使用自定义指标进行参数优化 - Quantstrat
Parameter Optimization with Custom indicators - Quantstrat
我在使用 自定义指标 时在 Quantstrat 中寻找参数优化代码示例。
我在网上可以找到的大多数示例都使用 SMA、MACD 和其他经典指标。它对我帮助不大，因为我最想使用自定义指标优化交易系统。
有没有人有相关链接或material？
更具体地说，我是第一次尝试指标优化选项。我使用的指标是 MACD 森林的动量。我正在尝试评估该指标与通常的 MACD 交叉信号线的有效性。
但我不想过分关注这个特定指标，因为我的目标是为我将要使用的未来自定义指标提供可行的优化代码。
这是代码，数据是日内 EURUSD 报价（5 分钟蜡烛图）：
library(quantstrat)
Sys.setenv(TZ = "UTC")
currency(c('EUR', 'USD'))
exchange_rate(c('EURUSD'), tick_size=0.0001)
init_date <- "2018-01-07"
start_date <- "2018-01-08"
end_date <- "2018-01-08"
init_equity <- 1e8 # 0,000,000
adjustment <- FALSE
basic_symbols <- function() {
symbols <- c(
"EUR"
)
}
symbols <- basic_symbols()
portfolio.st <- "Port.Luxor.Opt"
account.st <- "Acct.Luxor.Opt"
strategy.st <- "Strat.Luxor.Opt"
rm.strat(portfolio.st)
rm.strat(account.st)
initPortf(name = portfolio.st,
symbols = symbols,
initDate = init_date)
initAcct(name = account.st,
portfolios = portfolio.st,
initDate = init_date,
initEq = init_equity)
initOrders(portfolio = portfolio.st,
symbols = symbols,
initDate = init_date)
strategy(strategy.st, store = TRUE)
fastMA_custom = 12
fastMA_custom2 = 12
slowMA_custom = 26
slowMA_custom2 = 26
signalMA_custom = 9
signalMA_custom2 = 9
maType="EMA"
MAforest = 3
forest <- function(x){
step1 <- EMA(x,fastMA_custom)
step2 <- EMA(x,slowMA_custom)
step3 <- step1-step2
step4 <- EMA(step3,signalMA_custom)
step5 <- step3-step4
return(step5)
}
smaforest <- function(x){
step1 <- EMA(x,fastMA_custom2)
step2 <- EMA(x,slowMA_custom2)
step3 <- step1-step2
step4 <- EMA(step3,signalMA_custom2)
step5 <- step3-step4
step6 <- EMA(step5,MAforest)
return(step6)
}
add.indicator(strategy = strategy.st,
name ="forest",
arguments = list(x=quote(Cl(mktdata))),
label="forest")
add.indicator(strategy=strategy.st,
name ="smaforest",
arguments = list(x=quote(Cl(mktdata))),
label="smaforest")
add.distribution(strategy.st,
paramset.label = "forestopt",
component.type = "indicator",
component.label = "forest",
variable = list(fastMA_custom= 8:14),
label = "fastMA_custom")
add.signal(strategy = strategy.st,
name="sigCrossover",
arguments = list(columns = c("forest", "smaforest"),
relationship = "gte"),
label = "long")
add.signal(strategy = strategy.st,
name="sigCrossover",
arguments = list(columns = c("forest", "smaforest"),
relationship = "lte"),
label = "short")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "long",
sigval = TRUE,
orderqty = 100000,
ordertype = "market",
orderside = "long",
TxnFees = -1,
replace = FALSE),
type = "enter",
label = "EnterLONG")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "short",
sigval = TRUE,
orderqty = -100000,
ordertype = "market",
orderside = "short",
replace = FALSE,
TxnFees = -1
),
type = "enter",
label = "EnterSHORT")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "short",
sigval = TRUE,
orderside = "long",
ordertype = "market",
orderqty = "all",
TxnFees = -1,
replace = TRUE),
type = "exit",
label = "Exit2SHORT")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "long",
sigval = TRUE,
orderside = "short",
ordertype = "market",
orderqty = "all",
TxnFees = -1,
replace = TRUE),
type = "exit",
label = "Exit2LONG")
addPosLimit(portfolio.st, 'EUR', timestamp=initDate, maxpos=500, minpos=0)
resultsopt <- apply.paramset(strategy.st,
paramset.label = "forestopt",
portfolio.st = portfolio.st,
account.st = account.st,
nsamples = 0)
没有优化部分的回测完美运行。确实是导致错误的优化位。
这是我得到的错误：
error calling combine function: "simpleError in fun(result.1,
result.2, result.3, result.4, result.5, result.6, result.7):
attempt to select less than one element"
下次请提供可复现的数据集。
这是与您的非常相似的示例代码，可以优化自定义指标。希望您应该清楚如何调整代码。你做错了几件事。特别是，您必须为 forest 或您想要在 quantstrat 中优化的任何其他自定义函数提供形式参数。通常，默认情况下全局定义所有 "n" 变量是个坏主意。将它们直接提供给 add.indicator 调用中的参数。就像下面为 forest 函数所做的一样。
library(quantstrat)
Sys.setenv(TZ = "UTC")
currency(c('USD'))
symbols <- "AAPL"
getSymbols(symbols)
stock(symbols, currency = "USD")
AAPL <- AAPL["2016/"]
init_date <- "2017-01-07"
start_date <- "2018-01-31"
end_date <- "2018-01-31"
init_equity <- 1e8 # 0,000,000
adjustment <- FALSE
portfolio.st <- "Luxor.Opt"
account.st <- "Luxor.Opt"
strategy.st <- "Luxor.Opt"
rm.strat(name = portfolio.st)
initPortf(name = portfolio.st,
symbols = symbols)
initAcct(name = account.st,
portfolios = portfolio.st,
initEq = init_equity)
initOrders(portfolio = portfolio.st,
symbols = symbols)
strategy(strategy.st, store = TRUE)
fastMA_custom2 = 12
slowMA_custom2 = 26
signalMA_custom2 = 9
maType="EMA"
MAforest = 3
forest <- function(x, fastMA_custom, slowMA_custom, signalMA_custom){
step1 <- EMA(x,fastMA_custom)
step2 <- EMA(x,slowMA_custom)
step3 <- step1-step2
step4 <- EMA(step3,signalMA_custom)
step5 <- step3-step4
return(step5)
}
smaforest <- function(x){
step1 <- EMA(x,fastMA_custom2)
step2 <- EMA(x,slowMA_custom2)
step3 <- step1-step2
step4 <- EMA(step3,signalMA_custom2)
step5 <- step3-step4
step6 <- EMA(step5,MAforest)
return(step6)
}
add.indicator(strategy = strategy.st,
name ="forest",
arguments = list(x=quote(Cl(mktdata)),
fastMA_custom = 12,
slowMA_custom = 26,
signalMA_custom = 9
),
label="forest")
add.indicator(strategy=strategy.st,
name ="smaforest",
arguments = list(x=quote(Cl(mktdata))),
label="smaforest")
add.signal(strategy = strategy.st,
name="sigCrossover",
arguments = list(columns = c("forest", "smaforest"),
relationship = "gte"),
label = "long")
add.signal(strategy = strategy.st,
name="sigCrossover",
arguments = list(columns = c("forest", "smaforest"),
relationship = "lte"),
label = "short")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "long",
sigval = TRUE,
orderqty = 100000,
ordertype = "market",
orderside = "long",
TxnFees = -1,
replace = FALSE),
type = "enter",
label = "EnterLONG")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "short",
sigval = TRUE,
orderqty = -100000,
ordertype = "market",
orderside = "short",
replace = FALSE,
TxnFees = -1
),
type = "enter",
label = "EnterSHORT")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "short",
sigval = TRUE,
orderside = "long",
ordertype = "market",
orderqty = "all",
TxnFees = -1,
replace = TRUE),
type = "exit",
label = "Exit2SHORT")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "long",
sigval = TRUE,
orderside = "short",
ordertype = "market",
orderqty = "all",
TxnFees = -1,
replace = TRUE),
type = "exit",
label = "Exit2LONG")
addPosLimit(portfolio.st, symbols[], timestamp=init_date, maxpos=500, minpos=0)
# applyStrategy(strategy.st, portfolio.st)
#
# updatePortf(portfolio.st)
# tradeStats(portfolio.st, symbols)
# Portfolio Symbol Num.Txns Num.Trades Net.Trading.PL Avg.Trade.PL Med.Trade.PL Largest.Winner Largest.Loser Gross.Profits Gross.Losses Std.Dev.Trade.PL Std.Err.Trade.PL Percent.Positive Percent.Negative Profit.Factor
# AAPL Luxor.Opt AAPL 127 63 -45123.5 -28493.01 -46000.1 1328999 -1047001 10371975 -12167035 474783 59817.04 42.85714 57.14286 0.8524653
# Avg.Win.Trade Med.Win.Trade Avg.Losing.Trade Med.Losing.Trade Avg.Daily.PL Med.Daily.PL Std.Dev.Daily.PL Std.Err.Daily.PL Ann.Sharpe Max.Drawdown Profit.To.Max.Draw Avg.WinLoss.Ratio Med.WinLoss.Ratio Max.Equity Min.Equity
# AAPL 384147.2 305999.5 -337973.2 -298000.6 -28493.01 -46000.1 474783 59817.04 -0.952672 -3182097 -0.01418043 1.13662 1.026842 985976.4 -2196121
# End.Equity
# AAPL -45123.5
add.distribution(strategy.st,
paramset.label = "forestopt", #The label we will use when we want to run this optimisation in paramset
component.type = "indicator", # The custom function is of indicator type (not other alternatives including signal or rule)
component.label = "forest", #this is the name of your custom function
variable = list(fastMA_custom = seq(8, 12, by = 2)),
label = "myForestOptLabel") #choose whatever you want
resultsopt <- apply.paramset(strategy.st,
paramset.label = "forestopt",
portfolio.st = portfolio.st,
account.st = account.st,
nsamples = 0)
# Check, results are equal in the case where fastMA_custom = 12 compared to when running `applyStrategy`:
resultsopt$tradeStats
# myForestOptLabel Portfolio Symbol Num.Txns Num.Trades Net.Trading.PL Avg.Trade.PL Med.Trade.PL Largest.Winner Largest.Loser Gross.Profits Gross.Losses Std.Dev.Trade.PL Std.Err.Trade.PL Percent.Positive Percent.Negative
# 1 8 Luxor.Opt.1 AAPL 97 48 2609902.7 23790.64 -34500.75 1405999 -1055001 9615975 -8474024 492362.4 71066.39 43.75000 56.25000
# 2 10 Luxor.Opt.2 AAPL 105 52 -340106.5 -34770.27 -52500.75 1405999 -1047001 8538977 -10347031 480790.5 66673.64 40.38462 59.61538
# 3 12 Luxor.Opt.3 AAPL 127 63 -45123.5 -28493.01 -46000.10 1328999 -1047001 10371975 -12167035 474783.0 59817.04 42.85714 57.14286
# Profit.Factor Avg.Win.Trade Med.Win.Trade Avg.Losing.Trade Med.Losing.Trade Avg.Daily.PL Med.Daily.PL Std.Dev.Daily.PL Std.Err.Daily.PL Ann.Sharpe Max.Drawdown Profit.To.Max.Draw Avg.WinLoss.Ratio Med.WinLoss.Ratio Max.Equity
# 1 1.1347590 457903.6 386998.5 -313852.8 -237000.5 23790.64 -34500.75 492362.4 71066.39 0.7670464 -2444032 1.06786773 1.458976 1.632902 3585933.7
# 2 0.8252586 406617.9 334998.1 -333775.2 -245002.2 -34770.27 -52500.75 480790.5 66673.64 -1.1480282 -3152090 -0.10789872 1.218239 1.367327 1343982.9
# 3 0.8524653 384147.2 305999.5 -337973.2 -298000.6 -28493.01 -46000.10 474783.0 59817.04 -0.9526720 -3182097 -0.01418043 1.136620 1.026842 985976.4
# Min.Equity End.Equity
# 1 -1122012 2609902.7
# 2 -1808107 -340106.5
# 3 -2196121 -45123.5
# Results for fastMA_custom = 12 are identical.
上面我还展示了如何快速检查 apply.paramsets 输出，通过 apply.Strategy 将其与 n = 12 的个别情况进行比较。这样做是个好主意各种支票。
我在使用 自定义指标 时在 Quantstrat 中寻找参数优化代码示例。 我在网上可以找到的大多数示例都使用 SMA、MACD 和其他经典指标。它对我帮助不大，因为我最想使用自定义指标优化交易系统。
有没有人有相关链接或material？
更具体地说，我是第一次尝试指标优化选项。我使用的指标是 MACD 森林的动量。我正在尝试评估该指标与通常的 MACD 交叉信号线的有效性。 但我不想过分关注这个特定指标，因为我的目标是为我将要使用的未来自定义指标提供可行的优化代码。
这是代码，数据是日内 EURUSD 报价（5 分钟蜡烛图）：
library(quantstrat)
Sys.setenv(TZ = "UTC")
currency(c('EUR', 'USD'))
exchange_rate(c('EURUSD'), tick_size=0.0001)
init_date <- "2018-01-07"
start_date <- "2018-01-08"
end_date <- "2018-01-08"
init_equity <- 1e8 # 0,000,000
adjustment <- FALSE
basic_symbols <- function() {
symbols <- c(
"EUR"
)
}
symbols <- basic_symbols()
portfolio.st <- "Port.Luxor.Opt"
account.st <- "Acct.Luxor.Opt"
strategy.st <- "Strat.Luxor.Opt"
rm.strat(portfolio.st)
rm.strat(account.st)
initPortf(name = portfolio.st,
symbols = symbols,
initDate = init_date)
initAcct(name = account.st,
portfolios = portfolio.st,
initDate = init_date,
initEq = init_equity)
initOrders(portfolio = portfolio.st,
symbols = symbols,
initDate = init_date)
strategy(strategy.st, store = TRUE)
fastMA_custom = 12
fastMA_custom2 = 12
slowMA_custom = 26
slowMA_custom2 = 26
signalMA_custom = 9
signalMA_custom2 = 9
maType="EMA"
MAforest = 3
forest <- function(x){
step1 <- EMA(x,fastMA_custom)
step2 <- EMA(x,slowMA_custom)
step3 <- step1-step2
step4 <- EMA(step3,signalMA_custom)
step5 <- step3-step4
return(step5)
}
smaforest <- function(x){
step1 <- EMA(x,fastMA_custom2)
step2 <- EMA(x,slowMA_custom2)
step3 <- step1-step2
step4 <- EMA(step3,signalMA_custom2)
step5 <- step3-step4
step6 <- EMA(step5,MAforest)
return(step6)
}
add.indicator(strategy = strategy.st,
name ="forest",
arguments = list(x=quote(Cl(mktdata))),
label="forest")
add.indicator(strategy=strategy.st,
name ="smaforest",
arguments = list(x=quote(Cl(mktdata))),
label="smaforest")
add.distribution(strategy.st,
paramset.label = "forestopt",
component.type = "indicator",
component.label = "forest",
variable = list(fastMA_custom= 8:14),
label = "fastMA_custom")
add.signal(strategy = strategy.st,
name="sigCrossover",
arguments = list(columns = c("forest", "smaforest"),
relationship = "gte"),
label = "long")
add.signal(strategy = strategy.st,
name="sigCrossover",
arguments = list(columns = c("forest", "smaforest"),
relationship = "lte"),
label = "short")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "long",
sigval = TRUE,
orderqty = 100000,
ordertype = "market",
orderside = "long",
TxnFees = -1,
replace = FALSE),
type = "enter",
label = "EnterLONG")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "short",
sigval = TRUE,
orderqty = -100000,
ordertype = "market",
orderside = "short",
replace = FALSE,
TxnFees = -1
),
type = "enter",
label = "EnterSHORT")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "short",
sigval = TRUE,
orderside = "long",
ordertype = "market",
orderqty = "all",
TxnFees = -1,
replace = TRUE),
type = "exit",
label = "Exit2SHORT")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "long",
sigval = TRUE,
orderside = "short",
ordertype = "market",
orderqty = "all",
TxnFees = -1,
replace = TRUE),
type = "exit",
label = "Exit2LONG")
addPosLimit(portfolio.st, 'EUR', timestamp=initDate, maxpos=500, minpos=0)
resultsopt <- apply.paramset(strategy.st,
paramset.label = "forestopt",
portfolio.st = portfolio.st,
account.st = account.st,
nsamples = 0)
没有优化部分的回测完美运行。确实是导致错误的优化位。
这是我得到的错误：
error calling combine function: "simpleError in fun(result.1, result.2, result.3, result.4, result.5, result.6, result.7): attempt to select less than one element"
下次请提供可复现的数据集。
这是与您的非常相似的示例代码，可以优化自定义指标。希望您应该清楚如何调整代码。你做错了几件事。特别是，您必须为 forest 或您想要在 quantstrat 中优化的任何其他自定义函数提供形式参数。通常，默认情况下全局定义所有 "n" 变量是个坏主意。将它们直接提供给 add.indicator 调用中的参数。就像下面为 forest 函数所做的一样。
library(quantstrat)
Sys.setenv(TZ = "UTC")
currency(c('USD'))
symbols <- "AAPL"
getSymbols(symbols)
stock(symbols, currency = "USD")
AAPL <- AAPL["2016/"]
init_date <- "2017-01-07"
start_date <- "2018-01-31"
end_date <- "2018-01-31"
init_equity <- 1e8 # 0,000,000
adjustment <- FALSE
portfolio.st <- "Luxor.Opt"
account.st <- "Luxor.Opt"
strategy.st <- "Luxor.Opt"
rm.strat(name = portfolio.st)
initPortf(name = portfolio.st,
symbols = symbols)
initAcct(name = account.st,
portfolios = portfolio.st,
initEq = init_equity)
initOrders(portfolio = portfolio.st,
symbols = symbols)
strategy(strategy.st, store = TRUE)
fastMA_custom2 = 12
slowMA_custom2 = 26
signalMA_custom2 = 9
maType="EMA"
MAforest = 3
forest <- function(x, fastMA_custom, slowMA_custom, signalMA_custom){
step1 <- EMA(x,fastMA_custom)
step2 <- EMA(x,slowMA_custom)
step3 <- step1-step2
step4 <- EMA(step3,signalMA_custom)
step5 <- step3-step4
return(step5)
}
smaforest <- function(x){
step1 <- EMA(x,fastMA_custom2)
step2 <- EMA(x,slowMA_custom2)
step3 <- step1-step2
step4 <- EMA(step3,signalMA_custom2)
step5 <- step3-step4
step6 <- EMA(step5,MAforest)
return(step6)
}
add.indicator(strategy = strategy.st,
name ="forest",
arguments = list(x=quote(Cl(mktdata)),
fastMA_custom = 12,
slowMA_custom = 26,
signalMA_custom = 9
),
label="forest")
add.indicator(strategy=strategy.st,
name ="smaforest",
arguments = list(x=quote(Cl(mktdata))),
label="smaforest")
add.signal(strategy = strategy.st,
name="sigCrossover",
arguments = list(columns = c("forest", "smaforest"),
relationship = "gte"),
label = "long")
add.signal(strategy = strategy.st,
name="sigCrossover",
arguments = list(columns = c("forest", "smaforest"),
relationship = "lte"),
label = "short")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "long",
sigval = TRUE,
orderqty = 100000,
ordertype = "market",
orderside = "long",
TxnFees = -1,
replace = FALSE),
type = "enter",
label = "EnterLONG")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "short",
sigval = TRUE,
orderqty = -100000,
ordertype = "market",
orderside = "short",
replace = FALSE,
TxnFees = -1
),
type = "enter",
label = "EnterSHORT")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "short",
sigval = TRUE,
orderside = "long",
ordertype = "market",
orderqty = "all",
TxnFees = -1,
replace = TRUE),
type = "exit",
label = "Exit2SHORT")
add.rule(strategy.st,
name = "ruleSignal",
arguments = list(sigcol = "long",
sigval = TRUE,
orderside = "short",
ordertype = "market",
orderqty = "all",
TxnFees = -1,
replace = TRUE),
type = "exit",
label = "Exit2LONG")
addPosLimit(portfolio.st, symbols[], timestamp=init_date, maxpos=500, minpos=0)
# applyStrategy(strategy.st, portfolio.st)
#
# updatePortf(portfolio.st)
# tradeStats(portfolio.st, symbols)
# Portfolio Symbol Num.Txns Num.Trades Net.Trading.PL Avg.Trade.PL Med.Trade.PL Largest.Winner Largest.Loser Gross.Profits Gross.Losses Std.Dev.Trade.PL Std.Err.Trade.PL Percent.Positive Percent.Negative Profit.Factor
# AAPL Luxor.Opt AAPL 127 63 -45123.5 -28493.01 -46000.1 1328999 -1047001 10371975 -12167035 474783 59817.04 42.85714 57.14286 0.8524653
# Avg.Win.Trade Med.Win.Trade Avg.Losing.Trade Med.Losing.Trade Avg.Daily.PL Med.Daily.PL Std.Dev.Daily.PL Std.Err.Daily.PL Ann.Sharpe Max.Drawdown Profit.To.Max.Draw Avg.WinLoss.Ratio Med.WinLoss.Ratio Max.Equity Min.Equity
# AAPL 384147.2 305999.5 -337973.2 -298000.6 -28493.01 -46000.1 474783 59817.04 -0.952672 -3182097 -0.01418043 1.13662 1.026842 985976.4 -2196121
# End.Equity
# AAPL -45123.5
add.distribution(strategy.st,
paramset.label = "forestopt", #The label we will use when we want to run this optimisation in paramset
component.type = "indicator", # The custom function is of indicator type (not other alternatives including signal or rule)
component.label = "forest", #this is the name of your custom function
variable = list(fastMA_custom = seq(8, 12, by = 2)),
label = "myForestOptLabel") #choose whatever you want
resultsopt <- apply.paramset(strategy.st,
paramset.label = "forestopt",
portfolio.st = portfolio.st,
account.st = account.st,
nsamples = 0)
# Check, results are equal in the case where fastMA_custom = 12 compared to when running `applyStrategy`:
resultsopt$tradeStats
# myForestOptLabel Portfolio Symbol Num.Txns Num.Trades Net.Trading.PL Avg.Trade.PL Med.Trade.PL Largest.Winner Largest.Loser Gross.Profits Gross.Losses Std.Dev.Trade.PL Std.Err.Trade.PL Percent.Positive Percent.Negative
# 1 8 Luxor.Opt.1 AAPL 97 48 2609902.7 23790.64 -34500.75 1405999 -1055001 9615975 -8474024 492362.4 71066.39 43.75000 56.25000
# 2 10 Luxor.Opt.2 AAPL 105 52 -340106.5 -34770.27 -52500.75 1405999 -1047001 8538977 -10347031 480790.5 66673.64 40.38462 59.61538
# 3 12 Luxor.Opt.3 AAPL 127 63 -45123.5 -28493.01 -46000.10 1328999 -1047001 10371975 -12167035 474783.0 59817.04 42.85714 57.14286
# Profit.Factor Avg.Win.Trade Med.Win.Trade Avg.Losing.Trade Med.Losing.Trade Avg.Daily.PL Med.Daily.PL Std.Dev.Daily.PL Std.Err.Daily.PL Ann.Sharpe Max.Drawdown Profit.To.Max.Draw Avg.WinLoss.Ratio Med.WinLoss.Ratio Max.Equity
# 1 1.1347590 457903.6 386998.5 -313852.8 -237000.5 23790.64 -34500.75 492362.4 71066.39 0.7670464 -2444032 1.06786773 1.458976 1.632902 3585933.7
# 2 0.8252586 406617.9 334998.1 -333775.2 -245002.2 -34770.27 -52500.75 480790.5 66673.64 -1.1480282 -3152090 -0.10789872 1.218239 1.367327 1343982.9
# 3 0.8524653 384147.2 305999.5 -337973.2 -298000.6 -28493.01 -46000.10 474783.0 59817.04 -0.9526720 -3182097 -0.01418043 1.136620 1.026842 985976.4
# Min.Equity End.Equity
# 1 -1122012 2609902.7
# 2 -1808107 -340106.5
# 3 -2196121 -45123.5
# Results for fastMA_custom = 12 are identical.
上面我还展示了如何快速检查 apply.paramsets 输出，通过 apply.Strategy 将其与 n = 12 的个别情况进行比较。这样做是个好主意各种支票。