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In the dataset, click on the Download button. Select the XLSX format and choose Copy Link Alternatively, you can use the Data Snapshot link from any pipeline. Remember to choose the XLSX link. Step 2. Integrating with Excel Open Excel on your Mac, go to the Data tab in the top menu bar. Click on Get Data (Power Query) and Select Get Data (Power Query) from the dropdown menu. Choose data source: Blank query from the options In the Connect to the data source tab, you need to paste the dataset URL and then run the following query. After that, press Next to visualize the table. Excel.Workbook(Web.Contents("XLSX link")) Alternatively, you may choose to use a CSV Link provided by Alphacast. To do so, you should use: Csv.Document(Web.Contents("CSV link")) Finally, set up your table by selecting the first row as headers. Step 3. There is no step 3....

By Francisco Fernandez More Alphacast Highlights available at this link The Argentine economy has a significant part of its production, employment, and consumption destined to Small and Medium Enterprises (SMEs). To understand their economic and financial situation, especially considering the scarcity of relevant high frequency official statistics, it is possible to use data on their financing. For this special segment of the economy, ECHEQs are fundamental - and Alphacast has a real-time updated Stock Market dataset of them, discriminating by maturity, asset type, and currency. The complexity of the dataset, in addition to the amount of data and variables available, makes it crucial to know how to process and analyze them. Therefore, in this hands-on guide, we will show how to filter the data, transform them, and present them graphically for their best use. For this, we will use the Alphacast pipeline engine to transform and plot data from guaranteed ECHEQs. Since the source is available in our database, the information is updated regularly and automatically, as soon as it is made public - both in the original dataset and in all uses. Step by step First, we start by creating a pipeline. This is simple: select "create new", then...

From a Dataset First of all, filter the information you want to use. Excel and Google Sheet limit the information that can be downloaded to a maximum, so it is convenient to apply the filters you need. Once you've done the filter, click on the Download button. Then select the format you want: TSV (for integrations with Excel for Mac), CSV, or Excel and choose Copy Link. You will notice the URL already has your personal API key (which should not be shared publicly with other users, outside your team). From a Pipeline Within any pipeline, you can create a Data Snapshot to get a Download Link for the data up to that point. Use the XLSX Download link and follow Step 2. Step 2. Integrating to Excel or Google Sheets Excel Now go to Excel, select Get Data > From Other Sources > From Web, and enter the URL you copied from the Alphacast website. The following GIF guides you through a successful Excel integration! Google Sheets It is VERY easy to connect any Alphacast dataset to Google Sheets and leave it connected to update itself. In Google Sheets type =IMPORTDATA() and paste the URL (the one copied earlier)...

The pipeline engine "Apply Transform" step incorporates a new transformation that allows changing the source unit: Convert to dollar official or to Blue Chip Swap (for Argentina only). The pipeline is separated into Two steps Select ("Fetch") the dataset and its columns "Apply transform" by selecting "usddaily", "usdmonthly" or whatever corresponds to the frequency and/or CCL_daily to convert to CCL. Important: This operation uses the "Country" field to identify the country and know which currency to use. In the case of the official currencies, use this reference dataset for the conversion, and in the case of the Argentine CCL...

Removing seasonality from time series is always complicated and laborious. The standard deseasonalization method is X-13ARIMA-SEATS or some other version of the methodologies maintained by the United States Census Bureau. Denationalizing usually includes using some application such as Eviews, Demetra or Stata or Python, combining it with the files that are downloaded from Census. Anyone who has also tried to seasonally adjust in Excel knows that it is cumbersome. Just for reference, about the reasons why it is important to deseasonalize the series for the analysis of the situation I wrote this article a while ago. Deseasonalize with Pipelines in Alphacast is very easy and includes only 4 steps: Fetch dataset Filter the columns we want to use (this is not strictly necessary) Step "Apply Transform", choosing the "Seasonal Adjustment" option Chart, download, or publish the dataset As an example, Let's see how to deseasonalize the Monthly Estimator of Economic Activity of Mexico -> This data and Argentina's EMAE -> This...

Surely in your usual work with data, you needed to join several data sources and if your calculation tool is Excel you may solve it with some combination of the VLOOKUP, HLOOKUP, and/or MATCH formulas. Excel is a great solution in many cases, but it can be difficult in some scenarios. For example when... ...You have MANY rows. VLOOKUP can have performance issues and be very slow ...you need to search more than one field to combine the data ...the position of rows or columns changes ...you only need the data that is in both datasets ...some of the data sources change the number of rows and you have to copy or adjust the formulas. With Alphacast you can use pipelines to combine datasets and keep them connected. Step 1. Choose a data source To merge two datasets, first click the Create new button and choose pipeline. Once there use Fetch dataset to select the required dataset. You can also begin the process by clicking "Transform data" in the dataset you want to merge Step 2. Select the data source to "Merge" Then click Add step below and choose the option Merge with Dataset, there you select the dataset you...

Pipelines are an easy way to apply transformations to datasets that update automatically every time data is updated. Suppose we have a daily data series for which we need only the last value of each month. It is possible to do that in Excel. For example, an auxiliary column is added that is TRUE / FALSE depending on whether or not we are on the last day of the month, and then a pivot table or a filter is created, or another table and a vlookup. EOMONTH, for example, is used for that. Doing it with pipelines basically you don't have to do anything. The transformation is done by itself Step 1. Find the dataset and click "Transform Data" Suppose we want a monthly exchange rate series for all countries. Something like this exists in this BIS dataset but with daily frequency: "Financial - Global - BIS - Main Currencies" With the source, dataset identified click "Transform Data" on the dataset view You will now be in the Pipelines Editor Step 2. Choose the step "Resample frequency" A step is added to perform a "Frequency Resample". The source data is daily - this will be automatically detected - and then...

By Maia Mindel (mmindel@alphacast.io) Read more Alphacast Highlights here In this hands-on guide, we will show you how to use Alphacast Pipelines to create coincident indeces and monitor key economic variables. What is a coincident index? A coincident index provides a gauge for the movement of a key economic variable through the use of related data series. In this case, the coincident investment index mirrors two of the two largest components of GDP, Private Consumption and Private Fixed Investment. Since both indeces have a certain complexity, the methodology will be shown separately, though there are major similarities between the two. How to make the Investment Index In this case, we will show you how to create the coincident investment index. The index is divided in sections, which are weighed in accordance with their relevance to the variable at hand, and estimated using the latest available data for each. Given that the index uses the Alphacast pipelines engine and data available on the platform's database, that means that the Index will be updated to reflect it as soon as the data is uploaded in the Alphacast site - in a completely automated manner. The first step is identifying the data series...

By Milagros Ricchini (mricchini@alphacast.io) Read more Alphacast Highlights here In this hands-on guide, we will show you how to use Alphacast Pipelines to automatize Latin American economies' analysis. In this case, we are going to make a graph of Ecuador's fiscal result and calculate its primary expenditure, which is not a variable of the dataset. First, we look for the dataset which contains the variables we need: Fiscal - Ecuador - BCE - Non Financial Public Sector Operations. The data excerpt in the overview of the dataset shows it contains multiple entities, this is useful information for the next steps in this guide, where we are going to transform the data using pipelines and create the graphs we need. So, the next step is to transform de data by clicking on the right top button "Transform Data" Now it's time for action! As we saw the dataset has multiple entities, first we are going to filter the ones we are interested in, which are overall result, primary result, total expenditures and total revenue included into the entity "Totals": When we click "Save & Preview Data" we can see the dataset we have now: As the entities "Fist Level" and "Second...

If you work with data you probably have come to the scenario where you have found the data you need but not in the shape that you need. A typical example is when data that should be row values are columns or otherwise, a situation that can not be solved by simply transposing the data. "Wide to Long" and "Long to Wide" steps are useful to solve this. Let's see an example. See the pipeline here Let's begin by loading some Yahoo Finance data. You will notice the Tickers (Apple, Microsoft, Google. etc) values in the column "Ticker". Let's assume that you need them to be columns, side by side and that you only need the Closing price and not Open-High-Low-Close. Next, add a step "Long to Wide (Unstack" to the pipeline. And the resulting dataset will look like these If you want to reverse this process use the step "Wide to Long" in which case you need to define what will be the name of the column that will now host the tickers and the name of the columns that will have the values. Also you can check and uncheck which columns should be "melted" and uncheck those that...

If you work with economic data you probably know FRED, the massive database created by the US Federal Reserve Bank of Saint Louis, that claims to have 816,000 US and international time series from 108 sources. You can now access all that data directly into Alphacast, using Alphacast pipelines. Creating a dataset or pipeline by importing from Yahoo finance is as simple as 1 Create a new pipeline (Top right --> Create New --> Pipeline) and choose "FRED" as your data source. 2.To load data into Alphacast you need to know the series Code. Each series has a unique code that can be found in two places on FRED site. On the URL and next to its name. In the following example the series code is T10Y2Y and MORTGAGE30US 3.Include as many series as you like, separating them by a comma. See a working example here 4.Ready! You can now publish the dataset, chart the data, process it or any other cool option available in...

Each dataset has a frequency. They can be daily, weekly, monthly, quarterly, or yearly, This step is used to change that frequency and recalculate the values of the variables. With Change Frequency users can resample the time-frequency of the dataset moving from Daily to monthly, quarterly, and yearly back and forth. Step 1. Create a pipeline and choose the data source Step 2. add a "Change Sample Frequency" step. Once the step is selected the following Interface will appear. The system will automatically identify the input data frequency. Step 3. Select the output data. This will define whether the step is an interpolation or Downscaling. When going from a lower frequency to a higher frequency (i.e. monthly to daily) you have to select from the 8 interpolation options: Repeat the value, linear interpolation, quadratic, cubic, polynomial, and piecewise, Splines and Krogh interpolation. Alternatively, when going from higher to lower (I,e. monthly to quarterly) there are 6 options: average all the values, sum, select the min or max, or the last and first. These are examples of the different interpolation methods applied on the same input...

The Entity columns are those necessary to uniquely identify a row of the dataset and, to that extent, the combinations of Entities cannot be repeated. This implies that trying to remove or change entities are delicate task as they can corrupt the data Let's say, for example, that you have a Dataset with Date and Country as entities (the most common combo in Alphacast). This means that you will have many rows for the same date for different countries. In this example, you can not drop the country column/entity because dates will then be repeated and entities have to be unique The way to deal with this is by regrouping entities using pipelines. Step 1. Create a Pipeline and select the Dataset source. Step 2. add the step "Regroup Entities" Step 3. Decide which entities will be dropped by deselecting them. Step 4. Decide what formula you will use to group the rows with repeated values in the Entity (the Date In the previous example) For example, you can sum all the values of every country for a given date, calculate the mean or the min or max value. The optimal formula depends on the content and context of the...

Yahoo Finance has information on hundreds of thousands of financial assets, stocks, bonds, ETFs, Indices, which are now easily accessible using Alphacast pipelines. Creating a dataset or pipeline by importing from Yahoo finance is as simple as 1 Create a new pipeline 2 Choose as data source "Yahoo Finance" 3 Choose as many tickers as you want, separated by commas: Example "NQ=F, AAPL, MSFT" and the period you need 4 Press "Save" to save the changes in the Step 5 Ready! You can now publish the dataset that will include open, close, high, low, volume, dividends, and stock split...