Name
动量方向发散策略Momentum-Direction-Divergence-Strategy
Author
ChaoZhang
Strategy Description
动量方向发散策略(Momentum Direction Divergence Strategy)是一个根据威廉·布劳(William Blau)在他的书《动量,方向和发散》(Momentum, Direction and Divergence)中描述的技术之一。该策略关注三个关键方面:动量、方向和发散。布劳先生是一个电气工程师,后来成为一名交易员,他深入研究了价格和动量之间的关系。在此基础上,他分析了其他oscillator的不足,并引入了一些创新技术,包括一个新的看涨oscillator的解释。在方向性问题上,他分析了ADX的复杂性,并提供了一个独特的方法来帮助定义趋势和非趋势周期。
该策略绘制了动量方向发散指数(Ergotic CSI)和其平滑线,用于过滤噪音。
代码开头定义了一个自适应动向指数(ADX)的函数fADX,它接受参数Len表示平滑周期。该函数计算真实范围(TR)的选通移动平均(RMA)作为分母,计算多头动量和空头动量的RMA作为分子,再相除得到比率,表示多头和空头的相对强度。最后通过一个公式结合多头强度和空头强度得到ADX的值。
然后是策略参数的定义。r表示ATR的平滑参数,Length表示ADX的长度,BigPointValue表示大点值,SmthLen表示平滑CSI的长度,SellZone和BuyZone表示符合条件的卖出和买入区域。reverse表示是否反转交易信号。
关键逻辑在计算CSI。首先计算真实波动幅度ATR和ADX。然后计算惩罚系数K,包含了大点值、ATR和ADX的考量。计算标准化残差nRes,它结合了ATR、ADX和收盘价的信息。最后计算CSI值,再求其平滑SMA。
根据CSI的SMA值判定交易方向。如果高于BuyZone就做多,低于SellZone就做空。绘制CSI曲线和其SMA,并用颜色标记不同的交易区间。
该策略结合了动量指标ATR和趋势指标ADX的优点,既考虑了市场波动性,也考虑了趋势程度,避免了仅使用ATR和仅使用ADX的局限性。惩罚系数K的设计巧妙地融合了这些指标与大点值的关系。
标准化残差nRes加入了对价格信息的利用,不仅关注动量趋势,也关注价格的绝对水平,这与一般oscillator不同,增强了策略的表现力。
平滑处理和区段判断为策略决策提供了清晰的交易信号,有利于实盘操作。
该策略对参数设置比较敏感,如ATR和ADX的周期长度,大点值的设定,CSI平滑参数等都会影响策略表现。需要通过大量回测确定合适的参数组合。
CSI作为一个新提出的oscillator,其有效性需要在更多不同市场中验证。如果该指标效果不佳,会影响策略profitability。
策略本身没有止损机制,直接按照CSI信号做多做空,存在一定程度上的风险,需要结合止损来运用。
可以测试不同市场中的参数组合,找到一个较为通用的组合。
可以引入一个动态ADX周期长度的机制,根据市场状态来调整ADX的参数。
可以结合其他oscillator指标来决定买卖时机,使策略更加稳健。比如底部发散,顶部背离等效果。
可以加入止损策略使整体策略更加完善。
动量方向发散策略整合了多种指标的优点,利用价格、动量、趋势多个维度设计CSI指标并进行交易。该策略参数设置灵活,表现力强,值得进一步测试和优化,可以成为量化交易的有利工具。
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The Momentum Direction Divergence Strategy is one of the techniques described by William Blau in his book "Momentum, Direction and Divergence". The strategy focuses on three key aspects: momentum, direction and divergence. Blau, who was an electrical engineer before becoming a trader, thoroughly examines the relationship between price and momentum. From this grounding, he then looks at the deficiencies in other oscillators and introduces some innovative techniques, including a fresh twist on Stochastics. On directional issues, he analyzes the intricacies of ADX and offers a unique approach to help define trending and non-trending periods.
The strategy plots Ergotic CSI and its smoothed line to filter out noise.
The code starts by defining an Adaptive Direction Index (ADX) function fADX, which takes parameter Len as the smoothing period. The function calculates the RMA of True Range (TR) as denominator, and RMA of upside momentum and downside momentum as numerator, then divides them to get ratios indicating the relative strength of upside and downside. Finally, ADX is obtained by combining upside strength and downside strength.
Then strategy parameters are defined. r is the smoothing parameter for ATR, Length is the length of ADX, BigPointValue is big point value, SmthLen is the smoothing length for CSI, SellZone and BuyZone are sell and buy zones that meet the criteria. reverse indicates whether to reverse the trading signals.
The key logic is in CSI calculation. First ATR and ADX are computed. Then the penalty coefficient K is calculated, incorporating considerations of big point value, ATR and ADX. The standardized residual nRes combines information from ATR, ADX and close price. Finally CSI value is obtained, and its SMA is calculated.
Trading direction is determined according to SMA value of CSI. Go long if above BuyZone, go short if below SellZone. Plot CSI curve and its SMA, color code different trading zones.
The strategy combines the advantages of momentum indicator ATR and trend indicator ADX, considering both market volatility and trend strength, avoiding limitations of using ATR or ADX alone. The design of penalty coefficient K cleverly integrates the relationships between these indicators and big point value.
The standardized residual nRes incorporates price information, paying attention not only to momentum trend but also absolute price level, which is different from typical oscillators, enhancing the strategy's performance.
The smoothing process and zone determination provide clear trading signals for practical trading.
The strategy is quite sensitive to parameter settings like periods of ATR and ADX, big point value etc., which can affect performance. Extensive backtests are needed to determine proper parameter combinations.
As a newly proposed oscillator, the efficacy of CSI needs validation across more diverse markets. Poor CSI performance would hurt strategy profitability.
The strategy itself does not have a stop loss mechanism, just directly long or short per CSI signals. There are risks that need to be mitigated by incorporating stop loss.
Test parameter combinations across different markets to find relatively universal settings.
Introduce a dynamic ADX period mechanism adjusting ADX parameters based on market states.
Incorporate other oscillator indicators to determine entries and exits to make the strategy more robust. Effects like bottom divergence, top divergence can be useful.
Add stop loss mechanisms to complete the strategy.
The Momentum Direction Divergence Strategy integrates advantages of multiple indicators, utilizing price, momentum, trend dimensions to design CSI indicator for trading. With flexible parameter settings and strong capacity, the strategy deserves further testing and optimization, and can become a beneficial quantitative trading tool.
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Strategy Arguments
| Argument | Default | Description |
|---|---|---|
| v_input_1 | 32 | r |
| v_input_2 | true | Length |
| v_input_3 | true | BigPointValue |
| v_input_4 | 5 | SmthLen |
| v_input_5 | 0.004 | SellZone |
| v_input_6 | 0.024 | BuyZone |
| v_input_7 | false | Trade reverse |
Source (PineScript)
/*backtest
start: 2022-12-20 00:00:00
end: 2023-12-26 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=2
////////////////////////////////////////////////////////////
// Copyright by HPotter v1.0 20/06/2018
// This is one of the techniques described by William Blau in his book
// "Momentum, Direction and Divergence" (1995). If you like to learn more,
// we advise you to read this book. His book focuses on three key aspects
// of trading: momentum, direction and divergence. Blau, who was an electrical
// engineer before becoming a trader, thoroughly examines the relationship between
// price and momentum in step-by-step examples. From this grounding, he then looks
// at the deficiencies in other oscillators and introduces some innovative techniques,
// including a fresh twist on Stochastics. On directional issues, he analyzes the
// intricacies of ADX and offers a unique approach to help define trending and
// non-trending periods.
// This indicator plots Ergotic CSI and smoothed Ergotic CSI to filter out noise.
//
// You can change long to short in the Input Settings
// WARNING:
// - For purpose educate only
// - This script to change bars colors.
////////////////////////////////////////////////////////////
fADX(Len) =>
up = change(high)
down = -change(low)
trur = rma(tr, Len)
plus = fixnan(100 * rma(up > down and up > 0 ? up : 0, Len) / trur)
minus = fixnan(100 * rma(down > up and down > 0 ? down : 0, Len) / trur)
sum = plus + minus
100 * rma(abs(plus - minus) / (sum == 0 ? 1 : sum), Len)
strategy(title="Ergodic CSI Backtest")
r = input(32, minval=1)
Length = input(1, minval=1)
BigPointValue = input(1.0, minval=0.00001)
SmthLen = input(5, minval=1)
SellZone = input(0.004, minval=0.00001)
BuyZone = input(0.024, minval=0.001)
reverse = input(false, title="Trade reverse")
hline(BuyZone, color=green, linestyle=line)
hline(SellZone, color=red, linestyle=line)
source = close
K = 100 * (BigPointValue / sqrt(r) / (150 + 5))
xTrueRange = atr(1)
xADX = fADX(Length)
xADXR = (xADX + xADX[1]) * 0.5
nRes = iff(Length + xTrueRange > 0, K * xADXR * xTrueRange / Length,0)
xCSI = iff(close > 0, nRes / close, 0)
xSMA_CSI = sma(xCSI, SmthLen)
pos = iff(xSMA_CSI > BuyZone, 1,
iff(xSMA_CSI <= SellZone, -1, nz(pos[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)
barcolor(possig == -1 ? red: possig == 1 ? green : blue )
plot(xCSI, color=green, title="Ergodic CSI")
plot(xSMA_CSI, color=red, title="SigLin")
Detail
https://www.fmz.com/strategy/436767
Last Modified
2023-12-27 15:37:31