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。
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:
计算价格变动值xMom = close - close[1]
计算价格变动绝对值 xMomAbs = abs(close - close[1])
对价格变动值进行滤波,如果小于阈值Filter则记为0
对价格变动绝对值进行滤波,如果小于阈值Filter则记为0
计算最后n天内滤波后的价格变动值之和nSum
计算最后n天内滤波后的价格变动绝对值之和nAbsSum
计算动量值:nRes = 100 * nSum / nAbsSum
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。
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:
123反转策略可以在转折点捕捉反转趋势,避免被套。
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。
两者结合可以验证信号,降低错误交易概率,提高胜率。
该策略主要存在以下风险:
单一时间周期分析,可能错过更大级别的趋势。
参数设置过于死板,不能适应市场变化。
双重验证可能错过部分机会,降低盈利空间。
质量不高的交易信号也会被验证通过,导致亏损。
该策略可以从以下几个方面进行优化:
增加多时间周期验证,避免被套。
设置自适应参数,根据市场调整指标参数。
优化过滤阈值,减少信号误报率。
增加止损策略,控制单笔亏损。
调整仓位管理,优化资金利用效率。
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。
/*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 )