Exploring Alphacast [Outdated]

By alphacast

Exploring Alphacast [Outdated]'s insights

Importing data into Excel

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Excel allows to add data from different sources. We offer our Alphacast Excel Add-In to simplify the process of downloading data. However, some customers may not have the chance to install an Add-In. Here you can find an alternative way to embed data into Excel, by using our TSV data source: Getting TSV download links When you navigate through Datasets in Alphacast, you will find a Download link. If you filter variables, download links will consider variables selection, and only filtered variables will be downloaded. You will now find an option to download Tab Separated Values (TSV), which can make it easier to embed data into Excel: Right click on the TSV Download link, and choose "Copy Link Address". You will notice the URL already has your personal api_key, and should not be shared publicly with other users, outside your team. Now go to Excel, and select Get Data -> From Other Sources -> From Web, and enter the URL you copied from Alphacast website. The Excel team published a super easy video, describing how to do...

¿Cómo unir el contenido de dos datasets?

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Seguramente en tu trabajo habitual con datos necesitaste cruzar varias fuentes de datos y si tu herramienta de cálculo es Excel posiblemente lo resuelvas con alguna combinación de las formulas VLOOKUP, HLOOKUP y/o MATCH. Excel es una gran solución en muchos casos, pero suele traer dificultades en algunos escenarios. Por ejemplo, cuando... ...tenés MUCHAS filas. VLOOKUP puede tener problemas de performance y ser muy lento ...necesitás buscar mas de un campo para combinar los datos ...cambia la posición de las filas o las columnas ...solo necesitás los datos que estén en los dos conjuntos de datos ...alguna de las fuentes de datos cambio de cantidad de filas y tenés que copiar o ajustar las formulas. Con Alphacast podés utilizar los pipelines para combinar datasets y mantenerlos conectados. Paso 1.Elegir una fuente de datos Para hacer merge dos datasets primero hay que dirigirse al botón Create new y elegir pipeline v2.0. Una vez allí, seleccionar el repositorio donde se guardará el pipeline y escribir el nombre deseado. En Fetch dataset seleccionar el dataset requerido. Apretar el botón Save. Paso 2. Seleccionar la fuente de datos a "Mergear" Luego clickear Add step below y elegir la opción Merge with Dataset, ahí se...

Alphacast's Integrations: Interacting with the API on R

Integrating Alphacast with R (YOU ARE HERE) Integrating Alphacast with Python How to install the Excel add-in Download data with the Excel add-in Introduction and prerequisites Getting data from Alphacast with R is really easy. You need the Alphacast API Key and some common R packages. With a few simple steps, you can get entire dataframes, dataset indexes, repository names and repository content in any format, ready for processing and analysis. To work correctly with the Alphacast API from R we recommend installing and loading the following libraries: install.packages(c("dplyr", "httr", "reshape2")) library(dplyr) library(httr) library(reshape2) To make your API workflow easier, we recommend creating an object named "alphacastapikey" with your own key. This will make working with your Alphacast credentials faster. Remember that you can get the API credentials from the Alphacast Settings menu. For example: alphacastapikey <- "YOUR_API KEY" Getting all available datasets in Alphacast Before starting to work with the API, you may find it useful to have an index with all the datasets available on the platform with your user level. With a few lines of code to achieve this is possible in a very simple way by using the mentioned libraries. First, we indicate to R the link to the Alphacast website that will bring the index in JSON format. Authentication will be completed with the authenticate() function. It should be remembered that the Alphacast API does not need a user and password but works with a single API Key. datasets <- GET("https://api.alphacast.io/datasets", authenticate(user = alphacastapikey, password = "")) To clean up the dataset and make it useful, use the bind_rows() command from the dplyr package in conjunction with the content() function from the httr package to get the response in dataframe format. datasets <- bind_rows(content(datasets))[ ,-5] head(datasets) | id|name |database | |----:|:--------------------------------------------------------|:----------------------------------------------| | 5208|High Frequency CPI - Argentina - Wide - Weekly |Alphacast Basics: Argentina High Frequency CPI | | 5225|High Frequency CPI - Argentina - Weekly |Alphacast Basics: Argentina High Frequency CPI | | 5226|High Frequency CPI - Argentina - SEIDO vs INDEC - Weekly |Alphacast Basics: Argentina High Frequency CPI | | 5231|Public Opinion - Latin America |SEIDO: Latin American Public Opinion | | 5236|Public Opinion - Argentina |SEIDO: Latin American Public Opinion | | 5241|Public Opinion - Argentina - COVID-19 |SEIDO: Latin American Public Opinion | Getting dataframes from Alphacast To obtain a dataframe it is necessary to call the GET function (from the HTTR library) with the number of dataset you want and your API key. For example, if you want to get the data from dataset 6659 (Apple Mobility Report): dataset_id <- 6659 apple_mob <- GET(paste("https://api.alphacast.io/datasets/", datasetid,".csv", sep=""), authenticate(user = alphacastapi_key, password = "")) applemob <- readr::readcsv(content(applemob, as ="text"), guessmax = 100000) head(apple_mob) |Entity |Year | driving| walking| driving - 7drunningav| walking - 7drunningav|transit |transit - 7drunningav | |:-------|:----------|-------:|-------:|-----------------------:|-----------------------:|:-------|:-----------------------| |Albania |2020-01-13 | 100.00| 100.00| NA| NA|NA |NA | |Albania |2020-01-14 | 95.30| 100.68| NA| NA|NA |NA | |Albania |2020-01-15 | 101.43| 98.93| NA| NA|NA |NA | |Albania |2020-01-16 | 97.20| 98.46| NA| NA|NA |NA | |Albania |2020-01-17 | 103.55| 100.85| NA| NA|NA |NA | |Albania |2020-01-18 | 112.67| 100.13| NA| NA|NA |NA | The previous code allows to save the dataframe of the "Apple Mobility Report" in the object "apple_mob". From here, you can do whatever you want with it: graph, analyze, export as csv or JSON, among other things. It is also easy to transform the dataframe to LONG format using the reshape2 package, since all Alphacast datasets contain the "Year" and "Entity" columns. applemoblong <- melt(apple_mob, id.vars = c("Entity", "Year")) Getting repositories and its datasets You can get all available repositories from Alphacast with your level of access. repos <- GET("https://api.alphacast.io/repositories", authenticate(user = alphacastapikey, password = "")) repos <- bind_rows(content(repos)) You can also access the index of the datasets of a given repo. In this case, you can get all the datasets from the repo "Argentina's daily financial data" through the following functions: repo_id <- 21 reposdatasets <- GET("https://api.alphacast.io/datasets", query = list(repoid = repo_id), authenticate(user = alphacastapikey, password = "")) reposdatasets <- bindrows(content(repos_datasets)) head(repos_datasets) | id|name |createdAt |updatedAt | repositoryId| |----:|:------------------------------------------------------------|:-------------------|:-------------------|------------:| | 5266|Base FCI - Renta Variable |2020-10-22T22:38:21 |2020-10-22T22:38:21 | 21| | 5273|Base FCI - Renta Fija |2020-10-27T16:43:04 |2020-10-27T16:43:04 | 21| | 5288|Financial - Argentina - FX premiums - Daily |2020-11-01T17:32:02 |2020-11-01T17:32:02 | 21| | 5289|Financial - Argentina - FX premiums - Daily_Long |2020-11-01T17:33:03 |2020-11-01T17:33:03 | 21| | 5341|Financial - Argentina - Sovereign Bonds |2020-11-12T12:30:03 |2020-11-12T12:30:03 | 21| | 5357|Financial - Argentina - Sovereign Bonds - Last Price - Daily |2020-11-19T16:30:03 |2020-11-19T16:30:03 | 21| Creating repositories in Alphacast You can create your own repository to later upload the dataset. First, you have to set some variables in your R Environment. url <- "https://api.alphacast.io/repositories"` form <- list( "name" = "Repo's Name", "description" = "Test Repo - description", "privacy" = "Private", "slug" = "test-rrr-repo") And then, you post in the Alphacast server through the function POST. r <- POST(url = url, body = form, config = authenticate(user = alphacastapikey, password = "")) content(r) | id|name |description |privacy |slug | |---:|:-----------|:-----------------------|:-------|:-------------| | 610|Repo's Name |Test Repo - description |Private |test-rrr-repo | In this way, the "610" repo is created and can be checked from your admin on the Alphacast web. Uploading data to your repo Once the repo is created, it is necessary to create the slot for the dataset that you want to upload. The system will automatically generate the id of the dataset. url <- "https://api.alphacast.io/datasets" form <- list( "name" = "test_datasets", "repositoryId" = 610)` r <- POST(url = url, body = form, config = authenticate(user = alphacastapikey, password = "")) content(r) $id 6822 In this example, id number 6822 was assigned for the dataset. The next thing is to create the PUT function to upload the CSV to Alphacast and make it appear in the repo. dataset_id <- 6822 url <- paste("https://api.alphacast.io/datasets/", dataset_id, "/data?deleteMissingFromDB=True&onConflictUpdateDB=True", sep = "") Finally, you can upload the dataset in CSV format, with columns named Entity (for countries) and Year (for dates, in YYYY-MM-DD format). In this case, the "tcn.csv" file is uploaded from the indicated path (located in the root folder of the R project). r <- PUT(url, body = list(data= upload_file("tcn.csv")), config = authenticate(user = alphacastapikey, password = "")) content(r) | id|status |createdAt | datasetId| |---:|:---------|:--------------------------|---------:| | 614|Requested |2021-07-26T20:59:21.494134 | 6822| And in this way the file is uploaded to its own repository, generating the possibility of sharing it, transforming it or graphing it. Previous Next

Technical Analysis - Pattern Recognition

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Two Crows (CDL2CROWS) Three Black Crows (CDL3BLACKCROWS) Three Inside Up/Down (CDL3INSIDE) Three-Line Strike (CDL3LINESTRIKE) Three Outside Up/Down (CDL3OUTSIDE) Three Stars In The South (CDL3STARSINSOUTH) Three Advancing White Soldiers (CDL3WHITESOLDIERS) Abandoned Baby (CDLABANDONEDBABY) Advance Block (CDLADVANCEBLOCK) Belt-hold (CDLBELTHOLD) Breakaway (CDLBREAKAWAY) Closing Marubozu (CDLCLOSINGMARUBOZU) Concealing Baby Swallow (CDLCONCEALBABYSWALL) Counterattack (CDLCOUNTERATTACK) Dark Cloud Cover (CDLDARKCLOUDCOVER) Doji (CDLDOJI) Doji Star (CDLDOJISTAR) Dragonfly Doji (CDLDRAGONFLYDOJI) Engulfing Pattern (CDLENGULFING) Evening Doji Star (CDLEVENINGDOJISTAR) Evening Star (CDLEVENINGSTAR) Up/Down-gap side-by-side white lines (CDLGAPSIDESIDEWHITE) Gravestone Doji (CDLGRAVESTONEDOJI) Hammer (CDLHAMMER) Hanging Man (CDLHANGINGMAN) Harami Pattern (CDLHARAMI) Harami Cross Pattern (CDLHARAMICROSS) High-Wave Candle (CDLHIGHWAVE) Hikkake Pattern (CDLHIKKAKE) Modified Hikkake Pattern (CDLHIKKAKEMOD) Homing Pigeon (CDLHOMINGPIGEON) Identical Three Crows (CDLIDENTICAL3CROWS) In-Neck Pattern (CDLINNECK) Inverted Hammer (CDLINVERTEDHAMMER) Kicking (CDLKICKING) Kicking - bull/bear determined by the longer marubozu (CDLKICKINGBYLENGTH) Ladder Bottom (CDLLADDERBOTTOM) Long Legged Doji (CDLLONGLEGGEDDOJI) Long Line Candle (CDLLONGLINE) Marubozu (CDLMARUBOZU) Matching Low (CDLMATCHINGLOW) Mat Hold (CDLMATHOLD) Morning Doji Star (CDLMORNINGDOJISTAR) Morning Star (CDLMORNINGSTAR) On-Neck Pattern (CDLONNECK) Piercing Pattern (CDLPIERCING) Rickshaw Man (CDLRICKSHAWMAN) Rising/Falling Three Methods (CDLRISEFALL3METHODS) Separating Lines (CDLSEPARATINGLINES) Shooting Star (CDLSHOOTINGSTAR) Short Line Candle (CDLSHORTLINE) Spinning Top (CDLSPINNINGTOP) Stalled Pattern (CDLSTALLEDPATTERN) Stick Sandwich (CDLSTICKSANDWICH) Takuri (Dragonfly Doji with very long lower shadow) (CDLTAKURI) Tasuki Gap (CDLTASUKIGAP) Thrusting Pattern (CDLTHRUSTING) Tristar Pattern (CDLTRISTAR)...

Technical Analysis - Overlap Studies

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Bollinger Bands (BBANDS) Double Exponential Moving Average (DEMA) Exponential Moving Average (EMA) Hilbert Transform - Instantaneous Trendline (HT_TRENDLINE) Kaufman Adaptive Moving Average (KAMA) Moving average (MA) MESA Adaptive Moving Average (MAMA) Moving average with variable period (MAVP) MidPoint over period (MIDPOINT) Midpoint Price over period (MIDPRICE) Parabolic SAR (SAR) Parabolic SAR - Extended (SAREXT) Simple Moving Average (SMA) Triple Exponential Moving Average (T3) (T3) Triple Exponential Moving Average (TEMA) Triangular Moving Average (TRIMA) Weighted Moving Average...

Technical Analysis - Momentum Indicators

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Average Directional Movement Index (ADX) Average Directional Movement Index Rating (ADXR) Absolute Price Oscillator (APO) Aroon (AROON) Aroon Oscillator (AROONOSC) Balance Of Power (BOP) Commodity Channel Index (CCI) Chande Momentum Oscillator (CMO) Directional Movement Index (DX) Moving Average Convergence/Divergence (MACD) MACD with controllable MA type (MACDEXT) Moving Average Convergence/Divergence Fix 12/26 (MACDFIX) Money Flow Index (MFI) Minus Directional Indicator (MINUS_DI) Minus Directional Movement (MINUS_DM) Momentum (MOM) Plus Directional Indicator (PLUS_DI) Plus Directional Movement (PLUS_DM) Percentage Price Oscillator (PPO) Rate of change : ((price/prevPrice)-1)*100 (ROC) Rate of change Percentage: (price-prevPrice)/prevPrice (ROCP) Rate of change ratio: (price/prevPrice) (ROCR) Rate of change ratio 100 scale: (price/prevPrice)*100 (ROCR100) Relative Strength Index (RSI) Stochastic (STOCH) Stochastic Fast (STOCHF) Stochastic Relative Strength Index (STOCHRSI) 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA (TRIX) Ultimate Oscillator (ULTOSC) Williams' %R...

¿Cómo import datos desde Yahoo Finance?

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Yahoo Finance cuenta con información de cientos de miles de activos financieros, acciones, bonos, ETFs, Indices, que son accesible de forma muy facil utilizando los pipelines de Alphacast. Crear un dataset o un pipeline importando desde Yahoo finance es tan simple como Crear un pipeline nuevo Elegir como fuente de datos "Yahoo Finance" Elegir tantos tickers como quieras, separados por coma: Ejemplo "NQ=F, AAPL, MSFT" y el periodo que necesitas Apreta "Save" para guardar los cambios en el Step Listo! Ya podes publicar el dataset que incluira datos open, close, high, low, volumen, dividends y stock...

¿Cómo pasar una serie a dólar oficial o CCL?

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En el nuevo engine de pipelines incorporamos una nueva transformación que permite cambiar la moneda de la serie original y que en el caso de Argentina tiene dos opciones: Convertir a dólar oficial o a CCL. El pipeline se separa en tres Seleccionar ("Fetch") el dataset y sus columnas (este paso no es necesario, pero remueve data innecesaria) "Apply transform" seleccionando "usddaily", "usdmonthly" o lo que corresponda a la frecuencia y/o CCL_daily para convertir a CCL. Publicar el dataset Importante: Esta operación utiliza el campo "Country" para identificar el país y saber qué moneda utilizar. En el caso de las monedas oficiales utiliza este dataset de referencia para la conversión y en el caso del CCL argentino...

¿Cómo calcular una serie mensual de fin de periodo?

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Los pipelines son una forma facil de aplicar transformaciones a los datasets que se actualice de forma automática cada vez que se actualice un dato. Supongamos que tenemos una serie de datos diarios para la cual necesitamos solamente el último valor de cada mes. Es posible hacer eso en Excel. Por ejemplo se agrega una columna auxiliar que sea TRUE / FALSE según si estamos o no en el último día del mes, y luego se arma una tabla pivot o un filtro, u otra tabla y un vlookup. EOMONTH, por ejemplo sirve para eso. Haciendolo con pipelines básicamente no hay que hacer nada. La transformación se hace sola 1. Buscar el dataset original Supongamos que queremos una serie mensual de tipo de cambio de todos los países. Algo así existe en este dataset del BIS pero con frecuencia diaria. "Financial - Global - BIS - Main Currencies" 2. Crear el pipeline y elegir la fuente Con el dataset de origen identificado vamos a crear un pipeline (con el Engine v2.0). Como primera opción en "Fetch Dataset" seleccionamos el dataset que encontramos en el paso anterior 3. Resamplear la frecuencia y elegir la variable y publicar a un dataset En...

¿Cómo se desestacionaliza una Serie de Tiempo?

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Quitarle la estacionalidad a las series de tiempo siempre es complicado y laborioso. El método estándar de desestacionalización es X-13ARIMA-SEATS o alguna otra versión de las metodologías que mantiene el Census Bureau de Estados Unidos. Desestacoinalizar suele incluir usar alguna aplicaciones como Eviews, Demetra o Stata o Python, combinándola con los archivos que se bajan de Census. Cualquiera que haya tratado también de desestacionalizar en Excel sabe que es engorroso. Solo de referencia, sobre las razones de por qué es importante desestacionalizar las series para el análisis de coyuntura escribi hace un tiempo este artículo. Desestacionalizar con Pipelines en Alphacast es muy facil e incluye solo 4 pasos: Fetch dataset Filtrar las columnas que queremos usar (esto no es estrictamente necesario) Apply Transform, eligiendo la opción de \\\"Seassonal Adjustment\\\" Publicar el dataset A modo de ejemplo puede ver como desestacionalizamos el Estimador Mensual de Actividad Económica (EMAE) de Argentina Este dataset --> Con este pipelines --> Publicado en este nuevo dataset El beneficio de usar series desestacionalizadas se ve inmediatamente. La serie azul del siguiente grafico muestra como evoluciona en el tiempo la estimacion mensual del rubro \\\"Agricultura, Ganaderia y Pesca\\\". Notaran que sube muchisimo en Mayo cuando se cosecha...

¿Cómo conectar los datos de Alphacast a Google Sheets?

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Es MUY facil conectar cualquier dataset de Alphacast a Google Sheet, y dejarlo conectado para que se actualice solo. Paso 1. Encontrá el dato que te interesa. Por ejemplo, el siguiente dataset tiene el dolar blue de Argentina https://www.alphacast.io/datasets/5288 Podes encontrar cientos de datasets con info acá https://www.alphacast.io/datasets Paso 2. Filtra la informacion que querés usar. Google Sheet limita la informacion que se puede descargar a un máximo, con lo cual conviene aplicar los filtros que necesitas. Paso 3. En Download apreta botón derecho donde dice CSV y copia la dirección. Va a verse algo parecido a esto https://api.alphacast.io/datasets/5288/data?apiKey=YOURAPIKEY&%24filter=%27Date%27+ge+2021-12-01T03%3A00%3A00.000Z&$format=csv Paso 4. En google sheets escribi +IMPORTDATA() y pega la URL la URL que copiaste antes. Paso 5. Cambio el formato de la columna fecha a fecha Paso 6. Planifica en que vas a usar todo el tiempo que te vas a ahorrar por no tener que actualizar nunca mas este...

Charts and Maps

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Intro to charts and maps (You are here) Creating charts & maps Intro to Charts and Maps With Alphacast, you will be able to transform your data into useful and visually attractive information in an easy and fast way. You can create and customize different types of graphs or dynamic maps in just a few minutes. Next, you will see the different options available to create and share. Maps State Level Maps We added Sub National level maps. You can easily map the data by having the state/province as your entity. The editor will recognize the state/province and map it accordingly and if it doesn't you can match your data with the map geographies. World Map, US, Brazil & Argentina are already on board and we are adding new maps on a daily basis. If you need a specific map just let us know! Line chart Scatter plot Time Scatter Stacked area Discrete bar Slope chart New charts and editing options Many many many new options for charting. From the chart editor now you can: mix graphs with bars and lines, add a secondary axis on any series, select the position of the legend, font family, color, axis width, hide markers and gridlines, smooth lines, stacked and unstacked bars, and more. Now, you can make multiple chart from one chart! [Previous] [Next]

Teams and users

Managing your user Once logged in, you can access your profile by clicking on your picture, on the upper right, next to the "Create new" button. In "View profile", you will be able to see your own insights, charts, datasets, and repositories or the ones from your team. You can also check who you follow on the platform. If you click on "Settings", you can modify your public data: You can change the display name, write a brief description and add a profile and cover picture. You can also see the email which you have logged in with and get your API key. In the next section we will explain the access and the advantages of belonging to a team. To logout, you can do so by clicking "Logout", that is displayed on the options that appear when you click on your profile picture. Creating and managing your team On settings, you can also access the Teams tab. The main objective of this function is team collaboration between different users either to collaborate on a project or for a specific work area. When you belong to a team, the information will be visible only to its members. Create a New...

Repositories & Teams

Repositories (you are here) Teams Intro to repositories A repository is a categorical grouping. There you can find charts, insights and datasets related to one specific topic. Exploring and watching repositories From our home page, you can access the repositories that are on the left. There you can find the repositories that you are following, are the owner of and that have been shared with you. Also, you can click on "Explore repositories" and find new ones. When you open a repository, you will be able to examine the datasets, insights and charts related to that topic. Furthermore, you can follow a repository and it will appear on the left of your home page. How to create a new repository and permissions In order to create a new repository, there's a blue button on the top right called "Create new". Select the option "New repository". Choose the name, write a brief description of what the repository is about and decide whether you want it private or public. Once the repository is created, you can create charts, upload datasets and write insights, you can store them in that repository. You will be able to find them in the tabs charts, datasets...

Embedding charts, images, and videos

Alphacast's charts can be added in an insight by using '@chart' and '@Ncharts'. The first option is to paste one chart, the second one is to paste two charts together. To paste an interactive chart, write '@chart' and paste the URL of your selected chart like it is shown in the following gif: If you want to add a video that was uploaded in another website, you can insert it in your insight by using the embed code generated by that site. For example, this is the first Alphacast webinar, that was posted on YouTube. In order to add an image, click on the "Image" icon on the bar above and choose the file from your computer. This is, for instance, Alphacast's logo. Publishing and sharing your insights Once you are done writing your insight, you can publish it. If your repository is private, you can choose between two options: restricted (your insight is restricted to users with repository access) and anyone with the link (Even users without access to your repository will be able to read your insight. Insight will not be listed to public users.). If your repository is public, your insight can be seen by everyone. Also,...

Insights and Dashboards

Intro to insights (You are here) Embedding charts, images, and videos Publishing and sharing your insights What's an insight? “Insights” are pieces of content where you can combine text, charts, tables, or images to share your ideas. It can be anything you want. You can create: Presentations Dashboards Recurrent reports Insights – like charts and datasets - are stored in your repositories and can be created and edited by you or anyone in your team. Once published, they will have a permanent URL address that can be easily shared (more sharing options will be available soon). How to create an insight To create and publish an insight, find and click the button in the upper right of the site "Create New" and select "Insight". You can also select one of your or your teams repositories and click on “New insight” located on the upper right. The window on the left is the editor. There you can write, insert charts or format your insight. In the window of the right, you can preview your insight. Follow the instructions already written to help you in the process. Delete them to start writing! Headers can be included by using #, ##, ###, Alphacast...

Pipelines

Creating a pipeline Through the Pipelines, you can create your own datasets derived from any other dataset on the platform. First, you must click on the dataset you want the data from. In the upper right, you will see a button called Create pipe. When you click on it, it will take you to another page where you will select the repository you want to store the data and make your own dataset. Select the repository and choose a name. Click on Read columns. Next, you can filter and rename the variables, rescale the frequency, combine and make transformations. Then, a new dataset will be created in your repository, it will be automatically updated every time the original dataset is updated either by Alphacast or by yourself. Just click on Save pipeline and the dataset will be generated. Every time a dataset is updated or a pipeline runs it will be reported on the "Activity Tab". In addition, more information is provided in case of failure. Once the dataset is created, you can edit it. In the upper right you will see the button Edit. Choose the option Edit pipeline, you can make any change you...

Datasets and Pipelines

Datasets and Pipelines Datasets (you are here) Pipelines What is a dataset? To begin with, a dataset is where the information is stored. Each dataset has one or multiple time series from one or many unique entities. Entities can be, for example, countries. Think about the datasets as an Excel spreadsheet, a Python Pandas DataFrame, or simply as a table with rows and columns. Now you will find datasets created by Alphacast and some featured publishers, but soon there will be many more. Searching and downloading data To explore and search for a dataset follow the "Explore" tab, located at the upper left bar and then click on the "Datasets" tab. This tab has multiple features (and many more are coming soon). For instance, filter by region and country, categories, frequency, sources, sector, and more, where we are permanently tagging the data to categorize it. You can also toggle "Show details" button to hide or expose datasets metadata, sort your results by name, popularity or last update time and favourite your frequently used datasets too. When searching for a dataset utilize the bar on the upper right corner. Use keywords in order to find the data you are looking for. Then, navigate to see the dataset details and find the repository that stores it. In the dataset view you can explore and download data. There's a brief description of the dataset, including the source. Next, you will see: A list of the variables that make up the dataset. Usually, as columns. The transformations that have been made to the data to make it useful and practical, as well as an excerpt of the dataset as an excel sheet. Charts using that dataset, from that repository and others. You will also see three gray buttons. Sync Now allows you to update the data, when you click it, the activity of the dataset will change. We will explain what you can do with Create pipe in the following insight. The last button will filter variables so you don't have to download unnecessary information, you can also create charts with this feature. Last but not least, once you decide what data you need, download your dataset. When downloading the dataset, you can choose between different formats, such as CSV or XLSX, for example. You can also decide whether you want the variables as columns or rows. If you regularly check this dataset, please follow it! Another way of finding the datasets you want is by clicking on a repository. There is a tab that includes all the datasets related to that specific topic. Select any repository of your interest and click on the tab called "Datasets". Choose the dataset you want from the list. When clicking on its name, it will redirect you to dataset view. Creating a dataset To create a dataset you need to upload a CSV or XLS following certain rules. First, you need to access one of your repositories and click Upload dataset. Import your data: upload the CSV or XLS that you want. First column should be country. You can put anything there, but if you put countries they can be used in the maps engine. Second column should be date, on the format YYYY-MM-DD. Both country and date are mandatory. Then one column for each variable. Configure your data: click on each column and select the data and column type. Date should be treated as entity, respecting the date format. Country should be treated as entity as well, its data type is text. You can ignore the other columns. Name your dataset: give your data a name that will be easy to identify. You can also add the country or source in the name. Also, choose the repository you want to store tha dataset. Click on Save and you will create the dataset. Wait a minute a refresh the page, you will be able to see your new dataset. To update the data, the process is the same. You will only have to rewrite the dataset you want to update. Transformations When exploring a dataset, you can see the list of transformations made to it. We call transformations to data that has been modified in order to make comparisons, make it more useful and more. The most common ones in economics and finances are: Seasonally adjusted: a statistical technique that attempts to measure and remove the influences of predictable seasonal patterns. When it says sa_orig, it means we did not make to transformation, we took it from the source. Constant prices: a way of measuring the real change in output. A year is chosen as the base year. Cumulative sum: used to display the total sum of data as it grows with time. It could be 3 months or 12 months, for example. Year over Year/Month over Month: comparisons between figures according to chosen frecuency. % GDP: enabling a ratio in order to make comparisons.

Beginners guide to Alphacast

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Alphacast is an integrated platform for economic and financial analysis. Think GitHub, but for economy and finance. There are many things you and your team can do with Alphacast. Download and create data, integrate with R or Python, Power-bi or Tableau, create interactive charts, maps, insights, or full auto-updated presentations. In this guide, we will guide you through the basics of Alphacast, follow the links for more in-depth information Alphacast in 5 minutes Downloading your first dataset Let's start by finding some data you need, say, for example. Argentina's Consumer Price Index. Go to Search at the top left of your screen Start typing "Inflation Argentina" and select "Inflation - Argentina - INDEC - Consumer Price Index - Groups - Monthly" Click Filter variables on the top right and then "Nivel General" followed by the transformation "Year over Year". You should be logged in to do this. Click Download on the top right and select either CSV o XLSX, and whether you want a transposed version of the data. That's it. The Browser will start downloading your dataset Creating your first chart Now let's create your first chart. To create a chart with data from a dataset you need to...

Alphacast "How to" Guide

Beginners guide (start here) Repositories and teams Intro to repositories Exploring and watching Repositories How to create a new repository Permissions Repository Themes Managing your user Creating and managing your teams Datasets and Pipelines Intro to datasets Searching Data Downloading data Creating Datasets Transformations Charts and Maps Intro to charts and Maps (Chart types) Creating charts & maps: Variable selection Creating charts & maps: Customize Data Creating charts & maps: Customize texts Creating charts & maps: Customize colors Creating charts & maps: Publishing, interacting and sharing charts Plotting Forecasts Insights Intro to insights Embedding charts, images, and videos Publishing and sharing your insights Integrations Integrating Alphacast with R Integrating Alphacast with Python How to install the Excel add-in Download data with the Excel...