README.rmd

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output: github_document
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# rb3 <img src="man/figures/logo.png" align="right" width="120" />

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[B3](https://www.b3.com.br)  is the main financial exchange in Brazil, offering 
support and access to trading systems for equity and fixed income markets. 
In its website you can find a vast number of datasets regarding prices and transactions 
for contracts available for trading at these markets, including:

* equities/stocks
* futures
* FII (Reits)
* options
* BDRs
* historical yield curves (calculated from futures contracts)
* B3 indexes composition

For example, you can find the current yield curve at this [link](https://www.b3.com.br/pt_br/market-data-e-indices/servicos-de-dados/market-data/consultas/mercado-de-derivativos/precos-referenciais/taxas-referenciais-bm-fbovespa/). Package **rb3** uses webscraping tools 
to download and read these datasets from 
[B3](https://www.b3.com.br), making it easy to consume it in R in a structured way. 

The available datasets are highly valuable, going back as early as 2000's, and 
can be used by industry practitioners or academics. None of these datasets are available 
anywhere else, which makes rb3 an unique package for data importation from the 
Brazilian financial exchange.


# Documentation

The documentation is available in its [pkgdown page](https://ropensci.github.io/rb3/),
where articles (vignettes) with real applications can be found.

## Installation

Package rb3 is available in its stable form in CRAN and its development version in Github. 
Please find the installation commands below:

```r
# stable (CRAN)
install.packages("rb3")

# github (Development branch)
if (!require(devtools)) install.packages("devtools")
devtools::install_github("ropensci/rb3")
```

## Examples

### Yield curve

In this first example we'll import and plot the historical yield curve for Brazil using 
function `yc_get`.

```{r setup}
library(rb3)
library(ggplot2)
library(stringr)

df_yc <- yc_mget(
  first_date = Sys.Date() - 255 * 5,
  last_date = Sys.Date(),
  by = 255
)

p <- ggplot(
  df_yc,
  aes(
    x = forward_date,
    y = r_252,
    group = refdate,
    color = factor(refdate)
  )
) +
  geom_line() +
  labs(
    title = "Yield Curves for Brazil",
    subtitle = "Built using interest rates future contracts",
    caption = str_glue("Data imported using rb3 at {Sys.Date()}"),
    x = "Forward Date",
    y = "Annual Interest Rate",
    color = "Reference Date"
  ) +
  theme_light() +
  scale_y_continuous(labels = scales::percent)

print(p)
```


### Futures prices

Get settlement future prices with `futures_get`.

```{r message=FALSE}
library(rb3)
library(dplyr)

df <- futures_mget(
  first_date = "2022-04-01",
  last_date = "2022-04-29",
  by = 5
)

glimpse(
  df |>
    filter(commodity == "DI1")
)
```

### Equity data

Equity closing data (without **ANY** price adjustments) is available thru `cotahist_get`.

```{r}
library(rb3)
library(bizdays)

# fix for ssl error (only in linux)
if (Sys.info()["sysname"] == "Linux") {
  httr::set_config(
    httr::config(ssl_verifypeer = FALSE)
  )
}

date <- preceding(Sys.Date() - 1, "Brazil/ANBIMA") # last business day
ch <- cotahist_get(date, "daily")

glimpse(
  cotahist_equity_get(ch)
)
```

### Funds data

One can also download hedge fund data with `cotahist_etfs_get`.

```{r}
glimpse(
  cotahist_etfs_get(ch)
)
```

### FIIs (Brazilian REITs) data

Download FII (Fundo de Investimento Imobiliário) data with `cotahist_fiis_get`:


```{r}
glimpse(
  cotahist_fiis_get(ch)
)
```

### BDRs data

Download BDR (Brazilian depositary receipts) with `cotahist_bdrs_get`:

```{r}
glimpse(
  cotahist_bdrs_get(ch)
)
```

### Equity options

Download equity options contracts with `cotahist_option_get`:

```{r}
glimpse(
  cotahist_equity_options_get(ch)
)
```


### Indexes composition

The list with available B3 indexes can be obtained with `indexes_get`.

```{r}
indexes_get()
```

And the composition of a specific index with `index_comp_get`.

```{r}
(ibov_comp <- index_comp_get("IBOV"))
```

With the index composition you can use COTAHIST to select their quotes.

```{r}
glimpse(
  cotahist_get_symbols(ch, ibov_comp)
)
```


## Template System

One important part of `rb3` infrastructure is its `Template System`.

All datasets handled by the rb3 package are configured in a template, that is
an YAML file.
The template brings many information regarding the datasets, like its
description and its metadata that describes its columns, their types and how
it has to be parsed.
The template fully describes its dataset.

Once you have the template implemented you can fetch and read downloaded
data directly with the functions `download_marketdata` and `read_marketdata`.

For examples, let's use the template `FPR` to download and read data regarding
primitive risk factor used by B3 in its risk engine.

```{r}
f <- download_marketdata("FPR", refdate = as.Date("2022-05-10"))
f
```

`download_marketdata` returns the path for the downloaded file.

```{r}
fpr <- read_marketdata(f, "FPR")
fpr
```

`read_marketdata` parses the downloaded file according to the metadata
configured in the template `FPR`.

Here it follows a view of the `show_templates` adding that lists the available
templates.

```r
show_templates()
```

<img src="man/figures/rb3-templates.png" width="100%" />