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cITMre

The cITMre library—Colombian Index Tool (Market Rate Exchange)—responds to the researcher's economics and financial sciences needs to use the colombian Representative Market Rate Exchange. This package presents a practical solution for downloading the RMRE database.

The tool allows us to obtain:

Download the data series in daily, monthly, quarterly, and half-yearly frequencies.
Can split the time series through start and end function
Can transform the data set in log returns or level
Can make a Dynamic graph through Plotly, or if is your preference can make a normal plot.

Motivation

Obtaining information from the Colombian RMRE is relatively straightforward; the search in the official state database Portal de Datos Abiertos allows the data to be downloaded in .xls or .csv. In economics or financial sciences, obtaining and loading this information into R can be frustrating, forcing many users to create a routine function linked to the database limited to the user's expertise. Thus, this tool aims to facilitate both the loading of data and the use of essential RMRE time series analysis tools.

Note: The information discounts weekends and holidays; the function approximates the nearest trade date.

Parameters

Parameter	Description	Default Value
start_date	An initial date in the "YYYY-MM-DD" type; by default, the series starts on the first date of the resource.	First day of the resource
end_date	A final date in the "YYYY-MM-DD" form; by default, it shows the updated last date on the resource.	Updated last date on the resource
log_return	Show the log return of the RMRE (Representative Market Rate Exchange) dataset; if it is TRUE, show the log return dataset; if it is FALSE, show the level dataset; in default, show the level dataset.	FALSE
plot_data	Show a Plotly linear graph data set; by default, the argument is false, and the graph is built in the Viewer option. You can use the basic plot if the user does not use the plot_data option.	FALSE
frequency	Show frequencies for the data set in daily (365), month (12), quarter (4), and half-year (2); in default, the dataset is the daily frequency.	Daily (365)
type	It works only with 12, 4, 2 frequencies, showing the dataset using the last date ("last_date") or doing a mean ("mean") in the frequency series. By default, the type is "last_date".	"last_date" by default

Applied Example

If you want to use citmre, perform the package installation process using pip install cITMre=0.1.0 and load the from citmre.citmre_fun import rmre_data. Once the package is installed, use the rmre_data() function to obtain the total RMRE series (the Colombian state has RMRE data since 1991-12-02); the data loaded is an XTS series.

from citmre.citmre_fun import rmre_data
import pandas as pd

data = rmre_data(plot_data= True)
data.head()

#>Date
#>1991-12-02    643.42
#>1991-12-03    639.22
#>1991-12-04    635.70
#>1991-12-05    631.51
#>1991-12-06    627.16

Example Image

In economic or financial research, it is not necessary to take the whole time series; use start_date and end_date under the format "YYYYY-MM_DD" to obtain a specific start and end date. For example, we want to get the RMRE from March 18, 2005, to June 26, 2019, in an object called data simplifying function result.

data = rmre_data(start_date = "2005-03-18", end_date = "2019-06-26", plot_data= True)
data.head()

#>Date
#>2005-03-18    2374.46
#>2005-03-22    2371.43
#>2005-03-23    2361.78
#>2005-03-28    2382.30
#>2005-03-29    2397.25

Example Image

In some research, the historical volatility is expected to be analysed for advanced econometric or financial studies. It is possible to use the function log_return=TRUE to change the series to log return based on the formula: $ lr(RMRE) = ln(\frac{Present~value}{Past~Value}) $, in Default the series is presented in level data.

data_log = rmre_data(start_date = "2005-03-18", end_date = "2019-06-26", log_return = True, plot_data= True)
print(data_log.head())
print(data_log.tail())

#>Date
#>2005-03-22   -0.001277
#>2005-03-23   -0.004078
#>2005-03-28    0.008651
#>2005-03-29    0.006256
#>2005-03-30   -0.001641
#>Name: log_return, dtype: float64

#>Date
#>2019-06-19   -0.006609
#>2019-06-20   -0.004934
#>2019-06-21   -0.014541
#>2019-06-25   -0.003391
#>2019-06-26   -0.001261
#>Name: log_return, dtype: float64

Example Image

On some occasions, economic or financial variables do not necessarily use the same time-frequency of the daily series as in the RMRE. Colombia's GDP (Gross Domestic Product) is quarterly; therefore, the RMRE daily series must be transformed into a quarterly one. The frequency function displays the RMRE series in monthly (12), quarterly (4) and half-yearly (2) series. By default, the daily series will be (365). Frequencies can also be transformed to log_return.

The type function can approximate the series on mean or last date data. When type = "mean" is used, the series gets the average value of the series in frequency. Iftype = "last_date" is used, the last data of the series is used in frequency. By default, the type is set to last_date.

### Monthly RMRE
data = rmre_data(start_date = "2005-03-18", end_date = "2019-06-26", plot_data= True, frequency = 12)
print(data.head())
print(data.tail())

#>Date
#>2005-03-18    2374.46
#>2005-03-22    2371.43
#>2005-03-23    2361.78
#>2005-03-28    2382.30
#>2005-03-29    2397.25
#>Name: rmre, dtype: float64

#>Date
#>2019-06-19    3264.98
#>2019-06-20    3248.91
#>2019-06-21    3202.01
#>2019-06-25    3191.17
#>2019-06-26    3187.15
#>Name: rmre, dtype: float64

Example Image

### Quarterly RMRE
data_q = rmre_data(start_date = "2005-03-18", end_date = "2019-06-26", plot_data= True, frequency = 4)
print(data_q.head())
print(data_q.tail())

#>Quarter
#>2005Q1    2376.48
#>2005Q2    2331.81
#>2005Q3    2289.61
#>2005Q4    2282.35
#>2006Q1    2289.98

#>Name: rmre, dtype: float64
#>Quarter
#>2018Q2    2945.09
#>2018Q3    2989.58
#>2018Q4    3275.01
#>2019Q1    3190.94
#>2019Q2    3187.15
#>Name: rmre, dtype: float64

Example Image

### Half-year RMRE
data_s = rmre_data(start_date = "2005-03-18", end_date = "2019-06-26", plot_data= True, frequency = 2)
print(data_s.head())
print(data_s.tail())

#>Semester
#>2005-1S    2331.81
#>2005-2S    2282.35
#>2006-1S    2633.12
#>2006-2S    2233.31
#>2007-1S    1958.09
#>Name: rmre, dtype: float64

#>Semester
#>2017-1S    3038.26
#>2017-2S    2971.63
#>2018-1S    2945.09
#>2018-2S    3275.01
#>2019-1S    3187.15
#>Name: rmre, dtype: float64

Example Image

Final considerations

This tool can be used for time series analysis with an xts class condition; therefore, the user can transform the series to ts if any tool conflicts with an xts series.

References

Source: Portal de Datos Abiertos