DavisVaughan

a=-1.24458046630025
b=-1.25191834103316 
c=-1.81590817030519 
d=-1.90866735205054

n <- 10000000
x <- vector("numeric", n)
y <- vector("numeric", n)
x[1] <- 0
y[1] <- 0

DavisVaughan

``` r
library(odbc)
#> Warning: package 'odbc' was built under R version 3.4.2
library(DBI)

con <- DBI::dbConnect(odbc::odbc(),
                      Driver   = "PostgreSQL ODBC Driver(ANSI)",
                      Server   = "localhost",
                      Database = "postgres",
                      UID      = 'postgres',

DavisVaughan

``` r
library(tibbletime)
library(dplyr)

options(digits.secs = 6)

# 1000 values
ex <- create_series('2013' ~ '2013-01-01 00:00:00.999', period = "1 millisec")

# Random data

DavisVaughan

library(tibbletime)
library(dplyr)
library(zoo)

data(FB)

window <- 6
rolling_mean <- rollify(mean, window = window)

FB_roll <- FB %>%

DavisVaughan

library(DBI)
library(dplyr)
library(pool)

pool <- dbPool(drv = RPostgres::Postgres(),
               dbname = "rstudioconftweets",
               host = "rstudioconf-tweets.c0azx4micevq.us-east-1.rds.amazonaws.com",
               user = "guest",
               password = "guest",
               port = 5432)

DavisVaughan

library(tibbletime)
#> 
#> Attaching package: 'tibbletime'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':

DavisVaughan

#### Example with flights dataset

library(readr)
library(fst)
library(data.table)

data(flights, package = "nycflights13")

# Write
write.fst(flights, path = "flights.fst")

DavisVaughan

library(magrittr)

time_horizon <- 5

# Starts at 12/12 (1) goes to 60/12 (5) (notice how the numerators are 12->60)
seq(1, time_horizon, by = 1/12) %>% print() %>% length()
#>  [1] 1.000000 1.083333 1.166667 1.250000 1.333333 1.416667 1.500000
#>  [8] 1.583333 1.666667 1.750000 1.833333 1.916667 2.000000 2.083333
#> [15] 2.166667 2.250000 2.333333 2.416667 2.500000 2.583333 2.666667
#> [22] 2.750000 2.833333 2.916667 3.000000 3.083333 3.166667 3.250000

DavisVaughan

library(Rcpp)
library(microbenchmark)

cppFunction(
  'int looped_sum(int x, const int num_loops) {
        for(int i = 0; i < num_loops; i++) {
          x = x + 1;
        }
        return x;
    }')

DavisVaughan

# This example simulates a stock price 1 time and 10k times.
# It uses a functional approach and a loop approach.
# A speed test is done at the end.

# This example for GBM ignores the fact that 
# 1) There is an explicit solution to GBM where simulation isn't needed
# 2) Along the same lines, there is a vectorized form where pure matrix mult can be used.
# These are ignored for the sake of the example, because there are often cases
# where this isn't the case and you HAVE to do the discrete loop.