Auction Write [Python]

An easy step by step tutorial about how to write an Auction Time Serie in Python SDK.

Artesian gives you straightforward access to the data history to perform an analysis and produce a plan in the most compatible way.

Let’s see step-by-step how to proceed.

The goal

Extract the data of an Auction Time Series Market Data.

The reference data is fictitious, created exclusively for this case. With Artesian, it is possible to write any data attributable to a Time Series, making it suitable for saving your data production.

Let’s see how to proceed step by step.

Import of Artesian libraries and configuration

To write the Time Series in Python, first install the Artesian SDK in the python environment used, using the command “pip install artesian-SDK”, then importing the necessary dependencies with the command “from Artesian import ArtesianConfig, Granularity, Market Data “.

To write the Time Series and use the TimeZone, we need to import “datetime” from the datetime library and “tz” from the dateutil library.

First, import the necessary libraries, and then we can configure Artesian by entering the essential link and the API key.

To extract these two essential pieces of data, you can refer to the tutorial “How to Configure Artesian Python SDK “.

Once the Artesian configuration is complete, we can configure the MarketData Service.

					from Artesian import ArtesianConfig, Granularity, MerketData
from Artesian import datetime
from dateutil import tz

cfg = ArtesianConfig("{tenantName}/", "{api-key}")
mkservice = MarketData.MarketDataService(cfg)

Il MarketData Identifier e i dati necessari per la scrittura del Auction TimeSeries

Once Artesian and the Market Data Service have been configured, we can define the MarketData Identifier; that is, we can give a name to our MarketData.

In this case, the Provider’s name will be “PythonSDK”, while the name of the Market Data will be “AuctionWrite”. The definition of these two fields is necessary for two reasons:

  1. The Provider and Market Data’s names represent the unique identifier of our curve on Artesian. The value combination is then translated into the MarketDataID.
  2. The Provider and Market Data’s names are necessary to find the data within the portal through the free text or category filter.

Once the market data and provider names are defined, we can decide on the essential characteristics of our Time Series, such as the type of granularity, the type of the Time Series, the Time Zone, any Aggregation Rule and the Tags.

Artesian can support different granularities such as 10min, 15min, 30min, Hour, Day, Week, Month, Quarter, Season and Year.

When we decide the type of granularity of our market data: we must write it accordingly, indicating the values. For example, in the case of Granularity Day, the data will correspond to a specific day of a certain month in a particular year. In the case of Granularity Hour, the data will correspond to a specific hour (minute and second) of a certain day in a particular month and year.

The TimeZones: must be enhanced with the one corresponding to the data we are saving; this will help the system to apply the necessary conversions to the data in the case of extractions in a TimeZone different from the original.

The Type of the Time Series: in this case is Auction, but it could also be Actual, Versioned, MarketAssessment or BidAsk. See the other tutorials.

The Tags: these are not mandatory but can help categorize the data and allow us to locate them faster by scrolling through the portal menu. In our specific case, we will set the tags as “TutorialSDKPython” with “PythonValue5” inside for our market data.

					mkdir = MarketData.MarketDataIdentifier("PythonSDK","AuctionWrite")

mkd = MarketData.MarketDataEntityInput(
        "TutorialSDKPython": ["PythonValue5"]

Control and registration of the Market Data

First, set the MarketData base; you must check if this Time Series already exists. To do this, we need to insert the Provider’s name, the market data, and unique identifiers to see if there is a match in Artesian. The data already exists if there is a match and can not be overwritten. On the other hand, if there is no response, the data is saved on Artesian through the command “registerMarketData“.

					registered = mkservice.readMarketDataRegistryByName(mkdir.provider,
if(registered is None):
    registered = mkservice.registerMarketData(mkd)

Writing the MarketData values

The last part of our code consists of the configuration of our write to Artesian.

The required parameters for this step are:

The MarketData identifier: that we defined at the beginning of our code

The reference TimeZone of the data we are writing: this must be “UTC” in the case of data with hourly or lower granularity (with adequate data conversion if necessary). It must correspond to the Original Timezone in the case of daily granularity data or higher. This data conversion in the case of hourly or lower granularity is necessary for Artesian to correctly manage the data sent (e.g. change of Winter/Summer time)

The Auction rows are a dictionary containing tuples of values for “bid” and “offer” and their respective associated value.

To conclude and proceed with writing our Auction TimeSeries, we must define the “DownloadedAt“. Represented metadata type of information data when written in Artesian.

Once the previous steps are complete, we can load the Market Assessment Time Series into the system using the command “upsertData“.

					auctionRows = MarketData.UpsertData(MarketData.MarketDataIdentifier(mkdir, 'CET', 
      datetime(2020,1,1): MarketData.AuctionBids(datetime(2020,1,1), 
              MarketData.AuctionBidValue(11.0, 12.0),
              MarketData.AuctionBidValue(13.0, 14.0),
              MarketData.AuctionBidValue(21.0, 22.0),
              MarketData.AuctionBidValue(23.0, 24.0),

Visualization of the new MarketData on the Artesian portal

Unless there are errors to report, nothing will appear in the terminal. However, returning to the Artesian portal, we can verify that our TimeSeries appears under the ProviderName category with the previously given name “PythonSDK”. Scrolling through the menu, we can also notice the item “TutorialSDKPython”, which is nothing more than our tag.

It is sufficient to employ just once and then have it entirely reproducible and automated in our workflow.

Not only does it save you time, but it allows you to minimize human errors caused by repeated operations on substantial amounts of data or different Excel files.

An undeniable advantage that allows us to focus on data analysis instead of its management and optimization.