wlinInspire

library(IBrokers)
tws <- twsConnect(verbose = FALSE)
symbol <- 'ABC'
quantity <- 100
lmtPrice <- 10
placeOrder(twsconn=tws,
           Contract=twsSTK(symbol),
           Order=twsOrder(reqIds(tws),
                          "BUY",
                          quantity,

wlinInspire

mail_query <- paste0('from:"@xxx.com" ',
                     '{subject:trade subject:sell} ',
                     'after:', Sys.Date())

list <- gmailr::threads(
  search = mail_query,
  include_spam_trash = TRUE, num_results = 99999)

ids <- sapply(list, function(x) sapply(x$threads, function(y) y$id)) %>%
  unlist()

wlinInspire

while(hour(Sys.time()) < 16) {

  # check email
  try(get_alert_email(), silent = TRUE)

  # Place order if needed
  tws_order()

  Sys.sleep(5)
}

wlinInspire

27 9 * * 1-5 /usr/lib/R/bin/R -f '/home/ubuntu/TraderR/script/trading_robot.R' > '/home/ubuntu/TraderR/script/trading_robot.log' 2>&1

wlinInspire

lw <- function(v) {
  if (sum(v, na.rm = TRUE) == 0) {
    1 / length(v)
  } else {
    v / sum(v, na.rm = TRUE)
  }
}

wlinInspire

quick_date <- function(x, …) {
    if (anyDuplicated(x)) {
        ux <- unique(x)
        idx <- match(x, ux)
        y <- as.Date.character(ux, …)
        return(y[idx])
    }
    as.Date.character(x, …)
}

wlinInspire

cc <- function(df) {
    print(df %>% is.na() %>% colSums())
}

cd <- function(df) {
    print(df %>% is.na() %>% colMeans())
}

wlinInspire

h2o_start <- function() {
    h2o::h2o.init(nthreads = -1, enable_assertions = FALSE, min_mem_size = '8g',
                  strict_version_check = FALSE, port = 54321)
}

h2o_stop <- function() {
    h2o::h2o.shutdown(prompt = FALSE)
}

wlinInspire

library(survival)
library(data.table)
library(ggplot2)

data <- fread('https://raw.githubusercontent.com/IBM/invoke-wml-using-cognos-custom-control/master/data/Telco-Customer-Churn.csv')

churn_data <- data[, .(churn_flag = ifelse(Churn == 'Yes', 1, 0), tenure)]
km_curve <- survfit(Surv(tenure, churn_flag) ~ 1, data=churn_data)

# Calculate Survival and Churn Rate

wlinInspire

library(survival)
library(data.table)
library(ggplot2)

data = fread('https://raw.githubusercontent.com/IBM/invoke-wml-using-cognos-custom-control/master/data/Telco-Customer-Churn.csv')

churn_data <- data[, .(churn_flag = ifelse(Churn == 'Yes', 1, 0), tenure)]
km_curve <- survfit(Surv(tenure, churn_flag) ~ 1, data=churn_data)

# Calculate Survival and Churn Rate