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https://github.com/ropensci/rb3
A bunch of downloaders and parsers for data delivered from B3
https://github.com/ropensci/rb3
brazil exchange-data finance financial-data financial-services market-data r
Last synced: 3 months ago
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A bunch of downloaders and parsers for data delivered from B3
- Host: GitHub
- URL: https://github.com/ropensci/rb3
- Owner: ropensci
- License: other
- Created: 2014-12-15T11:04:39.000Z (almost 10 years ago)
- Default Branch: main
- Last Pushed: 2024-07-13T20:57:28.000Z (4 months ago)
- Last Synced: 2024-07-30T11:43:47.618Z (3 months ago)
- Topics: brazil, exchange-data, finance, financial-data, financial-services, market-data, r
- Language: R
- Homepage: https://docs.ropensci.org/rb3/
- Size: 40.5 MB
- Stars: 69
- Watchers: 14
- Forks: 28
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- Contributing: Contributing.md
- License: LICENSE
Awesome Lists containing this project
- awesome-quant - rb3 - A bunch of downloaders and parsers for data delivered from B3. (R / Data Sources)
README
# rb3 
[](https://www.repostatus.org/#active)
[](https://app.codecov.io/gh/wilsonfreitas/rb3?branch=main)
[](https://github.com/ropensci/rb3/actions)
[](https://CRAN.R-project.org/package=rb3)
[](https://cran.r-project.org/package=rb3)
[](https://github.com/ropensci/software-review/issues/534)
[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
library(rb3)
library(ggplot2)
library(stringr)
df_yc <- yc_mget(
first_date = Sys.Date() - 255 * 5,
last_date = Sys.Date(),
by = 255
)
#> Warning: Automatic coercion from double to character was deprecated in purrr 1.0.0.
#> ℹ Please use an explicit call to `as.character()` within `map_chr()` instead.
#> ℹ The deprecated feature was likely used in the rb3 package.
#> Please report the issue at .
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
library(rb3)
library(dplyr)
df <- futures_mget(
first_date = "2022-04-01",
last_date = "2022-04-29",
by = 5
)
glimpse(
df |>
filter(commodity == "DI1")
)
#> Rows: 153
#> Columns: 8
#> $ refdate 2022-04-01, 2022-04-01, 2022-04-01, 2022-04-01, 2022…
#> $ commodity "DI1", "DI1", "DI1", "DI1", "DI1", "DI1", "DI1", "DI1…
#> $ maturity_code "J22", "K22", "M22", "N22", "Q22", "U22", "V22", "X22…
#> $ symbol "DI1J22", "DI1K22", "DI1M22", "DI1N22", "DI1Q22", "DI…
#> $ price_previous 99999.99, 99172.50, 98159.27, 97181.87, 96199.14, 951…
#> $ price 100000.00, 99172.31, 98160.23, 97185.43, 96210.42, 95…
#> $ change 0.01, -0.19, 0.96, 3.56, 11.28, 21.61, 34.93, 48.85, …
#> $ settlement_value 0.01, 0.19, 0.96, 3.56, 11.28, 21.61, 34.93, 48.85, 5…
```
### Equity data
Equity closing data (without **ANY** price adjustments) is available
thru `cotahist_get`.
``` r
library(rb3)
library(bizdays)
#>
#> Attaching package: 'bizdays'
#> The following object is masked from 'package:stats':
#>
#> offset
# 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)
)
#> Rows: 367
#> Columns: 13
#> $ refdate 2023-03-03, 2023-03-03, 2023-03-03, 2023-03-03,…
#> $ symbol "AERI3", "AESB3", "AFLT3", "AGRO3", "AGXY3", "BR…
#> $ open 1.15, 10.00, 8.89, 26.15, 8.45, 25.89, 28.83, 14…
#> $ high 1.17, 10.18, 8.89, 26.60, 9.06, 26.35, 29.24, 15…
#> $ low 1.11, 9.96, 8.71, 26.12, 8.39, 25.80, 28.76, 14.…
#> $ close 1.12, 10.16, 8.71, 26.33, 9.06, 25.92, 28.92, 14…
#> $ average 1.14, 10.08, 8.80, 26.39, 8.75, 26.13, 29.00, 14…
#> $ best_bid 1.12, 10.14, 8.71, 26.32, 8.92, 25.92, 28.81, 14…
#> $ best_ask 1.13, 10.16, 8.99, 26.33, 9.06, 26.08, 28.92, 14…
#> $ volume 4481724, 19128087, 1760, 7341008, 234691, 689933…
#> $ traded_contracts 3926400, 1897500, 200, 278100, 26800, 26400, 305…
#> $ transactions_quantity 2818, 5573, 2, 1399, 177, 140, 7403, 17092, 1464…
#> $ distribution_id 101, 105, 119, 113, 101, 145, 145, 123, 102, 166…
```
### Funds data
One can also download hedge fund data with `cotahist_etfs_get`.
``` r
glimpse(
cotahist_etfs_get(ch)
)
#> Rows: 100
#> Columns: 13
#> $ refdate 2023-03-03, 2023-03-03, 2023-03-03, 2023-03-03,…
#> $ symbol "AGRI11", "IBOB11", "OGIN11", "BOVB11", "BOVS11"…
#> $ open 43.51, 84.31, 9.51, 104.52, 79.95, 104.50, 10.40…
#> $ high 43.63, 84.31, 9.63, 105.11, 80.57, 105.74, 10.51…
#> $ low 42.91, 84.17, 9.35, 104.52, 79.74, 104.49, 10.40…
#> $ close 43.10, 84.17, 9.57, 104.68, 80.13, 104.90, 10.45…
#> $ average 43.40, 84.17, 9.50, 104.68, 80.22, 105.17, 10.46…
#> $ best_bid 41.50, 83.51, 9.44, 104.68, 80.13, 104.90, 10.45…
#> $ best_ask 46.24, 84.18, 9.57, 113.00, 90.00, 105.11, 10.49…
#> $ volume 2274762.37, 1375010.67, 14024.14, 241499.09, 444…
#> $ traded_contracts 52405, 16336, 1476, 2307, 554, 1733901, 336092, …
#> $ transactions_quantity 50, 6, 24, 15, 452, 11558, 1949, 5, 25, 4402, 4,…
#> $ distribution_id 100, 100, 102, 100, 100, 101, 100, 101, 100, 100…
```
### FIIs (Brazilian REITs) data
Download FII (Fundo de Investimento Imobiliário) data with
`cotahist_fiis_get`:
``` r
glimpse(
cotahist_fiis_get(ch)
)
#> Rows: 268
#> Columns: 13
#> $ refdate 2023-03-03, 2023-03-03, 2023-03-03, 2023-03-03,…
#> $ symbol "BZLI11", "AFHI11", "IBCR11", "IDGR11", "LUGG11"…
#> $ open 17.00, 94.69, 76.73, 80.50, 71.98, 94.83, 11.50,…
#> $ high 17.00, 94.70, 77.00, 80.50, 72.00, 95.88, 11.50,…
#> $ low 17.00, 93.72, 76.39, 80.50, 71.16, 94.02, 11.49,…
#> $ close 17.00, 94.20, 77.00, 80.50, 71.61, 94.31, 11.49,…
#> $ average 17.00, 94.19, 76.70, 80.50, 71.53, 94.59, 11.49,…
#> $ best_bid 17.00, 94.20, 76.74, 1.00, 71.42, 94.31, 10.96, …
#> $ best_ask 18.00, 94.42, 77.00, 80.50, 71.61, 94.32, 11.49,…
#> $ volume 357.00, 453376.17, 54150.68, 8050.00, 46068.54, …
#> $ traded_contracts 21, 4813, 706, 100, 644, 11197, 10, 70, 207, 100…
#> $ transactions_quantity 2, 1670, 75, 1, 203, 3116, 4, 6, 58, 73, 2650, 2…
#> $ distribution_id 100, 124, 121, 112, 140, 152, 213, 102, 226, 144…
```
### BDRs data
Download BDR (Brazilian depositary receipts) with `cotahist_bdrs_get`:
``` r
glimpse(
cotahist_bdrs_get(ch)
)
#> Rows: 507
#> Columns: 13
#> $ refdate 2023-03-03, 2023-03-03, 2023-03-03, 2023-03-03,…
#> $ symbol "ADBE34", "CSCO34", "I1QV34", "I1QY34", "I1RM34"…
#> $ open 34.60, 50.53, 283.92, 19.76, 283.50, 500.19, 60.…
#> $ high 35.86, 51.44, 287.00, 20.00, 283.50, 500.19, 61.…
#> $ low 34.60, 50.53, 283.92, 19.70, 283.50, 500.19, 60.…
#> $ close 35.48, 51.44, 287.00, 19.96, 283.50, 500.19, 61.…
#> $ average 35.40, 51.18, 285.46, 19.75, 283.50, 500.19, 60.…
#> $ best_bid 35.48, 48.45, 0.00, 19.55, 249.50, 200.00, 59.40…
#> $ best_ask 39.98, 51.44, 335.10, 0.00, 310.00, 0.00, 70.00,…
#> $ volume 600948.59, 437857.01, 570.92, 39665.04, 1134.00,…
#> $ traded_contracts 16972, 8555, 2, 2008, 4, 472, 77, 500, 535693, 1…
#> $ transactions_quantity 230, 144, 2, 25, 1, 2, 2, 2, 1041, 13, 54, 92, 2…
#> $ distribution_id 101, 149, 100, 100, 112, 113, 102, 146, 117, 112…
```
### Equity options
Download equity options contracts with `cotahist_option_get`:
``` r
glimpse(
cotahist_equity_options_get(ch)
)
#> Rows: 6,656
#> Columns: 14
#> $ refdate 2023-03-03, 2023-03-03, 2023-03-03, 2023-03-03,…
#> $ symbol "ABCBC200", "ABCBO176", "ABEVF160", "ABEVP300", …
#> $ type Call, Put, Call, Put, Put, Put, Put, Put, Call, …
#> $ strike 19.69, 17.69, 14.82, 18.82, 28.82, 14.82, 13.82,…
#> $ maturity_date 2023-03-17, 2023-03-17, 2023-06-16, 2023-04-20,…
#> $ open 0.07, 0.28, 0.30, 5.48, 15.30, 1.50, 0.90, 2.76,…
#> $ high 0.07, 0.28, 0.30, 5.48, 15.30, 1.50, 0.98, 2.76,…
#> $ low 0.06, 0.28, 0.28, 5.48, 15.28, 1.50, 0.90, 2.76,…
#> $ close 0.06, 0.28, 0.28, 5.48, 15.28, 1.50, 0.98, 2.76,…
#> $ average 0.06, 0.28, 0.28, 5.48, 15.28, 1.50, 0.92, 2.76,…
#> $ volume 76, 140, 86, 5480, 7643, 1500, 7530, 1380, 10423…
#> $ traded_contracts 1100, 500, 300, 1000, 500, 1000, 8100, 500, 3898…
#> $ transactions_quantity 2, 1, 3, 2, 4, 1, 3, 1, 89, 2, 13, 8, 2, 174, 12…
#> $ distribution_id 142, 143, 124, 124, 124, 124, 124, 124, 125, 125…
```
### Indexes composition
The list with available B3 indexes can be obtained with `indexes_get`.
``` r
indexes_get()
#> [1] "AGFS" "BDRX" "GPTW" "IBOV" "IBRA" "IBXL" "IBXX" "ICO2" "ICON" "IDIV"
#> [11] "IEEX" "IFIL" "IFIX" "IFNC" "IGCT" "IGCX" "IGNM" "IMAT" "IMOB" "INDX"
#> [21] "ISEE" "ITAG" "IVBX" "MLCX" "SMLL" "UTIL"
```
And the composition of a specific index with `index_comp_get`.
``` r
(ibov_comp <- index_comp_get("IBOV"))
#> [1] "ABEV3" "ALPA4" "AMER3" "ASAI3" "AZUL4" "B3SA3" "BBAS3" "BBDC3"
#> [9] "BBDC4" "BBSE3" "BEEF3" "BIDI11" "BPAC11" "BPAN4" "BRAP4" "BRFS3"
#> [17] "BRKM5" "BRML3" "CASH3" "CCRO3" "CIEL3" "CMIG4" "CMIN3" "COGN3"
#> [25] "CPFE3" "CPLE6" "CRFB3" "CSAN3" "CSNA3" "CVCB3" "CYRE3" "DXCO3"
#> [33] "ECOR3" "EGIE3" "ELET3" "ELET6" "EMBR3" "ENBR3" "ENEV3" "ENGI11"
#> [41] "EQTL3" "EZTC3" "FLRY3" "GGBR4" "GOAU4" "GOLL4" "HAPV3" "HYPE3"
#> [49] "IGTI11" "IRBR3" "ITSA4" "ITUB4" "JBSS3" "JHSF3" "KLBN11" "LCAM3"
#> [57] "LREN3" "LWSA3" "MGLU3" "MRFG3" "MRVE3" "MULT3" "NTCO3" "PCAR3"
#> [65] "PETR3" "PETR4" "PETZ3" "POSI3" "PRIO3" "QUAL3" "RADL3" "RAIL3"
#> [73] "RDOR3" "RENT3" "RRRP3" "SANB11" "SBSP3" "SLCE3" "SOMA3" "SULA11"
#> [81] "SUZB3" "TAEE11" "TIMS3" "TOTS3" "UGPA3" "USIM5" "VALE3" "VBBR3"
#> [89] "VIIA3" "VIVT3" "WEGE3" "YDUQ3"
```
With the index composition you can use COTAHIST to select their quotes.
``` r
glimpse(
cotahist_get_symbols(ch, ibov_comp)
)
#> Rows: 88
#> Columns: 13
#> $ refdate 2023-03-03, 2023-03-03, 2023-03-03, 2023-03-03,…
#> $ symbol "BRAP4", "CSAN3", "CSNA3", "LREN3", "LWSA3", "PC…
#> $ open 28.83, 14.73, 17.90, 18.27, 4.58, 14.36, 13.12, …
#> $ high 29.24, 15.06, 18.52, 18.27, 4.81, 14.70, 13.22, …
#> $ low 28.76, 14.64, 17.85, 17.74, 4.58, 14.19, 12.99, …
#> $ close 28.92, 14.74, 18.24, 17.75, 4.75, 14.41, 13.11, …
#> $ average 29.00, 14.85, 18.29, 17.93, 4.70, 14.47, 13.09, …
#> $ best_bid 28.81, 14.73, 18.24, 17.74, 4.74, 14.41, 13.11, …
#> $ best_ask 28.92, 14.74, 18.25, 17.76, 4.75, 14.45, 13.12, …
#> $ volume 88684490, 108449481, 151985388, 179220052, 42526…
#> $ traded_contracts 3057800, 7302400, 8307600, 9995000, 9041300, 263…
#> $ transactions_quantity 7403, 17092, 13794, 28476, 12587, 7990, 34400, 2…
#> $ distribution_id 145, 123, 258, 215, 102, 166, 126, 115, 134, 144…
```
## 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
#> [1] "C:/Users/wilso/R/rb3-cache/FPR/7a2422cc97221426a3b2bd4419215481/FP220510/FatoresPrimitivosRisco.txt"
```
`download_marketdata` returns the path for the downloaded file.
``` r
fpr <- read_marketdata(f, "FPR")
fpr
#> $Header
#> # A tibble: 1 × 2
#> tipo_registro data_geracao_arquivo
#>
#> 1 1 2022-05-10
#>
#> $Data
#> # A tibble: 3,204 × 11
#> tipo_r…¹ id_fpr nome_…² forma…³ id_gr…⁴ id_ca…⁵ id_in…⁶ orige…⁷ base base_…⁸
#>
#> 1 2 1422 VLRAPT4 Basis … 1 BVMF 2.00e11 8 0 0
#> 2 2 1423 VLPETR3 Basis … 1 BVMF 2.00e11 8 0 0
#> 3 2 1424 VLSEER3 Basis … 1 BVMF 2.00e11 8 0 0
#> 4 2 1426 VLJBSS3 Basis … 1 BVMF 2.00e11 8 0 0
#> 5 2 1427 VLKLBN… Basis … 1 BVMF 2.00e11 8 0 0
#> 6 2 1428 VLITUB3 Basis … 1 BVMF 2.00e11 8 0 0
#> 7 2 1429 VLITSA4 Basis … 1 BVMF 2.00e11 8 0 0
#> 8 2 1430 VLHYPE3 Basis … 1 BVMF 2.00e11 8 0 0
#> 9 2 1431 VLGRND3 Basis … 1 BVMF 2.00e11 8 0 0
#> 10 2 1433 VLUGPA3 Basis … 1 BVMF 2.00e11 8 0 0
#> # … with 3,194 more rows, 1 more variable: criterio_capitalizacao , and
#> # abbreviated variable names ¹tipo_registro, ²nome_fpr, ³formato_variacao,
#> # ⁴id_grupo_fpr, ⁵id_camara_indicador, ⁶id_instrumento_indicador,
#> # ⁷origem_instrumento, ⁸base_interpolacao
#>
#> attr(,"class")
#> [1] "parts"
```
`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()
```
