多种指标组合使用

动态 trailing stop

完善的风险管理

丰富的交易信号

风险分析

交易频率过高

指标参数敏感

突破信号回撤率大

优化方向

测试不同参数组合

优化信号使用策略

测试在不同品种的表现

总结

Overview

Strategy Logic

Advantage Analysis

Combination of Multiple Indicators

Dynamic Trailing Stop

Comprehensive Risk Management

Abundant Trading Signals

Risk Analysis

High Trading Frequency

Sensitive to Parameter Settings

Higher Drawdown for Breakout Signals

Optimization Directions

Test Different Parameter Combinations

Optimize Signal Usage Strategies

Test Performance Across Different Products

Conclusion