Luciano Cohan's Public Repo

Luciano Cohan

Unnamed Dashboard

Total Industry

Money Markets

Renta Fija T + 1

Renta Fija > 50% CER

Renta Fija > 50 DLK

Renta Fija > 50% ARS

Renta Variable

Alphacast Dashboards 

Alphacast

Argentina's Mutual Funds Industry Dashboard V1.4

General

Argentina: Petroleo y Gas

Nominal Policy Rates

Spreads vs USA Monetary Policy Rate (Daily basis)

Global: Policy Rates Tracker

# Argentina's Fiscal Dashboard

## Tax Revenue

@chart https://charts.alphacast.io/p/alphacast/argentina-macro-basics/mecon---payroll-activity-trade-related-taxes?tab=chart&year=latest

@chart https://charts.alphacast.io/p/alphacast/argentina-macro-basics/fiscal-argentina-tax-revenue-by-tax?tab=chart&stackMode=absolute&year=latest

## Fiscal Expenditure, Income, and results

@chart https://charts.alphacast.io/p/alphacast/argentina-macro-basics/argentina-fiscal-result?tab=chart&stackMode=absolute&year=latest

@chart https://charts.alphacast.io/p/alphacast/argentina-macro-basics/ingresos-totales-y-gasto-primario?tab=chart&stackMode=absolute&year=latest&time=2017-05-01..latest

@chart https://charts.alphacast.io/p/alphacast/argentina-macro-basics/primary-expenditure-detail-consatan-prices-sa?tab=chart&stackMode=absolute&year=latest

@chart https://charts.alphacast.io/p/seido/seido-macro-dashboards/transfers-to-treasury-?tab=chart&stackMode=absolute&year=latest

## Debt management & Profile

@chart https://charts.alphacast.io/p/seido/macro-research/fiscal-argentina-cumulative-rollover-rate?tab=chart&stackMode=absolute&year=latest&time=2021-01-18..latest

@chart https://charts.alphacast.io/p/seido/macro-research/fiscal-argentina-debt-auctions-results?tab=chart&stackMode=absolute&year=latest&time=earliest..latest

@chart https://charts.alphacast.io/p/seido/macro-research/argentina-public-net-debt-profile-2021-22-ars?tab=chart&stackMode=absolute&year=latest 

@chart https://charts.alphacast.io/p/seido/macro-research/argentina-public-net-debt-profile-2021-22-ars-stacked?tab=chart&stackMode=absolute&year=latest&time=2022-03-01..latest

@chart https://charts.alphacast.io/p/seido/macro-research/fiscal---argentina---debt-auctions---monthly-forecast?tab=chart&stackMode=absolute&year=latest


Tax Revenue

Fiscal Expenditure, Income, and Results

Debt Management & Profile

Argentina: Fiscal Trends

USDA Soybeans Production Forecast

Inflación de bienes y servicios en America

Countries around the globe are recovering from the pandemic's impact at varying speeds. This post examines the different economic trajectories through ten insightful charts. Access the dashboard of this insight [here](https://www.alphacast.io/p/isavanti_/insights/2024-6-4-wOaGGs)

### Global Economic Growth Rates
As we move beyond the pandemic, economic growth rates have become less synchronized and have not yet recovered to pre-shock trends. While, for example, growth is strong in the USA, Latin America, or India, it is one step lower in some regions and countries such as China, and the Euro Area.

@chart https://www.alphacast.io/p/alphacast/charts/gross-domestic-product-30384?tab=chart&stackMode=absolute&year=latest&time=earliest..2024-01-01&country=Emerging%20and%20Developing%20Asia~Emerging%20and%20Developing%20Europe~Euro%20Area%20(ex%20G7)~Major%20Advanced%20Economies%20(G7)~Other%20Advanced%20Economies

### Inflation Rates Comparison
Global inflation surged during the pandemic recovery, but recent trends reveal varied inflation rates, with some countries managing to stabilize prices more effectively than others. The 0%-2% range of inflation in the past has now shifted to 2%-4%, even in Japan.

@chart https://www.alphacast.io/p/isavanti_/charts/Headline-Consumer-Price-Index-30297?tab=chart&stackMode=absolute&year=latest&country=undefined-0~undefined-1~undefined-2~undefined-3

### Monetary Policy Divergence
Monetary policies diverge as countries tailor their approaches to unique economic circumstances. For instance, the US is expected to cut rates, while Japan plans to raise rates by October.

@chart https://www.alphacast.io/p/isavanti_/charts/Monetary-Policy-Rate-30303?tab=chart&stackMode=absolute&year=latest&country=undefined-0~undefined-1~undefined-2~undefined-3

### US Economic Performance
The US economy has outperformed other developed markets, growing at or above trend. However, inflation, particularly driven by housing prices, remains a significant concern.
@chart https://www.alphacast.io/p/isavanti_/charts/United-States-GDP-Growth-and-Inflation-Rate-30321?tab=chart&stackMode=absolute&year=latest&country=United%20States-0~United%20States-1

### European Economic Struggles
Europe's economic performance has lagged, with weaker GDP and consumer spending. Climate change, demographics, and Brexit continue to hinder growth.
@chart https://www.alphacast.io/p/isavanti_/charts/Euro-Zone-Gross-Domestic-Product-30341?tab=chart&stackMode=absolute&year=latest&country=~Europe

### Japan's Economic Prospects
Japan is set to raise interest rates in October, driven by efforts to create domestic inflation and a virtuous cycle between wages and prices.
@chart https://www.alphacast.io/p/isavanti_/charts/Japan-GDP-Growth-Inflation-Rate-and-Wages-30349?tab=chart&stackMode=absolute&year=latest&country=Japan-1~Japan-0~Japan-2

### China's Economic Rebalancing
China faces challenges from its prolonged pandemic impact and property market slowdown. However, recent policy decisions aim to stabilize the sector and boost economic confidence.
@chart https://www.alphacast.io/p/isavanti_/charts/China-GDP-and-Exports-Growth-30350?tab=chart&stackMode=absolute&year=latest

### Asia's Export Recovery
Asia's export cycle recovery, particularly in sectors like semiconductors, chemicals, and autos, continues to bolster the region's growth outlook.
@chart https://www.alphacast.io/p/isavanti_/charts/Asia-Exports-30356?tab=chart&stackMode=absolute&year=latest&country=China~India~Indonesia~Japan~Saudi%20Arabia~Taiwan~Thailand

### Monetary Policy in Asia
Asian central banks show diverse monetary policies, with some focusing on financial stability and others on aligning with US Fed policy. Rate cuts are expected in several countries, while others may hold or hike rates.
@chart https://www.alphacast.io/p/isavanti_/charts/Monetary-Policy-Rate-in-Asia-30340?tab=chart&stackMode=absolute&year=latest&country=India~Indonesia~Malaysia~Philippines~South%20Korea~Thailand

### Structural Changes in Asian Trade
Asia is undergoing structural changes in trade dynamics with China. Countries like India, Vietnam, and Malaysia are emerging as key beneficiaries of the China-plus-one strategy, adjusting their trade dependencies.

@Ncharts https://www.alphacast.io/p/alphacast/charts/share-of-imports-coming-from-china-30360?stackMode=absolute&year=latest https://www.alphacast.io/p/alphacast/charts/share-of-imports-coming-from-china-30360?stackMode=absolute&year=2001-01-01



Charts of the Week

Diverging paths ahead in global economy

Excel and Google Sheets allow adding data from different sources. Here you can find an alternative way to embed data into Excel, by using our TSV data source:

## Step 1. Getting the download link

### <ins>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.

![dow 4.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/2caf66be-8a28-4f13-afd2-24860bf58462/dow-4.png)


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).


![dow 1.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/795e51b6-2f49-4bf7-a1c9-06324a404ca4/dow-1.png)


![dow 2.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c18f899c-5b8c-458c-a9e8-81a17c1378bc/dow-2.png)

### <ins>From a Pipeline</ins>

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.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/d7ee3160-1430-4796-84ca-a4c77e518f21/image.png)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/bb4aa440-e1e1-48da-aae1-cfccbb54f447/image.png)

## 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! 

![chrome_uyIGgkuxvn.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/b66c14d5-b77e-404e-9686-a0a6d80fed82/chrome_uyIGgkuxvn.gif)

### 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) inside the formula.


```
=IMPORTDATA("LINK CSV")
```


![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/40e4d156-4f0d-4807-9310-5a7e371dac13/image.png)

Change the format of the date column to date.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/4a5026c4-0572-47b8-bc02-d7e343fd378b/image.png)

If the regional settings of your spreadsheet are in Spanish, you have to type =IMPORTDATA() and paste the URL (the one you copied earlier) inside the formula, and add ;",";"en-US" at the end.

```
=IMPORTDATA("LINK CSV";",";"en-US")
```


## Step 3. There is no step 3. Enjoy!


Alphacast Quick Tips

How to integrate Alphacast with Excel and Google Sheets

Automatic Tables and Charts 

Mutual Funds Tracking

Mutual Fund Tracking

My Public Repo

Alphacast_Support

Title

# How to convert a series to the official USD or Blue Chip Swap?

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
1. Select ("Fetch") the dataset and its columns
2. "Apply transform" by selecting "usd_daily", "usd_monthly" 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](https://www.alphacast.io/datasets/5288) for the conversion, and in the case of the [Argentine CCL this](https://www.alphacast .io/datasets/5288)

@chart https://charts.alphacast.io/p/alphacast/pipelines-templates/pipeline/3164-1651678278351

@chart https://charts.alphacast.io/p/alphacast/pipelines-templates/pipeline/3164-1651678349588

# How is a Time Series seasonally adjusted?

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](https://www.census.gov/data/software/x13as.html). 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](http://econserialcronico.blogspot.com/2017/03/arte-ciencia-y-tortura -medieval.html).

Deseasonalize with Pipelines in Alphacast is very easy and includes only 4 steps:
1. Fetch dataset
2. Filter the columns we want to use (this is not strictly necessary)
3. Step "Apply Transform", choosing the "Seasonal Adjustment" option
4. 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](https://www.alphacast.io/datasets/5390) and Argentina's EMAE -> [This dataset](https://www.alphacast.io/datasets/5331)


@chart https://charts.alphacast.io/p/alphacast/pipelines-templates/pipeline/3157-1651670481625

@chart https://charts.alphacast.io/p/alphacast/pipelines-templates/pipeline/3157-1651671142405

# How to merge the content of two datasets?

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...

1. ...You have MANY rows. VLOOKUP can have performance issues and be very slow
2. ...you need to search more than one field to combine the data
3. ...the position of rows or columns changes
4. ...you only need the data that is in both datasets
5. ...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 want to add. The best dataset combinations are obtained with data that share the frequency (daily, monthly, quarterly, or yearly). 

## Step 3. Choose the common fields

If we have two datasets, we have to tell the system what the "splice" method is between both datasets. That is, what will be the fields that must be in one and another dataset from which to join them.

- Usually there will be only one **Date**, in which case both datasets will be **Merged** by their dates.
- In addition to the date, datasets can have more than one entity. For example, they can have data by date and by country. In this case, it will be necessary to identify, if any, which is the field of the second dataset that corresponds to the country field.
- If a field for the second country is not selected, the connection will only be through the date field. In this case, the rows in dataset B may appear duplicated if there is more than one occurrence of your date in dataset A.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c9690c4e-7b6b-4dcd-8ed8-b54d30140e7b/image.png)

In this example, we used two datasets with a monthly frequency and the same entity (Argentina). The result of this combination, when choosing the Left Join option, is that all the data from the first dataset (EMAE) will remain. Those that will be incorporated will be those data from the Consumer Price Index that coincide in date and entity.

## Step 4.  Choose the Matching type

There are four types of criteria for joining

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/ceea19d5-7c24-4843-96ba-42b49e841b7d/image.png)

- **Inner join:** The new dataset will have only those rows that can be **matched**.
- **Left join:** All the rows of dataset A will be present and the unmatched rows of dataset B are discarded.
- **Right join:** Reverse to the previous one. All those from dataset B and discarded the unmatched ones from A.
- **Outer join:** The data from both datasets will remain even if they do not match.

## Paso 5. Publish

As a result of the previous step, the combination of the columns of Dataset A and Dataset B will be obtained. From here you can continue processing it or publish it in a new dataset.

@chart https://charts.alphacast.io/p/alphacast/pipelines-templates/pipeline/3159-1651677087305

# How to calculate a monthly end-of-period series?

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](https://www.alphacast.io/datasets/15172): **"Financial - Global - BIS - Main Currencies"**

With the source, dataset identified click "Transform Data" on the dataset view

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/22517a60-5d28-4043-96b0-9f5b56d26824/image.png)

You will now be in the Pipelines Editor

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e95c63bb-2b48-4432-89da-6ac45736a066/image.png)

## 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 you can select the new frequency - monthly, quarterly o yearly. 

Once the frequency is selected you have to choose how you will aggregate daily data into a single value. Average? sum? Min o Max? In this example we will choose the "Last" value of the month.. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c48a12fa-20c8-4f23-a185-6bb3b2ac8bee/image.png)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e35fdfc2-6e93-4672-8569-8b40911a869b/image.png)

## Step 3. Results

@chart https://charts.alphacast.io/p/alphacast/pipelines-templates/pipeline/3155-1651658829429

@chart https://charts.alphacast.io/p/alphacast/pipelines-templates/pipeline/3155-1651653272969

# Monthly Sales

@chart https://www.alphacast.io/p/alphacast/charts/14858

@chart https://www.alphacast.io/p/alphacast/charts/14859

@chart https://www.alphacast.io/p/alphacast/charts/14843



Sales Dataset Sample

# Debt Sustainability Analysis - Costa Rica  Dec 2022

Our Debt Sustainability Model uses the standard debt dynamic equation to generate forecasts for the Debt/GDP ratio throughout the next 10 years. Specifically, we use the following equation:
$$
d_{t+1} =d_{t}\biggl[(1-\alpha)\biggl(\frac{1+i_{dt}}{(1+g_t)(1+\pi_t)}\biggl) +(\alpha)\biggl(\frac{(1+\Delta\epsilon_t)(1+i_{ft})}{(1+g_t)(1+\pi_t)}\biggl)\biggl] + pd_t
$$
In which:

$$d_{t+1}$$ is the ratio Debt/GDP for the t+1 period;

$$\alpha$$ is the share of foreign debt in relation to total debt;

$$g_t$$ is the real growth of GDP at the period t;

$$\pi_t$$ is the variation of the GDP deflator for the period t;

$$\Delta\epsilon_t$$ is the devaluation of the exchange rate between the local currency to the US dollar during the period t;

$$i_{dt}$$ is the implicit interest rate for the domestic debt, it is calculated as the ratio between the interest paid for the internal debt during the period t and the internal debt in the period t-1;

$$i_{ft}$$ is the implicit interest rate for the foreign debt, it is calculated as the ratio between the interest paid for the internal debt during the period t and the internal debt in the period t-1;

$$pd_t$$ is the primary deficit of the government during the period t.

We account for uncertainty by applying random shocks for each variable. These shocks are constructed by simulating a Multivariate Normal Distribution that uses as input the historical covariance matrix between those variables and their forecasts for the next 10 years. We simulate 2000 shocks for each year and compute the quantiles of the Debt/GDP forecasts according to our DSA equation.

## Model Results. Debt / GDP Dynamic

@chart https://www.alphacast.io/p/luciano/charts/debt-dynamics-14609

## Baseline Assumption

The parameters of this simulation can be changed updated and running [this pipeline](https://www.alphacast.io/pipelines/2022-12-6-QlXtuS/edit?step=1670340264269)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/a4dfad61-bd4a-49ad-bf87-f0483d612edf/image.png)

The interest rate forecast is estimated using a starting average implied rate of 9.3% and 4.6% respectively for internal and external. 20% of the debt is renewed at the new marginal rate of 7.2% (3.7% USD 5y + 350bp) for external and 8% for internal

## Scenarios and sensibility Analysis

The chart below reports the results of the simulation of shocks over a single variable, while the other variables are kept at their baseline levels

@chart https://www.alphacast.io/p/luciano/charts/sensibility-analysis-14610

## Annex: Model inputs

@chart https://www.alphacast.io/p/luciano/charts/exchange-rate-14611

@chart https://www.alphacast.io/p/luciano/charts/growth-14612

@chart https://www.alphacast.io/p/luciano/charts/implied-internal-rate-14613

@chart https://www.alphacast.io/p/luciano/charts/implied-external-rate-14614

@chart https://www.alphacast.io/p/luciano/charts/primary-deficit-14615

@chart https://www.alphacast.io/p/luciano/charts/inflation-14616


Latam DSA - Dec 2022

# Debt Sustainability Analysis - Colombia Dec 2022

Our Debt Sustainability Model uses the standard debt dynamic equation to generate forecasts for the Debt/GDP ratio throughout the next 10 years. Specifically, we use the following the equation:
$$
d_{t+1} =d_{t}\biggl[(1-\alpha)\biggl(\frac{1+i_{dt}}{(1+g_t)(1+\pi_t)}\biggl) +(\alpha)\biggl(\frac{(1+\Delta\epsilon_t)(1+i_{ft})}{(1+g_t)(1+\pi_t)}\biggl)\biggl] + pd_t
$$
In which:

$$d_{t+1}$$ is the ratio Debt/GDP for the t+1 period;

$$\alpha$$ is the share of foreign debt in relation to total debt;

$$g_t$$ is the real growth of GDP at the period t;

$$\pi_t$$ is the variation of the GDP deflator for the period t;

$$\Delta\epsilon_t$$ is the devaluation of the exchange rate between the local currency to the US dollar during the period t;

$$i_{dt}$$ is the implicit interest rate for the domestic debt, it is calculated as the ratio between the interest paid for the internal debt during the period t and the internal debt in the period t-1;

$$i_{ft}$$ is the implicit interest rate for the foreign debt, it is calculated as the ratio between the interest paid for the internal debt during the period t and the internal debt in the period t-1;

$$pd_t$$ is the primary deficit of the government during the period t.

We account for uncertainty by applying random shocks for each variable. These shocks are constructed by simulating a Multivariate Normal Distribution that uses as input the historical covariance matrix between those variables and their forecasts for the next 10 years. We simulate 2000 shocks for each year and compute the quantiles of the Debt/GDP forecasts according to our DSA equation.

## Model Results. Debt / GDP Dynamic

@chart https://www.alphacast.io/p/alphacast/charts/debt-dynamics-14510

## Baseline Assumption

The parameters of this simulation can be changed updated and running [this pipeline](https://www.alphacast.io/pipelines/2022-12-1-dOWdhv/edit?step=1669900873464)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/15de0bb7-602a-43c9-a489-b48f8bd9a468/image.png)

The interest rate forecast is estimated using a starting average implied rate of 9.3% and 4.6% respectively for internal and external. 20% of the debt is renewed at the new marginal rate of 7.2% (3.7% USD 5y + 350bp) for external and 8% for internal

## Scenarios and sensibility Analysis

The chart below reports the results of the simulation of shocks over a single variable, while the other variables are kept at their baseline levels

@chart https://www.alphacast.io/p/alphacast/charts/sensibility-analysis-14511

## Annex: Model inputs

@chart https://www.alphacast.io/p/alphacast/charts/exchange-rate-14513

@chart https://www.alphacast.io/p/alphacast/charts/inflation-14583

@chart https://www.alphacast.io/p/alphacast/charts/primary-deficit-14582

@chart https://www.alphacast.io/p/alphacast/charts/growth-forecast-14514

@chart https://www.alphacast.io/p/alphacast/charts/14516

@chart https://www.alphacast.io/p/alphacast/charts/14581


# Argentina Short-term Monitor

## FX Premiums

@chart https://charts.alphacast.io/p/marianosanchez/public-repo/dlar-brecha-respecto-a-cotizacion-oficial

@chart https://charts.alphacast.io/p/alphacast/argentina-markets/argentina-fx---30-days-change

## Inflation Linked denominated Debt

@chart https://charts.alphacast.io/p/marianosanchez/public-repo/stock-de-deuda-cer-COPY

@chart https://charts.alphacast.io/p/marianosanchez/public-repo/composicion-del-stock-de-deuda-en-ars

@chart https://charts.alphacast.io/p/marianosanchez/public-repo/outstanding-inflation-linked-cer-debt-stock

## Fiscal result and expenditure

@chart https://charts.alphacast.io/p/alphacast/argentina-macro-basics/ingresos-totales-y-gasto-primario

@chart https://charts.alphacast.io/p/alphacast/argentina-macro-basics/argentina-fiscal-result?tab=chart&stackMode=absolute&year=latest&time=2019-02-01..2022-05-01&country=ARG-0~ARG-2

## Central Bank and market financing to the treasury


@chart https://charts.alphacast.io/p/seido/seido-macro-dashboards/transfers-to-treasury-

@chart https://charts.alphacast.io/p/seido/seido-public-repository/fiscal-argentina-debt-auctions-results

## Short term debt profile

@chart https://charts.alphacast.io/p/seido/macro-research/argentina-public-net-debt-profile-2021-22-ars-stacked

@chart https://charts.alphacast.io/p/marianosanchez/public-repo/fiscal---argentina---public-debt-profile---quarterly

## Sovereign Debt yields

@chart https://charts.alphacast.io/p/marianosanchez/public-repo/cer-bonds-crossing-the-paso-2023s-rubicon

@chart https://charts.alphacast.io/p/marianosanchez/public-repo/argentina-cer-bills-yields-

## Monetary and FX dynamics

@chart https://charts.alphacast.io/p/seido/seido-macro-dashboards/realeffectivemultilateralbilateralexchangerates

@chart https://charts.alphacast.io/p/seido/seido-macro-dashboards/agro-sales-in-usd?tab=chart&year=latest&time=2019-01-01..latest

@chart https://charts.alphacast.io/p/marianosanchez/public-repo/argentina-ex-post-real-rate-COPY

@chart https://charts.alphacast.io/p/alphacast/argentina-bcra/drivers-of-change-in-money-base---30-days-running-sum?tab=chart&stackMode=absolute&year=latest&country=~ARG-3

## Rates and Futures

@chart https://charts.alphacast.io/grapher/rofex---estimated-implied-curve

@chart https://charts.alphacast.io/p/alphacast/argentina-bcra/rofex-badlar-spot-reverse-repo


SEIDO Public Repository

SEIDO

# A short guide to Argentina's Mutual Funds Industry Analysis

**In this short tutorial, we will guide you on calculating **ranks of YTD Total Returns** for different funds and management companies.**

Alphacast hosts a number of daily updated datasets of Argentinas Mutual Funds Industry. Two weeks ago we began publishing **detailed datasets for based on CAFCI daily reports**,  mostly in these two repositories ([1](https://alphacast.io/repositories/3202) and [2](https://alphacast.io/repositories/1207) ). Data covers Assets Under Management (AUM) by Fund, [Detailed Portfolio by Fund](https://www.alphacast.io/datasets/29744), Price and Subscription Effect by Fund and Class (Data and [Calculation](https://www.alphacast.io/pipelines/2022-6-7-JGmbel/view)) and Metadata per [Fund](https://www.alphacast.io/datasets/29796) and per [Asset](https://www.alphacast.io/datasets/29795). 

@Ncharts https://charts.alphacast.io/p/luciano/home/pipeline/3915-1655912566028 https://charts.alphacast.io/p/luciano/home/pipeline/3909-1655914380385?tab=chart&stackMode=absolute&region=World

## Calculating the total return of each Fund Subsegment (Clase)

Price, quantities, and AUM data from CAFCI are provided for each subsegment of each fund (Clase A - B - C, etc). We will begin by calculating a measure of total return for each class. You can see [this pipeline](https://www.alphacast.io/pipelines/2022-6-22-PsgSqJ/view) for a working example of this section.

**The first steps load and filter the data that we will be using**. We will be working with data from the dataset [Financial - Argentina - Fondos Comunes de Inversion - 2022](https://www.alphacast.io/datasets/29786) from CAFCI.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/9d0d8f69-5231-4731-8b9f-0e23ff2d2f4a/image.png)

**The next two steps calculate the contribution of the "price effect" and "subscription effect" to the daily change in the fund AUM**. Basically, the net wealth of a fund can change both because its price has changed ("Valor mil Cuotapartes") or because investors have redeemed or increased their holdings of the fund. We called that Price and Subscription effects, which are calculated as follows.

```
Price Effect = (Price per Share)[T] * (Number of Shares)[T - 1] - (Price Share)[T - 1) * (Number of Shares)[T - 1]
Subscription Effect  = (AUM)[T] - (AUM)[T-1] - (Price Effect)[T]
Daily Return = (Price Effect)[T] / (AUM) [T -1]
Total Return = cum_product(1+ (Daily Return)[T])
```
**That's it. This basic calculation provides a Total Return index for each class, that you can find on [this dataset.](https://www.alphacast.io/datasets/29914)**

## Grouping by fund or fund management company

Working with data at the class level may be too much to handle. Just for the first half of 2022 there are more than 230,000 daily data points for 2,200 fund classes. We may rather reduce the dimensionality to analyze the return at the fund level or management company. That task is performed in [this pipeline.](https://www.alphacast.io/pipelines/2022-6-22-KTzEWk/view)

In order to do that we need to bring additional information to our pipeline. Each data point has information on the fund class, but not on the fund itself or the management company. That information is in another dataset that stores the [Metadata](https://www.alphacast.io/datasets/29492).

The step that achieves that is the "Merge with Dataset", which will combine the current dataset in memory with another. This requires what field will be used to know which rows from the first dataset correspond to the rows on the second dataset. In this example "Fondo Comun de Inversion" Matches "clase_fondo_nombre" from metadata datasets.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0ba21f30-5daf-4cac-8332-74eb0aaccc2e/image.png)

The data in memory will now have many more columns, but what we want to achieve is to combine under a single value, for each day,  all the data points from subclasses that correspond to the same fund, identified by their name. We will use the "Regroup Entities" step, unchecking every other entity but "nombre" (or gerente_nombre if we want to group management companies) and define that we want to group individual values by summing them.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0dc2af6f-5857-4e5d-a9ca-578ebfe95476/image.png)

[Click here to see interactive chart](https://charts.alphacast.io/p/alphacast/cafci---fondos-comunes-de-inversion---portfolio-details/total-return-per-managment-company?tab=chart&year=latest&time=2022-06-06..2022-06-21)
@Ncharts https://charts.alphacast.io/p/alphacast/cafci---fondos-comunes-de-inversion---portfolio-details/total-return-per-managment-company?tab=chart&year=latest&time=2022-06-06..2022-06-21 https://charts.alphacast.io/p/alphacast/cafci---fondos-comunes-de-inversion---portfolio-details/total-return-per-fund?tab=chart&year=latest&time=earliest..882

That's it. We now have a pipeline that generates a total return per fund and or per management company.

- [Total Return per Fund](https://www.alphacast.io/datasets/29917)
- [Total Return per Managment Company](https://www.alphacast.io/datasets/29919)

## Cleaning outliers

Unfortunately, raw data is not flawless and some outliers appear now and then. To prevent that, and to maximize the automatization of the calculations, we have added a number of filters to exclude outliers:

1. We exclude data points from funds with less than ARS 1,000,000 in AUM
2. We exclude data points that fulfill the following two conditions:
a. the daily price change (positive or negative) is higher than 10% and 
b. (fund daily price change - average daily price change of all funds) / (standard deviation of all daily changes that day > 10

Less than 20 data points are excluded, but they had a huge impact on global results.

## Filtering best and worst

The final step is to use the information that we have collected so far to create a ranking of the funds with the best and worst performance. This is achieved in these two pipelines: [Top](https://www.alphacast.io/pipelines/2022-6-22-ZweFcB/view) and [Bottom](https://www.alphacast.io/pipelines/2022-6-22-qdFhct/view).

To do that, we have to create a new variable using the formula rank, and grouping by Date. This will order the YTD total return - already calculated - from lowest to highest **for each date**. We will also calculate the maximum rank for each date so we can filter to obtain the top and bottom 20.

```
rank = +rank(@total_return_YTD, @Date)
max_rank = max(@rank, @Date)

The formula for filter rows step is
if( @rank > (@max_rank-20),True,False)
```

That's it!! You have a filtered dataset with the top and bottom funds for each date.

If you want to do the same for management companies, simply change the initial dataset to [this one](https://www.alphacast.io/datasets/29919).

@Ncharts https://charts.alphacast.io/p/luciano/home/pipeline/3915-1655912566028 https://charts.alphacast.io/p/luciano/home/pipeline/3909-1655914380385?tab=chart&stackMode=absolute&region=World


Alphacast Data Guides

Mariano Sanchez Moreno

A short guide to Argentina's Mutual Funds Industry Analysis

# A short guide to Argentina's Financial and Monetary Data

**There are more than 2.000 datasets in Alphacast, and there are plenty of hidden gems. This is a short "Must see" guide for those interested in Argentina's financial and monetary data.**

**Would you like to know more?
Write to hello@alphacast.io o [book a demo!](https://meetings.hubspot.com/chiara-maximo)**

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/b6764b6a-ddaf-47fc-9ce5-0c435d15fa0d/image.png)

There are lots of based on the data from IAMC daily reports. We encourage you to take advantage of the following datasets:

* Bonds: [Sovereign](https://www.alphacast.io/datasets/15925), [Subnational](https://www.alphacast.io/datasets/financial-argentina-iamc-fixed-income-report-subnational-bonds-37255) and [Corporate](https://www.alphacast.io/datasets/financial-argentina-iamc-fixed-income-report-corporate-37348)
* IAMC's bonds index in [ARS](https://www.alphacast.io/datasets/16301) and [USD](https://www.alphacast.io/datasets/16302).
* Treasury Bills: both [fixed-rate (LEDES)](https://www.alphacast.io/datasets/29775) and [inflation-linked (LECER)](https://www.alphacast.io/datasets/29775)
* [Leading Equity](https://www.alphacast.io/datasets/financial-argentina-iamc-leading-companies-equities-29857)
* [Trading Volumes](https://www.alphacast.io/datasets/financial-argentina-iamc-trading-volumes-29864)

Moreover, historical data series for sovereign bonds can be found in [this dataset](https://www.alphacast.io/datasets/5359)  while this one is always updated with the [last price](https://www.alphacast.io/datasets/5357)

Users have also created some pipelines based on this data. For example, you can see (and clone) this pipeline that calculates [C vs D spreads](https://www.alphacast.io/pipelines/2022-6-6-baesbT/view)  or this one, that estimates [MEP and CCL from bonds prices](https://www.alphacast.io/pipelines/2022-6-10-lKIVor/view)

-----------------------------------

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/a48a7a77-288b-4dce-8d1c-972c46a5469e/image.png)

Two weeks ago we began publishing **detailed datasets for the Mutual Funds industry,** based on CAFCI daily reports. You can access that data in these two repositories ([1](https://alphacast.io/repositories/3202) and [2](https://alphacast.io/repositories/1207) )

- Net Worth by Fund
- Detailed Portfolio by Fund (https://www.alphacast.io/datasets/29744)
- Effect Price and Subscription by Fund and Class (Data and Calculation (https://www.alphacast.io/pipelines/2022-6-7-JGmbel/view) )
- Metadata per Fund (https://www.alphacast.io/datasets/29796) and per Asset (https://www.alphacast.io/datasets/29795)

-----------------------------------


![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0b0bb40a-1f17-4faa-892b-cf37117c40e8/image.png)

You can find data on dozens of ARS/USD rates out there. MEP, CCL, Blue Chips Swap, GGAL, and the like.

The most popular FX datasets in Alphacast are:

* [FX premiums](https://www.alphacast.io/datasets/5288)
* [Global Daily FX](https://www.alphacast.io/datasets/5307) 
* [BCRA - Reference Exchange Rate - Com. A3500](https://www.alphacast.io/datasets/6756)
* [BNA - Retail Fx](https://www.alphacast.io/datasets/14405)
* [BNA - Wholesale Fx](https://www.alphacast.io/datasets/14644)

For the FX Futures market the most popular datasets are:

* [ROFEX estimated Implied curve](https://www.alphacast.io/datasets/5361)
* [ROFEX open interest](https://www.alphacast.io/datasets/5363)
* [ROFEX prices](https://www.alphacast.io/datasets/5663)
* [BCRA futures position](https://www.alphacast.io/datasets/6072)

-----------------------------------


![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/be034125-9b18-425b-9414-8055103b2125/image.png)

There are more than 50 monetary and banking datasets for Argentina in Alphacast, most of them in [this repository](https://www.alphacast.io/repositories/17). However, these are the most frequently downloaded datasets

* [SERIESE y BAS - Selected - Daily](https://www.alphacast.io/datasets/5282) & [Monthly](https://www.alphacast.io/datasets/5277)
* [SERIESE BCRA Daily Instruments](https://www.alphacast.io/datasets/6074)
* [Panorama monetario y financiero](https://www.alphacast.io/datasets/6054)
* [Monetary Aggregates](https://www.alphacast.io/datasets/7835)
* [BCRA's Weekly Balance Sheet](https://www.alphacast.io/datasets/5602)
* [BCRA Daily Financial Liabilities](https://www.alphacast.io/datasets/5687)
* [Deposits](https://www.alphacast.io/datasets/7930) and [loans](https://www.alphacast.io/datasets/7925) by Categories
* [Tasas de interés - BADLAR](https://www.alphacast.io/datasets/6109)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c8145bbc-bf89-426e-943d-9730b4c3d802/image.png)

What about equity, commodities, or rates? This dataset has the most requested and downloaded data both for Argentina and the World

* [Merval daily closing price](https://www.alphacast.io/datasets/5816)
* [BYMA's repo (Cauciones in Spanish)](https://www.alphacast.io/datasets/financial-argentina-byma-repo-daily-35162)
* [Global Equity Indexes](https://www.alphacast.io/datasets/5423)
* [Global Commodities](https://www.alphacast.io/datasets/5309)
* [Global Rates](https://www.alphacast.io/datasets/5420)
* [Credit Default Swap (CDS)](https://www.alphacast.io/datasets/financial-global-cds-daily-35148)
* [Latin America EMBI](https://www.alphacast.io/datasets/5293)
* [USA - FRB Interest Rates](https://www.alphacast.io/datasets/5824)

**Would you like to know more?
Write to hello@alphacast.io o [book a demo!](https://meetings.hubspot.com/chiara-maximo)**



Exploring Alphacast

## Friday Update -  June 10th, 2022
# It's one small step for Alphacast, one giant leap for data

Hello, everyone! It's been a couple of weeks since our last update. We have been doing a lot of work under the hood, particularly trying to improve the readiness of the data on the platform, the overall performance of the Notebooks, and the content you see when you interact with Alphacast (i.e. through emails, related datasets, etc). 

However, there have been major additions to our pipeline engine. Enjoy X new step!

### New Steps: FRED!

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. [Learn more here.](https://www.alphacast.io/p/luciano/insights/2022-5-5-bPQhEU)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/d3d22cd8-a3c1-4698-ad60-83cfdaeeff9a/image.png)

### New Steps: Wide to Long (melt), Long to Wide (unstack) 

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 it. A typical example is when data that should be row values are columns or otherwise, a situation that cannot be solved by simply transposing the data. **"Wide to Long" and "Long to Wide" steps are useful to solve this.** [Learn more here.](https://www.alphacast.io/p/luciano/insights/2022-5-5-QQtMIe)

![Untitled Diagram.drawio (2).png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/7b03ceaf-082a-4008-a759-a7218f89c6a0/Untitled-Diagram.drawio-%282%29.png)

### New Steps: Append

Append is used to combine datasets with the same structure one on top of the other. Say, for example, that you have data for different months or years in different datasets and you need to combine them into a single one. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/6a56e127-78f2-416d-99ea-30e06b29b415/image.png)

### Improved Steps: Regroup Entities and add new entities

The step that regroups entities now allows the addition of new entities to the datasets. This may sound too techie, but a big number of problems can now be solved with pipelines. 

Let's say, for example, that you have the prices of Corn, Wheat, and Soybeans and you want to calculate the average price of Grains and Oilseed. If the category where an entity you can use "Regroup entity" as usual, remove the "commodity" entity and calculate the average.  But now let's assume that the category information is currently not on your dataset but on another dataset. In this case, you have to merge it to combine both datasets BUT if you do so, the category will not be an entity. Voila! With this step, you can now add the entity and regroup it!

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/70f8a7f7-7347-4ed0-a2ac-f72712c624a7/image.png)

### New Steps: Reorder Columns and Sort Rows

Pretty straightforward. You can now change the column orders and sort the values of the rows.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/d443f032-6775-41a5-9099-ed7fd6c59a06/image.png)

Have a nice weekend!



Alphacast Dev Diary

# How can I reshape my dataset from "Long" format to "Wide" format and otherwise?

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.

![Untitled Diagram.drawio (2).png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/7b03ceaf-082a-4008-a759-a7218f89c6a0/Untitled-Diagram.drawio-%282%29.png)

### Let's see an example. [See the pipeline here](https://www.alphacast.io/pipelines/2022-6-10-UYhLlh/view)

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.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/a3b35a70-5c3b-47ec-8c7f-c094a7f9b7e5/image.png)

Next, add a step "Long to Wide (Unstack"  to the pipeline.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/4891bc9f-969c-4fd1-b463-73ef50e1430e/image.png)

And the resulting dataset will look like these

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/65fd0a22-399d-4b3e-8203-a670b4b810a6/image.png)

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 will prevail as columns.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/4c31f31b-23ab-4568-b722-98ebaab7e7a1/image.png)

It will now look like these

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/88d0f61d-e0ba-4ec5-9a5f-24ebcda1796b/image.png)

# How can I download Data from the Federal Reserve Bank of St. Louis Database (FRED) ?

If you work with economic data you probably know [FRED](https://fred.stlouisfed.org/), 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**
 
![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c236e159-f7f7-4d1a-a6f3-19d2acb014e7/image.png)

3.Include as many series as you like, separating them by a comma. See a working example [here](https://www.alphacast.io/pipelines/2022-6-10-xaJGgt/view)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/508b20d4-9bbe-4ed6-9a18-f152f9020d47/image.png)

4.Ready! You can now publish the dataset, chart the data, process it or any other cool option available in pipelines.




# Pipelines examples and tutorials

### [How do I calculate a monthly end-of-period series from daily data?](https://www.alphacast.io/pipelines/2022-5-4-VFTtwv/insight)
### [How do I change the frequency of the data?](https://www.alphacast.io/p/luciano/insights/2022-2-5-EGkmez)
### [How can I merge two datasets into one?](https://www.alphacast.io/pipelines/2022-5-4-hARfHC/insight)
### [How can I connect the results of my pipeline to Excel?]()
### [How is a time series seasonally adjusted?](https://www.alphacast.io/pipelines/2022-5-4-ZEJVEI/insight)
### [How do I change a local currency series to dollars?](https://www.alphacast.io/pipelines/2022-5-4-tcmamh/insight)
### [How can I download Data from Yahoo Finance?](https://www.alphacast.io/p/luciano/insights/2022-2-4-gQqdpX)
### [How can I download Data from FRED?](https://www.alphacast.io/p/luciano/insights/2022-5-5-bPQhEU)
### [How can I remove or regroup entities of my dataset?](https://www.alphacast.io/p/luciano/insights/2022-2-5-rGeoMU)
### [How can i reshape my dataset from "Wide" format to "Long" format and otherwise?](https://www.alphacast.io/p/luciano/insights/2022-5-5-QQtMIe)



# Pipeline's Steps Library

## Index
### - [Data Sources](#data-sources)
### - [Dataset Transforms](#dataset-transforms)
### - [Basic Arithmetics](#basic-arithmetics)
### - [Finance](#finance)
### - [Publish data](#publish-data)


-----------------------------

### Data Sources
**Fetch Dataset**: This step allows you to choose the dataset to which the sequence of pipeline steps can be added. This step is already predefined and is usually the first step of a pipeline [Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-5-QfCasD)

**Fetch Yahoo Finance**:  Yahoo Finance has information on hundreds of thousands of financial assets, stocks, bonds, ETFs, Indices, which are now easily accessible using Alphacast pipelines. [Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-4-gQqdpX)

**Fetch FRED**: 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.. [Learn More](https://www.alphacast.io/p/luciano/insights/2022-5-5-bPQhEU)

--------------------------------

### Dataset Transforms

**Select Columns**: This step allows to filter columns and reduce the number of data to be processed in the following step. The variable selector will show the data up to that point in the pipeline. [Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-5-IONULW)

**Rename Columns**: the user can choose the name for each column by filling in the blank space to the right, or keeping the original name by leaving the space blank. [Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-5-IONULW)

**Regroup Entities**: this step allows the user to remove an entity and regroup the data by the remaining ones selecting the formula to deal with the aggregation. Similar to "Group By" in other data frameworks such as Pandas or SQL.  [Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-5-rGeoMU)

**Change Frequency**: 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. When doing so the user has to select the aggregation formula if moving from higher to a lower frequency - such as average or end of the period - or the interpolation formula if moving from lower to higher, such as linear interpolation or splines. [Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-5-EGkmez)

**Merge with Dataset**: users can combine datasets through common entities. You can choose which and how the entities will match and the type of matching i.e. if there is no entity match, they can keep the data from the first dataset, the second, or both datasets, or if there is a match, only that data will remain in the pipeline [Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-5-gKcGcd)

**Wide to Long / Long to Wide**: 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. [Learn More](https://www.alphacast.io/p/luciano/insights/2022-5-5-QQtMIe)

**Append**: Append is used to combine datasets with the same structure one on top of the other. Say, for example, that you have data for different months or years in different datasets and you need to combine them into a single dataset. 

--------------------------------

### Basic Arithmetics
**Calculate Variables:** here the user will be able to perform different types of arithmetic, trigonometric and other functions on the variables of the previously selected dataset. For this, it is necessary to mention the name of the variable with the symbol @ and then type the name of the column. 

[Learn more here](https://www.alphacast.io/p/luciano/insights/2022-2-3-pcmmUv)
[Full list of Available Functions](https://www.alphacast.io/p/luciano/insights/2022-2-1-pOLoak)

**Apply Transform**: In this step, the user will be able to make changes to the original data depending on the frequency of the data and combine the different transformations. Some of them make it possible to change the data from current prices to constant prices, deseasonalize time series, change from local currency to dollars, or even express them as a percentage of GDP or per capita. Variations can also be made month over month, year over year, the sum of 12 months, variations a year ago, and many more

--------------------------------

### Financial Analysis

**Technical Analysis**: With this *step** users can estimate 130 metrics for Technical Analysis of financial assets, Metrics include a number of cycles, momentum, volatility, and volume  Indicators, standard overlap studies, pattern recognition techniques, or statistics functions.[Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-5-NLiniD)

**Debt Sustainability Analysis***. Alphacast has integrated into its platform a Debt Sustainability Analysis (DSA) Tool. Basically, this tool can help you forecast the Debt/GDP ratio of a country, taking into account specific characteristics such as growth, inflation, interest rates on domestic and foreign debt, exchange rate depreciation, and primary surplus. The tool also applies shocks to generate a fan-chart style chart so it can account for uncertainty in the forecasts. [Click here to learn more](https://www.alphacast.io/p/lucascosta93/insights/2022-2-4-UYaHBY)

**Portfolio Stats and Tear Sheets Calculator**. Alphacast pipelines can be used to design and test portfolio and trading strategies. With the "Portfolio Analysis" Step on the pipeline editor, you can create tear-sheets from daily returns and also dynamic rolling stats for different timeframes. [Learn More Here](https://www.alphacast.io/p/luciano/insights/2022-3-3-FLOLIf)



--------------------------------

### Publish data

**Publish to Dataset**: allows the user to choose the name of the dataset and the repository where it will be stored


# Alphacast Wednesday Update - April 13th, 2022

The last weeks have been great. We have seen lots of users flowing into what we think is becoming the core feature of Alphacast: the **pipelines engine**. We have been interviewing users and seeing how you are interacting with these new features: We discovered that users are not only creating pipelines to automatize processes but also as a workspace, to analyze the data and be able to "tell stories" with it.

**That's why we have come to a new product roadmap for Q2:2022: Notebooks!**

## Introducing Alphacast Notebooks!

Creating content in Alphacast often involves a back and forth between tabs. Search data, then go from Dataset to Pipeline, Back to Datasets, then to create a chart and then an insight to embed it, and back to square one. Complex pipelines and reports require no less than five open Tabs. 

**We are working to combine the full experience of creating content into a single screen. Load the data, transform and visualize it, include comments, and tell the story. Use the new "Notebook" as a workspace to find, analyze and share data.**

As a first step, a new feature is in test mode:  you can now **create and edit a chart directly in the pipelines editor**. You can see a visual output of your work and edit it right on the spot without the need to leave the screen. That chart will update itself every time the pipeline runs. 

**The chart editor and the pipelines editor are now a single thing.**

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/a7dd9d0e-51e2-4b13-9979-15144ea52d8c/image.png)

Down the road, we will be recombining the building blocks of the platform into Notebooks, including things such as
- **New sources and integrations**: We will work to make the Notebooks the single place to input data into Alphacast, integrating part of the process that is now "Create Dataset" such as Google Drive or import CSV.  Also other sources such as Custom URLs or even manual input, i.e. to copy & paste from Excel.
- **Connect Excel or use the API to get the data of any pipeline or step**. We want to simplify the way data flows in and out of Alphacast. Users will be able to get the data directly from the pipelines, without having to create ad-hoc datasets.
- **Markdown or text editor** to use the Notebooks as a way to tell a story, integrating the experience and look and feel to what is now the "insights" space. 
- **More "Dashboards" options**. Place the content - charts, comments, tables - the way you prefer and create more flexible dashboards, reports, presentations, etc. 
- **Tables!**: Tables have been a recurrent request. A basic experience for creating tables is still in [experimental fase](https://www.alphacast.io/p/matias/insights/2022-3-3-HkUjMR), and we plan to improve the experience and versatility of the tables.
- **Combine transform steps into sections / templates**. Once you create a specific sequence of transforms in your Notebook you will be able to combine and share it, so users can better understand the process behind your work or reuse pieces of it.

We are super excited about how the whole Alphacast experience will change once the Notebooks are in place.

Have a nice week!
 




# How can I create a Portfolio Tear-Sheet?

Alphacast pipelines can be used to design and test portfolio and trading strategies. With the "Porfolio Analysis" Step on the pipeline editor you can create tear-sheets from daily returns and also dynamic rolling stats for different timeframes. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/431498f9-5382-4f5b-9674-d43b556349e1/image.png)

# Step 1. Load the data and calculate a daily return variable.

You can use any dataset you want to calculate daily returns. As an example, let's load Yahoo Finance data and calculate the daily return of three assets

- In the pipeline editor (Click on create new at the top right --> Pipeline) 
- Add Yahoo Finance as your source
- add the tickers that you want to analyze (i.e. MSFT, AAPL, GOOG)
- Select the period
- add a new step to "Calculate Variable" write the formula 

      pct_change(@Close,1,@Ticker)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/7d29d901-992c-4b42-ae67-2524790a7bac/image.png)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c665069f-e0f7-47aa-943e-379c3e864246/image.png)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/17ff76ff-915b-4ba9-b311-fbdb7050a2a8/image.png)

# Step 2. Calculate the portfolio Tear Sheet

- Add a new step "Portfolio Analysis"
- Select the metrics that you want to calculate for your portfolio. Available stats are'**Annual return, Cumulative returns, Annual volatility, Sharpe ratio, Calmar ratio, Stability, Max drawdown, Omega ratio, Sortino ratio, Skew, Kurtosis, Tail ratio, Daily value at risk, Total Return
- Select the variable with the daily returns. "pct_change" in this example

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/549832b1-0ba9-4932-bd89-449402a67009/image.png)

### If Tear-sheet option is on then a single Tear sheet with results will be calculate for the whole sample period

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/3409f79a-c217-4bbd-b691-b7444fbec1ca/image.png)

### If Tear-sheet option is off then stats will be estimated recursively for each day of the sample, estimating the stats calculated with data from that day up to the most current day.

In this case, two additional parameters are required
- dropLastN implies that stats will not be calculated from the timeframes that begin in the last N days. Default excludes 252 trading days.
- Skip days mean that the start will be calculated from timeframes with start days every N days. This parameter is important for performance reasons. Default is 10 days.

The resulting dynamic Tear Sheet will [look like this dataset](https://www.alphacast.io/datasets/23156)

@chart https://charts.alphacast.io/p/luciano/exploring-alphacast-new/sharpe-ratio?tab=chart&stackMode=absolute&year=latest

# Optional. Calculate Technical analytics for your portfolio

Remember that with the **Technical Analysis**: *step** you can estimate 130 metrics for Technical Analysis of financial assets, Metrics include a number of cycles, momentum, volatility and volume  Indicators, standard overlap studies, patterns recognition techniques, or statistic functions.[Learn More](https://www.alphacast.io/p/luciano/insights/2022-2-5-NLiniD)


# Alphacast Friday Update - April 1st, 2022

March and Q1 have come to an end and, when we compare the platform today vs. what we had just one month ago we see changes almost everywhere! Let us do a short recap of what happened in the last two weeks.


## A new, super-powerful formula editor: Group, rolling  windows, time functions and much more.

The "Calculate Variable" is getting VERY powerful and versatile allowing more complex calculations to be done with a simple excelish formula parser. 
[See the full list of formulas here](https://www.alphacast.io/p/luciano/insights/2022-2-3-pcmmUv)

The first step is to create a pipeline (click on "Create new --> Pipeline" on the top right) and select a data source, followed by "add step below --> Calculate Variable"

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/82925794-6d32-48bd-841e-bae02d2060ba/image.png)

### Video Tutorial (In Spanish)

<iframe width="560" height="315" src="https://www.youtube.com/embed/ZixcrNYDVEU" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>


### The editor is really powerful, just to catch a glimpse on some formulas that can be created 
``` 
log(@Close)+ sqrt(@Volume))
log(exp(@Close))

Example: Calculate for each ticker the cumulative price change vs the first price 
@Close / first(@Close, @Ticker)

Example: Calculate the cumulative sales per salesperson
cumsum(@sales, @salesperson)

Example: Calculate for each date the share of sales explained by each product vs the total sales of that date
@sales / sum(@sales, @date)

Example: calculate the 20 day moving average for each ticker
rollingmean(@Close, 20, @Ticker)

Example: Extract the year from the date entity
year(@Date)

``` 


## Revamping of the chart editor, chart view, dataset view, and landing page.
The flow of navigation between sections has improved dramatically. You will notice the better speed and lower loading times. Also, there are now homogeneous styles between all the sections. No more the odd "what's going on" feeling when entering the charts editor or when viewing a chart.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/086c90bc-5fe4-456b-85c3-09fa609c4116/image.png)

## New Home section 

Hearing feedback from our users we have updated the content in the Home Section. With these changes now it's much easier to find your content and the content you've been working on 

You will now find
- Your Followed Datasets
- Your latest insights, pipelines, and datasets
- Your repositories and those of your teams

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e50b9f7f-5114-452d-9cdf-e6151104a478/image.png)

## A growing community

We have seen impressive engagement in Alphacast. We are about to reach 2.000 signups and users are creating datasets, pipelines and charts as never before. We are sincerely grateful for the support that you are giving us 

If you would like to give us some feedback, we would like to [hear from you with this short survey](https://www.research.net/r/YLPNB66)



# How to calculate a variable with the formula editor

The calculate variable step is an all-purpose formula parser to create new variables in a more "Excelish" way. There are dozens of operators, comparisons, and functions that allow endless combinations. For a comprehensive list of functions and operands see [here](https://www.alphacast.io/p/luciano/insights/2022-2-1-pOLoak)

## Video Tutorial (Spanish)

<iframe width="560" height="315" src="https://www.youtube.com/embed/ZixcrNYDVEU" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

## First step. Writing a basic formula

The first step is to create a pipeline (click on "Create new --> Pipeline" on the top right) and select a data source, followed by "add step below --> Calculate Variable"

There are a couple of rules to creating new variables
- Select a name on the textbox. If you select a variable name that already exists it will be overwritten
- You can use @ to select variables of the dataset. Start writing the name of the variables and options will show up
- You can do basic arithmetic operations such as (+, -, *, /, ^): @column1 + 3
- You can use functions by typing the name followed by parenthesis and commas to separate parameters: round(@column) + @column2. Full list of functions is here: [here](https://www.alphacast.io/p/luciano/insights/2022-2-1-pOLoak).
- The formula will always return the same number of rows as the original datasets, even if it involves calculations that usually reduce the dimensionality such as "Total sum per group". Check group and rolling windows functions below.


``` 
@Close + @Volume * 2)
``` 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/53ab6c01-6233-4e2b-acf1-3a31ec3648d9/image.png)

## Row Functions

Row functions are those that are calculated using only information from a single row in the dataset. That is, no values from other periods or entities are used in the calculation.  This includes functions such as power, exp, sqrt, log, abs, trunc, round,sin, cos, etc.

``` 
log(@Close)+ sqrt(@Volume))
``` 

## Nested calculations

Calculations can be nested inside other calculations to be used as parameters

``` 
log(exp(@Close))
``` 

## Group Functions

Group functions are used to make calculations for subgroups within the dataset. The data is split by the unique values of the entities defined in the parameters and calculations are done for each separate group. Entities can be the real entities of the dataset or any other variable or even the result of nested calculations.

There are dozens of group functions. See a comprehensive list [here](https://www.alphacast.io/p/luciano/insights/2022-2-1-pOLoak)

``` 
Example: Calculate for each ticker the cumulative price change vs the first price 
@Close / first(@Close, @Ticker)

Example: Calculate the cumulative sales per salesperson
cumsum(@sales, @salesperson)

Example: Calculate for each date the share of sales explained by each product vs the total sales of that date
@sales / sum(@sales, @date)
``` 

## Rolling Window Functions

Rolling windows are a special class of group function where calculations are made within a group AND within a rolling window. The user has to define the number of periods to be included in the rolling calculation

``` 
Example: calculate the 20 day moving average for each ticker
rollingmean(@Close, 20, @Ticker)

``` 

## Date and time Functions

Date and time functions are performed over DateTime variables (usually the @Date entity of the dataset) and include functions such as year, month or day, week, dao_of_year and more

``` 
Example: Extract the year from the date entity
year(@Date)

``` 

# Formula Editor: All functions and operators

## Operators

            "+":  Add. A + B
            "-":  Substraction. A - B
            "*":  Multiply. A * B
            "/":  Divide. A / B
            "^":  Power. A ^ B

## Comparison 
            ">":  Greater. 
            ">=":  Greater Than
            "<=":  Less or Equal Than
            "=":  Equal
            "!=":  Not Equal
            "<>": Not Equal
            "<":  Less than

## Logical 
            "and": and(conditionA, conditionB)
            "or": or(conditionA, conditionB)
            "xor": xor(conditionA, conditionB)
            "not": not(conditionA)
            "is_null": is_null(@variable)

## Shift and IF

``` 
Shift: Shift index by the number of periods.
SHIFT(Variable, lags_or_leads)

Example: Calculate daily percent  percent change of variable @Close
@Close  / Shift( @Close , 1) - 1

Use of IF. Returns a value based on Logical conditions
IF(condition, confition_if_true, condition_if_false)

Example: If Pct_change is positive then return 1, else return 0
IF(@pct_change >= 0, 1, -1)

``` 
## Row Functions

These functions are calculated through all the values of the column (different than group functions below)

            "power": Power(A, B)
            "exp": Exponential(A, B)
            "sqrt": Square Root(A)
            "log":  Log(A)
            "log10": Log10(A)
            "sgn": Sign. Positive = 1, Negative -1. sng(A)
            "abs": Absolute value. abs (A)
            "trunc": Truncate integer. trunc(A)
            "round": Round to N digits. round(A, N)
            "floor": Closest intereg lower or equal than value. floor(A)
            "ceil": Closest intereg higher or equal than value. ceil(A)
            "sin": sin
            "cos": cos
            "tan": tan

## Group Functions

These functions are calculated within groups. Groups are defined by the columns defined by the parameters. The default groups are the entities of the Dataset (excluding the Date columns)

    "rank": Compute numerical data ranks (1 through n)  rank(@Variable, @entity1, @entity2, ...)
    "cummax": Cumulative max per group. cummax(@Variable, @entity1, @entity2, ...)
    "cummin": Cumulative min per group
    "cumprod": Cumulative min per group
    "cumsum": Cumulative sum per group
    "cumcount": Cumulative count per group
    "backfill": backfills NA values
    "pad": forward fills NA values
    "pct_change": pct_change vs N lead or lags values within the 
    "quantile": quantile position within the group. quantile(@Variable, quantile, @entity1, @entity2, ...)
    "count": count values within the group
    "nunique": number of unique values in the group
    "first": first value of the group 
    "last": last value of the group
    "max": max value of the group values
    "min": min value of the group values
    "mean": mean value of the group values
    "median": median value of the group values
    "prod": product of the group values
    "size": the size  of the group
    "sem": standard error of the mean of the group
    "std": standard error  of the group
    "sum": sum of the values of the group
    "var": variance of the values of the group

## Rolling Window functions
These functions perform calculation on rolling windows and are calculated within groups. Groups are defined by the columns defined by the parameters. The default groups are the entities of the Dataset (excluding the Date columns)

    "rollingcount": count per group over a rolling window
    "rollingsum": sum per group over a rolling window
    "rollingmean": mean per group over a rolling window 
    "rollingmedian": median per group over a rolling window
    "rollingvar": variance per group over a rolling window
    "rollingstd": variance per group over a rolling window
    "rollingmin": variance per group over a rolling window
    "rollingmax": variance per group over a rolling window
    "rollingsem":  standard error of the mean of groups over a rolling window
    "rollingrank":  Compute numerical data ranks (1 through n) over a rolling window. rank(@Variable, window_N, @entity1, @entity2, ...)

## Date and time functions
    "now": returns current time. now()
    "today": returns current date. today()
    "year": The year of the datetime
    "month": The month of the datetime
    "day": The days of the datetime
    "hour": The hour of the datetime
    "minute": The minutes of the datetime
    "second": The seconds of the datetime
    "microsecond": The microseconds of the datetime
    "nanosecond": The nanoseconds of the datetime
    "date": The date of the datetime
    "time": The time of the datetime
    "dayofyear": The ordinal day of year
    "day_of_year": The ordinal day of year
    "weekofyear": The week ordinal of the year
    "week": The week ordinal of the year
    "dayofweek": The number of the day of the week with Monday=0, Sunday=6
    "day_of_week": The number of the day of the week with Monday=0, Sunday=6
    "weekday": The number of the day of the week with Monday=0, Sunday=6
    "quarter": Quarter of the date: Jan-Mar = 1, Apr-Jun = 2, etc.
    "days_in_month": The number of days in the month of the datetime
    "is_month_start": True / False indicating if first day of month (defined by frequency)
    "is_month_end": True / False indicating if last day of month (defined by frequency)
    "is_quarter_start": True / False indicating if first day of quarter (defined by frequency)
    "is_quarter_end": True / False indicating if last day of quarter (defined by frequency)
    "is_year_start": True / False  indicating if first day of year (defined by frequency)
    "is_year_end": True / False indicating if last day of year (defined by frequency)
    "is_leap_year": True / False indicating if the date belongs to a leap year



# Alphacast Economic Breakfast - Martes 22/03 9:30
## Registrate al evento haciendo [click acá](https://tiny.one/alphacast-breakfast)
[![2203_-_930_2.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/5e81bbe9-f342-452f-8e7c-01c2d0927f1c/2203_-_930_2.png)](https://tiny.one/alphacast-breakfast)

# Alphacast Friday Update - March 11th, 2022

Welcome to another update! There are new features and improvements almost everywhere in the platform, as we have been focusing on User Experience, navigation, and bug fixing. You should notice **faster navigation**, better search results, and small updates everywhere. 

The most highlighted pieces of news are: 

## Publishers Publishing!

We have seen a lot of [publishers' activity this week.](https://www.alphacast.io/insights/community) Find out content by:
- [Econviews](https://www.alphacast.io/p/IsaiasMarini/insights/2022-2-3-PnWmAo)
-  [Econ.uy](https://www.alphacast.io/p/econuy/insights/2022-2-3-nYddUd)
- Enrique Roura [1](https://www.alphacast.io/p/eroura/insights/2022-2-2-MSSZGo) [2](https://www.alphacast.io/p/eroura/insights/2021-11-4-EeWQPV)
- [Ivan Baumann](https://www.alphacast.io/p/ivanfbf/insights/2022-2-5-wvQOVZ)
- [CREEBBA](https://www.alphacast.io/p/CREEBBA/insights/2022-2-5-LTKzZK)

## The course "Automation of Economic and Financial Analysis" has begun

Watch the video and [presentation](https://www.alphacast.io/p/luciano/insights/2022-2-3-PYPvTD) of the first class, or [subscribe here](https://forms.gle/XF8VAq168xM3VsWNA). Next class, on **Wednesday 16th**, will be about pipelines and transformations.


<iframe width="560" height="315" src="https://www.youtube.com/embed/Nnr-GiAYBtI?start=3" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>


## New Learning Center & Home Page

We have made a full revamp of our [Learning Center ](https://www.alphacast.io/learn) and our [Home Page](https://www.alphacast.io/features/find).  In the new Learning Center, you will find improved documentation, examples, tutorials, and more. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/88b5ff25-bc6b-48c0-bf54-19a00554d8d0/image.png)


## Advanced formula editor (conditionals, shift, and more)

We have powered up the "Calculate Variable" step in the pipelines editors. There is now more flexibility to calculate new columns in a more "Excelish" way. There are now two new functionalities to the formula editor: **Conditionals** and the possibility to **Shift the data N periods**: A positive N lead will move the data N periods, and a  negative N will lag it.

Begin by creating a pipeline with the step "Calculate Variable".

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c5f698d6-f5c2-43ab-b481-60029a8712dc/image.png)

``` 
Use of Shift
SHIFT(Variable, lags_or_leads)

Example: Calculate daily percent  percent change of variable @Close
@Close  / Shift( @Close , 1) - 1

Usen of IF
IF(condition, confition_if_true, condition_if_false)

Example: If Pct_change is positive then return 1, else return 0
IF(@pct_change >= 0, 1, -1)

``` 

## Alphacast hosted at "El Economista" newspaper

The economic newspaper "El Economista" has adopted Alphacast's chart engine on their online portal. We are super excited to see our visualizations in over a dozen articles! 

See some examples [here](https://eleconomista.com.ar/economia/la-inflacion-febrero-fue-4-mensual-alimentos-escalando-55-n51124),  [here](https://eleconomista.com.ar/agro/el-trigo-vuela-gobierno-busca-desacoplar-precios-n51186) and [here](https://eleconomista.com.ar/economia/dolar-blue-hoy-cuanto-cotizo-martes-8-marzo-n51256).

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/1ea8676f-dd45-4db2-9a35-11082c08868f/image.png)

## Other UX improvements on Charts, Datasets and Pipelines views, Home repo, and more

**Users now have a Default Private Home Repo** that will store all the content that has not been assigned to a repository yet. This greatly simplifies the steps needed to create any assets (chart, insight, pipeline, or dataset). You can now first create and assign to a Repo Later.

There are now more actions that can be done directly from the charts. User can:
- Insert the chart directly to insights in one click
- Jump to the Chart editor by clicking edit (no more searching for the chart to edit it)
- Find the related datasets and the data by clicking on the source
![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/602ad1ad-10dc-48b4-88bc-f39d112aa343/image.png)

In any dataset, clicking "Transform Data" will take you to a Pipeline that is already connected to the dataset.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/29649d44-1c4a-45e0-b711-543ee298cd6e/image.png)

## Have a nice weekend!


# Debt Sustainability Analysis - V1

Our Debt Sustainability Model uses the standard debt dynamic equation to generate forecasts for the Debt/GDP ratio throughout the next 10 years. Specifically, we use the following the equation:
$$
d_{t+1} =d_{t}\biggl[(1-\alpha)\biggl(\frac{1+i_{dt}}{(1+g_t)(1+\pi_t)}\biggl) +(\alpha)\biggl(\frac{(1+\Delta\epsilon_t)(1+i_{ft})}{(1+g_t)(1+\pi_t)}\biggl)\biggl] + pd_t
$$
In which:

$$d_{t+1}$$ is the ratio Debt/GDP for the t+1 period;

$$\alpha$$ is the share of foreign debt in relation to total debt;

$$g_t$$ is the real growth of GDP at the period t;

$$\pi_t$$ is the variation of the GDP deflator for the period t;

$$\Delta\epsilon_t$$ is the devaluation of the exchange rate between the local currency to the US dollar during the period t;

$$i_{dt}$$ is the implicit interest rate for the domestic debt, it is calculates as the ratio between the interest paid for the internal debt during the period t and the internal debt in the period t-1;

$$i_{ft}$$ is the implicit interest rate for the foreign debt, it is calculates as the ratio between the interest paid for the internal debt during the period t and the internal debt in the period t-1;

$$pd_t$$ is the primary deficit of the government during the period t.

We account for uncertainty by applying random shocks for each variable. These shocks are constructed by simulating a Multivariate Normal Distribution that uses as input the historical covariance matrix between those variables and their forecasts for the next 10 years. We simulate 2000 shocks for each each year and compute the quantiles of the Debt/GDP forecasts according to our DSA equation.

## Model Results. Debt / GDP Dynamic

@chart https://charts.alphacast.io/p/marianosanchez/dsa-private-repo/dsa-test-results-d_gdp

## Scenarios and sensibility Analysis

The chart below reports the results of the simulation of shocks over a single variable, while the other variables are kept at their baseline levels

@chart https://charts.alphacast.io/p/marianosanchez/dsa-private-repo/sensiblity-analysis-scenarios?tab=chart&stackMode=absolute&year=latest

## Annex: Model inputs

@Ncharts https://charts.alphacast.io/p/marianosanchez/dsa-private-repo/dsa-test-results-delta_e?tab=chart&stackMode=absolute&time=2015-01-01..2030-01-01&region=World https://charts.alphacast.io/p/marianosanchez/dsa-private-repo/dsa-test-results-pi?tab=chart&stackMode=absolute&year=latest

@Ncharts https://charts.alphacast.io/p/marianosanchez/dsa-private-repo/dsa-test-results-i_interna?tab=chart&stackMode=absolute&year=latest https://charts.alphacast.io/p/marianosanchez/dsa-private-repo/dsa-test-results-i_externa?tab=chart&stackMode=absolute&year=latest

@Ncharts https://charts.alphacast.io/p/marianosanchez/dsa-private-repo/dsa-test-results-g?tab=chart&stackMode=absolute&year=latest https://charts.alphacast.io/p/marianosanchez/dsa-private-repo/dsa-test-results-sp?tab=chart&stackMode=absolute&year=latest

DSA Model

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/30372a7b-22ba-4050-a29e-d397efcbeec5/image.png)
# Automatización de Análisis de Coyuntura - Clase 1

## Introducción general. Objetivos del Curso



### Alphacast Basics
1. [HOY] Datasets, Charts, Maps y Dashboards
2.  Data Flows: Pipelines y Transformaciones 
3. Integrations: Python, Excel, Google Drive, Power Bi

### Aplicaciones y casos de uso

4. Intro to Web Scrapping in Python
5. Intro a Algotrading
6. Intro to Time Series Forecasting
7. Intro a Machine Learning


## Alphacast Basics 

### Nuestro objetivo es ayudar a los equipos y profesionales de Economía y Finanzas a encontrar, procesar y compartir datos y analisis.

### Perfil de usuarios de Alphacast: Estudiantes, aficionados a la economía y finanzas, Consultoras, Analistas de Research de Bancos, Fondos de Inversion, Brokers, Centros de Investigación y Think Tanks

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/7934d791-3db0-4660-8c8a-fcd33fc27788/image.png)

## El contenido de la plataforma. ¿Que? ¿Quien? ¿Como? ¿Donde?

@chart https://charts.alphacast.io/p/alphacast/dev-diary/number-of-datasets-per-country?stackMode=absolute&year=earliest

@chart https://charts.alphacast.io/p/alphacast/dev-diary/number-of-datasets-per-category?tab=chart&stackMode=absolute&time=2020-09-18..latest&region=World

### ¿Quien produce el contenido?

- Alphacast [Open source](https://github.com/alphacastio/connectors-main)
- Featured Publishers
- Los Usuarios

**El contenido puede ser publico, privado o bajo suscripción. Alphacast es una plataforma donde puede comprar y vender datos y analisis**

Algunos ejemplos: Econviews, Enrique Roura, Ivan Baumann, CREEBA, ECON-UY


@chart https://charts.alphacast.io/p/alphacast/dev-diary/total-number-of-datasets-by-provider-type?tab=chart&stackMode=absolute&year=latest&time=2020-09-29..latest

### ¿Cómo encuentro el contenido?
- Search
- Explore
- Community
- Tags / Filters

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/71c19fa2-b198-498b-a0d9-27ad7446add0/image.png)

## ¿Las distintas "Clases" de contenido?
![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/78560f44-00a9-4409-b023-ad7e1cf232af/image.png)

# Creacion de Datasets, Charts, Maps y Dashboards

## Creación, actualización y manejo de datos

### La vista del dataset
- Download
- Filter
- Transform Data [proxima clase]
- Sync Now --> [Open Source](https://github.com/alphacastio/connectors-main)

**¿Que son las entities?**

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/9658f375-397c-46e5-ac00-659fce2ff0ec/image.png)

## Conectando un Dataset a Excel
## Creación de un Chart
- [Tipos de Charts](https://www.alphacast.io/p/luciano/insights/2022-2-3-uSxhhG)
## Creación de un Insight, dashboards o reportes.
## Algunas opciones de charts avanzadas.
Sample Dataset: [Technical Analysis Sample Metrics](https://www.alphacast.io/datasets/18042)
- Lines & Bars
- Dual Axis
- Annotations
- Dual Charts
- Relative Change / Sum. Etc
## Creación de mapas interactivos
- Matching de entidades
- Mapas regionales
- Proyecciones




# How to create basic maps?

In order to create a basic map, click on the **Create new** button in the upper right corner and choose the **Chart** option. Once in the editor, make sure you have the **Map tab** option checked (below the Type of chart dropdown). You can create a chart with a line chart and a map, notice that in the chart viewer, in the lower left corner are the chart and map options. If you only want the map chart, uncheck the Chart tab option. 

Note that selecting the Map tab option opens a **new tab called Map** that allows you to make changes. The most important is **Unmatched Entities**, which allows you to match entities that have not been recognized by the editor.

You can only make a map with a single variable that has data for different countries or provinces. For example, the only variable may be GDP and there are different data for the countries. It is important that the country in question is in the country field. If in the Data tab it appears as undefined, a change must be made in the entities of the dataset.

![1PiMNHcwN8.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e79f1d67-60db-40ab-8c0b-5797d19b3e59/1PiMNHcwN8.gif)



# Teams Frequently Asked Questions

## What is a Team?

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. 

## How do I create a team and add participants?

To create a Team, you must first go to **Your profile** in the upper right corner of the site. Once in the profile view, click on settings and then on the **Teams** tab. There select **Create new team**. Finally, a name is assigned to the team, the users who are members of the team are added et voilá!

To add them you can do it with the **Alphacast username** or with the e-mail. If you put the wrong user, you can easily remove it by pressing **Remove**.

![Teams.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/715573c3-6f0d-4a06-8a6f-b6dca5f2d051/Teams.gif)

Even if you have already created the group, you can also change its members at any time. When you access the **Teams tab**, the groups listed appear, and there you can delete them. If you do it that way, don't forget to hit **Update team** to save your changes.

![Teams2.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/cf6f9949-7259-476a-8942-2918c4618883/Teams2.gif)

           
## How do I change the permissions that team members have?

There are no different levels of permissions within a team, that is, all members have the same hierarchy. The user who created the team is the one who can add and remove people from a team. Permissions are assigned on the repositories, not on the teams, that is, a particular team can be assigned read, write, admin or ownership permission of a repo. Read more

## Where do I see which teams I belong to?

A user can belong to more than one team. To see all the teams you're on, go to Your profile in the top right corner of the site. Once in the profile view, click on settings and then on the Teams tab. There you can see all the teams and the members of each team. Read more

## What are the privacy levels of the repositories?

The privacy levels of a repo are 4. Read allows access to read the content of the repo. Write allows editing within the repo in addition to writing. The Admin can, besides reading and editing, add users to a repo and grant them different levels of permissions. Finally the owner can, in addition all of the above, change the ownership of the repo. Read more

## How can I give other users access to my private repositories?

To give a user or a team access to your own private repository, you must go to the repo in question. Click on the settings tab and in the Add permission box put the name of the user or team that you want to add to the repo, assign the type of permission you want to assign and click on Add. Read more


# How to connect to STATA or Power BI?

Connecting Alphacast to other tools is really simple using the following steps

## Find the data you want to connect to your tool and copy the download link

When you navigate through Datasets in Alphacast, you will find a Download link (you have to be logged in). 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 Comma Separated Values (CSV):

![Screen Shot 2021-08-03 at 12.31.17 PM.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/74f8297f-17d0-4952-8ee8-0c429bd781dc/Screen-Shot-2021-08-03-at-12.31.17-PM.png)

Right click on the CSV 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.

It will look like this

```
THE_URL = https://api.alphacast.io/datasets/5288/data?apiKey=YOUR_API_KEY&format=csv
```

## Connecting to STATA

Data can be connected to STATA with a single line 
```
insheet using "THE_URL"
```

## Connecting to Power BI

Integrating Alphacast with Power BI is straightforward. You will need the complete URL for the dataset that you want to integrate, which you can find by clicking the Download button and then copying (not downloading!) the URL attached to a TSV file. 

Once you have the corresponding URL, you should click on “Get Data”, and then “Web”.
![powerbi1.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/1e3ce0a1-c06a-4e2b-8eec-79b936594de5/powerbi1.png)

Then paste the corresponding URL and click “Accept”.
![powerbi2.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/3d5f27d1-26e2-40cd-87b4-9e3d8dd8390f/powerbi2.png)

If the data previsualization looks ok, click “Load”. Otherwise, you can make changes by clicking on “Transform Data”.
![powerbi3.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/66973c30-f053-44d1-84a4-137faa9c7f2d/powerbi3.png)

**Tip: Check out the decimal separator before trying to integrate Alphacast with Power BI.** Alphacast uses English configuration but if your Power BI uses Spanish configuration, it can cause problems because it will interpret commas as a decimal separator and periods as thousand separator. To address this, go to Settings & Configuration and change the regional configuration. You can change this globally (for all Power BI files) or locally (for a specific file).

# Beginners guide to Alphacast

Alphacast is an integrated platform for economic and financial analysis. 

Our goal is to help you use your time wisely. Stop spending your day copying & pasting data from sources that frequently change their formats, or updating charts. Keep your files, scripts, and charts always up to date. Have all the data and charts you need in a single place and discover new data and ideas from the community.

There are many things you and your team can do with Alphacast. Download and create data or dashboards, integrate with R or Python or Power-bi, create interactive charts o maps or transform the data with pipelines. 
 
but let's start from the beginning

---

## Downloading your first dataset

Let's start by finding some data you need, say, for example. Argentina's Consumer Price Index.
1. Go to **Search** at the top right of your screen. Type "Inflation Argentina", search and select any dataset, for example ["Inflation - Argentina - INDEC - Consumer Price Index - Groups - Monthly"](https://www.alphacast.io/datasets/5515)
3. Filter the data that you need. You can filter variables, entities and, in some datasets, also pre-defined transformations. You will see how the preview on the right bar changes (if you want to get adventurous, click "Create chart" and head to the chart creator).
4. Click **Download** on the top right - You should be logged in to do this - and select the format you want. That's it. The Browser will start downloading your dataset. 

![7oHl0D4Are.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/6054da06-d418-427b-9c6d-606392c6e189/7oHl0D4Are.gif)

##  Connecting the data to Excel

To have this data always connected to Excel you have to follow two simple steps:

**(1) Getting CSV/XLS download links from the Dataset. ** Click again on download but instead of clicking download, click on "copy link" on either CSV or XLSX
**(2). Now go to Excel**, select Get Data -> From Other Sources -> From Web, and enter the URL you copied from the Alphacast website.

This simple video describes how to do it:

<iframe width="560" height="315" src="https://www.youtube.com/embed/cLWCUgCYl5Y" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

### More content:
- 
- [How to use Alphacast Python Library?](https://www.alphacast.io/p/luciano/insights/2022-2-4-RahGZH)
- [How to integrate Alphacast with Google Sheets?](https://alphacast-python-sdk.readthedocs.io/en/latest/)
- [How to download data with the API? ](https://documenter.getpostman.com/view/17184186/TzzDLb94)
- [How to connect to STATA or Power BI?](https://www.alphacast.io/p/luciano/insights/2022-2-4-RKkcTA)
- [How can I create a new Dataset?](https://www.alphacast.io/p/luciano/insights/2022-2-3-GgrRgP) 
- [Where do I find Alphacast open-source code?](https://github.com/alphacastio/connectors-main).
---

## Creating your first dashboard

Dashboards are a great way to monitor data that you have to see frequently. See some examples of dashboards [here](https://www.alphacast.io/p/flor/insights/2022-6-5-gOxAsY), [here](https://www.alphacast.io/p/milagrosricchini/insights/2022-6-2-zDQRZO), or [here](https://www.alphacast.io/p/marianosanchez/insights/2022-5-2-YgOUGX).

### Step 1. Clipping charts

The easiest way to build a dashboard is by preselecting charts you like, either yours or those you find in the community.  This is done by "clipping" charts, and adding them to the clipboard (Access it via the hook button at the top right of the screen). Click on the **clip chart** below any graphic and voila, it is already saved in the clipboard.

### Dashboard --> clipping charts
![ECXyhz9SJW.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/059fe2bd-7c7f-4927-a7bc-c6634442f49f/ECXyhz9SJW.gif)

### Step 2. Adding Chart to dashboard

To create and publish a dashboard, find and click the button in the upper right of the site "Create New" and select "Insight". The window on the left is the editor. There you can add a title, subtitles, and all the charts you want to visualize on your dashboard. 

To begin adding charts delete everything on the editor 

Once you have already clipped all the charts you are interested in, it is time to add them to your dashboard! 
To do this, you have to click on the clipboard button and drag the charts to the left one by one.
It's not important to keep the order since later you can order them as you want!

![GIF 4.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/f384c7a3-7de9-453b-af2f-9fab29b65650/GIF-4.gif)

### Step 3. Sorting, rescaling, and publishing.

Then click on **switch to dashboard view** to see what your dashboard would look like

you can change the order of the charts by dragging them from one position to another and you can also resize them from the bottom right corner of each chart to have a better view of your dashboard as seen below

![GIF 5.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/614750c1-5060-4396-9b52-645e3610f7f2/GIF-5.gif)

---
## Creating your first chart

Now let's create your first chart. There are two ways to create a chart: 
 
* **From scratch**: click **Create new** on the upper right corner and then **Chart**. You will be redirected to the pipeline. Select **Fetch Dataset Alphacast** and **Select Datasets**. Type the name of the dataset and select it. filter "Nivel Gerneral" variable. Select the step "Create a chart" on the left. Select the variable and click save. 
 
* **From any dataset**: you can create a chart from any dataset. If you are still on the dataset [Inflation - Argentina - INDEC - Consumer Price Index - Groups - Monthly](https://www.alphacast.io/datasets/5515) filter again the variable "Nivel General" and its transformation "Year over Year".  When doing so, in the upper right you will see the **Chart Preview**. Then click on **Create chart**. It will redirect you to the pipeline, follow the next steps to finish your chart. 

![chrome_7BAbNityNE.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/cb4a9eec-e344-4fb4-a9ea-dd262321213e/chrome_7BAbNityNE.gif)


Once you are editing your chart you can add other variables or change the chart type by clicking the top right blue button. There you can choose variables from the dataset you are working on or from others. Following our example we add two more variables from the same dataset and then we click on **Save button**. You can also customize the chart as you wish by selecting the different tabs on the right side of the chart editor.

![chrome_K4CnUu8odQ.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/5dc5d8da-7e27-4393-8319-65ecd27b9536/chrome_K4CnUu8odQ.gif)

Finally, click on Save. That's it! You have just created your first chart. It is saved in the pipeline and you can modify it whenever you want. You can access it with its link (click on the title of the chart)



## Transforming data with pipelines 

With pipelines, you can process the data within the platform. A pipeline is a sequence of steps, transformations and others to be applied to a particular dataset that has the characteristic of updating automatically every time a data is updated. There are two different ways to do this:
1. **From scratch:** click Create new on the upper right corner and then Pipeline.
2. **From any dataset:** you can create a pipeline from any dataset. When entering to the dataset view, on the right you will find the button Transform Data Both options will lead you to the Pipeline Engine. 
Here you will need to choose the repository where the pipeline will be stored and select a name for it. After that is where the fun begins! 

First, you will have to choose the data you want to work with it could be fretch form: An **Alphacast Dataset**, **FRED** or **Yahoo Finance**. Once you save it, you will be able to choose different steps to modify and transform your data as you wish.

As an example, we chose the following dataset: [Inflation - Argentina - INDEC - Consumer Price Index - Groups - Monthly](https://www.alphacast.io/datasets/5515) and we applied the following steps:
1. *Select columns:* to leave only the variable we were interested in and leave behind the other ones.
2. *Rename columns:* to change the name of one of the variables
3. *Change Frequency:* to rescale the frequency from monthly to quarterly

Once you finish working with the transformations you want to apply to your dataset, you can choose which output works for you: Publish Dataset, Create a Chart, Data Snapshot, or Add Markdown text. 

Remember to click the **Save button** each time you finish working on your step.

![chrome_g1jTjSx04C.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c41a81b3-ee83-43c1-826c-3a4c11c862f6/chrome_g1jTjSx04C.gif)


# How can I search and download data? 


To **explore and search** for a **dataset** you can go to the explore tab, located at the upper left bar or access it from the home clicking in Explore Dataset. You can filter by **main category**, for example prices, activity or financial and when selecting each of them, a list of referred datasets is displayed.

![chrome_3QV4c2uOuV.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e842c8d5-71b8-4180-b1af-4d4abe6ab22e/chrome_3QV4c2uOuV.gif)

In addition, you can explore the **most recent** or **most popular** datasets simply by clicking "see more" in the corresponding column. Also, below you can find a list of recommended datasets for you, to facilitate your search.

![chrome_GZT3r3SdXh.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/55141adc-7f4a-4585-8e71-bb27f078d033/chrome_GZT3r3SdXh.gif)

In the left column there are different **filters** such as country, frequency or source, which will be of great help to simplify the search.

![chrome_FSZs4f23uk.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/2f5cbcdf-d483-4b4d-befc-dc5a709fa631/chrome_FSZs4f23uk.gif)

When searching for a specific 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.

![chrome_OwKtl1LFtZ.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/ad5be603-4598-413a-b71d-3e320dd572fa/chrome_OwKtl1LFtZ.gif)

You can build **lists of favorite datasets** to rapidly acces. You can go to the view of the dataset you want and press the Follow option, located under the publisher. To remove it from the list, simply select Unfollow. Another way, is in the dataset listings, when browsing categories and tags, you will see the Follow button on the right, next to the update information. Your selected datasets will then appear on your home page, in the followed datasets section

![chrome_44UgkLOWsQ.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/6aeca914-8e1f-46f4-8745-aa9565a78d3f/chrome_44UgkLOWsQ.gif)

In the **dataset view** you can explore and download data. Once you decide what data you need, you can download it by clicking the Download on the dataset menu. You can choose between different formats for download., such as CSV or XLSX, for example.

![chrome_jVgFxoHD8K.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/5b67e518-cccd-48b2-bdaa-b90ae86cf575/chrome_jVgFxoHD8K.gif)

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.

![chrome_bPe4bOse9T.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/4ed980e2-b38b-429c-a025-bda472125f5b/chrome_bPe4bOse9T.gif)








# How can I create a new Dataset 

There are 4 ways to create datasets:
- Uploading by hand from a CSV or XLS. 
- Automatically connecting it to a file in Google Drive
- As a result of running a pipeline (read more here)
- Using the API or Python library (read more here)

For the first two, on the Create new button at the top right, click on Dataset, after which you can drag the CSV or XLSX file you want to upload or select the Google Drive file you want to connect.

## How to connect your data

Make sure your CSV, XLSX or file from Google Drive has a column with a **Date**.  If you choose the first option, simply select the file from your computer. For the second option, make sure to sign in with Google in order to synchronize your data from Google Drive. Once you have given Alphacast permission, you will be able to add a file from your Google Drive. Select the file you desire. With this option, updates will be made automatically. If you choose an XLSX or CSV, when data is added, you will have to upload your file again.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/8cd55540-1da0-4128-8c70-af23281da619/image.png)

## How to configure the columns of a dataset?

Once the file is selected, you must choose to configure the dataset, defining the Entity columns and the variables.
The Entity columns are those necessary to uniquely define a row of the dataset (to see more about entities click here) and all datasets must have at least one “Entity” column with the date (to see the formats of accepted date click here)
Then you define the type of each variable (if it is a text or a number), and indicate which variables you want to ignore. Finally, the last step allows you to choose the name of the dataset and its repository.

## What is an “Entity”?

The Entity columns are necessary to uniquely define a row of the dataset, the combinations of Entities cannot be repeated. For example, if your only entity is Date your dataset cannot have repeating dates. If your entities are Date and Country the dates can be repeated, but the combinations of date and country must be unique.

## What are the accepted formats for date columns when creating a dataset?
When creating a new dataset, the date column must be selected, marked as entity and, in data type, select the format. YY-MM-DD (year, month, day) is generally used, but can be changed with the Change Date Format button.

For instance, in the following case the format is YYYY-MM-DD and you can click on **Change Date Format** to match it with the format you have on your file, like it is shown in the image. Also, you can see the Python date formats for reference. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/f93accea-6c77-4957-98b4-b83732d12f8c/image.png)

## Naming and publishing a Dataset

The last tab **Name your dataset** allows you to choose the name of your dataset and the repository where it will be stored. 


# How to create a basic chart? 

## Step 1. Creating the chart 

There are two ways to create a chart: 
 
* **From scratch**: click **Create new** on the upper right corner and then **Chart**. You will be redirected to the chart editor
 
* **From any dataset**: you can create a chart from any dataset. When entering to the dataset view, on the right you will find the button **Filter**. Click on it and below, all the variables will be listed. You can select the variables you want and its transformations.  When doing so, in the upper right you will see the **Chart Preview**. Once all the desired variables are chosen, click on **Create chart**. It will redirect you to the chart editor, follow the next steps to finish your chart. 

![k8O4Ka4qU9.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/3bd1ff74-5fce-4206-ae4a-8b6bae9ed2b8/k8O4Ka4qU9.gif)

## Step 2. Chart editor and variables selection

Clicking on **Add variables** will display a list with the repositories that you own, the ones that you are part of and those you follow. If you select one of these, it will lead you to a list of variables grouped by datasets. , for example, ''EMAE ''. Inside each dataset, you can also look up for the name of a specific variable in the search option and once selected click "set variables". It is also possible to combine variables from different repositories, both your own and from other users or publishers.

![XNm9BUf7Ku.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/8f56c973-9c6f-4467-88d1-33b3ed678bc5/XNm9BUf7Ku.gif)

## Step 3. Publishing and sharing charts

Once the graph is finished, you can save it as a draft and continue later or publish. The graphic's editor also lets you modify graphics that are already created and saved as new pressing **Save as new**. If you are going to publish it, you can choose the repository to publish it in **Select repository for chart**. Finally click on **Publish**. Another interesting feature offered by the platform is the possibility of downloading the chart, either in .png or .svg simply by clicking on **dowland** and choosing the option you prefer. Also, Alphacast allows easily share it. Options to share data appear below your graph. You can publish it on Twitter, Facebook, or you can embedded it in your own website or blog. In adittion, you can create your own [insight](https://www.alphacast.io/p/Florencia%20Suarez/insights/2021-6-4-wgVUoB) in our platform and [include charts and maps on its.](https://www.alphacast.io/p/Florencia%20Suarez/insights/2021-6-1-PmtQKx)

![Step 2.6.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/961f25bb-15dc-467c-acee-614707793e7e/Step-2.6.gif)

Graphics are interactive. The graph is displayed in **mobile view** or **desktop view** format by checking the option below the graph. Also, if preferred, you can view it in table form. You can hover over any variable and see the determined values of the variables in a certain period of time. Also, the timeline can be changed directly from the chart by sliding the blue bar below it.

![Step 2.1.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/4c497c2d-5a64-482e-bf83-0b3be1a3eed1/Step-2.1.gif)

# Creating a pipeline *do's* and *dont's* 

With pipelines you can process the data within the platform. A pipeline is a sequence of steps, transformations and others to be applied to a particular dataset that has the characteristic of updating automatically every time a data is updated. There are two different ways to do this:
* **From scratch:** click **Create new** on the upper right corner and then **Pipeline**. 
* **From any dataset:** you can create a pipeline from any dataset. When entering to the dataset view, on the right you will find the button **Create Pipe**

Both options will lead you to the **Pipeline Engine**. Here you will need to choose the repository where the pipeline will be stored and select a name for it. After that is where the fun begins! First, you will have to choose the dataset you want to work with. Once you save it, you will be able to choose **[different steps](https://www.alphacast.io/p/luciano/insights/2022-2-3-Rlvhpa)** to modify and transform your data as you wish. 

As an example we chose the following dataset: [Inflation - Argentina - INDEC - Consumer Price Index - Groups - Monthly](https://www.alphacast.io/datasets/5515) and we applied the following steps:
- Rename columns: to change the name of one of the variables
- Change Frequency: to rescale the frequency from monthly to quarterly 
- Select columns: to leave only the variable we were interested in and leave behind the other ones. 


*Remember to click the **Save** button each time you finish working on your step.*

Once you finish working on your pipeline, you will need to click the default step called **Publish to dataset** to assign a name and repository to your new dataset. It will be automatically updated every time the original dataset is updated either by Alphacast or by yourself or when the pipeline is run. After that, you can save it and preview it or you can just go to the new dataset to start [charting!](https://www.alphacast.io/p/luciano/insights/2022-2-3-RGggBE)


![DLnTIG77LP.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/fff442e5-eca0-46af-9766-6a6a939b483a/DLnTIG77LP.gif)


# Financial Analysis

A couple of months ago we began to work silently on a new set of features requested by users and clients that are finally coming to light. The final objective is to use the pipeline's engine to perform full no-code financial analysis, trading strategy design, and portfolio backtesting. 

As a first step towards this goal, we have just launched a new **step** on the pipelines to estimate 130 metrics on financial assets, Metrics include a number of cycles, momentum, volatility and volume  Indicators, standard overlap studies, patterns recognition techniques, or statistic functions.

[This is an example dataset of every metric estimated for AAPL](https://www.alphacast.io/datasets/18042)

Check out this insights for a full list of functions
- [Cycle Indicators](https://www.alphacast.io/p/luciano/insights/2022-1-5-lIgCzo)
- [Momentum Indicators](https://www.alphacast.io/p/luciano/insights/2022-1-5-wOAPuL)
- [Volume & Volatility](https://www.alphacast.io/p/luciano/insights/2022-1-5-BVRJfr)
- [Statistics Functions](https://www.alphacast.io/p/luciano/insights/2022-1-5-OFzxUP)
- [Price Transforms](https://www.alphacast.io/p/luciano/insights/2022-1-5-ZqyMKw)
- [Pattern Recognition](https://www.alphacast.io/p/luciano/insights/2022-1-5-tHKxNm)
- [Overlap Studies](https://www.alphacast.io/p/luciano/insights/2022-1-5-gZBnJk)

Calculating any metrics involves two steps:
1. Load the financial data you want to analyze. Most of the metrics will require open, close, high, low, volume variables to work properly. **You can load data directly from Yahoo Finance** (see more below)
2. Add a step "Technical analysis". The interface is quite straightforward. Select the function group, the function, the parameters, the prefix (optional) and press save. The new variables will be added to the dataset. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/2a97923b-343b-4849-9769-3aebd744cef4/image.png)

# How to convert a series to the official dollar or Blue Chip Swap?

@chart https://charts.alphacast.io/p/luciano/public-repo/merval-mep-usd?tab=chart&stackMode=absolute&year=latest&time=2005-09-01..2022-02-02

In the [new pipeline engine](https://www.alphacast.io/pipelines/create) we incorporate a new transformation that allows changing the currency of the original series and that in the case of Argentina has two options: Convert to dollar official or to BCS.

The pipeline is separated into three
1. Select ("Fetch") the dataset and its columns (this step is not necessary, but it removes unnecessary data)
2. "Apply transform" by selecting "usd_daily", "usd_monthly" or whatever corresponds to the frequency and/or CCL_daily to convert to CCL.
3. Publish the dataset

**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](https://www.alphacast.io/datasets/5288) for the conversion and in the case of the [Argentine CCL this](https://www.alphacast .io/datasets/5288)


![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/d8f867fb-2caf-475b-b576-2de23b21c93d/image.png)

 

# How is a Time Series seasonally adjusted?

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](https://www.census.gov/data/software/x13as.html). 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](http://econserialcronico.blogspot.com/2017/03/arte-ciencia-y-tortura -medieval.html).

Deseasonalize with Pipelines in Alphacast is very easy and includes only 4 steps:
1. Fetch dataset
2. Filter the columns we want to use (this is not strictly necessary)
3. Apply Transform, choosing the "Seasonal Adjustment" option
4. Publish the dataset

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e2e1cfc0-81b3-4d5f-bea3-e0ca53a06f6c/image.png)

As an example, you can see how we deseasonalize the Monthly Estimator of Economic Activity (EMAE) of Argentina
This dataset --> With this pipelines --> Posted to this new dataset

The benefit of using seasonally adjusted series is seen immediately. The blue series in the following graph shows how the monthly estimate for the category \\\"Agriculture, Livestock and Fishing\\\" evolves over time. You will notice that it goes up a lot in May when corn and soybeans are harvested. On the other hand, the orange series cleans up this seasonal effect and leaves a clean series that is much easier to compare month to month

See how the droughts of 2009, 2012 and 2018 jump out.18.

@chart https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World


## How to add the Information in a dashboard?

Building the graph above and adding it to a Dashboard that is updated only every month is very easy. Within the view of the dataset that contains the information, click on "Filter" and then choose the variable and its transformations. A small snapshot will appear. Pressing **Create Chart** there we go to the more detailed editor from where we can customize the chart and publish it.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/4aaad3cf-16df-4a89-a15a-c7780f87002d/image.png)

With the chart created, embed it in an \\\"insight\\\" by going to \\\"Create new\\\" at the top right.

Once in the editor of the insight, the way to insert it is as follows.
1. You have to copy the URL of the graphic. We get this by going to the Share button and choosing \\\"Copy Link\\\" or by right clicking on the title and copying the link address\
2. To embed it we have two commands to use: **Chart** or **Ncharts** (commands start with an @ before). The first is to embed a single graph per line, the second to embed many graphs
3. We write @\\chart (without the slash), space and then the link or @\Ncharts, space, link, space, link, etc.

@chart https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World\

@Ncharts https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World

@Ncharts https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World


# How to calculate a monthly end-of-period series?

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 original dataset

Suppose we want a monthly exchange rate series for all countries. Something like this exists in this [BIS dataset but with daily frequency](https://www.alphacast.io/datasets/15172). 

**"Financial - Global - BIS - Main Currencies"**

## Step 2. Create the pipeline and choose the source

With the source dataset identified we are going to create a pipeline (with Engine v2.0). As the first option in "Fetch Dataset" we select the dataset that we found in the previous step

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/797bea80-910a-4e94-bc3f-bec51a049b9e/image.png)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e1172ec6-cc47-4985-b7bf-351caeedd9cd/image.png)

## Step 3. Resample the frequency and choose the variable and publish to a dataset

Third, a step is added to perform a "Frequency Resample" and monthly is chosen as the destination frequency and "Last" as the information aggregation formula. By adding a new step we can then choose where we want that information to be saved.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e35fdfc2-6e93-4672-8569-8b40911a869b/image.png)


# Datasets FAQ

## How can I find and navigate data?
There are several ways to search for data:
- You can use the Search bar from the top menu. Simply write what you are looking for.
- You can also go to the Explore section of the top menu. On the left bar you will find filters to limit your search to what you need. Results can be sorted by name, popularity, last updated and most recent by clicking Sort and you can show or hide details by clicking Hide Details.
- In the Explore section you can browse the categories, find the most downloaded and popular datasets and discover data relevant to you based on your history on the site

## Can I filter the data search?
In the Explore section you will see, in the left column, different filters that help define and facilitate the search such as categories, country, source, etc.

## How do I build my list of favorite datasets?
There are two ways to build lists of favorite datasets:
- You can go to the view of the dataset you want and press the Follow option, located under the publisher. To remove it from the list, simply select Unfollow.
- In the dataset listings, when browsing categories and tags, you will see the Follow button on the right, next to the update information.
Your selected datasets will then appear on your home page, in the followed datasets section.

## How to upload/build a dataset?
There are 4 ways to create datasets::
- Uploading by hand from a CSV or XLS
- Automatically connecting it to a file in Google Drive
- As a result of running a pipeline (read more)
- Using the Python API or library (read more)
For the first two, on the Create new button at the top right, click on Dataset, after which you can drag the CSV or XLSX file you want to upload or select the Google Drive file you want to connect.

## How to configure the columns of a dataset when creating it?
Once the file is selected, you must choose to configure the dataset, defining the Entity columns and the variables.
The Entity columns are those necessary to uniquely define a row of the dataset (to see more about entities click here) and all datasets must have at least one “Entity” column with the date (to see the formats of accepted date click here)
Then you define the type of each variable (if it is a text or a number), and indicate which variables you want to ignore. Finally, the last step allows you to choose the name of the dataset and its repository.

## What is an “Entity”?

The Entity columns are those necessary to **uniquely** identify a row of the dataset. To that extent, the combinations of Entities cannot be repeated. 

For example, if your only entity is Date your dataset cannot have repeated dates. If your entities are Date and Country the dates can be repeated, but the combinations of date and country must be unique.

Entities are equivalent to **unique indexes** in other data frameworks

## What are the accepted formats for date columns when creating a dataset?
When creating a new dataset, the date column must be selected, marked as entity and, in data type, select the format. YY-MM-DD (year, month, day) is generally used, but can be changed with the Change Date Format button.

## Are all datasets public?
Datasets can be public or private. If the repository where it is stored is public, the datasets it includes will be freely accessible. On the other hand, if the repository is private, the datasets will have restricted access to its members.
Repository administrators can define the level of access. You can see how to do it by clicking here

## Can I edit a dataset?
Datasets can be modified by selecting the Edit option at the top of the dataset view. Within the metadata section you can change the name, add/modify a description, tags, source and reference links.

## Can I delete a dataset?
To delete a dataset, select the upper right Edit button of the dataset view and go to the bottom. Once there, passing the Metadata section, it can be deleted by pressing Delete this dataset in the Danger zone. Before deleting it, in Confirmation you have to write the name of the dataset to be deleted and pressing Delete will permanently delete it.

## What is a “Tag” and what is it for?
A tag or label is a word associated with the content that includes it, which allows the information to be categorized into different topics and facilitates subsequent searches.

## What are the “Activity” messages that appear to the right of a dataset?
When entering a dataset, the Activity column on the right shows the status of the actions performed on the dataset and the associated pipeline. Four stages may appear:
- Updated shows when was the last time a new data was added
- Processed means that the dataset finished running
- Processing assumes that you are loading the new information
- Error when something failed in the process.

## What does the “Sync Now” button do?
By clicking on Sync Now the user calls the process that automatically fetches the data. If there is new data in the original source it should be updated within a few seconds. 

## How can I filter data within the same dataset and download it?
From the dataset view, you can download all or part of the data. 

To download all the data, you simply need to click on Download and select the download format. By default, the data is downloaded as shown in the Data excerpt, but if you wish, you can download it by reversing rows by columns, that is, where the dates and entities are transposed as columns.

To filter information, you can do so by clicking Filter. You can filter variables, transformations, and the period it covers.

## What are transformations and how are they used?
The transformations are the results of processing a variable using the Pipelines tool. It includes processes such as changes in the unit of measurement of the data, seasonal adjustments, filters, changes and frequencies, and more.
To see more information about the pipelines and available transformations, click here.

## Can I change the data frequency?
To change the frequency of the data you have to edit the pipeline, adding a step that is Change Frequency. The pipeline will identify the frequency the data is in, and ask you to choose the new transformation by adding the data interpolation method(s). The interpolation can be from higher to lower frequency (repeat, linear, quadratic, spline, etc.) or vice versa (average, sum, min, last, etc.)

## What transformations are there and what does each one do?
There are different types of transformations depending on the frequency of the dataset. For example, transformations can be applied to a monthly variable, such as monthly average variation, annual accumulated variation, variation at constant prices, conversion to dollars or the seasonally adjusted variable. Also, you can change the frequency of the data and then apply transformations.
To learn more about each transformation read here

## Can I freely choose the transformations I want to apply to a dataset?
Data can undergo as many transformations as possible.




# How to connect to STATA or Power BI?

Connecting Alphacast to other tools is really simple using the following steps

## Find the data you want to connect to your tool and copy the download link

When you navigate through Datasets in Alphacast, you will find a Download link (you have to be logged in). 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 Comma Separated Values (CSV):

![Screen Shot 2021-08-03 at 12.31.17 PM.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/74f8297f-17d0-4952-8ee8-0c429bd781dc/Screen-Shot-2021-08-03-at-12.31.17-PM.png)

Right click on the CSV 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.

It will look like this

```
THE_URL = https://api.alphacast.io/datasets/5288/data?apiKey=YOUR_API_KEY&format=csv
```

## Connecting to STATA

Data can be connected to STATA with a single line 
```
insheet using "THE_URL"
```

## Connecting to Power BI

[Completar MATI]

# How to merge the content of two datasets?

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...

1. ...You have MANY rows. VLOOKUP can have performance issues and be very slow
2. ...you need to search more than one field to combine the data
3. ...the position of rows or columns changes
4. ...you only need the data that is in both datasets
5. ...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, you must first go to the **Create new** button and choose **pipeline**. Once there, select the repository where the pipeline will be saved and write the desired name. In **Fetch dataset** select the required dataset. Press the **Save** button.

## 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 want to add. The best dataset combinations are obtained with data that share the frequency (daily, monthly, quarterly, or yearly). 

## Step 3. Choose the common fields

If we have two datasets, we have to tell the system what the "splice" method is between both datasets. That is, what will be the fields that must be in one and another dataset from which to join them.

- Usually there will be only one **Date**, in which case both datasets will be **Merged** by their dates.
- In addition to the date, datasets can have more than one entity. For example, they can have data by date and by country. In this case, it will be necessary to identify, if any, which is the field of the second dataset that corresponds to the country field.
- If a field for the second country is not selected, the connection will only be through the date field. In this case, the rows in dataset B may appear duplicated if there is more than one occurrence of your date in dataset A.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/c9690c4e-7b6b-4dcd-8ed8-b54d30140e7b/image.png)

In this example, we used two datasets with a monthly frequency and the same entity (Argentina). The result of this combination, when choosing the Left Join option, is that all the data from the first dataset (EMAE) will remain. Those that will be incorporated will be those data from the Consumer Price Index that coincide in date and entity.

## Paso 4.  Choose the Matching type

There are four types of criteria for joining

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/ceea19d5-7c24-4843-96ba-42b49e841b7d/image.png)

- **Inner join:** The new dataset will have only those rows that can be **matched**.
- **Left join:** All the rows of dataset A will be present and the unmatched rows of dataset B are discarded.
- **Right join:** Reverse to the previous one. All those from dataset B and discarded the unmatched ones from A.
- **Outer join:** The data from both datasets will remain even if they do not match.

## Paso 5. Publish

As a result of the previous step, the combination of the columns of Dataset A and Dataset B will be obtained. From here you can continue processing it or publish it in a new dataset.

# How to change the frequency of a dataset?

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.

![Pipelines - Luciano.drawio (2).png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0308df9b-bcad-4d7e-93aa-b975321f6a02/Pipelines---Luciano.drawio-%282%29.png)

### 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. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/271a8678-8822-4df5-b1f0-c7be7d843a95/image.png)


### 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.

![methods.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/ab706eb7-a159-40b5-af70-cbb175248209/methods.png)

### These are examples of the different interpolation methods applied on the same input dataset.

@chart https://charts.alphacast.io/p/Alphacast_Support/public-repo/alphacast-interpolation-options?tab=chart&stackMode=absolute&country=ARG-0~ARG-7~ARG-6~ARG-3~ARG-2&region=World

@chart https://charts.alphacast.io/p/Alphacast_Support/public-repo/alphacast-interpolation-options?tab=chart&stackMode=absolute&country=ARG-0~ARG-8~ARG-4~ARG-1&region=World

# How can I remove or regroup entities of my dataset?

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**

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/3a6d2d1c-4e00-45dc-8eb4-f46568aedd37/image.png)

### 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 data.

The new dataset will have every entity except those you have just excluded. Also, it will have fewer rows than the original because rows with repeated entities will be grouped together


![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e72cc144-9a31-4c02-99f3-0e54333f7302/image.png)

# Selecting and renaming Columns in Pipelines

## How to select columns in pipelines?
This step allows to filter columns and reduce the number of data to be processed in the following step. The variable selector will show the data up to that point in the pipeline. 

**Remember to save the step before moving to the next one**

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/46f5ee5f-359d-4762-be70-01a0a2e67fb4/image.png)

## How to rename columns in pipelines?

the user can choose the name for each column by filling in the blank space to the right, or keeping the original name by leaving the space blank

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/21cde0e5-7571-4791-a088-12835d93cc0a/image.png)

# Fetching datasets in Pipelines

This step allows you to choose the dataset to which the sequence of pipeline steps can be added. This step is already predefined and is usually the first step of a pipeline

To select your dataset start writing in the search box and recommendations will show up.

**Remember to save the step before moving to the next one**

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/741fa715-a05a-49b8-a574-18a36b9d0bec/image.png)

# How can I download Data from Yahoo Finance?

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 data.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e13f7492-fd22-4447-addc-7ae9ce864d11/image.png)


# What is the content that I can find in Alphacast?

Alphacast Database grows on a daily basis, covering major macroeconomic and financial topics and with a global scope. Content is structured and curated both by our team and a thriving community. Explore the chart below to see its evolution.

@chart https://charts.alphacast.io/p/alphacast/dev-diary/number-of-datasets-per-country

@chart https://charts.alphacast.io/p/alphacast/dev-diary/total-number-of-datasets-by-provider-type?tab=chart&stackMode=absolute&year=latest&time=2020-09-29..latest

@chart https://charts.alphacast.io/p/alphacast/dev-diary/number-of-datasets-per-category?tab=chart&stackMode=absolute&time=2020-09-18..2022-02-24&region=World

# Advanced charts customization

## The "Basic" tab

On the left side of the chart, there are different options to edit it. In the **Basic tab**, you can find:

* A dropdown to select a chart type, you can change graphic style at any time
* At the bottom, in "Add Variables " is the Y-axis variable list, where you can make changes for each variable. 
* If you select a variable and click the arrow to the left, you have the option of making different changes such as change the name, unit of measurement, number of decimals places, line width, unit conversion factor, smoothing factor, offset date, offset value, tolerance and more.

Now, you are able to use the already made up settings as default for future charts. You can also remove variables by selecting the cross at the right side of the variable. 

![qLswD8WK9A.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/5d6ea762-7a5d-4345-aeb5-cbeb22bdc683/qLswD8WK9A.gif)

## The "Data" tab

In the "**Data**" tab you can choose whether users can or cannot change data. In "data to show" by selecting the brush to the left of each variable you can change the line color.

![V5aDf2fSWX.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/cc99dbde-a327-4e6e-a7ee-acd451ef60e5/V5aDf2fSWX.gif)

## The "Text" tab

In "**Text**" tab you can modify header, footer, and more

* The title of the chart describes, by default, the period it covers. To remove it, select the option "Hide automatic" time/entity/p/alphacast/examples. 
* Also, you can add a subtitle with additional information to interpret the graph. 
* If you want you are able to add a footer note, with any important clarification to avoid miscommunication.
* You can also change the font family, the color of the font, header and subheader.

![jxclAWuK1g.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/89633d6f-2afa-481d-b93b-460077991d43/jxclAWuK1g.gif)

## The "Customize" tab

The "**Customize**" tab allows you to: 

* Configure the axes (both right and left)
* Change the legibility of the legend
* Modify the time frame.

If you want you can also change the axis appearance by modifying the width, hiding the borders or the gridlines. 

Additionally, you can set the color scheme of your chart, either for the default colors or for the costume pallets previously set. Sometimes it is useful to have the option of hiding the legend or modifying the place of the title. You can find these options below in legend. 

Also, it may be practical to have and hide the relative change, stacking or cumulative sum on the chart. These options can be added in controls. 

![CGTPev5EcJ.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/795eef64-edac-4deb-a1cc-9c8d391ae517/CGTPev5EcJ.gif)

## The "Annotations" tab

In the ''**Annotations**'' tab you can add observations such as lines, circles, crosses, boxes and you can also draw with the pencil. You are able to choose the position, write comments on the label and edit the color, width and mark of the note. 

![48DtsyNbfa.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0db03141-ae73-4bd8-bedf-f75188a6aa5e/48DtsyNbfa.gif)

# Creating Themes for your Repositories

Creations need to be different, unique. Premium users now have more customization options for the charts, so it will no longer be something like “A customer can have a car painted any color he wants as long as it’s black”. Users can define a Default Theme for a repository and everything inside will look like that. Available parameters are:

* Custom Color Palettes
* Font Family and color
* Background and chart area color
* Header text-align (left / center)
* Line Width and show/hide line markers
* show/hide horizontal axis Gridlines
* show/hide axis borders

Here are some examples of themes that can be created with these options:

![Theme 1.PNG](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/1ecd1785-c9de-49c2-934a-024c04042ba5/Theme-1.PNG)
![Theme 2.PNG](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/15ad3d30-e4ee-4a23-89fa-931ae73eee2a/Theme-2.PNG)
![Theme 3.PNG](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0018dbc3-6e0c-4b79-b654-ca9044a6e57a/Theme-3.PNG)
![Theme 4.PNG](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/d60346a7-ec8f-4068-881d-b6204342c80f/Theme-4.PNG) In Line Charts 


# Dealing with data. Filter, edit, delete, and more

## How can I edit a dataset?
Datasets can be modified by selecting the Edit option at the top of the dataset view. Within the metadata section you can change the name, add/modify a description, tags, source and reference links.

## How can I delete a dataset?
To delete a dataset, select the upper right Edit button of the dataset view and go to the bottom. Once there, passing the Metadata section, it can be deleted by pressing Delete this dataset in the Danger zone. Before deleting it, in Confirmation you have to write the name of the dataset to be deleted and pressing Delete will permanently delete it.

## How can I move the dataset to another repository?

[COMPLETE]

## How can I Transform the data of a dataset?

The transformations are the results of processing a variable using the Pipelines tool. It includes processes such as changes in the unit of measurement of the data, seasonal adjustments, filters, changes and frequencies, and more.
To see more information about the pipelines and available transformations, click here.

[PIPELINES]

## What is a “Tag”?
A tag or label is a word associated with the content that includes it, which allows the information to be categorized into different topics and facilitates subsequent searches.

## What are the “Activity” messages that appear to the right of a dataset?
When entering a dataset, the Activity column on the right shows the status of the actions performed on the dataset and the associated pipeline. Four stages may appear:
- Updated shows when was the last time a new data was added
- Processed means that the dataset finished running
- Processing assumes that you are loading the new information
- Error when something failed in the process.

## What does the “Sync Now” button do?
By clicking on Sync Now the user calls the process that automatically fetches the data. If there is new data in the original source it should be updated within a few seconds. 

## Can I filter data before downloading it?
From the dataset view, you can download all or part of the data. To download all the data, you simply need to click on Download and select the download format. By default, the data is downloaded as shown in the Data excerpt, but if you wish, you can download it by reversing rows by columns, that is, where the dates and entities are transposed as columns.

To filter information, you can do so by clicking Filter. You can filter variables, transformations, and the period it covers.




# User Settings Frequently Asked Questions

## How can I view and edit my profile?

Once logged in, you can access your profile by clicking on your name, on the far left, above the search engine. In View profile, you will be able to see your own insights, datasets, and repositories or the teams that you belong. If you click in Settings, you can modify your public data. You can change the display name and add a photo, if you want. You can also see the email with which you have logged in and get your API key. 

To logout, you can do so by clicking Logout on your profile.

![User.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/6a095d36-1ffc-4a52-b317-a6daf31bb12b/User.gif)

## Where is my API KEY?

To access the API KEY, go to Your profile in the upper right corner of the site. Once in the profile view click on settings and then on the General tab. Finally click on Show KEY

## Can I reset my API KEY?

To reset your API KEY contact hello@alphacast.io

## How do I change my name, description, avatar or cover pic?

To make changes to personal data, go to Your profile in the upper right corner of the site. Once in the profile view click on settings and then on the General tab. There you will find all the options to modify

## How can I follow other users?

Search the user in the search bar, enter the profile and click on Follow

## How can I see which users I follow or which users follow me?

Coming soon

## Are the profiles public?

Yes, the users profiles are public


# Repositories Frequently Asked Questions

## What is a repository?

A repository is a collection of datasets, insights, charts, pipelines that are grouped under a particular theme. There are public access repositories, that is, any user can access the content of that repo, and there are also private access repos, which only members of the repo have access to. Read more

## How can I restrict access to my repositories? 

To restrict access to a repository you can make the repo private when creating it instead of giving it public access. Once created, add only the members or teams with whom you want to share the information of that repo. Read more

## How do I navigate among repositories?

Repositories can be navigated from two views: Home and Explore. In the left bar of the home page there are the repositories listed in the following order: those that the user follows (Following), then those that belong to the user, that is, in which the user is the owner, then those repos where the user is an administrator and , finally, the repositories that were shared with the user.

## What is the difference between being an owner of a repo or an admin?

The difference between being an owner or an admin of a repo is that the first one, in addition to having read, write and edit permissions, also has the ability to transfer ownership of the repo to another owner. Read more


# Alphacast Integration to R

## 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 "**alphacast_api_key**" 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:
```
alphacast_api_key <- "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 = alphacast_api_key, 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/", 
dataset_id,".csv", sep=""), authenticate(user = alphacast_api_key, password = "")) 
apple_mob <- readr::read_csv(content(apple_mob, as ="text"), guess_max = 100000)
head(apple_mob)
```


|Entity  |Year       | driving| walking| driving - 7d_running_av| walking - 7d_running_av|transit |transit - 7d_running_av |
|:-------|:----------|-------:|-------:|-----------------------:|-----------------------:|:-------|:-----------------------|
|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.

```
apple_mob_long <- 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 = alphacast_api_key, 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
repos_datasets <- GET("https://api.alphacast.io/datasets", query = list(repo_id = repo_id), 
authenticate(user = alphacast_api_key, password = ""))
repos_datasets <- bind_rows(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 = alphacast_api_key, 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 = alphacast_api_key, 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 = alphacast_api_key, 
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.


# Insights and dashboards FAQ

## How do I paste a chart into an insight? 

To paste a chart in an insight, write "@chart" in the left window of the editor and then paste the link of the chart in question. If you want to paste two charts together, type "@Ncharts", then paste the links of the desired charts separated by a space.

## How do I publish an insight?

Once the insight is finished, to publish it, touch the blue button Publish located in the upper right corner. A window will be displayed in which you can choose the repository where you want to save the insight. If the repository is public, all users will be able to see the insight. If the repository is private, there are two possibilities:
Restricted: insight is limited to only those users with access to the repository
Anyone with the link: even users without access to the repository will be able to read the insight. But the insight will not be listed publicly
Then, the insight can be tagged to make it easier to find. Finally, you can publish or unpublish if you want to make further changes.

## How do I create a Dashboard?

A dashboard is a type of insight that only contains automated graphics, so it will always be up to date. To paste a chart in an insight, write "@chart" in the left window of the editor and then paste the link of the chart in question. If you want to paste two charts together, type "@Ncharts", then paste the links to the desired charts separated by a space.

## How do I share an insight?

Once the insight is finalized and published, you can access its page. On it, in the upper right corner there are three dots. When clicking on these, the first option is the Share button. All the social networks in which you can share your insight will be displayed.

## Can an insight be exported as a PDF?

Once the insight is finalized and published, you can access its page. In the upper left corner, there is a button called Print. A print window will open, choosing the Save option will save the file in the folder of your choice in PDF format.

## How can I view and copy the markdown code of an insight?

Once the insight is finalized and published, you can access its page. On it, in the upper right corner there are three dots. When clicking on these, as a second option there is the View Markdown source button. To go back to the previous view, tap the three dots again and choose the Hide Markdown source option.

## If I have an insight in a private repository, can anyone who has the link see its content?

When having an insight in a private repository, there are two options when saving it:
Restricted: insight is limited to only those users with access to the repository
Anyone with the link: even users without access to the repository will be able to read the insight. But the insight will not appear in the listed publicly
So it depends on which option was chosen when saving the insight. If access is restricted, even having the link you cannot access the content.


# Charts and Maps Frequently Asked Questions

## How do I add annotations to a chart?
		
In the chart editor, there is a tab called Annotations. In it, there are several annotation options that can be included in the charts. They can be added by clicking on their name:
- Texts
- Lines (horizontal, vertical and more)
- Circles
- Boxes
- Crosses
- Drawings with a pencil

## How to edit an existing chart?

To edit an existing chart, it must be your own or belong to a repository in which you have editor or administrator permissions. If you meet this condition, when you go to the chart link, in the lower right corner there is an icon called Edit. Click on it, it will take you to the chart editor to make any necessary changes.

Don't forget to go to the Publish tab and click on the Update chart when finished.

## How do I replicate an existing chart?

To edit an existing chart, it must be your own or belong to a repository in which you have editor or administrator permissions. If you meet this condition, click on the Edit button and in the Publish tab there is a Save as new button that will replicate the chart.

## How do I change the colors and aesthetics of my graphics?

To change the colors and aesthetics of the charts, go to the Customize tab. There you can choose axis numbers, change axis appearance, choose timeline, color scheme and more. In case you want to choose specific colors and not a color scheme, go to the Data tab. Click on the brush next to the variable name and select the desired color.

## How do I download the data that is in a chart?

You must go to the link of the chart in question. In the lower right corner there is a button called Download. This allows you to download the chart as an image or download the data with which the chart is made in csv format (the blue button).

## How do I share a chart?

You must go to the link of the chart in question. In the lower right corner is this button:When you click on it, these options appear:
-Share on Twitter
-Share on Facebook
-Embed it
-Share it in other ways
-Copy the link


# Charts and Maps examples and tutorials

## How do I create a stacked bar or line chart?

To create a chart, click on the Create new button in the upper right corner and choose the Chart option. Once in the editor, choose the variables that will be used to make up the graph.	To the left of each variable, there is an arrow that will display editing options. The first dropdown option allows you to choose whether you want the graph to be a line chart (default Line option) or a bar chart (Bar option). If the bars option was chosen, the Absolute option can be selected, it will leave the bars side by side. On the other hand, the Stacked option makes the bars one on top of the other. The 100% Stacked option orders the bars on top of each other with an axis from 0 to 100%, this is the only option that does not support being combined with a line variable.

## How do I create a multi chart?

To create a chart, click on the Create new button in the upper right corner and choose the Chart option. Once in the editor, choose the variables that will be used to make up the graph. To the left of each variable, there is an arrow that will display editing options. Below Display name there is an option that allows you to select which chart the data will be placed on. On a single chart, you can build a chart with multiple levels.

## How do I create a Scatter Plot?

To create a chart, click on the Create new button in the upper right corner and choose the Chart option. Once in the editor, choose the Scatter option in the Type of chart dropdown menu. By doing so, you can choose the variables for the Y axis and the X axis, their size and color.

## How do I create a country map?

To create a chart, click on the Create new button in the upper right corner and choose the Chart option. Once in the editor, make sure you have the Map tab option checked (below the Type of chart dropdown). You can create a chart with a line chart and a map, notice that in the chart viewer, in the lower left corner are the chart and map options. If you only want the map chart, uncheck the Chart tab option. 
Note that selecting the Map tab option opens a new tab called Map that allows you to make changes. The most important is Unmatched Entities, which allows you to match entities that have not been recognized by the editor.
You can only make a map with a single variable that has data for different countries. For example, the only variable may be GDP and there are different data for the countries. It is important that the country in question is in the country field. If in the Data tab it appears as undefined, a change must be made in the entities of the dataset.

## How do I create a map of provinces?

To create a chart, click on the Create new button in the upper right corner and choose the Chart option. Once in the editor, make sure you have the Map tab option checked (below the Type of chart dropdown). You can create a chart with a line chart and a map, notice that in the chart viewer, in the lower left corner are the chart and map options. If you only want the map chart, uncheck the Chart tab option.

You can only make a map with a single variable that has data for different provinces. For example, the only variable may be expenses and there are different data for the provinces. It is important that the province in question is in the entity field. If in the Data tab it appears as undefined, a change must be made in the entities of the dataset.
Note that selecting the Map tab option opens a new tab called Map that allows you to make changes. In the Map dropdown you can choose the map of the country whose province data you want to show. If you want to make a map of a country that is not yet available, get in touch with customer support so that we can add it as soon as possible. Also, there is the Unmatched Entities option, which allows you to match entities that have not been recognized by the editor.

## How do I create a two-axis chart?

To create a two-axis graph, touch the arrow positioned to the left of the desired variable. The variables' editor will be displayed, locate the Secondary Axis button and mark it.
How do I build charts with information from other datasets?

Charts can be created with information from multiple datasets. But, if the datasets are not your own, you must follow the repository that contains them. To do this, click on the desired dataset, below the title there is a hyperlink to the repository. Once in the repository view, there is a button on the right in blue called Follow, by clicking on it you can use all the datasets of that repository to graph.



# How can I create basic insight, report or dashboard?
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 charts can be included by using '@chart' and '@Ncharts'. Then paste the URL of the chart you want to include
* Images can be inserted using the icon in the toolbar above
* Explore the toolbar for more options: Bold, Italic, Underline, Striketgeough and more

*Insights are created with Markdown language. For a full reference of markdown, features see [here ](https://github.com/lifeparticle/The-Ultimate-Markdown-Cheat-Sheet)*

In order to publish the insight you can choose among all the repositories you own, your team owns or you follow. You can also select the privacy of your insight. Finally, you can publish by pressing the _publish and view_ button. 

![OguNHgDHvz.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/1ac59d2d-2a14-47cd-a797-d01c05780ed7/OguNHgDHvz.gif)

# 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:

![sUcd2s0uuW.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/2dd53132-c714-4bf3-8996-c4f388d57824/sUcd2s0uuW.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.

![x16bSfyG6X.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/7a2f478c-b575-4de9-8a1f-1ec41e67f2f2/x16bSfyG6X.gif)

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. 

![Y7xSYI31JB.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/496d7e76-38b0-44f1-a92d-25ac46949c0c/Y7xSYI31JB.gif)

## 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, you can tag your insight so it will be easier for the community to find it. 

Finally, you can publish your insight. If you want to make more changes, click on cancel to keep editing. If you are satisfied, click on publish or publish and view. By clicking on the last option, it will take you to your published insight.

![jiuz51syyr.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0e22ccf7-7c29-4404-8e91-ff1b8cb9d8d0/jiuz51syyr.gif)

If you want to edit your insight, go to the link of the insight and click on the three dots located on the right. There are other options such as: sharing, deleting and viewing Markdown source. Click on edit and it will take you to the editor view again. 

![o58uBuYgoG.gif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0a87a248-57b7-4d0d-a99f-b395573282da/o58uBuYgoG.gif)


# Pipelines Frequently Asked Questions

## How can I edit a Pipeline?

To find the pipelines to edit, go to the repository where it was previously saved and select the pipelines tab. There choose the pipeline and click on edit and you will be redirected to the pipeline view

# Alphacast Friday MEGA update - February 25th, 2022

Today is one of those weeks where we can celebrate a new set of features that will have a major impact on the way users can interact with Alphacast. In our [previous update](https://www.alphacast.io/p/luciano/insights/2022-1-5-PkUzWT) we've described how we are working to improve the overall data search & explore experience. This week we have deployed in production three new groups of features (1) Financial & Technical Analysis Toolkit (2) new data integrations and (3) new community content sections.

## Financial and Technical Analysis Toolkit.

A couple of months ago we began to work silently on a new set of features requested by users and clients that are finally coming to light. The final objective is to use the pipeline's engine to perform full no-code financial analysis, trading strategy design, and portfolio back testing. 

As a first step towards this goal, we have just launched a new **step** on the pipelines to estimate 130 metrics on financial assets, Metrics include a number of cycles, momentum, volatility and volume  Indicators, standard overlap studies, patterns recognition techniques, or statistic functions.

[This is an example dataset of every metric estimated for AAPL](https://www.alphacast.io/datasets/18042)

Check out this insights for a full list of functions
- [Cycle Indicators](https://www.alphacast.io/p/luciano/insights/2022-1-5-lIgCzo)
- [Momentum Indicators](https://www.alphacast.io/p/luciano/insights/2022-1-5-wOAPuL)
- [Volume & Volatility](https://www.alphacast.io/p/luciano/insights/2022-1-5-BVRJfr)
- [Statistics Functions](https://www.alphacast.io/p/luciano/insights/2022-1-5-OFzxUP)
- [Price Transforms](https://www.alphacast.io/p/luciano/insights/2022-1-5-ZqyMKw)
- [Pattern Recognition](https://www.alphacast.io/p/luciano/insights/2022-1-5-tHKxNm)
- [Overlap Studies](https://www.alphacast.io/p/luciano/insights/2022-1-5-gZBnJk)

Calculating any metrics involves two steps:
1. Load the financial data you want to analyze. Most of the metrics will require open, close, high, low, volume variables to work properly. **You can load data directly from Yahoo Finance** (see more below)
2. Add a step "Technical analysis". The interface is quite straightforward. Select the function group, the function, the parameters, the prefix (optional) and press save. The new variables will be added to the dataset. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/2a97923b-343b-4849-9769-3aebd744cef4/image.png)

We are working on new steps to allow for full backtesting, no code trading rules, and more. Stay tuned!

## New data integrations. Yahoo Finance.

We now allow pipelines to be fed with data other than Alphacast datasets. Particularly we have created an integration with Yahoo Finance and many more are coming soon.  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](https://www.alphacast.io/p/luciano/insights/2022-1-4-jFrNcb) 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 data.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/9f82fccd-ba6e-401a-9bc0-7c4621cbec42/image.png)

## New Community Section

As we've described in our [last update](https://www.alphacast.io/p/luciano/insights/2022-1-5-PkUzWT) user-created content is growing. We want the community to be at the heart of the platform experience so we have created a new *Community Section* where you can find data from our publishers, the content of those you follow, latest and trending insights, staff picks, and new Alphacast content, and more

Also, you can now filter the content while exploring insights. Search specific categories or countries

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/7ee444d5-423e-472d-8235-50b3f6a3cc1f/image.png)

Hope you enjoy using the new features as much as we are enjoying creating it!!!!!!

Have a nice weekend

# Technical Analysis - Volume & Volatility

- Average True Range (ATR)
- Normalized Average True Range (NATR)
- True Range (TRANGE)
- Chaikin A/D Line (AD)
- Chaikin A/D Oscillator (ADOSC)
- On Balance Volume (OBV)
@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis---volume-volatility?tab=chart&stackMode=absolute&year=latest

Exploring Alphacast [Outdated]

# Technical Analysis - Statistics Functions
- Beta (BETA)
- Pearson's Correlation Coefficient (r) (CORREL)
- Linear Regression (LINEARREG)
- Linear Regression Angle (LINEARREG_ANGLE)
- Linear Regression Intercept (LINEARREG_INTERCEPT)
- Linear Regression Slope (LINEARREG_SLOPE)
- Standard Deviation (STDDEV)
- Time Series Forecast (TSF)
- Variance (VAR)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-statistics-functions?tab=chart&stackMode=absolute&year=latest

# Technical Analysis - Price Transforms

- Average Price (AVGPRICE)
- Median Price (MEDPRICE)
- Typical Price (TYPPRICE)
- Weighted Close Price (WCLPRICE)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis---price-transforms?tab=chart&stackMode=absolute&year=latest

# Technical Analysis - Pattern Recognition

- 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)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-pattern-recognition-1?tab=chart&stackMode=absolute&year=latest

- 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)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-pattern-recognition-2?tab=chart&stackMode=absolute&year=latest

- 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)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-pattern-recognition-3?tab=chart&stackMode=absolute&year=latest

- 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)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-pattern-recognition-4?tab=chart&stackMode=absolute&year=latest

- 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)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-pattern-recognition-6?tab=chart&stackMode=absolute&year=latest

- 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)
- Unique 3 River (CDLUNIQUE3RIVER)
- Upside Gap Two Crows (CDLUPSIDEGAP2CROWS)
- Upside/Downside Gap Three Methods (CDLXSIDEGAP3METHODS)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-pattern-recognition-5?tab=chart&stackMode=absolute&year=latest


# Technical Analysis - Overlap Studies
- Bollinger Bands (BBANDS)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-overlap-studies-1?tab=chart&stackMode=absolute&year=latest

- 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)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-overlap-studies-2?tab=chart&stackMode=absolute&year=latest

- 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 (WMA)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-sample-metrics-3?tab=chart&stackMode=absolute&year=latest

# Technical Analysis - Momentum Indicators

- Average Directional Movement Index (ADX)
- Average Directional Movement Index Rating (ADXR)
- Absolute Price Oscillator (APO)
- Aroon (AROON)
- Aroon Oscillator (AROONOSC)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-momentum-indicators-1?tab=chart&stackMode=absolute&year=latest

- Balance Of Power (BOP)
- Commodity Channel Index (CCI)
- Chande Momentum Oscillator (CMO)
- Directional Movement Index (DX)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-momentum-indicators-2?tab=chart&stackMode=absolute&year=latest

- Moving Average Convergence/Divergence (MACD)
- MACD with controllable MA type (MACDEXT)
- Moving Average Convergence/Divergence Fix 12/26 (MACDFIX)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-momentum-indicators-3?tab=chart&stackMode=absolute&year=latest
 
- 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)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-momentum-indicators-4?tab=chart&stackMode=absolute&year=latest

- 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)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-momentum-indicators-5?tab=chart&stackMode=absolute&year=latest

- 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 (WILLR)OSC)

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis-momentum-indicators-6?tab=chart&stackMode=absolute&year=latest

# Technical Analysis - Cycle Indicators
Available transforms
- Hilbert Transform - Dominant Cycle Period (HT_DCPERIOD)
- Hilbert Transform - Dominant Cycle Phase (HT_DCPHASE)
- Hilbert Transform - Phasor Components (HT_PHASOR)
- Hilbert Transform - SineWave (HT_SINE)
- Hilbert Transform - Trend vs Cycle Mode (HT_TRENDMODE)

### Click "Add Indicator" in the chart below to see a real example with AAPL data

@chart https://charts.alphacast.io/p/alphacast/exploringalphacast/technical-analysis---cycle-indicators?tab=chart&stackMode=absolute&year=latest




# ¿Cómo import datos desde Yahoo Finance?

[Yahoo Finance](https://finance.yahoo.com/) 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
1. Crear un pipeline nuevo
2. Elegir como fuente de datos "Yahoo Finance"
3. Elegir tantos tickers como quieras, separados por coma: Ejemplo "NQ=F, AAPL, MSFT" y el periodo que necesitas
4. Apreta "Save" para guardar los cambios en el Step
5. Listo! Ya podes publicar el dataset que incluira datos open, close, high, low, volumen, dividends y stock splits. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e13f7492-fd22-4447-addc-7ae9ce864d11/image.png)



# Alphacast Friday Update - February 18th

Hi! It's been a couple of weeks since our last update. We are eager to show you our recent work. 

## We have reached 1,500 users!!

We are very happy to see our community growing. Seven months after relaunching and opening the platform in June 2021 we have reached 1.500 users. You are not only downloading data but also, at a growing speed, creating content. There are now almost 500 user-created datasets, that complement the 1.500 coming from the Alphacast Team.

**We are very grateful for your support and engagement**

![total-signups (2).png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/5e882c75-4ed6-4ae8-b2eb-96ced9b1360f/total-signups-%282%29.png)

![total-number-of-datasets-by-provider-type.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/97e03478-7e34-4015-9919-081e0fe1a9e8/total-number-of-datasets-by-provider-type.png)

## Explore explore explore! New explore Home and recommended-for-you 

Finally, the new explore experience is here. We have been working on making it easier to discover new data on the platform in three ways:

- A new ***Home* for the Explore section**, where you can navigate categories, trending datasets, new content and have a more comprehensive view of what can be found on Alphacast.
- **Recommended for you**: We have been doing some data alchemy to find out what you may be interested in based on your behavior on the website.
- **Improved search results**: The search results from the top bare are getting smarter and smarter. Check them out!

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/7ced407d-c12f-4fe7-90c9-460c5b98bb09/image.png)

## Alphacast "Automation of economic and financial outlook analysis"

Last week we've announced a new two-months course, starting in March, and we had an amazing response with more than 700 subscriptions. If you like to [subscribe just fill out this form](https://docs.google.com/spreadsheets/d/18T-p-iQYardaO_f1KEKKiLYZu7t2CeQMaMlpr4sYsUE/edit?resourcekey#gid=1347114536). Given that most of the subscriptions are from Latin American countries, the course will be given in Spanish. If you are not fluent in Spanish please leave us a comment in the form and we will organize new courses in English.

![FLpHL09WYAAghA2.jfif](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/bf0debd8-3b92-4a5f-a717-8fd92d5da7f8/FLpHL09WYAAghA2.jfif)

## New Pipelines Engine stabilization
Behind the scenes, we have been stabilizing the new pipeline engine, fixing multiple bugs, and improving overall performance.  We have been populating the Learning Center with examples and use cases:
- [How to change the currency of a variable?](https://www.alphacast.io/p/luciano/insights/2022-1-5-CqTqOe)
- [How to seasonally adjust time series?](https://www.alphacast.io/p/luciano/insights/2022-1-4-DpGBxC)    
- [How to change the frequency from daily to monthly end of period?](https://www.alphacast.io/p/luciano/insights/2022-1-3-TVDHKl)

We have also been responding to users' requests and working on new steps that will be available soon, such as Technical Analysis or Backtesting tools, Principal Components Analysis, Hodrick Prescott Filters, and more. 

### Stay tuned for more news!!

# ¿Cómo pasar una serie a dólar oficial o CCL?

@chart https://charts.alphacast.io/p/luciano/public-repo/merval-mep-usd?tab=chart&stackMode=absolute&year=latest&time=2005-09-01..2022-02-02

En el [nuevo engine de pipelines](https://www.alphacast.io/pipelines/create) 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
1. Seleccionar ("Fetch") el dataset y sus columnas (este paso no es necesario, pero remueve data innecesaria)
2. "Apply transform" seleccionando "usd_daily", "usd_monthly" o lo que corresponda a la frecuencia y/o CCL_daily para convertir a CCL.
3. 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](https://www.alphacast.io/datasets/5288) de referencia para la conversión  y en el caso del [CCL argentino este](https://www.alphacast.io/datasets/5288)


![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/d8f867fb-2caf-475b-b576-2de23b21c93d/image.png)

 

# ¿Cómo calcular una serie mensual de fin de periodo?

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](https://www.alphacast.io/datasets/15172). **"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

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/797bea80-910a-4e94-bc3f-bec51a049b9e/image.png)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e1172ec6-cc47-4985-b7bf-351caeedd9cd/image.png)

## 3. Resamplear la frecuencia y elegir la variable y publicar a un dataset

En tercer lugar se agrega un step para hacer un "Resample de Frequency" y se elige mensual como frecuencia de destino y "Last" como fómula de agregación de la información. Agregando un nuevo step podemos elegir luego dónde queremos que se guarde esa información.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e35fdfc2-6e93-4672-8569-8b40911a869b/image.png)

## 4. Conectar el dataset a Excel

Ya tenemos un dataset que se actualiza solo cada vez que se actualiza el dataset original. Con el nuevo dataset podemos, por ejemplo, crear charts y dashboards, conectarlo a un script de Python vis API o conectarlo directamente a Excel.

Para integrarlo a excel hay que hacer 5 pasos

1. Ir al dataset y apretar download
2. Botón derecho en "Download" al lado del XLSX y copiar la dirección del enlace. Se ve algo así:

https://api.alphacast.io/datasets/15174/data?apiKey=ak_xxxxxxxxxxxxxxxxxx&&$format=xlsx

3. En el excel vamos a Data --> Get Data --> From other Sources --> From Web. En el cartel que aparece copiar la URL que copiamos en el paso anterior
4. Elegimos la "Sheet1" en el cartel que aparece, luego "Close and load".
5. Listo, el dataset en alphacast esta conectado a excel, y solo hay que clickear en "refresh" para que se actualice

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/bc68fc2d-84a4-4059-87cb-7a0940b6a296/image.png)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/5355c443-a668-4bcc-9376-3ecf3bfcff64/image.png)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/a11f600d-cb7b-4792-990b-00aee8ad4bc2/image.png)

# ¿Cómo se desestacionaliza una Serie de Tiempo?

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](https://www.census.gov/data/software/x13as.html). 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](http://econserialcronico.blogspot.com/2017/03/arte-ciencia-y-tortura-medieval.html).

Desestacionalizar con Pipelines en Alphacast es muy facil e incluye solo 4 pasos:
1. Fetch dataset
2. Filtrar las columnas que queremos usar (esto no es estrictamente necesario)
3. Apply Transform, eligiendo la opción de \\\"Seassonal Adjustment\\\"
4. Publicar el dataset

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/e2e1cfc0-81b3-4d5f-bea3-e0ca53a06f6c/image.png)

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 el maiz y la soja, En cambio la serie naranja limpia este efecto estacional y deja una serie limpia que es mucho mas facil de comparar mes a mes.

Vean como salta la vista las sequías de 2009, 2012 y 2018.

@chart https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World


## ¿Cómo agregar la Información en un dashboard?

Armar el gráfico de arriba y sumarlo a un Dashboard que se actualice sólo todos los meses es muy facil. Dentro de la vista del dataset que contiene la información hay que clickear en \\\"Filter\\\" y luego elegir la variable y sus transformaciones. Va a aparecer un pequeño snapshot. Apretando **Create Chart** ahí vamos al editor mas detallado desde donde podemos customizar el gráfico y publicarlo.\
\
![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/4aaad3cf-16df-4a89-a15a-c7780f87002d/image.png)

Con el gráfico creado, hay que embeberlo en un \\\"insight\\\" yendo a \\\"Create new\\\" arriba a la derecha. 

Ya en el editor del insight la forma de insertarlo es la siguiente.
1. Hay que copiar la URL del gr+afico. Esta la obtenemos yendo al botón de Share y eligiendo \\\"Copy Link\\\" o bien haciendo click derecho en el título y copiando la dirección de enlance\
2. Para embeberlo tenemos dos comandos para usar: **Chart** o **Ncharts** (los comandos comienzan poniendo una @ antes). El primero es para embeber un solo gráfico por línea, el segundo para embeber muchos gráficos
3. Escribimos @\\chart (sin la barra), espacio y luego el link o bien @\Ncharts, espacio, link, espacio, link, etc

@chart https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World\

@Ncharts https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World

@Ncharts https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World https://charts.alphacast.io/p/luciano/public-repo/emae-seassonaly-adjusted?tab=chart&stackMode=absolute&time=2004-01-01..2021-11-01&region=World


# ¿Cómo conectar los datos de Alphacast a Google Sheets?

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.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/38f65ef0-845d-4ccd-9891-714e536dc279/image.png)

## Paso 3. En Download apreta botón derecho donde dice CSV y copia la dirección. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/7351a332-d967-44d5-8b47-bbb0fa410732/image.png)

Va a verse algo parecido a esto

https://api.alphacast.io/datasets/5288/data?apiKey=YOUR_API_KEY&%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.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/db3cc619-8c4b-4f33-8710-9d3a65f4ec64/image.png)

## Paso 5. Cambio el formato de la columna fecha a fecha

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/4a5026c4-0572-47b8-bc02-d7e343fd378b/image.png)

## Paso 6. Planifica en que vas a usar todo el tiempo que te vas a ahorrar por no tener que actualizar nunca mas este dataset.



# Alphacast Friday Update - January 28th, 2022

## The new pipeline engine is finally here. 

We are very happy to announce that the Beta version of our new pipelines engine is now live. Just click "Create new" on the top right and then "Pipeline V2.0". To create a Pipeline define its name and the Repository where it will be stored.  Endless possibilities are now opening with the ability to process the data within the platform. One step closer to our aim of creating a single global collaborative database.

The first step in a Pipeline is selecting a dataset as a source. Once selected there are seven **steps** currently available to transform and process the data. These steps are the following:

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/2afb22ed-20ff-491b-a145-2e963d9ad5f0/image.png)

## Rename Column & Select Column

**Rename Column & Select Column** are used to, well, filter columns from the dataset and change their names. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/63bcb9e0-c953-427f-968b-0664c109b6ec/image.png)

## Calculate variable

**Calculate variable** is a general-purpose formula parser where you can write the transformation that you need. Most standard arithmetic is accepted.  If you need some specific transform just let us not

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/1834b5ab-b8df-4931-a81a-7e3b9794a615/image.png)
## Change frequency

With **Change Frequency** users can resample the time-frequency of the dataset moving from Daily to monthly, quarterly, and yearly back and forth. When doing so the user has to select the aggregation formula if moving from higher to a lower frequency - such as average or end of the period - or the interpolation formula if moving from lower to higher (such as linear interpolation or splines)

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/22538bea-2dde-40ce-9dc6-9c50fea34f2e/image.png)

## Merge with another Dataset

**Merge Datasets** is our new and awesome feature that allow combining any dataset on the fly. A whole world of use cases now opens with this new feature. No more downloading and vlookuping on excel. You can create any dataset by combining other datasets on the platform. 

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/b1b623e9-c95d-460c-8fe7-6269dd5b392d/image.png)

## Apply Transform

**Apply transform** is a many-in-one step where you can find most of the transforms that were available in the first version of our pipeline's engine. Most of this will eventually become steps on their own. But meanwhile, you can still do the standard transforms by picking this option (unit conversions, basic arithmetic, variations, differences, etc.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/381e045e-a3f4-473e-aa56-f150d0d6009e/image.png)

## Publish to a dataset

Finally, you can publish the content of your pipeline into a dataset. The pipeline does not have to end there, you can add further steps and create more than one dataset!

Have a nice weekend!

# Alphacast Friday Update - January 21st

What a week! As we've described in the recent Friday update, we are focusing our efforts on two fronts: **More Data and a more powerful Pipeline Engine**. Let us describe where we are standing and where we are heading

## New premium Publisher on the marketplace. Econ.uy
![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/d96cdfc7-25a6-4686-a34f-e7e832ebdb93/image.png)

We are excited to introduce you to econ.uy. our new Premium publisher providing **A LOT** of new content for Uruguay,with [60 public datasets](https://www.alphacast.io/repositories/1633) and lots of content for a subscription. You can subscribe to their content and receive daily updates of more than 50 private datasets for just USD 20 per month [following this link](https://www.alphacast.io/t/econ-uy/products)

## Lots of new global data 
There are hundreds of new datasets in Alphacast, and the pace of the addition of new content is accelerating. We look forward to adding lots of new content in the coming weeks.

* [Paraguay Macro Basics](https://www.alphacast.io/repositories/39) now has 51 datasets
* [Colombia Macro Basics](https://www.alphacast.io/repositories/27) now has 40 datasets
* [Peru Macro Basics](https://www.alphacast.io/repositories/41) now has 100 datasets
* [Mexico Macro Basics](https://www.alphacast.io/repositories/37) now has 75 datasets
* [Africa Information Highway](https://www.alphacast.io/repositories/1711) and [African Development Bank Socio-Economic Database](https://www.alphacast.io/repositories/1710)
* [New Data for Argentina Markets](https://www.alphacast.io/repositories/21), mostly coming from [MAVSA](https://www.alphacast.io/repositories/1428) and MAE
* [INDEC's household survey](https://www.alphacast.io/repositories/1304)

With so much data comes great responsibility. We understand that entropy will tend to disorganize the content in Alphacast so we are doing efforts to improve the overall search and explore experience. We have some ideas that we plan to be exploring in the coming weeks such as: 
- A new "Netflix like" home for the explore section to organize the data with categories, trending datasets, What’s new (from your network? In Alphacast?), - People section, to discover what other users are creating
- Related content based on the user activity (you may be interested in) and on the dataset or the chart that you are seeing 
- Filter by tags on insights, filter by users, and more.
- Improved metadata of the content
- Language standardization and multilanguage support

## New pipelines engine is on beta testing.

The new pipelines engine is on beta testing. We are working to stabilize the overall experience and make a publicly available version soon. 

We have to be cautious in not overpromising, but our aim with the new pipelines engine is to have a much more powerful full and flexible infrastructure to process the data within Alphacast and, eventually, to let the users program their own custom transformations in Python. 

Our goal is to go beyond basic transformations or arithmetic on the data and include transformations such as Smoothing & Filters, complex unit conversions, econometrics models and statistics, and more (i.e. finance-related algorithms). To catch a glimpse on our road map for the coming month we hope to be adding the following steps:

* **Basic transforms**: 	Filter rows, 	Filter columns, 	Resample frequency, 	Group by, 	Pivot, 	Melt, 	Merg, Shift (lead and lag)
* **Basic Arithmetics**: 	Variation, Difference, 	X to date sum, change and average and custom formulas.
* **Smoothing & Filters**: 	Seasonal adjustment X-12, 	Moving average, 	Running sum, 	Hodrick-Prescott Filters, 	Kalman filter, 	Data Normalization and remove Holidays
* **Unit conversion**:  % of GDP, USD / LCU, USD (BCS) / LCU, Constant Prices, Per Capita or PPP conversion
* **Models and related**: 	Linear Regression, ARIMAs,	Principal Component Analysis, Recurrent Neural Network, and VARs and VECs
* **Statistics and Tests**:	Basic Summary Stats, Covariance Matrix, Histograms, Granger Causality, Johanson's Cointegration Test, Augmented Dickey-Fuller Test (ADF Test) - Stationarity, Durbin Watson Statistic, Rank & Percentiles.
* **Finance**: Nelson Siegel Svensson and splines o cubic splines for yield curve interpolation.

As you can see, this is lots of ground to cover! Looking forward to hearing your toughs




# Alphacast Thursday Update - December 30th

This short week between Christmas and the end of the year was highly productive. We'd like to share with you some news on Alphacast

## User Survey

We received great feedback from you with the user survey. We now have a better understanding on what is the value given to the different features of the platform. This is a critical input for us. Thank you all for the time you spent answering it. We really appreciate it. 

If you did not answer the survey and would like to do it now please [follow this link](https://www.research.net/r/3JFQKMP)

## Print insights
Among the most requested feature in the survey was having the ability to print and download the insights to PDF. You can now do that! Next to the "Open in Fullscreen" link, you will find the option to print the insight.

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/2ff8e5d0-e906-4e68-9a9f-b48c41928a9f/image.png)

## Tags on datasets and insights

Tags, tags, tags. You have also requested to have the ability to organize your data easier. Users can now add tags to datasets and insights and we will soon add the experience to search and explore tags. 

### Tags on datasets
![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/5a7256e9-df18-4df2-b415-3223e863e176/image.png)

### Tags on insights
![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/0bb5902f-ca28-4998-bb14-b182864ba67f/image.png)

## Multiple charts on a single chart and other features!

We have also done updates to our chart engine.

You can now create **multiple chart charts**. You can split your charts moving your series up and down and combining lines and bars. To do so, when choosing the variables in the "Basic tab" in the charts editor you can click on "Select in which chart will this data be rendered"

@chart https://charts.alphacast.io/p/alphacast/dev-diary/financial---global---commodities---daily?tab=chart&stackMode=absolute&year=latest

You can also stack and unstack the series, and choose whether you want the stack to be just an addition or to rescale it to add to 100%

@chart https://charts.alphacast.io/p/alphacast/argentina-oil-gas/oil-production-sample?tab=chart&stackMode=absolute&year=latest

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/f1619658-ee60-41c7-9501-06bd032c04e6/image.png)

## Annotation: New Features

Based on the request from the survey, there are now new annotations available to add to the charts. You can now include **Horizontal and Vertical Lines and Text**

![image.png](https://alphacast-images.s3.us-west-2.amazonaws.com/next-s3-uploads/eca72ff0-63a7-4382-8b0b-584ac3fe66d0/image.png)

## Happy new Year!!! See you on 2022!



Luciano Cohan

@luciano

Luciano Cohan

@luciano

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Luciano Cohan

@luciano

Luciano Cohan

@luciano

luciano's insights

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