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strategies/双动量反转策略Momentum-Reversal-Trading-Strategy.md
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Name

双动量反转策略Momentum-Reversal-Trading-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

双动量反转策略融合了反转策略和动量策略的优势,利用两类指标的信号进行组合,在突破点进行反向操作,以期获利。

策略原理

该策略由两部分组成:

第一部分是123反转策略。它的原理是:

  • 当收盘价连续2天高于前一日收盘价,并且9日平均慢速K线低于50时,做多;

  • 当收盘价连续2天低于前一日收盘价,并且9日平均快速K线高于50时,做空。

第二部分是滤波动量指标。该指标的计算步骤为:

  1. 计算价格变动值xMom = close - close[1]

  2. 计算价格变动绝对值 xMomAbs = abs(close - close[1])

  3. 对价格变动值进行滤波,如果小于阈值Filter则记为0

  4. 对价格变动绝对值进行滤波,如果小于阈值Filter则记为0

  5. 计算最后n天内滤波后的价格变动值之和nSum

  6. 计算最后n天内滤波后的价格变动绝对值之和nAbsSum

  7. 计算动量值:nRes = 100 * nSum / nAbsSum

  8. 判断动量值与界限TopBand和LowBand的关系,输出交易信号

该指标的特点是过滤掉小幅波动,只提取大趋势中的动量信息。

最后,当两类指标信号一致时产生交易信号,做多或做空。

优势分析

该策略融合两种不同类型指标的优势,可以提高信号的质量:

  1. 123反转策略可以在转折点捕捉反转趋势,避免被套。

  2. 滤波动量指标只关注大幅波动,可以过滤噪音,抓住主要趋势。

  3. 两者结合可以验证信号,降低错误交易概率,提高胜率。

风险分析

该策略主要存在以下风险:

  1. 单一时间周期分析,可能错过更大级别的趋势。

  2. 参数设置过于死板,不能适应市场变化。

  3. 双重验证可能错过部分机会,降低盈利空间。

  4. 质量不高的交易信号也会被验证通过,导致亏损。

优化方向

该策略可以从以下几个方面进行优化:

  1. 增加多时间周期验证,避免被套。

  2. 设置自适应参数,根据市场调整指标参数。

  3. 优化过滤阈值,减少信号误报率。

  4. 增加止损策略,控制单笔亏损。

  5. 调整仓位管理,优化资金利用效率。

总结

综上所述,双动量反转策略结合反转策略和滤波动量指标的优点,在一定程度上可以提高信号质量和盈利效率。但该策略也存在一些问题,如忽略更大级别趋势、参数死板、信号误报等风险。通过多时间框架验证、自适应参数、设置止损等方法可以对该策略进行优化,减少风险,提高稳定盈利能力。

||

Overview

The Momentum Reversal trading strategy combines the advantages of reversal and momentum strategies by utilizing signals from both types of indicators to take counter-directional trades at turning points for profit.

Strategy Logic

The strategy consists of two parts:

The first part is the 123 reversal strategy. Its logic is:

  • Go long when the close price is higher than the previous close for 2 consecutive days and the 9-day slow Stochastic Oscillator is below 50.

  • Go short when the close price is lower than the previous close for 2 consecutive days and the 9-day fast Stochastic Oscillator is above 50.

The second part is the filtered momentum indicator. Its calculation steps are:

  1. Calculate price change xMom = close - close[1]

  2. Calculate absolute price change xMomAbs = abs(close - close[1])

  3. Filter xMom if less than threshold Filter to 0

  4. Filter xMomAbs if less than threshold Filter to 0

  5. Calculate sum of filtered xMom over n days nSum

  6. Calculate sum of filtered xMomAbs over n days nAbsSum

  7. Calculate momentum value: nRes = 100 * nSum / nAbsSum

  8. Generate signal based on nRes and bands TopBand, LowBand

This indicator filters out small fluctuations and extracts momentum information from major trends.

Finally, trading signals are generated when the signals from both indicators align for long or short.

Advantage Analysis

The strategy combines the advantages of two different types of indicators to improve signal quality:

  1. The 123 reversal strategy catches reversal trends at turning points, avoiding being trapped.

  2. The filtered momentum indicator focuses on large moves only, filtering out noise and catching major trends.

  3. Combining them verifies signals and reduces incorrect trades, improving win rate.

Risk Analysis

The main risks of this strategy include:

  1. Single timeframe analysis may miss larger trend.

  2. Static parameter settings cannot adapt to market changes.

  3. Double verification may miss some opportunities, reducing profit potential.

  4. Low quality signals may also be verified, leading to losses.

Optimization Directions

The strategy can be optimized in several aspects:

  1. Add multi-timeframe verification to avoid being trapped.

  2. Use adaptive parameters to adjust indicators based on market conditions.

  3. Optimize filter threshold to reduce false signals.

  4. Add stop loss to control single trade loss amount.

  5. Adjust position sizing to optimize capital utilization efficiency.

Conclusion

In conclusion, the Momentum Reversal strategy combines the strengths of reversal and filtered momentum strategies to improve signal quality and profitability to some extent. However, it also has some flaws like ignoring larger trends, static parameters, false signals etc. Methods like multi-timeframe validation, adaptive parameters, stop loss can optimize the strategy by reducing risks and improving steady profitability.

[/trans]

Strategy Arguments

Argument Default Description
v_input_1 14 Length
v_input_2 true KSmoothing
v_input_3 3 DLength
v_input_4 50 Level
v_input_5 9 LengthCMO
v_input_6 3 Filter
v_input_7 70 TopBand
v_input_8 -70 LowBand
v_input_9 false Trade reverse

Source (PineScript)

/*backtest
start: 2023-09-08 00:00:00
end: 2023-10-08 00:00:00
period: 1h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
////////////////////////////////////////////////////////////
//  Copyright by HPotter v1.0 25/09/2019
// This is combo strategies for get a cumulative signal. 
//
// First strategy
// This System was created from the Book "How I Tripled My Money In The 
// Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
// The strategy buys at market, if close price is higher than the previous close 
// during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. 
// The strategy sells at market, if close price is lower than the previous close price 
// during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
//
// Second strategy
// This indicator plots a CMO which ignores price changes which are less 
// than a threshold value. CMO was developed by Tushar Chande. A scientist, 
// an inventor, and a respected trading system developer, Mr. Chande developed 
// the CMO to capture what he calls "pure momentum". For more definitive 
// information on the CMO and other indicators we recommend the book The New 
// Technical Trader by Tushar Chande and Stanley Kroll.
// The CMO is closely related to, yet unique from, other momentum oriented 
// indicators such as Relative Strength Index, Stochastic, Rate-of-Change, etc. 
// It is most closely related to Welles Wilder`s RSI, yet it differs in several ways:
// - It uses data for both up days and down days in the numerator, thereby directly 
// measuring momentum;
// - The calculations are applied on unsmoothed data. Therefore, short-term extreme 
// movements in price are not hidden. Once calculated, smoothing can be applied to the 
// CMO, if desired;
// - The scale is bounded between +100 and -100, thereby allowing you to clearly see 
// changes in net momentum using the 0 level. The bounded scale also allows you to 
// conveniently compare values across different securities.
//
// WARNING:
// - For purpose educate only
// - This script to change bars colors.
////////////////////////////////////////////////////////////
Reversal123(Length, KSmoothing, DLength, Level) =>
    vFast = sma(stoch(close, high, low, Length), KSmoothing) 
    vSlow = sma(vFast, DLength)
    pos = 0.0
    pos := iff(close[2] < close[1] and close > close[1] and vFast < vSlow and vFast > Level, 1,
	         iff(close[2] > close[1] and close < close[1] and vFast > vSlow and vFast < Level, -1, nz(pos[1], 0))) 
	pos

fFilter(xSeriesSum, xSeriesV, Filter) =>
    iff(xSeriesV > Filter, xSeriesSum, 0)

CMOfilt(Length,Filter, TopBand, LowBand) =>
    pos = 0
    xMom = close - close[1]
    xMomAbs = abs(close - close[1])
    xMomFilter = fFilter(xMom, xMomAbs, Filter)
    xMomAbsFilter = fFilter(xMomAbs,xMomAbs, Filter)
    nSum = sum(xMomFilter, Length)
    nAbsSum = sum(xMomAbsFilter, Length)
    nRes =   100 * nSum / nAbsSum
    pos := iff(nRes > TopBand, 1,
	         iff(nRes < LowBand, -1, nz(pos[1], 0))) 
    pos

strategy(title="Combo Backtest 123 Reversal & CMOfilt", shorttitle="Combo", overlay = true)
Length = input(14, minval=1)
KSmoothing = input(1, minval=1)
DLength = input(3, minval=1)
Level = input(50, minval=1)
//-------------------------
LengthCMO = input(9, minval=1)
Filter = input(3, minval=1)
TopBand = input(70, minval=1)
LowBand = input(-70, maxval=-1)
reverse = input(false, title="Trade reverse")
posReversal123 = Reversal123(Length, KSmoothing, DLength, Level)
posCMOfilt = CMOfilt(LengthCMO,Filter, TopBand, LowBand)
pos = iff(posReversal123 == 1 and posCMOfilt == 1 , 1,
	   iff(posReversal123 == -1 and posCMOfilt == -1, -1, 0)) 
possig = iff(reverse and pos == 1, -1,
          iff(reverse and pos == -1 , 1, pos))	   
if (possig == 1) 
    strategy.entry("Long", strategy.long)
if (possig == -1)
    strategy.entry("Short", strategy.short)	 
if (possig == 0) 
    strategy.close_all()
barcolor(possig == -1 ? #b50404: possig == 1 ? #079605 : #0536b3 )

Detail

https://www.fmz.com/strategy/428816

Last Modified

2023-10-09 17:21:27

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