Superalgos - Data Mining - Fetching Data From APIs

Data Mining - Fetching Data From APIs

Summary: Superalgos allows its users to fetch data from any API on the Internet without needing to write code. Instead, users design an , which is a set of definitions for how a Bot can access a certain API and how to interpret the responses. With this , an can be configured to extract data from any and store inside the system. This raw data can later be processed and consumed by other Bots.

Main Workflow

How to Fetch Data from an API?

The main workflow to pull this off is quite straight forward. It does not require coding at all. Follow this guide and you will be using data from any source on the Internet for your trading strategies in no time.

1. Choose an API: The current version is limited to public APIs only. Secured APIs coming soon.

2. Design an API Map: Read the APIs online documentation and identify the Endpoints that return the data you would like to fetch. Carefully map the Endpoints defining the parameters the API Server expects at each call, and also how the API Server response is formatted.

3. Setup an API Data Fetcher Bot: Create a Data Mine for this API and add an API Data Fetcher Bot. From that Bot's Record Properties you will need to reference the fields returned by the API Server defined at the API Map. This is how you define which fields from each response you are going to save to disk. You might also need to define query or path parameters here.

4. Setup an Indicator Bot for Dataset Type Conversion: The Raw Data saved to disk needs to be organized for consumption. You will need to setup an Indicator Bot that will do a Dataset Type Conversion from Raw Data to a standard Dataset Type. This process will do data aggregation in order to generate the elements with a fixed time range for every Time Frame possible.

5. Setup Indicator Bots: Once the data is converted, you can extract specific Datasets from it. These datasets can be used to define a set of indicators you can build from that data. These Indicators can be as simple as reorganizations of the data (with some adjustments to the names of fields and splitting the data into different indicators), or it can include some data processing / transformations if you wish (this would require some coding).

6. Setup Plotters: After you have set up your indicators you can design plotters for them if you wish.

7. Use Data for Trading Strategies: These indicators can be consumed from your trading strategies.

Tip: There is a called Demo with a simple working example of the previous steps you can use as a reference.

Data Aggregation

How Does Data Aggregation Work?

When is fetched from the Internet, it needs to be converted into a either of or types. Usually is a timestamped dataset with a timesStamp field, or it has begin and end properties if they are fragmented in time slots of one minute.

Wenn (Rohdaten) aus dem Internet abgerufen werden, müssen sie in einen umgewandelt werden, der entweder zum Typ oder gehört. In der Regel handelt es sich bei um einen Datensatz mit Zeitstempel und einem timesStamp Feld, oder sie haben Anfangs- und Endeigenschaften, wenn sie in Zeitschlitze von einer Minute unterteilt sind.

Когда необработанные данные извлекаются из интернета, их необходимо преобразовать в тип набора данных либо в тип или . Обычно необработанные данные представляют собой набор данных с отметкой времени с полем timesStamp или у него есть свойства начала и конца, если они фрагментированы во временных интервалах в одну минуту.

From there the conversion process needs to generate elements with time slots of 2 min, 3 min, 4 min, 5 min, 10, min,... up to 24hs. To achieve this, each field needs to be aggregated in a certain way.

At the Bot where this conversion happens, the nodes needs to have an aggregationMethod config property that defines how this is going to be handled. Here is a list of the supported aggregation methods that can be used during this conversion procedure:

Im Bot, in dem diese Konvertierung stattfindet, müssen die nodes der Datensatzeigenschaft über eine aggregationMethod config property (Konfigurationseigenschaft) verfügen, die definiert, wie dies gehandhabt werden soll. Hier ist eine Liste der unterstützten aggregation methods (Aggregationsmethoden), die während dieses Konvertierungsvorgangs verwendet werden können:

В боте, где происходит это преобразование, узлы должны иметь свойство конфигурации aggregationMethod, которое определяет, как это будет обрабатываться. Вот список поддерживаемых методов агрегирования, которые можно использовать во время этой процедуры преобразования:

Bu dönüşümün gerçekleştiği Bot'ta, Kayıt Özelliği ( ) düğümlerinin, bunun nasıl ele alınacağını tanımlayan bir aggregationMethod yapılandırma özelliğine sahip olması gerekir. Bu dönüştürme prosedürü sırasında kullanılabilecek desteklenen toplama yöntemlerinin listesi aşağıda verilmiştir:

First: This means that the first value found within the time range being processed, will define the value of the Record Property.

Last: This means that from all values found at the current time range, the last will be selected for the final value of the Record Property node being evaluated.

Min: This means that the mathematical minimum value from all values found in the time range will be used as the value for the Record Property.

Max: This means that the mathematical maximum value from all values found in the time range will be used as the value for the Record Property.

Sum: This means that the sum of all values found in the time range will be used as the value for the Record Property.

Avg: This means that the average of all values found in the time range will be used as the value for the Record Property.

Step By Step

Step by Step Guide to Fetch Data from an API

This is a detailed guide on how to setup a data mining operation fetching data from an Internet API.

Step #1: Open the API Data Fetcher Bot Demo Plugin Workspace and study it for a while. Run the tasks in there, watch while data is processed, check the charts and finally run the Trading Task that uses the data processed.

Step #2: Open a second UI (this will prevent the previous one from saving the changes, but you don't need to do that). You will use the first UI to check from time to time how nodes are configured there, and the second UI to build your own data mining operation from scratch. The rest of the steps you will do within the second UI.

Step #3: Select an API from the Internet with data that might be useful at your Trading Strategy.

Step #4: Read the API's documentation, indentify the endpoints, what parameters they require and what data they will provide you. Another helpful way to see how the api responds is by typing the api endpoint into your browser and looking at the raw output.

Step #5: At Superalgos, get a clean workspace, without plugins pre-installed.

Step #6: Create an API Map node, and start mapping the documented API by adding children to it. Especially pay attention to unnamed arrays and objects that may wrap the data.

Step #7: For each node you add, read the node's doc page. This will save you time trying to guess how all this works.

Step #8: Define one API Endpoint first, once you have the first one working, go for the rest of the endpoints available.

Step #9: Define the query, or path parameters, and the responses. Read each node's help for details on how to do it right.

Step #10: Create a Trading System with an empty Strategy. This will allow the Install New Market procedure to create trading tasks referencing this Trading System.

Step #11: Create a new Data Mine with the name of the company that hosts the API.

Step #12: Within the Data Mine add an API Data Fetcher Bot. Configure the Bot's nodes following the example at the UI #1.

Step #13: Install a new market. This procedure is going to setup many things for you. It will detect the API Map, and the Data Mine you created and will create appropiate Tasks to run your API Fetcher Bot.

Step #14: Pay attention to the nodePath property at the API Response Schema node of the API Map. Check the node's docs page for details.

Step #15: At your API Data Fetcher Bot's Product Definition Record Properties, define the fields that you would like to save from the API Server response. You don't need to save everything, only what makes sense to you.

Step #16: Establish a reference from each Record Property child API Response Field Reference node to a node at the API Map of type API Response Field. Do it carefully for every Record Property.

Step #17: The timestamp record property is the only required property. It helps keep the records you save organized. Additionally, the timestamp may live in the header of the API response. Read the docs of the API Response Field Reference node to set up the nodePath property if necessary.

Note: It is a good idea to make sure you get the api fetcher bot running and debugged before moving on to the next step.

Step #18: At your Data Mine, set up an Indicator Bot and configure its Process Definition nodes to the framework that will convert from One-Min-Daily to Multi-Time-Frame-Daily and Multi-Period-Market types of Datasets. See how it was done at the UI #1.

Step #19: Within the Indicator Bot you just added, define the Record Property nodes based on the information you would like to convert from Raw Data. Make sure you add a begin and end Record Property even if it does not exist at the Raw Data in order for the indicator bot to run correctly.

Step #20: Configure the aggregationMethod property for each Record Property. Check the docs of that node for details.

Step #21: If you wish, you can create multiple Indicator Bots to cut the dataset in different groups of Record Properties.

Step #22: Setup a Plotter at your new Data Mine, and reference it from the Product Definition of the latest Indicator Bot created.

Step #23: Now you are ready to run the Task where the Data Fetcher Bot Process Instance is defined.

Step #24: If something did not work, check the workspace map to see if there is a node with an error. Locate the node, click on the error and see the error's page at the doc. Try to understand what is wrong, fix it and try again.

Step #25: Iterate through step #24 until everything is working. Re read node's documentation until you get the details of what it explains there. Reread this guide or this whole Topic from the start, until the concepts explained sinks in.

Step #26: If after digesting the docs, and following all this advice you still cannot get it to work, go to the Telegram Support group and ask for help.

Step #27: Once all is running well, set up your Trading Strategy in a way that allows you to test if you have access to the processed data (the one provided by the latest Indicator Bots created).

Step #29: Once the data is correctly consumed by your Trading Strategy, iterate through this guide by adding another API Endpoint to the API Map. You will need to add an addtional API Fetcher Bot.

Step #30: Once you have successfully mapped the whole API and you have all the needed API Data Fetchers, Indicator Bots and Plotters tested, you are ready to share your API Map and Data Mine with the community as a plugin, and receive SA Tokens in exchange for your contribution.

Data Mining — TOC

You just read page 14 in the topic.

1. Data Mining - Introduction

2. Data Mining - Workflows

3. Data Mining - Dataset Types

4. Data Mining - Datasets Type Conversions

5. Data Mining - Indicator Frameworks

6. Data Mining - Data Mine Hierarchy

7. Data Mining - Managing Execution

8. Data Mining - Data Dependencies

9. Data Mining - Status Reports

10. Data Mining - Status Dependencies

11. Data Mining - Deployments

12. Data Mining - Products

13. Data Mining - Plotting

14. Data Mining - Fetching Data From APIs

15. Data Mining - Consuming Data From Strategies

16. Data Mining - Indicator Bots

17. Data Mining - Indicator Bots - Process Definition

18. Data Mining - Indicator Bots - Product Definition

19. Data Mining - Indicator Bots - Data Mining Workflow

20. Data Mining - Indicator Bots - Coding the Indicator's Logic

21. Data Mining - Plotters

22. Data Mining - Plotters - Shapes

23. Data Mining - How to Build an Indicator - Video Series

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