（非常）使用逻辑回归的简单 quantstrat 交易模型

Question

我一直在使用 R 中的 quantstrat 回测包，我想就一个特别（差）的策略获得一些建议。

这个想法是在逻辑回归模型告诉我市场将要上涨时买入（在prediction 列中表示为 1）。每天逻辑回归都告诉我市场要上涨，我购买了.orderqtf = 10 的 Google 股票。逻辑回归告诉我价格会下跌的那一天（在prediction 列中用 0 表示）然后我们抛售我们目前在 Google 中的所有股票并重新开始，直到它告诉我们购买。

问题：

我的代码对我所描述的内容是否正确？

如果您注意到我已经落后于两个输入变量。即momentum(lag(GOOG$close), n = 12)

那是我想用t-1 day 来预测t 的日子。这也正确吗？我不想使用任何可能为预测提供偏差结果的指标

quantstrat 包对我来说似乎有点学习曲线，所以我只是想确保我得到了基本的正确。

型号：

rm(list=ls())
require(quantstrat)
require(PerformanceAnalytics)

set.seed(1234)

#setting up some initial parameters for the quantstrat trading model
initDate="2007-01-01"
from <- "2017-01-01"
to <- "2018-12-01"
init_equity <- 1000
adjustment <- TRUE

.orderqty <- 10
.txnfees <- -10

currency('USD')
Sys.setenv(TZ="UTC")

#Collect the data
symbols <- c('GOOG')
getSymbols(symbols, from=from, to=to, src="yahoo", adjust=TRUE)  

colnames(GOOG) <- c("open", "high", "low", "close", "volume", "adjusted")

# create the dependent variable for a logistic regression
GOOG$direction <- with(GOOG, ifelse(close >= open, 1, 0))

#create two basic input variables - lagged
GOOG$rsi <- RSI(lag(GOOG$close), nFast=14, nSlow = 26, nSig = 9, maType = SMA)
GOOG$momentum <- momentum(lag(GOOG$close), n = 12)

GOOG <- GOOG[complete.cases(GOOG), ] 

# create a training and test set
train_date <- nrow(GOOG) *0.8
train <- GOOG[1:train_date,]
test <- GOOG[-c(1:train_date),]

#Run a simple logistic regression and obtain predicted probabilities
lm.fit <- glm(direction ~ rsi + momentum, data = train, family = binomial)
summary(lm.fit)
pr.lm <- predict(lm.fit, test, type = "response")


# Extract the OHLC from the GOOG stock and match it with the test dates
TEST <- subset(GOOG, index(GOOG) %in% index(test))

#Add out predictions to the TEST data if its greater than 0.6
TEST$prediction <- ifelse(pr.lm > 0.6, 1, 0)

paste0("Accuracy", mean(TEST$direction == TEST$prediction))

# Now that we have a strategy we want to buy everytime the logistic model states that
# the direction would be a "1"

# Setting up the strategy
GOOG <- TEST
stock("GOOG", currency="USD", multiplier=1)
strategy.st <- portfolio.st <- account.st <- "LogisticRegressionStrategy"
rm.strat(strategy.st)
rm.strat(portfolio.st)
rm.strat(account.st)

initPortf(name = portfolio.st,
          symbols = symbols, 
          initDate = initDate, 
          currency = 'USD')

initAcct(name = account.st, 
         portfolios = portfolio.st, 
         initDate = initDate, 
         currency = 'USD',
         initEq = init_equity)

initOrders(portfolio.st,
           symbols = symbols,
           initDate = initDate)

strategy(strategy.st, store = TRUE)


# Adding the rules, enter at the low price when "prediction" = 1, taking transaction fees into account
add.rule(strategy = strategy.st,
         name = "ruleSignal",
         arguments = list(sigcol = "prediction",
                          sigval = 1,
                          orderqty = .orderqty,
                          ordertype = "market",
                          #orderside = "long", 
                          prefer = "Low", 
                          TxnFees = .txnfees, 
                          replace = FALSE),
         type = "enter",
         label = "EnterLONG")

# As soon as the Logistic regression predicts a "0" we dump all our shares in GOOG

add.rule(strategy.st, 
         name = "ruleSignal", 
         arguments = list(sigcol = "prediction", 
                          sigval = 0, 
                          #orderside = "short", 
                          ordertype = "market", 
                          orderqty = "all", 
                          TxnFees = .txnfees, 
                          replace = TRUE), 
         type = "exit", 
         label = "Exit2SHORT")


applyStrategy(strategy.st, portfolios = portfolio.st)

updatePortf(portfolio.st)
updateAcct(account.st)
updateEndEq(account.st)

chart.Posn(portfolio.st, Symbol = "GOOG", 
           TA="add_SMA(n = 10, col = 2); add_SMA(n = 30, col = 4)")

Answer 1

看起来你快到了。您在训练数据上拟合模型，并确保在测试集上进行回测，这是正确的做法。

您需要注意一些事情：不要在add.rule 中设置prefer = low 用于输入信号；您永远不会提前知道实际交易中的低点在哪里，以便在下一个柱上填充。

我让这里的逻辑回归预测比当前柱提前 1 个柱，因为如果您“在线”/实时进行这些预测，这就是您要做的。这没关系，只要我们只使用预测概率，显然永远不要使用direction_fwd 作为交易信号，因为它会引入前瞻偏差。

为了更轻松地重新运行代码，我还将市场数据存储在 .data 环境中，因此如果重新运行部分代码，您可以在 GOOG 中为 applyStrategy 重新生成数据，而无需再次从雅虎请求数据。

此外，您可能还想限制您进入某个职位的次数。您可以使用addPositionLimit 执行此操作。而且您可能不想在概率 > 0.6 时购买每根柱线，而只是第一次（交叉），所以我引入了signal 代码来处理这个问题。

请记住，在 quantstrat 中，默认情况下，订单会在 next 数据柱上成交（这里是下一个柱的开盘价，因为prefer = "Open"），默认情况下会成交更现实（这更适用于日内柱数据或分时数据行），但我认为这是你想要的，因为你不知道当前柱的 RSI 和动量值 end em> 当前柱，因此在下一个打开的柱上填充很有意义。

rm(list=ls())
require(quantstrat)
require(PerformanceAnalytics)

set.seed(1234)

#setting up some initial parameters for the quantstrat trading model
initDate="2007-01-01"
from <- "2017-01-01"
to <- "2018-12-01"
init_equity <- 1000
adjustment <- TRUE

.orderqty <- 10
.txnfees <- -10

currency('USD')
Sys.setenv(TZ="UTC")

#Collect the data
symbols <- c('GOOG')
.data <- new.env()
getSymbols(symbols, from=from, to=to, src="yahoo", adjust=TRUE, env = .data)  

colnames(.data$GOOG) <- c("open", "high", "low", "close", "volume", "adjusted")

mdata <- .data$GOOG

# create the dependent variable for a logistic regression
mdata$direction <- with(mdata, ifelse(close >= open, 1, 0))

#create two basic input variables - lagged
mdata$rsi <- RSI(mdata$close, nFast=14, nSlow = 26, nSig = 9, maType = SMA)
mdata$momentum <- momentum(mdata$close, n = 12)

mdata <- mdata[complete.cases(mdata), ] 
mdata$direction_fwd <- lag.xts(mdata$direction, k = -1)
# create a training and test set
train_date <- nrow(mdata) *0.8
train <- mdata[1:train_date,]
test <- mdata[-c(1:train_date),]



#Run a simple logistic regression and obtain predicted probabilities
lm.fit <- glm(direction_fwd ~ rsi + momentum, data = train, family = binomial)
summary(lm.fit)
pr.lm <- predict(lm.fit, test, type = "response")
test$pred_prob <- pr.lm

#Add out predictions to the TEST data if its greater than 0.6
test$prediction <- ifelse(pr.lm > 0.6, 1, 0)

paste0("Accuracy: ", mean(test$direction_fwd == test$prediction, na.rm = T))


# Simple way to run applyStrategy is to make sure the data for the symbol is in a variable with its name, like so:
GOOG <- test


stock("GOOG", currency="USD", multiplier=1)
strategy.st <- portfolio.st <- account.st <- "LogisticRegressionStrategy"
rm.strat(strategy.st)
rm.strat(portfolio.st)
rm.strat(account.st)



initPortf(name = portfolio.st,
          symbols = symbols, 
          initDate = initDate, 
          currency = 'USD')

initAcct(name = account.st, 
         portfolios = portfolio.st, 
         initDate = initDate, 
         currency = 'USD',
         initEq = init_equity)

initOrders(portfolio.st,
           symbols = symbols,
           initDate = initDate)

strategy(strategy.st, store = TRUE)

nMult_orderqty <- 2
addPosLimit(portfolio.st, symbol = "GOOG", timestamp = initDate, maxpos = nMult_orderqty * .orderqty)

# Buy when prob exceeds 0.6 for the first time, using cross= TRUE
add.signal(strategy = strategy.st,
         name = "sigThreshold",
         arguments = list(threshold=0.6, column="pred_prob", relationship="gt", cross= TRUE),
         label = "longSig")

 #exit when prob drops below 0.5 for the first time
add.signal(strategy = strategy.st,
           name = "sigThreshold",
           arguments = list(threshold=0.5, column="pred_prob", relationship="lt", cross= TRUE),
           label = "exitLongSig")

# Adding the rules, enter at the low price when "prediction" = 1, taking transaction fees into account
add.rule(strategy = strategy.st,
         name = "ruleSignal",
         arguments = list(sigcol = "longSig",
                          sigval = 1,
                          orderqty = .orderqty,
                          ordertype = "market",
                          orderside = "long",
                          osFUN = osMaxPos,
                          prefer = "Open",  #Never kknow the low in advance. Use the open, as it is for the next day (be aware that the open price for bar data has its own problems too)
                          TxnFees = .txnfees, 
                          replace = FALSE),
         type = "enter",
         label = "EnterLONG")

# As soon as the Logistic regression predicts a "0" we dump all our shares in GOOG

add.rule(strategy.st, 
         name = "ruleSignal", 
         arguments = list(sigcol = "exitLongSig", 
                          sigval = 1, 
                          ordertype = "market", 
                          orderside = "long",
                          orderqty = "all", 
                          TxnFees = .txnfees, 
                          replace = TRUE), 
         type = "exit", 
         label = "Exit2SHORT")


applyStrategy(strategy.st, portfolios = portfolio.st)

updatePortf(portfolio.st)
updateAcct(account.st)
updateEndEq(account.st)

chart.Posn(portfolio.st, Symbol = "GOOG", 
           TA="add_SMA(n = 10, col = 2); add_SMA(n = 30, col = 4)")