strategies/综合技术指标策略Multi-indicator-Combination-Strategy.md at 87a415edf7b08065fbcbfbfefb7351cd929e4dbd

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Name

综合技术指标策略Multi-indicator-Combination-Strategy

Author

ChaoZhang

Strategy Description

[trans]

概述

该策略综合运用多种技术指标,对价格趋势进行判断,以发出买入和卖出信号。

策略原理

该策略主要基于以下几种技术指标对价格趋势进行判断:

超级趋势指标(SuperTrend):根据ATR计算上轨和下轨,价格突破上轨做多,突破下轨做空;
简单移动平均线(SMA):价格上穿SMA做多,下穿SMA做空;
动量指标(Momentum):价格动量为正做多,为负做空;
MACD:DIFF向上突破DEA线做多,向下突破DEA线做空;
多空力量(Bull and Bear):多头力量大于空头力量做多,反之做空;
RSI:RSI上穿30线做多,下穿70线做空;
阳线阴线:连续N根阴线做空,连续N根阳线做多;
CCI:CCI大于100做多,小于-100做空;
DMI: DMI多头线大于空头线做多,反之做空;
市场波浪:判断价格在上升浪做多,下降浪做空;
随机指标:随机指标上穿20线做多,下穿80线做空。

这些Indicator计算出的结果,根据向上或向下的方向给出1或-1的点数。把所有Indicator的点数加总,得到总点数。当总点数上穿0线时产生买入信号;当总点数下穿0线时产生卖出信号。

优势分析

这种多Indicator组合策略最大的优势在于可靠性高,由于综合运用多种Indicator判断趋势方向,可以有效减少假信号,使信号更可靠。与单一Indicator相比,这种组合策略可靠性和稳定性会更好。

另一个优势是策略的灵活性和可定制性强。Indicator的种类、参数设置都可以根据不同市场调整,使策略更适应不同的行情环境。还可以根据回测结果,调整Indicator的权重。

风险分析

这类组合策略也存在一些风险需要注意:

如果选用的Indicator间相关性太高,会产生重复信号的风险。这就需要根据不同市场环境,选择相关性较低的Indicator进行组合。
如果Indicator数量太多,计算时间太长,会影响信号发出的及时性。需要权衡Indicator数量与及时性之间的关系。
Indicator参数设置不当也会影响策略效果,需要充分回测寻找最佳参数。
不同市场阶段,Indicator的效果也会有所不同。需要通过滚动回测不断检查其有效性。

优化方向

这种策略可以从以下几个方面进行优化:

优化Indicator的种类和数量,选择最优组合;
优化每个Indicator的参数设置;
调整Indicator的权重比例,提高关键Indicator的权重;
增加条件过滤,例如交易量激增等,避免假突破;
采用模型组合的方法,通过机器学习算法自动寻找最优策略组合。

总结

综上所述,这种多Indicator组合策略利用各类Indicator的优势,组合判断趋势方向,可以减少假信号,提高信号的可靠性。通过优化Indicator的选取、参数设定、权重分配等,可以不断提升策略的稳定性。这类组合策略适用于对指标信号稳定性要求高的策略交易者。

Overview

This strategy combines multiple technical indicators to judge the price trend and generate buy and sell signals.

Strategy Principle

The strategy mainly uses the following indicators to determine the price trend:

SuperTrend: Buy when price breaks above the upper band, sell when breaks below the lower band.
SMA: Buy when price crosses above SMA, sell when crosses below SMA.
Momentum: Go long when momentum is positive, go short when negative.
MACD: Buy when DIFF crosses above DEA, sell when crosses below.
Bull and Bear: Go long when bull power > bear power, vice versa.
RSI: Buy when RSI crosses above 30, sell when crosses below 70.
Candlesticks: Go long after N bullish bars, go short after N bearish bars.
CCI: Buy when CCI > 100, sell when CCI < -100.
DMI: Go long when DMI+ > DMI-, else go short.
Market Waves: Go long in upward waves, go short in downward waves.
Stochastics: Buy when %K crosses above 20, sell when crosses below 80.

The indicator signals are quantified as 1 or -1 depending on up or down direction. The total points are summed up. Buy when total points cross above 0, sell when cross below 0.

Advantage Analysis

The biggest advantage of this multi-indicator strategy is higher reliability by combining signals from various indicators to filter out false signals. It is more robust than single indicator strategies.

Another advantage is the flexibility to customize indicators and parameters for different market conditions. The indicator weights can also be adjusted based on backtest results.

Risk Analysis

Some risks to note in such combo strategies:

High correlation between indicators may generate duplicated signals. Indicators should be selected to have low correlation.
Too many indicators leads to delayed signals. There is a tradeoff between indicator quantity and timeliness.
Inappropriate indicator parameters impact strategy performance. Optimal parameters need to be found through thorough backtesting.
Indicator efficiency varies across market regimes. Rolling backtests should check indicator validity.

Optimization Directions

This strategy can be improved in several ways:

Optimize indicator selection and number to find best combination.
Optimize parameters for each indicator.
Adjust indicator weights to emphasize key indicators.
Add filters like volume spike to avoid false breakouts.
Use machine learning models to automatically find optimal combinations.

Conclusion

In summary, this multi-indicator strategy combines the strengths of various indicators to improve signal reliability and reduce false signals. Fine-tuning the indicator selection, parameters, and weights can further enhance the stability. It suits traders who require stable indicator signals.

[/trans]

Strategy Arguments

Argument	Default	Description
v_input_1	false	════════ Test Period ═══════
v_input_2	2017	Backtest Start Year
v_input_3	true	Backtest Start Month
v_input_4	true	Backtest Start Day
v_input_5	2019	Backtest Stop Year
v_input_6	12	Backtest Stop Month
v_input_7	31	Backtest Stop Day
v_input_8_close	0	Source: close
v_input_9	true	piz

Source (PineScript)

/*backtest
start: 2023-01-01 00:00:00
end: 2023-10-06 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/

//@version=4
strategy("Super indicator ", overlay=true, precision=2, initial_capital=10000, default_qty_type=strategy.percent_of_equity, default_qty_value=100, commission_type=strategy.commission.percent, commission_value=0.075)

/////////////// Time Frame ///////////////
_0 = input(false,  "════════ Test Period ═══════")
testStartYear = input(2017, "Backtest Start Year") 
testStartMonth = input(1, "Backtest Start Month")
testStartDay = input(1, "Backtest Start Day")
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay, 0, 0)

testStopYear = input(2019, "Backtest Stop Year")
testStopMonth = input(12, "Backtest Stop Month")
testStopDay = input(31, "Backtest Stop Day")
testPeriodStop = timestamp(testStopYear,testStopMonth,testStopDay, 0, 0)

testPeriod() =>true


hilow = ((high - low)*100)
openclose = ((close - open)*100)
vol1 = (volume / hilow)
spreadvol = (openclose * vol1)
VPT = spreadvol + cum(spreadvol)
window_len = 28

v_len = 14
price_spread = stdev(high-low, window_len)

vp =  spreadvol + cum(spreadvol)
smooth = sma(vp, v_len)
v_spread = stdev(vp - smooth, window_len)
shadow = (vp - smooth) / v_spread * price_spread

out1 = shadow > 0 ? high + shadow : low + shadow

//plot(out, style=line,linewidth=3, color=color)
len=5
vpt=ema(out1,len)


// INPUTS //
st_mult   =3
st_period = 7

// CALCULATIONS //
up_lev = vpt - (st_mult * atr(st_period))
dn_lev = vpt + (st_mult * atr(st_period))

up_trend   = 0.0
up_trend   := close[1] > up_trend[1]   ? max(up_lev, up_trend[1])   : up_lev

down_trend = 0.0
down_trend := close[1] < down_trend[1] ? min(dn_lev, down_trend[1]) : dn_lev

// Calculate trend var
trend10 = 0
trend10 := close > down_trend[1] ? 1: close < up_trend[1] ? -1 : nz(trend10[1], 1)

// Calculate SuperTrend Line
st_line = trend10 ==1 ? up_trend : down_trend

//
src = input(close, title="Source")
//sma
sma20 = sma(src, 20)
smapoint = 0
smapoint := src > sma20 ? smapoint + 1 : smapoint - 1


//AO
ao = sma(hl2,5) - sma(hl2,34)
aopoint = ao > 0 ? 1 : ao < 0 ? -1 : 0
//momentum
mom = src - src[14]
mompoint = mom > 0 ? 1 : mom < 0 ? -1 : 0
//MACD
fast_ma = ema(src, 12)
slow_ma = ema(src, 26)
macd = fast_ma - slow_ma
signal = ema(macd, 9)
hist = macd - signal
histpoint = hist > hist[1] ? 3 : -3

//Bull bear
Length = 30
r1=iff(close[1]<open,max(open-close[1],high-low),high-low)
r2=iff(close[1]>open,max(close[1]-open,high-low),high-low)
bull=iff(close==open,iff(high-close==close-low,iff(close[1]>open,max(high-open,close-low),r1),iff(high-close>close-low,iff(close[1]<open, max(high-close[1],close-low), high-open),r1)),iff(close<open,iff(close[1]<open,max(high-close[1],close-low), max(high-open,close-low)),r1))
bear=iff(close==open,iff(high-close==close-low,iff(close[1]<open,max(open-low,high-close),r2),iff(high-close>close-low,r2,iff(close[1]>open,max(close[1]-low,high-close), open-low))),iff(close<open,r2,iff(close[1]>open,max(close[1]-low,high-close),max(open-low,high-close))))
colors=iff(sma(bull-bear,Length)>0, color.green, color.red)
// barcolor(colors)
bbpoint = sma(bull-bear,Length)>0 ? 1 : -1
//UO
length7 = 7,
length14 = 14,
length28 = 28
average(bp, tr_, length) => sum(bp, length) / sum(tr_, length)
high_ = max(high, src[1])
low_ = min(low, src[1])
bp = src - low_
tr_ = high_ - low_
avg7 = average(bp, tr_, length7)
avg14 = average(bp, tr_, length14)
avg28 = average(bp, tr_, length28)
uoout = 100 * (4*avg7 + 2*avg14 + avg28)/7
uopoint = uoout > 70 ? 1 : uoout < 30 ? -1 : 0
//IC
conversionPeriods = 9
basePeriods = 26
laggingSpan2Periods = 52
displacement = 26
donchian(len) => avg(lowest(len), highest(len))
baseLine = donchian(basePeriods)
icpoint = src > baseLine ? 1 : -1

//HMA
hullma = wma(2*wma(src, 9/2)-wma(src, 21), round(sqrt(21)))
hmapoint = src > hullma ? 2 : -2
//
//
trendDetectionLength =4
float trend = na
float wave = na
float vol = na
mov = close>close[1] ? 1 : close<close[1] ? -1 : 0
trend := (mov != 0) and (mov != mov[1]) ? mov : nz(trend[1])
isTrending = rising(close, trendDetectionLength) or falling(close, trendDetectionLength)
wave := (trend != nz(wave[1])) and isTrending ? trend : nz(wave[1])
vol := wave == wave[1] ? (nz(vol[1])+volume) : volume
up1 = wave == 1 ? vol : 0
dn1 = wave == 1 ? 0 : vol
Weis= up1 > dn1 ? 2 : -2


//

roclen =20
ccilen =21
dilen = 5
dirmov(len) =>
	up = change(high)
	down = -change(low)
	truerange = rma(tr, len)
	plus = fixnan(100 * rma(up > down and up > 0 ? up : 0, len) / truerange)
	minus = fixnan(100 * rma(down > up and down > 0 ? down : 0, len) / truerange)
	[plus, minus]

f_draw_infopanel(_x, _y, _line, _text, _color)=>
    _rep_text = ""
    for _l = 0 to _line
        _rep_text := _rep_text + "\n"
    _rep_text := _rep_text + _text
    var label _la = na
    label.delete(_la)
    _la := label.new(
         x=_x, y=_y, 
         text=_rep_text, xloc=xloc.bar_time, yloc=yloc.price, 
         color=color.black, style=label.style_labelup, textcolor=_color, size=size.normal)

TD = 0
TS = 0
TD := close > close[4] ? nz(TD[1]) + 1 : 0
TS := close < close[4] ? nz(TS[1]) + 1 : 0
TDUp = TD - valuewhen(TD < TD[1], TD , 1 )
TDDn = TS - valuewhen(TS < TS[1], TS , 1 )
td = TDUp > 0 ? 2 : TDDn > 0 ? -2 : 0
roc = roc(close, roclen)
Roc=roc > 0 ? 1 : -1
cci = cci(close, ccilen)
CCI=cci > 0? 2 : -2
[plus, minus] = dirmov(dilen)
dmi = plus - minus
DMI= dmi >= 0? 2 : -2
//
STT=trend10 == 1 ? 1 : -1
//
periods = 2
smooth1 =  14
price = close
fn(src, length) => 
    MA_s= 0.0
    MA_s:=(src + nz(MA_s[1] * (length-1)))/length
    MA_s
r11 = ema( price, periods ) 
r22 = iff( price > r11, price - r11, 0 ) 
r3 = iff( price < r11, r11 - price, 0 ) 
r4 = fn( r22, smooth1 ) 
r5 = fn( r3, smooth1 ) 
rr = iff( r5 == 0, 100, 100 - ( 100 / ( 1 + ( r4 / r5 ) ) ) ) 

length = 20,fast = 7,slow = 13
//
src10 = rr
er = abs(change(src,length))/sum(abs(change(src10)),length)
dev = er*stdev(src10*2,fast) + (1-er)*stdev(src10*2,slow)
a = 0.
a := bar_index < 9 ? src10 : src10 > a[1] + dev ? src10 : src10 < a[1] - dev ? src10 : a[1]
//

rsi=fixnan(a > a[1] ? 3 : a < a[1] ?-3 : na)
//
totalpoints =rsi+td+STT+Roc+DMI+ CCI+Weis+smapoint  + aopoint + mompoint + histpoint  + bbpoint  + icpoint  + hmapoint
//
piz=input(1)
tt=sma(totalpoints,piz)

//

zero=0
down = crossunder(tt, 0) 
up = crossover(tt, -0) 

//Alerts
/////// Alerts /////
alertcondition(down,title="sell")
alertcondition(up,title="buy")
//
/////////////// Strategy /////////////// 
long = up
short = down

strategy.entry("Long", strategy.long, when = long) 
strategy.entry("Short", strategy.short, when = short)

Detail

https://www.fmz.com/strategy/428617

Last Modified

2023-10-07 15:34:33

13 KiB Raw Blame History

概述