ChaoZhang
概述
该策略通过选择快速和慢速两个不同的趋势指标,在快速趋势上穿慢速趋势时做多,下穿时做空,实现交易信号的产生。策略内置20多个不同的趋势计算方式,用户可以自由组合选择。
策略原理
该策略的核心是快速趋势指标和慢速趋势指标的选择和组合:
FastTrend = 用户选择的快速趋势指标
SlowTrend = 用户选择的慢速趋势指标
快速趋势指标包括SMA、EMA、KAMA等20多种趋势算法,慢速指标同样可自由选择。
交易信号的产生判断快慢趋势的关系:
if FastTrend > SlowTrend:
做多
if FastTrend < SlowTrend:
平仓
做多信号是快速趋势上穿慢速趋势时产生,做空信号是快速趋势下穿慢速趋势时产生。
优势分析
- 内置20多种指标可自由组合,灵活性强
- 可识别不同时间段的趋势态势
- 可通过参数优化找到最佳指标组合
- 可同时做多做空,捕捉双向趋势
- 可设置止损策略控制风险
风险分析
- 快慢趋势指标选择不当可能失效
- 趋势指标存在滞后,可能错过最佳入场点
- 震荡行情中容易产生错误信号
- 需要优化参数找到最佳组合指标
- 无法快速止损,存在亏损扩大风险
优化方向
该策略可以从以下几个方面进行优化:
调整快速和慢速趋势的指标和参数,寻找最佳组合。
增加过滤条件,避免在震荡行情中产生错误信号。例如增加交易量过滤。
增加止损策略,如跟踪止损,移动止损等方式。控制单笔亏损。
结合其他指标,如MACD、KDJ等,提高策略稳定性。
优化入场时机,不要只依赖趋势指标交叉入场。
总结
多重趋势交叉策略通过组合快慢趋势指标,能够识别不同时间周期的趋势变化。但该策略对市场震荡敏感,只适合趋势明显的市场环境。我们需要通过参数优化、风控等手段,提高策略的稳定性和盈利能力。
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Overview
This strategy generates trading signals by selecting fast and slow trend indicators and going long when the fast trend crosses over the slow trend, and going short when the fast trend crosses below the slow trend. The strategy incorporates over 20 different trend calculations to choose from.
Strategy Logic
The core of the strategy is the selection and combination of fast and slow trend indicators:
FastTrend = User selected fast trend indicator
SlowTrend = User selected slow trend indicator
Fast trend includes SMA, EMA, KAMA and 20+ trend algorithms. Slow trend can also be freely selected.
Trading signals are generated by judging the relationship between fast and slow trends:
if FastTrend > SlowTrend:
Go long
if FastTrend < SlowTrend:
Close position
Long signal is triggered when fast trend crosses over slow trend. Short signal is triggered when fast trend crosses below slow trend.
Advantage Analysis
- Incorporates 20+ indicators for flexible combinations
- Can identify trends over different timeframes
- Parameters can be optimized to find best combination
- Can go both long and short to capture trends in both directions
- Stop loss can be used to control risk
Risk Analysis
- Wrong fast/slow trend selection may cause strategy failure
- Trend indicators have lags, may miss best entry points
- Prone to generating false signals in ranging markets
- Need parameter optimization to find best indicator combinations
- Unable to quickly cut losses, risks of letting losses run
Optimization Directions
The strategy can be improved in the following aspects:
Adjust fast/slow trends and parameters to find optimal combinations.
Add filters like volume to avoid false signals during market choppiness.
Incorporate stop loss strategies like trailing stop loss to control single trade loss.
Combine with other indicators like MACD, KDJ to improve stability.
Optimize entry timing, don’t just rely on trend crossover.
Summary
The multi trend crossover strategy identifies trend changes across timeframes by combining fast and slow trends. But it is sensitive to market fluctuations and only works well in obvious trending markets. We need methods like parameter optimization and risk management to improve strategy stability and profitability.
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策略源码
/*backtest
start: 2023-08-21 00:00:00
end: 2023-09-20 00:00:00
period: 3h
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// @version=5
// Author = TradeAutomation
strategy(title="Multi Trend Cross Strategy Template", shorttitle="Multi Trend Cross Strategy", process_orders_on_close=true, overlay=true, commission_type=strategy.commission.cash_per_contract, commission_value=0.0035, initial_capital = 1000000, default_qty_type=strategy.percent_of_equity, default_qty_value=100)
// Backtest Date Range Inputs //
StartTime = input(defval=timestamp('01 Jan 2000 05:00 +0000'), group="Date Range", title='Start Time')
EndTime = input(defval=timestamp('01 Jan 2099 00:00 +0000'), group="Date Range", title='End Time')
InDateRange = true
// Trend Selector //
TrendSelectorInput = input.string(title="Fast Trend Selector", defval="EMA", group="Core Settings", options=["ALMA", "DEMA", "DSMA", "EMA", "HMA", "JMA", "KAMA", "Linear Regression (LSMA)", "RMA", "SMA", "SMMA", "Price Source", "TEMA", "TMA", "VAMA", "VIDYA", "VMA", "VWMA", "WMA", "WWMA", "ZLEMA"], tooltip="Select your fast trend")
TrendSelectorInput2 = input.string(title="Slow Trend Selector", defval="EMA", group="Core Settings", options=["ALMA", "DEMA", "DSMA", "EMA", "HMA", "JMA", "KAMA", "Linear Regression (LSMA)", "RMA", "SMA", "SMMA", "Price Source", "TEMA", "TMA", "VAMA", "VIDYA", "VMA", "VWMA", "WMA", "WWMA", "ZLEMA"], tooltip="Select your slow trend")
src = input.source(close, "Price Source", group="Core Settings", tooltip="This is the price source being used for the trends to calculate based on")
length = input.int(10, "Fast Trend Length", group="Core Settings", step=5, tooltip="A long is entered when the selected fast trend crosses over the selected slow trend")
length2 = input.int(200, "Slow Trend Length", group="Core Settings", step=5, tooltip="A long is entered when the selected fast trend crosses over the selected slow trend")
LineWidth = input.int(1, "Line Width", group="Core Settings", tooltip="This is the width of the line plotted that represents the selected trend")
// Individual Moving Average / Regression Setting //
AlmaOffset = input.float(0.85, "ALMA Offset", group="Individual Trend Settings", tooltip="This only applies when ALMA is selected")
AlmaSigma = input.float(6, "ALMA Sigma", group="Individual Trend Settings", tooltip="This only applies when ALMA is selected")
ATRFactor = input.float(3, "ATR Multiplier For SuperTrend", group="Individual Trend Settings", tooltip="This only applies when SuperTrend is selected")
ATRLength = input.int(12, "ATR Length For SuperTrend", group="Individual Trend Settings", tooltip="This only applies when SuperTrend is selected")
ssfLength = input.int(20, "DSMA Super Smoother Filter Length", minval=1, tooltip="This only applies when EDSMA is selected", group="Individual Trend Settings")
ssfPoles = input.int(2, "DSMA Super Smoother Filter Poles", options=[2, 3], tooltip="This only applies when EDSMA is selected", group="Individual Trend Settings")
JMApower = input.int(2, "JMA Power Parameter", group="Individual Trend Settings", tooltip="This only applies when JMA is selected")
phase = input.int(-45, title="JMA Phase Parameter", step=10, minval=-110, maxval=110, group="Individual Trend Settings", tooltip="This only applies when JMA is selected")
KamaAlpha = input.float(3, "KAMA's Alpha", minval=1,step=0.5, group="Individual Trend Settings", tooltip="This only applies when KAMA is selected")
LinRegOffset = input.int(0, "Linear Regression Offset", group="Individual Trend Settings", tooltip="This only applies when Linear Regression is selected")
VAMALookback =input.int(12, "VAMA Volatility lookback", group="Individual Trend Settings", tooltip="This only applies when VAMA is selected")
// Trend Indicators With Library Functions //
ALMA = ta.alma(src, length, AlmaOffset, AlmaSigma)
EMA = ta.ema(src, length)
HMA = ta.hma(src, length)
LinReg = ta.linreg(src, length, LinRegOffset)
RMA = ta.rma(src, length)
SMA = ta.sma(src, length)
VWMA = ta.vwma(src, length)
WMA = ta.wma(src, length)
ALMA2 = ta.alma(src, length2, AlmaOffset, AlmaSigma)
EMA2 = ta.ema(src, length2)
HMA2 = ta.hma(src, length2)
LinReg2 = ta.linreg(src, length2, LinRegOffset)
RMA2 = ta.rma(src, length2)
SMA2 = ta.sma(src, length2)
VWMA2 = ta.vwma(src, length2)
WMA2 = ta.wma(src, length2)
// Additional Trend Indicators Built In And/Or Open Sourced //
//DEMA
de1 = ta.ema(src, length)
de2 = ta.ema(de1, length)
DEMA = 2 * de1 - de2
de3 = ta.ema(src, length2)
de4 = ta.ema(de3, length2)
DEMA2 = 2 * de3 - de4
// Ehlers Deviation-Scaled Moving Average - DSMA [Everget]
PI = 2 * math.asin(1)
get2PoleSSF(src, length) =>
arg = math.sqrt(2) * PI / length
a1 = math.exp(-arg)
b1 = 2 * a1 * math.cos(arg)
c2 = b1
c3 = -math.pow(a1, 2)
c1 = 1 - c2 - c3
var ssf = 0.0
ssf := c1 * src + c2 * nz(ssf[1]) + c3 * nz(ssf[2])
get3PoleSSF(src, length) =>
arg = PI / length
a1 = math.exp(-arg)
b1 = 2 * a1 * math.cos(1.738 * arg)
c1 = math.pow(a1, 2)
coef2 = b1 + c1
coef3 = -(c1 + b1 * c1)
coef4 = math.pow(c1, 2)
coef1 = 1 - coef2 - coef3 - coef4
var ssf = 0.0
ssf := coef1 * src + coef2 * nz(ssf[1]) + coef3 * nz(ssf[2]) + coef4 * nz(ssf[3])
zeros = src - nz(src[2])
avgZeros = (zeros + zeros[1]) / 2
// Ehlers Super Smoother Filter
ssf = ssfPoles == 2
? get2PoleSSF(avgZeros, ssfLength)
: get3PoleSSF(avgZeros, ssfLength)
// Rescale filter in terms of Standard Deviations
stdev = ta.stdev(ssf, length)
scaledFilter = stdev != 0
? ssf / stdev
: 0
alpha1 = 5 * math.abs(scaledFilter) / length
EDSMA = 0.0
EDSMA := alpha1 * src + (1 - alpha1) * nz(EDSMA[1])
get2PoleSSF2(src, length2) =>
arg = math.sqrt(2) * PI / length2
a1 = math.exp(-arg)
b1 = 2 * a1 * math.cos(arg)
c2 = b1
c3 = -math.pow(a1, 2)
c1 = 1 - c2 - c3
var ssf2 = 0.0
ssf2 := c1 * src + c2 * nz(ssf2[1]) + c3 * nz(ssf2[2])
get3PoleSSF2(src, length2) =>
arg = PI / length2
a1 = math.exp(-arg)
b1 = 2 * a1 * math.cos(1.738 * arg)
c1 = math.pow(a1, 2)
coef2 = b1 + c1
coef3 = -(c1 + b1 * c1)
coef4 = math.pow(c1, 2)
coef1 = 1 - coef2 - coef3 - coef4
var ssf2 = 0.0
ssf2 := coef1 * src + coef2 * nz(ssf2[1]) + coef3 * nz(ssf2[2]) + coef4 * nz(ssf2[3])
// Ehlers Super Smoother Filter
ssf2 = ssfPoles == 2
? get2PoleSSF2(avgZeros, ssfLength)
: get3PoleSSF2(avgZeros, ssfLength)
// Rescale filter in terms of Standard Deviations
stdev2 = ta.stdev(ssf2, length2)
scaledFilter2 = stdev2 != 0
? ssf2 / stdev2
: 0
alpha12 = 5 * math.abs(scaledFilter2) / length2
EDSMA2 = 0.0
EDSMA2 := alpha12 * src + (1 - alpha12) * nz(EDSMA2[1])
//JMA [Everget]
phaseRatio = phase < -100 ? 0.5 : phase > 100 ? 2.5 : phase / 100 + 1.5
beta = 0.45 * (length - 1) / (0.45 * (length - 1) + 2)
alpha = math.pow(beta, JMApower)
var JMA = 0.0
var e0 = 0.0
e0 := (1 - alpha) * src + alpha * nz(e0[1])
var e1 = 0.0
e1 := (src - e0) * (1 - beta) + beta * nz(e1[1])
var e2 = 0.0
e2 := (e0 + phaseRatio * e1 - nz(JMA[1])) * math.pow(1 - alpha, 2) + math.pow(alpha, 2) * nz(e2[1])
JMA := e2 + nz(JMA[1])
beta2 = 0.45 * (length2 - 1) / (0.45 * (length2 - 1) + 2)
alpha2 = math.pow(beta2, JMApower)
var JMA2 = 0.0
var e02 = 0.0
e02 := (1 - alpha2) * src + alpha2 * nz(e02[1])
var e12 = 0.0
e12 := (src - e02) * (1 - beta2) + beta2 * nz(e12[1])
var e22 = 0.0
e22 := (e02 + phaseRatio * e12 - nz(JMA2[1])) * math.pow(1 - alpha2, 2) + math.pow(alpha2, 2) * nz(e22[1])
JMA2 := e22 + nz(JMA2[1])
//KAMA [Everget]
var KAMA = 0.0
fastAlpha = 2.0 / (KamaAlpha + 1)
slowAlpha = 2.0 / 31
momentum = math.abs(ta.change(src, length))
volatility = math.sum(math.abs(ta.change(src)), length)
efficiencyRatio = volatility != 0 ? momentum / volatility : 0
smoothingConstant = math.pow((efficiencyRatio * (fastAlpha - slowAlpha)) + slowAlpha, 2)
KAMA := nz(KAMA[1], src) + smoothingConstant * (src - nz(KAMA[1], src))
var KAMA2 = 0.0
momentum2 = math.abs(ta.change(src, length2))
volatility2 = math.sum(math.abs(ta.change(src)), length2)
efficiencyRatio2 = volatility2 != 0 ? momentum2 / volatility2 : 0
smoothingConstant2 = math.pow((efficiencyRatio2 * (fastAlpha - slowAlpha)) + slowAlpha, 2)
KAMA2 := nz(KAMA2[1], src) + smoothingConstant2 * (src - nz(KAMA2[1], src))
//SMMA
var SMMA = 0.0
SMMA := na(SMMA[1]) ? ta.sma(src, length) : (SMMA[1] * (length - 1) + src) / length
var SMMA2 = 0.0
SMMA2 := na(SMMA2[1]) ? ta.sma(src, length2) : (SMMA2[1] * (length2 - 1) + src) / length2
//TEMA
t1 = ta.ema(src, length)
t2 = ta.ema(t1, length)
t3 = ta.ema(t2, length)
TEMA = 3 * (t1 - t2) + t3
t12 = ta.ema(src, length2)
t22 = ta.ema(t12, length2)
t32 = ta.ema(t22, length2)
TEMA2 = 3 * (t12 - t22) + t32
//TMA
TMA = ta.sma(ta.sma(src, math.ceil(length / 2)), math.floor(length / 2) + 1)
TMA2 = ta.sma(ta.sma(src, math.ceil(length2 / 2)), math.floor(length2 / 2) + 1)
//VAMA [Duyck]
mid=ta.ema(src,length)
dev=src-mid
vol_up=ta.highest(dev,VAMALookback)
vol_down=ta.lowest(dev,VAMALookback)
VAMA = mid+math.avg(vol_up,vol_down)
mid2=ta.ema(src,length2)
dev2=src-mid2
vol_up2=ta.highest(dev2,VAMALookback)
vol_down2=ta.lowest(dev2,VAMALookback)
VAMA2 = mid2+math.avg(vol_up2,vol_down2)
//VIDYA [KivancOzbilgic]
var VIDYA=0.0
VMAalpha=2/(length+1)
ud1=src>src[1] ? src-src[1] : 0
dd1=src<src[1] ? src[1]-src : 0
UD=math.sum(ud1,9)
DD=math.sum(dd1,9)
CMO=nz((UD-DD)/(UD+DD))
VIDYA := na(VIDYA[1]) ? ta.sma(src, length) : nz(VMAalpha*math.abs(CMO)*src)+(1-VMAalpha*math.abs(CMO))*nz(VIDYA[1])
var VIDYA2=0.0
VMAalpha2=2/(length2+1)
ud12=src>src[1] ? src-src[1] : 0
dd12=src<src[1] ? src[1]-src : 0
UD2=math.sum(ud12,9)
DD2=math.sum(dd12,9)
CMO2=nz((UD2-DD2)/(UD2+DD2))
VIDYA2 := na(VIDYA2[1]) ? ta.sma(src, length2) : nz(VMAalpha2*math.abs(CMO2)*src)+(1-VMAalpha2*math.abs(CMO2))*nz(VIDYA2[1])
//VMA [LazyBear]
sc = 1/length
pdm = math.max((src - src[1]), 0)
mdm = math.max((src[1] - src), 0)
var pdmS = 0.0
var mdmS = 0.0
pdmS := ((1 - sc)*nz(pdmS[1]) + sc*pdm)
mdmS := ((1 - sc)*nz(mdmS[1]) + sc*mdm)
s = pdmS + mdmS
pdi = pdmS/s
mdi = mdmS/s
var pdiS = 0.0
var mdiS = 0.0
pdiS := ((1 - sc)*nz(pdiS[1]) + sc*pdi)
mdiS := ((1 - sc)*nz(mdiS[1]) + sc*mdi)
d = math.abs(pdiS - mdiS)
s1 = pdiS + mdiS
var iS = 0.0
iS := ((1 - sc)*nz(iS[1]) + sc*d/s1)
hhv = ta.highest(iS, length)
llv = ta.lowest(iS, length)
d1 = hhv - llv
vi = (iS - llv)/d1
var VMA=0.0
VMA := na(VMA[1]) ? ta.sma(src, length) : sc*vi*src + (1 - sc*vi)*nz(VMA[1])
sc2 = 1/length2
pdm2 = math.max((src - src[1]), 0)
mdm2 = math.max((src[1] - src), 0)
var pdmS2 = 0.0
var mdmS2 = 0.0
pdmS2 := ((1 - sc2)*nz(pdmS2[1]) + sc2*pdm2)
mdmS2 := ((1 - sc2)*nz(mdmS2[1]) + sc2*mdm2)
s2 = pdmS2 + mdmS2
pdi2 = pdmS2/s2
mdi2 = mdmS2/s2
var pdiS2 = 0.0
var mdiS2 = 0.0
pdiS2 := ((1 - sc2)*nz(pdiS2[1]) + sc2*pdi2)
mdiS2 := ((1 - sc2)*nz(mdiS2[1]) + sc2*mdi2)
d2 = math.abs(pdiS2 - mdiS2)
s12 = pdiS2 + mdiS2
var iS2 = 0.0
iS2 := ((1 - sc2)*nz(iS2[1]) + sc2*d2/s12)
hhv2 = ta.highest(iS2, length)
llv2 = ta.lowest(iS2, length)
d12 = hhv2 - llv2
vi2 = (iS2 - llv2)/d12
var VMA2=0.0
VMA2 := na(VMA2[1]) ? ta.sma(src, length2) : sc2*vi2*src + (1 - sc2*vi2)*nz(VMA2[1])
//WWMA
var WWMA=0.0
WWMA := (1/length)*src + (1-(1/length))*nz(WWMA[1])
var WWMA2=0.0
WWMA2 := (1/length2)*src + (1-(1/length2))*nz(WWMA2[1])
//Zero Lag EMA [KivancOzbilgic]
EMA1a = ta.ema(src,length)
EMA2a = ta.ema(EMA1a,length)
Diff = EMA1a - EMA2a
ZLEMA = EMA1a + Diff
EMA12 = ta.ema(src,length2)
EMA22 = ta.ema(EMA12,length2)
Diff2 = EMA12 - EMA22
ZLEMA2 = EMA12 + Diff2
// Trend Mapping and Plotting //
FastTrend = TrendSelectorInput == "ALMA" ? ALMA : TrendSelectorInput == "DEMA" ? DEMA : TrendSelectorInput == "DSMA" ? EDSMA : TrendSelectorInput == "EMA" ? EMA : TrendSelectorInput == "HMA" ? HMA : TrendSelectorInput == "JMA" ? JMA : TrendSelectorInput == "KAMA" ? KAMA : TrendSelectorInput == "Linear Regression (LSMA)" ? LinReg : TrendSelectorInput == "RMA" ? RMA : TrendSelectorInput == "SMA" ? SMA : TrendSelectorInput == "SMMA" ? SMMA : TrendSelectorInput == "Price Source" ? src : TrendSelectorInput == "TEMA" ? TEMA : TrendSelectorInput == "TMA" ? TMA : TrendSelectorInput == "VAMA" ? VAMA : TrendSelectorInput == "VIDYA" ? VIDYA : TrendSelectorInput == "VMA" ? VMA : TrendSelectorInput == "VWMA" ? VWMA : TrendSelectorInput == "WMA" ? WMA : TrendSelectorInput == "WWMA" ? WWMA : TrendSelectorInput == "ZLEMA" ? ZLEMA : SMA
SlowTrend = TrendSelectorInput2 == "ALMA" ? ALMA2 : TrendSelectorInput2 == "DEMA" ? DEMA2 : TrendSelectorInput2 == "DSMA" ? EDSMA2 : TrendSelectorInput2 == "EMA" ? EMA2 : TrendSelectorInput2 == "HMA" ? HMA2 : TrendSelectorInput2 == "JMA" ? JMA2 : TrendSelectorInput2 == "KAMA" ? KAMA2 : TrendSelectorInput2 == "Linear Regression (LSMA)" ? LinReg2 : TrendSelectorInput2 == "RMA" ? RMA2 : TrendSelectorInput2 == "SMA" ? SMA2 : TrendSelectorInput2 == "SMMA" ? SMMA2 : TrendSelectorInput2 == "Price Source" ? src : TrendSelectorInput2 == "TEMA" ? TEMA2 : TrendSelectorInput2 == "TMA" ? TMA2 : TrendSelectorInput2 == "VAMA" ? VAMA2 : TrendSelectorInput2 == "VIDYA" ? VIDYA2 : TrendSelectorInput2 == "VMA" ? VMA2 : TrendSelectorInput2 == "VWMA" ? VWMA2 : TrendSelectorInput2 == "WMA" ? WMA2 : TrendSelectorInput2 == "WWMA" ? WWMA2 : TrendSelectorInput2 == "ZLEMA" ? ZLEMA2 : SMA2
plot(FastTrend, color=color.green, linewidth=LineWidth)
plot(SlowTrend, color=color.red, linewidth=LineWidth)
//Short & Long Options
Long = input.bool(true, "Model Long Trades", group="Core Settings")
Short = input.bool(false, "Model Short Trades", group="Core Settings")
// Entry & Exit Functions //
if (InDateRange and Long==true and FastTrend>SlowTrend)
strategy.entry("Long", strategy.long, alert_message="Long")
if (InDateRange and Long==true and FastTrend<SlowTrend)
strategy.close("Long", alert_message="Close Long")
if (InDateRange and Short==true and FastTrend<SlowTrend)
strategy.entry("Short", strategy.short, alert_message="Short")
if (InDateRange and Short==true and FastTrend>SlowTrend)
strategy.close("Short", alert_message="Cover Short")
if (not InDateRange)
strategy.close_all(alert_message="End of Date Range")