(非常)使用逻辑回归的简单量子交易模型
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我一直在玩R中的quantstrat
回测试包,我想得到一些特别(差)策略的建议。
只要逻辑回归模型告诉我市场将会上涨(在prediction
专栏中显示为1),这个想法就是买入。每天逻辑回归告诉我市场将会上涨,我买了.orderqtf = 10
的谷歌股票。逻辑回归告诉我价格将下降的那一天(在prediction
专栏中以0表示)然后我们将所有当前股票转储到谷歌并重新开始,直到它告诉我们购买。
问题:
我的代码是否正确我所描述的内容?
如果你注意到我已经滞后了两个输入变量。即momentum(lag(GOOG$close), n = 12)
那就是我想用t-1
天预测一天t
。这也正确吗?我不想使用任何可能为预测提供bia结果的指标
对我来说,quantstrat
包似乎有一点学习曲线所以我只想确保我得到基本的正确。
该模型:
rm(list=ls())
require(quantstrat)
require(PerformanceAnalytics)
set.seed(1234)
#setting up some initial parameters for the quantstrat trading model
initDate="2007-01-01"
from <- "2017-01-01"
to <- "2018-12-01"
init_equity <- 1000
adjustment <- TRUE
.orderqty <- 10
.txnfees <- -10
currency('USD')
Sys.setenv(TZ="UTC")
#Collect the data
symbols <- c('GOOG')
getSymbols(symbols, from=from, to=to, src="yahoo", adjust=TRUE)
colnames(GOOG) <- c("open", "high", "low", "close", "volume", "adjusted")
# create the dependent variable for a logistic regression
GOOG$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)")
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