ChaoZhang
概述
黄金交叉交易策略是一种中长线趋势跟踪策略。它通过计算股价的SR指标和SR信号指标,识别股价的趋势方向,并结合神经网络绘制趋势通道,实现趋势跟踪操作。当SR指标上穿SR信号时生成买入信号;当SR指标下穿SR信号时生成卖出信号。该策略还使用自适应线性回归滤波技术优化通道曲线,有效抑制错误信号。
策略原理
该策略的核心指标是SR指标和SR信号指标。SR指标是以8周期为参数的WMA均线和SMA均线的二次合成。SR信号指标是以20周期为参数计算的SR指标。SR指标和SR信号的金叉死叉交给判断趋势方向。
该策略使用神经网络算法自动绘制股票价位的上下限,形成自适应通道。上限是以SR指标的历史极大值作为输入,下限是以历史极小值作为输入,然后分别计算出回归曲线作为通道上下限。通道曲线经过自适应线性回归滤波后更加平滑。
当SR指标上穿SR信号时,产生买入信号;当SR指标下穿SR信号时,产生卖出信号。做多做空信号发出后,股票价格与通道上下限的关系决定止损止盈位置。
优势分析
- 使用双线性合成技术消除价格震荡的影响, Accurately判断趋势方向;
- 自适应通道算法优化入场出场时机,避免假性突破;
- 通道曲线应用自适应线性回归滤波技术,避免曲线被极值影响;
- 止损止盈位置随通道动态变化,自动跟踪趋势获利。
风险分析
该策略主要 based on 趋势跟踪,存在如下主要风险:
- 震荡趋势中产生大量错误信号和过多无效操作;
- 突发事件导致 fast 向下突破通道下限造成较大亏损;
- 参数设置不当容易 caused 策略失效。
为控制风险,建议组合其他策略,避免单一策略操作;同时优化参数设置,适应不同市场环境。
优化方向
该策略可以从以下几个方面进行优化:
优化SR指标和信号指标的参数,提高交叉信号的稳定性;
优化自适应通道的长度周期,平滑通道曲线;
增加其他过滤指标,避免误操作。例如量能指标、波动率指标等;
结合深度学习算法实时优化通道曲线,提高自适应性。
总结
黄金交叉交易策略是一种有效跟踪中长线趋势的量化策略。它正确判断趋势方向的概率大、操作风险小。随着算法模型优化空间巨大,该策略有望成为跟踪股票趋势变化的有力工具。
策略源码
/*backtest
start: 2023-11-15 00:00:00
end: 2023-11-22 00:00:00
period: 30m
basePeriod: 15m
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
//@version=4
//
// ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ //
strategy(title = " Strategy PyramiCover",
shorttitle = "S-PC",
overlay = true,
precision = 8,
calc_on_order_fills = true,
calc_on_every_tick = true,
backtest_fill_limits_assumption = 0,
default_qty_type = strategy.fixed,
default_qty_value = 2,
initial_capital = 10000,
pyramiding=50,
currency = currency.USD,
linktoseries = true)
//
// ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ //
backTestSectionFrom = input(title = "═══════════════ From ═══════════════", defval = true, type = input.bool)
FromMonth = input(defval = 1, title = "Month", minval = 1)
FromDay = input(defval = 1, title = "Day", minval = 1)
FromYear = input(defval = 2014, title = "Year", minval = 2014)
backTestSectionTo = input(title = "════════════════ To ════════════════", defval = true, type = input.bool)
ToMonth = input(defval = 31, title = "Month", minval = 1)
ToDay = input(defval = 12, title = "Day", minval = 1)
ToYear = input(defval = 9999, title = "Year", minval = 2014)
backTestPeriod() => (time > timestamp(FromYear, FromMonth, FromDay, 00, 00)) and (time < timestamp(ToYear, ToMonth, ToDay, 23, 59))
//
// ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ //
per = input(14,title="🔹 Length")
//
up = 0.0
nup= 0.0
lowl = 0.0
nin = 0.0
//
srl=wma(close,8)
srr = sma(close,8)
sr = 2*srl - srr
//
srsl=wma(close,20)
srsr= sma(close,20)
srsignal = 2*srsl - srsr
//
if sr>srsignal
up := highest(sr,round(150))
nup :=highest(srsignal,round(20))
else
up := highest(srsignal,round(150))
nup := highest(sr,round(20))
//
if sr<srsignal
lowl := lowest(sr,round(150))
nin := lowest(srsignal,round(20))
else
lowl := lowest(sr,round(150))
nin := lowest(srsignal,round(20))
//reg alexgrover
f_reg(src,length)=>
x = bar_index
y = src
x_ = sma(x, length)
y_ = sma(y, length)
mx = stdev(x, length)
my = stdev(y, length)
c = correlation(x, y, length)
slope = c * (my / mx)
inter = y_ - slope * x_
reg = x * slope + inter
reg
//
up_=f_reg(up,per)
lowl_=f_reg(lowl,per)
nup_=f_reg(nup,per)
nin_=f_reg(nin,per)
//
plot(sr, title='SR', color=color.green, linewidth=2, style=plot.style_line,transp=0)
plot(srsignal, title='SR-Signal', color=color.red, linewidth=2, style=plot.style_line,transp=0)
plot(up_, title='Upper limit', color=color.blue, linewidth=3, style=plot.style_line,transp=0)
plot(lowl_, title='Lower limit', color=color.blue, linewidth=3, style=plot.style_line,transp=0)
a=plot(nup_, title='Neuronal Upper', color=color.gray, linewidth=1, style=plot.style_line,transp=0)
b=plot(nin_, title='Neuronal Lower', color=color.gray, linewidth=1, style=plot.style_line,transp=0)
fill(a, b, color=color.gray)
plotshape(crossunder(sr,nup_)? sr+atr(20):na, title="Sell", text="🐻", location=location.absolute, style=shape.labeldown, size=size.tiny, color=color.red, textcolor=color.black,transp=0)
plotshape(crossover(sr,nin_)? sr-atr(20):na, title="Buy", text="🐂", location=location.absolute, style=shape.labelup, size=size.tiny, color=color.green, textcolor=color.black,transp=0)
//
// ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ //
if backTestPeriod()
strategy.entry("Buy", true, 1, when = crossover(sr,nin_))
strategy.entry("Short", false, 1, when = crossunder(sr,nup_))
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