ChaoZhang
概述
猎户座交易策略(Orion Trading Strategy)是一个集成多种技术指标的量化交易策略。该策略旨在提前识别市场的高点和低点,以便交易者及时做出买入和卖出决策。该策略通过独特的预测曲线机制,尝试在价格实际转折前提前发出交易信号。
策略原理
该策略的核心是独创的猎户座信号曲线。该曲线综合多个技术指标,包括MACD、WPR、Stoch、RSI等,计算出一个综合信号。然后通过超平滑处理生成最终曲线。
关键是该曲线还配备了一个预测模型,它分析曲线的斜率变化情况,尝试预测1-2根K线之后的潜在转折。当预测曲线与实际曲线发生背离时,即可提前发出交易信号。
此外,策略还使用动量波指标判断更大级别上的趋势方向。当动量波改变方向时,提示可能出现较大级别的转折。
最后,策略在信号产生时,给出相应的买入卖出建议。用户可以自行决定是否按此入场。
优势分析
- 多指标综合判断,提高准确率
融合多个指标有助于确认趋势和发现转折点,避免单一指标误判风险。
- 预测模型提前发现反转机会
预测曲线可提前反转实际信号,为交易决策提供先机。
- 动量波判断大趋势方向
结合更高时间框架的动量波指标,可避免逆势操作。
- 可自定义参数,适应不同品种
用户可调整指标参数,适应不同品种的特点。
风险分析
- 预测模型可能造成过度交易
预测模型容易发出假信号,如果盲目跟随,可能导致过度交易。
- 多参数组合难以优化
参数数量多,要找到最优组合需要大量数据集和长时间测试。
- 需谨慎评估指标效果
各个指标对信号提升的实际作用需谨慎评估,避免使用冗余指标。
- 需考虑实盘交易成本因素
频繁交易会产生更多成本,这需要考虑在实盘条件下进行回测。
优化方向
- 评估预测模型效果,调整参数
评估预测模型准确率,优化调整预测参数以提高准确性。
- 简化模型,减少冗余指标
采用指标效果评估和模型简化方法,减少不必要的复杂度。
- 多市场回测验证稳定性
在更多市场中回测,验证参数优化结果和稳定性。
- 考虑实盘成本进行策略调整
根据回测引入实盘成本因素,调整策略参数以降低交易频率。
总结
猎户座策略综合运用多种指标和独特预测曲线,试图提前发现市场转折点。该策略具有一定优势,但可扩展性也存在局限性。需要保持谨慎态度,从交易信号效果和成本效果等方面不断优化调整,力求在交易自动化中获取长期稳定收益。
策略源码
/*backtest
start: 2023-09-17 00:00:00
end: 2023-09-21 22:00:00
period: 3m
basePeriod: 1m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// © OrionAlgo
// () /? | () |\| /\ |_ (_, () //
//@version=4
version = '2.0'
strategy("Orion Algo Strategy v"+version, shorttitle="Orion Algo Strategy v"+version, overlay=false, pyramiding=100)
// Getting inputs --------------------------------------------------------------
userAgreement = input(true, title='I understand that Orion Algo cannot be 100% accurate and overall performance will shift with market conditions. While Orion Algo increases my chances of entering better positions, I must use smart trade management. ', type=input.bool,group='User Agreement ─────────────',
tooltip='In order to use Orion Algo, you must click the checkbox to acknowledge the user agreement')
src = close
//smoothing inputs -------------------------------------------------------------
//superSmooth = input(true, title='Super Smooth', inline='Super Smooth', group='Smoothing ─────────────────')
superSmooth = true
smoothType = 1
superSmoothStrength = input(10, title='Super Smooth',minval = 3, inline='Super Smooth', group='Signal ────────────────────',
tooltip='Smooths the signal. Lower values move pivots to the left while increasing noise, higher values move pivots to the right and reduce noise. 8 is a good mix of both') // set to timeframe for decent results?
//trendSmoothing = input(30, title='Trend Smooth',minval = 3, group='Smoothing ─────────────────') // set to timeframe for decent results?
trendSmoothing = 30 // set to timeframe for decent results?
showPrediction = input(false, title='Prediction', group='Signal ────────────────────',inline='prediction')
predictionBias = input(0.45, minval = 0.,maxval=1., step=0.05, title='Bias', group='Signal ────────────────────',inline='prediction')
showPredictionCurve = input(true, title='Curve', group='Signal ────────────────────',inline='prediction', tooltip='Prediction model that attempts to predict short range reversals (0-2 bars). Adjust Bias to change the prediction curve.')
//momentum wave inputs ---------------------------------------------------------
showMomentumWave = input(true, 'Momentum Wave', group='Momentum Wave ─────────────', inline='mom')
momentumWaveLength = input(3, '', group='Momentum Wave ─────────────', inline='mom', tooltip='Secondary signal that shows medium to large movements based on the input variable. The wave will change depending on the current timeframe.')
momentumOutside = input(true, 'Position Outside', group='Momentum Wave ─────────────', inline='mom2', tooltip='Positions the wave outside of the main signal area.')
//visuals input-----------------------------------------------------------------
useDarkMode = input(true, 'Dark Mode', group='Visuals ───────────────────',inline='Colors')
// 0:backgroundlines, 1:signal, 2:bullish, 3:bearish, 4:hiddenbull, 5:hiddenbear, 6:deltav, 7:prediction, 8:predictionbull, 9:predictionbear, 10:dash, 11:mom2
visualMode = input('Pro', 'Mode',options=['Beginner', 'Pro'] ,group='Visuals ───────────────────')
dashOn = input(true, "Dashboard", group='Dashboard ─────────────────', inline='dash', tooltip='A dashboard with some usefual stats')
dashColor = color.new(#171a27, 100)
showPivots = input(true, title='Signal Pivots', group='Pivots ────────────────────',inline='pivots')
showPredictionPivots = input(false, title='Prediction Pivots', group='Pivots ────────────────────',inline='pivots')
// Functions -------------------------------------------------------------------
f_secureSecurity(_symbol, _res, _src) => security(_symbol, _res, _src,barmerge.gaps_on, lookahead = barmerge.lookahead_on)
f_slope(x) =>
slopePeriod = 1
(x - x[slopePeriod]) / slopePeriod
f_superSmooth(inputVal,smoothType) =>
smoothType==1? (hma(inputVal,superSmoothStrength)) :
smoothType==2? (ema((ema((ema(inputVal,3)),3)),superSmoothStrength)):
smoothType==3? linreg(inputVal,superSmoothStrength,0) :
smoothType==4? (hma(inputVal,superSmoothStrength * momentumWaveLength)) : na
f_bias(bias, min, max) =>
(bias * (max - min) ) + min
f_resInMinutes() =>
_resInMinutes = timeframe.multiplier * (
timeframe.isseconds ? 1. / 60. :
timeframe.isminutes ? 1. :
timeframe.isdaily ? 1440. :
timeframe.isweekly ? 10080. :
timeframe.ismonthly ? 43800. : na)
f_resFromMinutes(_minutes) =>
_minutes <= 0.0167 ? "1S" :
_minutes <= 0.0834 ? "5S" :
_minutes <= 0.2500 ? "15S" :
_minutes <= 0.5000 ? "30S" :
_minutes <= 1 ? "1":
_minutes <= 1440 ? tostring(round(_minutes)) :
_minutes <= 43800 ? tostring(round(min(_minutes / 1440, 365))) + "D" :
tostring(round(min(_minutes / 43800, 12))) + "M"
f_output_signal()=>
a = ((ema(close, 12) - ema(close, 26)) - ema((ema(close, 12) - ema(close, 26)), 8))/10
b = wpr(8)
c = (100 * ( close + 2*stdev( close, 21) - sma( close, 21 ) ) / ( 4 * stdev( close, 21 ) ))
d = (rsi(close - sma(close, 21)[11],8)*2)-100
e = (rsi(fixnan(100 * rma(change(high) > change(low) and change(high) > 0 ? change(high) : 0, 1) / rma(tr, 1)) - fixnan(100 * rma(change(low) > change(high) and change(low) > 0 ? change(low) : 0, 1) / rma(tr, 1)),8)*2)-100 //causes slow down
f = rsi((((close-( (sum(volume, 20) - volume)/sum(volume, 20)) + (volume*close/sum(volume, 20)))/((close+( (sum(volume, 20) - volume)/sum(volume, 20)) + (volume*close/sum(volume, 20)))/2)) * 100),8)-100
g = (rsi(sma(highest(high,14)-lowest(low,14)==0.0?0.0:(close-lowest(low,14))/highest(high,14)-lowest(low,14)-0.5,max(1,int(2))),8)*2)-100 //causes slow down
avg(a,b,c,d,e,f,g)*2
output_signal = f_output_signal()
output_signal := f_superSmooth(output_signal,1)
// output_signal2 = plot(f_superSmoothSlow(f_output_signal()), color=color.blue, linewidth=2)
//Orion Signal Higher Timeframe / Momentum Wave --------------------------------
f_momentumWave(wavelength,smooth) =>
currentMinutes = f_resInMinutes()
m = currentMinutes * wavelength //multiply current resolution by momentumWaveLength to get higher resolution
momentumWaveRes = f_resFromMinutes(m)
f_secureSecurity(syminfo.tickerid, momentumWaveRes,f_superSmooth(f_output_signal(),1))
// Plot ------------------------------------------------------------------------
f_color(x) =>
if userAgreement
white = useDarkMode ? #e5e4f4 : #505050ff
lightgray = useDarkMode ? #808080 : #909090ff
gray = useDarkMode ? #808080 : #505050ff
//blue = useDarkMode ? #007EA7 : #007EA7ff
blue = useDarkMode ? #2862FFFF : #2862FFFF
// 0:backgroundlines, 1:signal, 2:bullish, 3:bearish, 4:hiddenbull, 5:hiddenbear, 6:deltav, 7:prediction, 8:predictionbull, 9:predictionbear, 10:trendbull, 11:trendbear, 12:dash, 13:mom1, 14:mom2
x==0? lightgray : x==1? gray : x==2? white : x==3? blue : x==4? white : x==5? blue : x==6? blue : x==7? blue : x==8? white : x==9? blue : x==10? blue : x==11? blue : na
// Lines -----------------------------------------------------------------------
h1 = plot(0, "Mid Band", color=f_color(0),editable=0, transp=80)
// Signal ----------------------------------------------------------------------
orionSignal = plot(output_signal, title="Orion Signal Curve", style=plot.style_line,linewidth=1, transp=0, color= f_color(1), offset=0,editable=0)
// Momentum Wave ---------------------------------------------------------------
momWave = f_momentumWave(momentumWaveLength,1)
p_momWave = plot(showMomentumWave? momentumOutside? (momWave/2) -150 : momWave : na, color=f_color(11), linewidth=showMomentumWave and momentumOutside ? 1 : 2, editable =0, transp=50, style=momentumOutside? plot.style_area : plot.style_line, histbase=-200) //two tone color doesnt want to work with this for some reason.
// Divergence ------------------------------------------------------------------
osc = output_signal
plFound = osc > osc [1] and osc[1] < osc[2]
phFound = osc < osc [1] and osc[1] > osc[2]
// bullish
plot(
plFound and visualMode=='Pro'? osc[1] - 10 : na,
offset=0,
title="Regular Bullish",
linewidth=3,
color=showPivots ? f_color(2) :na,
transp=0,
style=plot.style_circles,
editable=0
)
plotshape(
plFound and visualMode=='Beginner'? osc[1] - 10 : na,
offset=0,
title="Regular Bullish",
size=size.tiny,
color=showPivots ? f_color(2) :na,
transp=0,
style=shape.labelup,
text = 'Buy',
textcolor= color.black,
location=location.absolute,
editable=0
)
// bearish
plot(
phFound and visualMode=='Pro'? osc[1] + 10: na,
offset=0,
title="Regular Bearish",
linewidth=3,
color=showPivots ? f_color(3):na,
transp=0,
style=plot.style_circles,
editable=0
)
plotshape(
phFound and visualMode=='Beginner'? osc[1] + 10: na,
offset=0,
title="Regular Bearish",
size=size.tiny,
color=showPivots ? f_color(3):na,
transp=0,
style=shape.labeldown,
text = 'Sell',
textcolor= color.white,
location=location.absolute,
editable=0
)
// Delta v ---------------------------------------------------------------------
slope = f_slope(output_signal)*1.5
// Prediction from Delta v -----------------------------------------------------
output_prediction = f_bias(predictionBias, slope, output_signal)
prediction_bullish = output_prediction>output_prediction[1] and output_prediction[1]<output_prediction[2] ?true:false
prediction_bearish = output_prediction<output_prediction[1] and output_prediction[1]>output_prediction[2] ?true:false
plot(showPrediction and showPredictionCurve?output_prediction:na,title='Prediction Curve', color=f_color(7), editable=0)
//prediction bull
plot(showPrediction?showPredictionPivots?output_prediction>output_prediction[1] and output_prediction[1]<output_prediction[2]?showPredictionCurve?output_prediction:output_signal:na:na:na,
title='Prediction Bullish',color=f_color(8), style=plot.style_circles, linewidth=2, editable=0)
//prediction bear
plot(showPrediction?showPredictionPivots?output_prediction<output_prediction[1] and output_prediction[1]>output_prediction[2]?showPredictionCurve?output_prediction:output_signal:na:na:na,
title='Prediction Bearish', color=f_color(9), style=plot.style_circles, linewidth=2, editable=0)
// User Aggreement -------------------------------------------------------------
plotshape(userAgreement==false?0:na,title='Welcome', text='Welcome to Orion Algo! Please double click me to enable signals',textcolor=color.black,color=color.white,offset=0,size=size.huge,style=shape.labeldown,location=location.absolute, transp=0, show_last=1, editable=0)
plotshape(userAgreement==false?0:na,title='Welcome', text='Welcome to Orion Algo! Please double click me to enable signals',textcolor=color.black,color=color.white,offset=-100,size=size.huge,style=shape.labeldown,location=location.absolute, transp=0, show_last=1, editable=0)
// Alerts ----------------------------------------------------------------------
alertcondition(plFound,title='1. Bullish (Big Dot)', message='Bullish Signal (Big Dot)')
alertcondition(phFound,title='2. Bearish (Big Dot)', message='Bearish Signal (Big Dot)')
alertcondition(prediction_bullish,title='3. Prediction Bullish (Small Dot)', message='Prediction Bullish Signal (Small Dot)')
alertcondition(prediction_bearish,title='4. Prediction Bearish (Small Dot)', message='Prediction Bearish Signal (Small Dot)')
// Strategy --------------------------------------------------------------------
i_strategy = input(defval='dca long', title='strategy', options=['simple','dca long'])
i_pyramid = input(10, 'pyramid orders')
// Simple Strat
if (i_strategy == 'simple')
longCondition = crossover(output_signal, output_signal[1])
if (longCondition)
strategy.entry("My Long Entry Id", strategy.long)
shortCondition = crossunder(output_signal, output_signal[1])
if (shortCondition)
strategy.entry("My Short Entry Id", strategy.short)
// DCA Strat
i_percent_exit = input(2.0,'percent exit in profit')/100
i_percent_drop = input(2.0,'percent drop before each entry')/100
var entryPrice = 0.0
var exitPrice = 0.0
var inTrade = false
var tradeCount = 0
var moneyInTrade = 0.0
if(output_signal > output_signal[1] and output_signal[1]<=output_signal[2] and i_strategy=='dca long')
//if (true)
if (inTrade==false)
strategy.entry('Long',long=true)
entryPrice:=close
moneyInTrade:=close
exitPrice:=entryPrice + (entryPrice*(i_percent_exit))
inTrade:=true
tradeCount := 1
if (inTrade==true and close <= (entryPrice-(entryPrice*(i_percent_drop) )))
//calculate DCA //math is incorrect!!!
if (tradeCount <= i_pyramid)
tradeCount := tradeCount+1
entryPrice:=close
moneyInTrade := moneyInTrade+close
exitPrice2 = moneyInTrade / tradeCount
exitPrice := exitPrice2 + (exitPrice2 *(i_percent_exit))
strategy.entry('Long',long=true)
if(close >= exitPrice and inTrade==true and output_signal <= output_signal[1] and output_signal[1]>=output_signal[2] and i_strategy=='dca long')
inTrade:=false
strategy.close('Long')
// Dashboard -------------------------------------------------------------------
//deltav
deltav = slope