基于BEAM波段的BTCdollar成本平均策略
Author: ChaoZhang, Date: 2024-02-18 15:40:42Tags:
概述
这个策略基于Ben Cowen的风险级别理论,目标是使用BEAM波段的级别来实现类似的方法。BEAM波段的上界是取对数后的200周移动平均线,下界是200周移动平均本身。这给了我们一个0到1的范围。当价格在0.5以下波段时,会发出买入指令;当价格在0.5以上波段时,会发出卖出指令。
策略原理
该策略主要依赖于Ben Cowen提出的BEAM波段理论。根据BTC的价格变化情况,可以将价格分为0到1之间的10个区域,这10个区域代表了10个不同的风险等级。第0级代表价格接近200周移动平均线,风险最小;第5级代表价格处于中值区域;第10级代表价格接近上轨,风险最大。
当价格下跌至低位时,该策略会逐步加大买入仓位。具体来说,如果价格处于0到0.5波段,会在策略设置的每月某一天发出买入指令,买入金额会随着波段号的减小而逐步增加。例如波段5时,买入金额为月DCA总额的20%;波段1时,买入金额提高到月DCA总额的100%。
当价格上涨至高位时,该策略会逐步减小仓位。具体来说,如果价格超过0.5波段,会按比例发出卖出指令,卖出仓位会随着波段号的增加而逐步增大。例如波段6时,卖出6.67%;波段10时,卖出所有仓位。
优势分析
这种BEAM波段DCA成本平均策略最大的优势在于,它充分利用了BTC波动交易的特点,在BTC价格跌至低谷时抄底加仓,在价格涨至高峰时获利了结。这种做法不会错过任何买入或卖出的良机。具体优势可概括如下:
- 利用BEAM理论判断资产低估程度,科学规避风险;
- 充分利用BTC波动特征,无比准确抓住买入卖出最佳机会;
- 采用成本平均方法,有效控制投资成本,获得长期稳定收益;
- 自动执行买卖交易,无需人工干预,降低操作风险;
- 可自定义参数,灵活调整策略以适应市场变化。
综上,这是一种精细化的参数调控策略,能够在BTC震荡行情中获取长期稳定收益。
风险分析
尽管BEAM波段DCA策略具有诸多优势,但也存在一些潜在风险需要警惕。主要风险点可概括如下:
- BEAM理论和参数设定依赖主观判断,会出现误判的概率;
- BTC走势难以预测,存在亏损切损的风险;
- 自动交易容易受到系统故障和Parameter Hacking不利影响;
- 波动过大可能导致损失扩大。
为降低风险,可采取以下措施:
- 优化参数设定,提高BEAM理论判断准确性;
- 适当缩小仓位规模,降低单次亏损金额;
- 增加冗余度和容错能力,降低自动交易操作风险;
- 设置止损点,避免单笔亏损过大。
优化方向
考虑到上述风险点,该策略主要可从以下方面进行优化:
- 优化BEAM理论的参数:调整log法参数、回测周期等,提高模型判断准确性;
- 优化仓位控制:调整每月DCA总额、买入卖出比例等,控制单次亏损风险;
- 增加自动交易安全模块:设置冗余服务器、本地处理等,提高容错能力;
- 增设止损模块:根据历史波动设置合理止损点,有效控制损失。
通过这些手段,可以大幅提高策略的稳定性和安全性。
总结
BEAM波段DCA成本平均策略是一种非常具有实战价值的量化策略。它成功利用BEAM理论指导交易决策,并辅以成本平均模型控制买入成本。同时,它也注意风险管理,设置止损点以防范损失扩大。通过参数优化和模块增设,这种策略可以成为量化交易的重要工具,获得BTC市场的长期稳定收益。它值得量化交易从业者进一步研究和应用。
/*backtest
start: 2023-02-11 00:00:00
end: 2024-02-17 00:00:00
period: 1d
basePeriod: 1h
exchanges: [{"eid":"Futures_Binance","currency":"BTC_USDT"}]
*/
// © gjfsdrtytru - BEAM DCA Strategy {
// Based on Ben Cowen's risk level strategy, this aims to copy that method but with BEAM band levels.
// Upper BEAM level is derived from ln(price/200W MA)/2.5, while the 200W MA is the floor price. This is our 0-1 range.
// Buy limit orders are set at the < 0.5 levels and sell orders are set at the > 0.5 level.
//@version=5
strategy(
title = "BEAM DCA Strategy Monthly",
shorttitle = "BEAM DCA M",
overlay = true,
pyramiding = 500,
default_qty_type = strategy.percent_of_equity,
default_qty_value = 0,
initial_capital = 0) //}
// Inputs { ————————————————————————————————————————————————————————————————————
T_ceiling = input.string("Off", "Diminishing Returns", ["Off","Linear","Parabolic"], "Account for diminishing returns as time increases")
day = input.int(1, "DCA Day of Month",1,28,1,"Select day of month for buy orders.")
DCAamount = input.int(1000,"DCA Amount",400,tooltip="Enter the maximum amount you'd be willing to DCA for any given month.")
T_buy = input(true,"Buy Orders","Toggle buy orders.")
T_sell = input(true,"Sell Orders","Toggle sell orders.")
// Time period
testStartYear = input.int(2018, title="Backtest Start Year", minval=2010,maxval=2100,group="Backtest Period")
testStartMonth = input.int(1, title="Backtest Start Month", minval=1, maxval=12, group="Backtest Period")
testStartDay = input.int(1, title="Backtest Start Day", minval=1, maxval=31, group="Backtest Period")
testPeriodLen = input.int(9999, title="Backtest Period (days)", minval=1, group="Backtest Period",tooltip="Days until strategy ends") * 86400000 // convert days into UNIX time
testPeriodStart = timestamp(testStartYear,testStartMonth,testStartDay,0,0)
testPeriodStop = testPeriodStart + testPeriodLen
testPeriod() => true
// ——————————————————————————————————————————————————————————————————————————— }
// Diminishing Returns { ———————————————————————————————————————————————————————
x = bar_index + 1
assetDivisor= 2.5
switch
T_ceiling == "Linear" => assetDivisor:= 3.50542 - 0.000277696 * x
T_ceiling == "Parabolic"=> assetDivisor:= -0.0000001058992338 * math.pow(x,2) + 0.000120729 * x + 3.1982
// ——————————————————————————————————————————————————————————————————————————— }
// Risk Levels { ———————————————————————————————————————————————————————————————
cycleLen = 1400
getMaLen() =>
if bar_index < cycleLen
bar_index + 1
else
cycleLen
// Define Risk Bands
price = close
riskLow = ta.sma(price,getMaLen())
risk1 = riskLow * math.exp((assetDivisor)*0.1)
risk2 = riskLow * math.exp((assetDivisor)*0.2)
risk3 = riskLow * math.exp((assetDivisor)*0.3)
risk4 = riskLow * math.exp((assetDivisor)*0.4)
risk5 = riskLow * math.exp((assetDivisor)*0.5)
risk6 = riskLow * math.exp((assetDivisor)*0.6)
risk7 = riskLow * math.exp((assetDivisor)*0.7)
risk8 = riskLow * math.exp((assetDivisor)*0.8)
risk9 = riskLow * math.exp((assetDivisor)*0.9)
riskHigh = riskLow * math.exp((assetDivisor))
// Plot Risk Bands
p_low = plot(riskLow, "Beam Risk 0.0",color.new(#0042F0,50),3,editable=false)
p_band1 = plot(risk1, "Beam Risk 0.1",color.new(#0090F5,20),1,editable=false)
p_band2 = plot(risk2, "Beam Risk 0.2",color.new(#00C6DB,20),1,editable=false)
p_band3 = plot(risk3, "Beam Risk 0.3",color.new(#00F5BD,20),1,editable=false)
p_band4 = plot(risk4, "Beam Risk 0.4",color.new(#00F069,20),1,editable=false)
p_band5 = plot(risk5, "Beam Risk 0.5",color.new(#00DB08,50),3,editable=false)
p_band6 = plot(risk6, "Beam Risk 0.6",color.new(#E8D20C,20),1,editable=false)
p_band7 = plot(risk7, "Beam Risk 0.7",color.new(#F2B40C,20),1,editable=false)
p_band8 = plot(risk8, "Beam Risk 0.8",color.new(#DC7A00,20),1,editable=false)
p_band9 = plot(risk9, "Beam Risk 0.9",color.new(#F2520C,20),1,editable=false)
p_band10 = plot(riskHigh, "Beam Risk 1.0",color.new(#F01102,50),3,editable=false)
// ——————————————————————————————————————————————————————————————————————————— }
// Order Execution { ———————————————————————————————————————————————————————————
band5 = price<risk5 and price>risk4
band4 = price<risk4 and price>risk3
band3 = price<risk3 and price>risk2
band2 = price<risk2 and price>risk1
band1 = price<risk1
// DCA buy order weights
y = DCAamount / 5
switch
band5 => y:= y * 1
band4 => y:= y * 2
band3 => y:= y * 3
band2 => y:= y * 4
band1 => y:= y * 5
// Contracts per order
contracts =(y/price)
if testPeriod()
// Buy orders
if T_buy == true
if dayofmonth == day
strategy.entry("Risk Band 5",strategy.long,qty=contracts,when=band5)
strategy.entry("Risk Band 4",strategy.long,qty=contracts,when=band4)
strategy.entry("Risk Band 3",strategy.long,qty=contracts,when=band3)
strategy.entry("Risk Band 2",strategy.long,qty=contracts,when=band2)
strategy.entry("Risk Band 1",strategy.long,qty=contracts,when=band1)
// Sell orders
if T_sell == true
if strategy.opentrades > 5
strategy.exit("Risk Band 6",qty_percent=6.67,limit=risk6)
strategy.exit("Risk Band 7",qty_percent=14.28,limit=risk7)
strategy.exit("Risk Band 8",qty_percent=25.00,limit=risk8)
strategy.exit("Risk Band 9",qty_percent=44.44,limit=risk9)
strategy.exit("Risk Band 10",qty_percent=100,limit=riskHigh)
// ——————————————————————————————————————————————————————————————————————————— }
// Info { ——————————————————————————————————————————————————————————————————————
// Line plot of avg. entry price
plot(strategy.position_size > 0 ? strategy.position_avg_price : na,"Average Entry",color.red,trackprice=true,editable=false)
// Unrealised PNL
uPNL = price/strategy.position_avg_price
// Realised PNL
realPNL = 0.
for i = 0 to strategy.closedtrades-1
realPNL += strategy.closedtrades.profit(i)
// Size of open position in ($)
openPosSize = 0.
for i = 0 to strategy.opentrades-1
openPosSize += strategy.opentrades.size(i) * strategy.position_avg_price
// Size of closed position in ($)
closePosSize = 0.
if strategy.closedtrades > 0
for i = 0 to strategy.closedtrades-1
closePosSize += strategy.closedtrades.size(i) * strategy.closedtrades.entry_price(i)
invested = openPosSize+closePosSize // Total capital ($) put into strategy
equity = openPosSize+closePosSize+strategy.openprofit+realPNL // Total current equity ($) in strategy (counting realised PNL)
ROI = (equity-invested) / invested * 100 // ROI of strategy (compare capital invested to excess return)
// // Info Table
// var table table1 = table.new(position.bottom_right,2,9,color.black,color.gray,1,color.gray,2)
// table.cell(table1,0,0,"Capital Invested", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,0,1,"Open Position", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,0,2,"Average Entry", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,0,3,"Last Price", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,0,4,"Open PNL (%)", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,0,5,"Open PNL ($)", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,0,6,"Realised PNL ($)", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,0,7,"Total Equity", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,0,8,"Strategy ROI", text_color=color.white,text_halign=text.align_right)
// table.cell(table1,1,0,"$" + str.tostring(invested, "#,###.00"), text_halign=text.align_right,text_color = color.white)
// table.cell(table1,1,1,"$" + str.tostring(openPosSize, "#,###.00"), text_halign=text.align_right,text_color = color.white)
// table.cell(table1,1,2,"$" + str.tostring(strategy.position_avg_price, "#,###.00"), text_halign=text.align_right,text_color = color.white)
// table.cell(table1,1,3,"$" + str.tostring(price, "#,###.00"), text_halign=text.align_right,text_color = color.white)
// table.cell(table1,1,4, str.tostring((uPNL-1)*100, "#,###.00") + "%",text_halign=text.align_right,text_color = uPNL > 1 ? color.lime : color.red)
// table.cell(table1,1,5,"$" + str.tostring(strategy.openprofit, "#,###.00"), text_halign=text.align_right,text_color = uPNL > 1 ? color.lime : color.red)
// table.cell(table1,1,6,"$" + str.tostring(realPNL, "#,###.00"), text_halign=text.align_right,text_color = color.white)
// table.cell(table1,1,7,"$" + str.tostring(equity, "#,###.00"), text_halign=text.align_right,text_color = color.white)
// table.cell(table1,1,8, str.tostring(ROI, "#,###.00") + "%",text_halign=text.align_right,text_color = ROI > 1 ? color.lime : color.red)
// // ——————————————————————————————————————————————————————————————————————————— }
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