wilsonfreitas

df <- as.data.frame(ds)
head(df)
#> # A tibble: 13,038 × 8
#>    refdate    symbol  commodity maturity_code previous_price  price price_change
#>    <date>     <chr>   <chr>     <chr>                  <dbl>  <dbl>        <dbl>
#>  1 2023-01-04 ABEVOF… ABEVO     F23                     14.3 1.43e1        0.07
#>  2 2023-01-04 ABEVOG… ABEVO     G23                     14.4 1.45e1        0.08 
#>  3 2023-01-04 AFSG23  AFS       G23                  17056   1.70e4     -101.
#>  4 2023-01-04 AFSH23  AFS       H23                  17093.  1.70e4     -101.   
#>  5 2023-01-04 AFSJ23  AFS       J23                  17142.  1.70e4     -105.   

wilsonfreitas

Download de curvas da B3

library(rb3)

fname <- download_marketdata("TaxasReferenciais",
                             refdate = as.Date("2024-01-12"),
                             curve_name = "TR")
df &lt;- read_marketdata(fname, "TaxasReferenciais", TRUE)

wilsonfreitas

class B3FilesURLDownloader(SingleDownloader):
    calendar = bizdays.Calendar.load('ANBIMA.cal')
    def download(self, refdate=None):
        filename = self.attrs.get('filename')
        refdate = refdate or self.get_refdate()
        logging.info('refdate %s', refdate)
        date = refdate.strftime('%Y-%m-%d')
        url = f'https://arquivos.b3.com.br/api/download/requestname?fileName={filename}&date={date}&recaptchaToken='
        res = requests.get(url)
        msg = 'status_code = {} url = {}'.format(res.status_code, url)

wilsonfreitas

from functools import wraps

try:
    from functools import lru_cache
except ImportError:
    def lru_cache(user_function):
        cache = {}
        @wraps(user_function)
        def wrapper(*args):
            key = tuple(args)

wilsonfreitas

f <- function(x) cos(x*20)
expi <- function(x) cos(x) + sin(x)*1i
par(mfrow = c(1,2))
L <- 40
medias_Re <- c()
fs <- c()
as <- seq(0.01, 1, l = 140)

animation::saveGIF({
  for(a in as) {

wilsonfreitas

library(rb3)
library(tidyverse)

top_weight <- function(.data, n = 10) {
  top_10 <- .data |>
    arrange(desc(weight)) |>
    slice_head(n = n) |>
    select(symbol, weight)
  total_weight <- sum(top_10$weight)
  others <- tibble(

wilsonfreitas

forwardrate method, for a SpotRateCurve and with arguments t1 and t2, computes the forward rate between two future terms that exist in the term structure.

library(rb3)
library(fixedincome)

df_yc <- yc_get("2022-05-20")

crv &lt;- spotratecurve(

wilsonfreitas

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled0.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNsy4RaWWJJXRN8opuCmESy",
      "include_colab_link": true

wilsonfreitas

Estou com dúvida em um tratamento aqui no R. Tenho um data frame que tem 3 colunas: data pregão, cód negociação ativo objeto e variação preço fechamento D0 x D-1. A partir dessas datas, o gostaria de incluir nesse data frame 5 datas úteis anteriores a cada uma dessas datas.

Vc tem alguma dica de como poderia fazer?

Tentei usar Index mas ele funciona bem para 1 data, agora quando temos várias datas...tentei um for tb mas não dei certo ainda....

wilsonfreitas

library(xts)
library(tidyverse)
library(PerformanceAnalytics)
library(quantmod)

stocks_names <- c(
  "ABEV3.SA",
  "B3SA3.SA",
  "CNTO3.SA",