Artesian.SDK

This Library provides read access to the Artesian API

Getting Started

Installation

You can install the package directly from pip.

pip install artesian-sdk

Alternatively, to install this package go to the release page .

How to use

The Artesian.SDK instance can be configured using API-Key authentication

from Artesian import ArtesianConfig

cfg = ArtesianConfig("https://arkive.artesian.cloud/{tenantName}/", "{api-key}")

BREAKING CHANGES: Upgrade v2->v3

The following breaking changes has been introduced in v3 respect to v2.

Python Version >=3.8

Python >=3.8 is required. Python 3.7 is not supported due missing 'typing' features.

SubPackaging

With Artesian-SDK v3 we introduced SubPkg to split the different part of the library. The new SubPkg are:

Artesian.Query: contains all classes for querying Artesian data.
Artesian.GMEPublicOffers: contains all classes for querying GME Public Offers
(NEW) Artesian.MarketData: contains all classes to interact with the MarketData registry of Artesian. Register a new MarketData, change its Tags, etc. See documentation below.

To upgrade is enough to prefix the QueryService with 'Query.' or import it from Artesian.Query.

Were was used:

from Artesian import *

cfg = ArtesianConfig("https://arkive.artesian.cloud/{tenantName}/", "{api-key}")
qs = QueryService(cfg)

now you have to:

from Artesian import ArtesianConfig
from Artesian.Query import QueryService

cfg = ArtesianConfig("https://arkive.artesian.cloud/{tenantName}/", "{api-key}")
qs = QueryService(cfg)

Enum entries Casing

To align the casing of the entries of the Enum, we adopted PascalCase to align it with the Artesian API.

Where before was used

  .inGranularity(Granularity.HOUR) \

now is

  .inGranularity(Granularity.Hour) \

MarketData QueryService

Using the ArtesianConfig cfg we create an instance of the QueryService which is used to create Actual, Versioned and Market Assessment time series queries

Actual Time Series Extraction

from Artesian import ArtesianConfig, Granularity
from Artesian.Query import QueryService, RelativeInterval

cfg = ArtesianConfig("https://arkive.artesian.cloud/{tenantName}/", "{api-key}")

qs = QueryService(cfg)
data = qs.createActual() \
    .forMarketData([100011484,100011472,100011477,100011490,100011468,100011462,100011453]) \
    .inAbsoluteDateRange("2018-01-01","2018-01-02") \
    .inTimeZone("UTC") \
    .inGranularity(Granularity.Hour) \
    .execute()

print(data)

To construct an Actual Time Series Extraction the following must be provided.

Actual Query	Description
Market Data ID	Provide a market data id or set of market data id's to query
Time Granularity	Specify the granularity type
Time Extraction Window	An extraction time window for data to be queried

Go to Time Extraction window section

Versioned Time Series Extraction

from Artesian import ArtesianConfig, Granularity
from Artesian.Query import QueryService, RelativeInterval

qs = QueryService(cfg)
q = qs.createVersioned() \
    .forMarketData([100042422,100042283,100042285,100042281,100042287,100042291,100042289]) \
    .inAbsoluteDateRange("2018-01-01","2018-01-02") \
    .inTimeZone("UTC") \
    .inGranularity(Granularity.Hour)

print(q)

ret = q.forMUV().execute()
print(ret)
ret = q.forLastNVersions(2).execute()
print(ret)
ret = q.forLastOfDays("2019-03-12","2019-03-16").execute()
print(ret)
ret = q.forLastOfDays("P0Y0M-2D","P0Y0M2D").execute()
print(ret)
ret = q.forLastOfDays("P0Y0M-2D").execute()
print(ret)
ret = q.forLastOfMonths("2019-03-12","2019-03-16").execute()
print(ret)
ret = q.forLastOfMonths("P0Y-1M0D","P0Y1M0D").execute()
print(ret)
ret = q.forLastOfMonths("P0Y-1M0D").execute()
print(ret)
ret = q.forVersion("2019-03-12T14:30:00").execute()
print(ret)
ret = q.forMostRecent("2019-03-12","2019-03-16").execute()
print(ret)
ret = q.forMostRecent("2019-03-12T12:30:05","2019-03-16T18:42:30").execute()
print(ret)
ret = q.forMostRecent("P0Y0M-2D","P0Y0M2D").execute()
print(ret)
ret = q.forMostRecent("P0Y0M-2D").execute()
print(ret)
ret = q.forMostRecent("2019-03-12","2019-03-16").execute()
print(ret)
ret = q.forMostRecent("P0Y-1M0D","P0Y1M0D").execute()
print(ret)
ret = q.forMostRecent("P0Y-1M0D").execute()
print(ret)

To construct a Versioned Time Series Extraction the following must be provided.

Versioned Query	Description
Market Data ID	Provide a market data id or set of market data id's to query
Time Granularity	Specify the granularity type
Versioned Time Extraction Window	Versioned extraction time window
Time Extraction Window	An extraction time window for data to be queried

Go to Time Extraction window section

Market Assessment Time Series Extraction

from Artesian import ArtesianConfig
from Artesian.Query import QueryService, RelativeInterval

qs = QueryService(cfg)
data = qs.createMarketAssessment() \
    .forMarketData([100000032,100000043]) \
    .forProducts(["D+1","Feb-18"]) \
    .inAbsoluteDateRange("2018-01-01","2018-01-02") \
    .execute()

print(data)

To construct a Market Assessment Time Series Extraction the following must be provided.

Mas Query	Description
Market Data ID	Provide a market data id or set of market data id's to query
Product	Provide a product or set of products
Time Extraction Window	An extraction time window for data to be queried

Go to Time Extraction window section

Bid Ask Time Series Extraction

from Artesian import ArtesianConfig
from Artesian.Query import QueryService, RelativeInterval

qs = QueryService(cfg)
data = qs.createBidAsk() \
    .forMarketData([100000032,100000043]) \
    .forProducts(["D+1","Feb-18"]) \
    .inAbsoluteDateRange("2018-01-01","2018-01-02") \
    .execute()

print(data)

To construct a Bid Ask Time Series Extraction the following must be provided.

Mas Query	Description
Market Data ID	Provide a market data id or set of market data id's to query
Product	Provide a product or set of products
Time Extraction Window	An extraction time window for data to be queried

Go to Time Extraction window section

Auction Time Series Extraction

from Artesian import ArtesianConfig
from Artesian.Query import QueryService, RelativeInterval

qs = QueryService(cfg)
data = qs.createAuction() \
    .forMarketData([100011484,100011472,100011477,100011490,100011468,100011462,100011453]) \
    .inAbsoluteDateRange("2018-01-01","2018-01-02") \
    .inTimeZone("UTC") \
    .execute()

print(data)

To construct an Auction Time Series Extraction the following must be provided.

Auction Query	Description
Market Data ID	Provide a market data id or set of market data id's to query
Time Extraction Window	An extraction time window for data to be queried

Go to Time Extraction window section

Extraction Windows

Extraction window types for queries.

Date Range

 .inAbsoluteDateRange("2018-08-01", "2018-08-10")

Relative Interval

 .inRelativeInterval(RelativeInterval.RollingWeek)

Period

 .inRelativePeriod("P5D")

Period Range

 .inRelativePeriodRange("P-3D", "P10D")

Filler Strategy

All extraction types (Actual,Versioned, Market Assessment and BidAsk) have an optional filler strategy.

var versionedSeries = qs
  .createVersioned() \
  .forMarketData([100000001]) \
  .forLastNVersions(1) \
  .inGranularity(Granularity.Day) \
  .inAbsoluteDateRange(new Date("2018-1-1"), new Date("2018-1-10")) \
  .withFillLatestValue("P5D") \
  .execute()

Use 'Null' to fill the missing timepoint with 'None' values.

 .withFillNull()

Use 'None' to not fill at all: timepoints are not returned if not present.

 .withFillNone()

Custom Value can be provided for each MarketDataType.

Custom Value for Actual extraction type.

.withFillCustomValue(123)

Custom Value for BidAsk extraction type.

.withFillCustomValue(
  bestBidPrice = 15.0,
  bestAskPrice = 20.0,
  bestBidQuantity = 30.0,
  bestAskQuantity = 40.0,
  lastPrice = 50.0,
  lastQuantity = 60.0)

Custom Value for Market Assessment extraction type.

.withFillCustomValue(
settlement = 10.0,
open = 20.0,
close = 30.0,
high = 40.0,
low = 50.0,
volumePaid = 60.0,
volueGiven = 70.0,
volume = 80.0)

Custom Value for Versioned extraction type.

.withFillCustomValue(123)

Latest Value to propagate the latest value, not older than a certain threshold only if there is a value at the end of the period.

 .withFillLatestValue("P5D")

 .withFillLatestValue("P5D", "False")

Latest Value to propagate the latest value, not older than a certain threshold even if there's no value at the end.

 .withFillLatestValue("P5D", "True")

Query written Versions or Products

Using MarketDataService is possible to query all the Versions and all the Products curves which has been written in a MarketData.

from Artesian.MarketData import MarketDataService

mds = MarketDataService(cfg)

To list MarketData curves

page = 1
pageSize = 100
res = mds.readCurveRange(100042422, page, pageSize, versionFrom="2016-12-20" , versionTo="2019-03-12")

Search the MarketData collection with faceted results

Using MarketDataService is possible to query and search the MarketData collection with faceted results. Supports paging, filtering and free text.

from Artesian.MarketData import MarketDataService

mds = MarketDataService(cfg)

To list MarketData curves

page = 1
pageSize = 100
searchText = "Riconsegnato_"
filters = {"ProviderName": ["SNAM", "France"]}
sorts=["MarketDataId asc"]
doNotLoadAdditionalInfo=True
res = mds.searchFacet(page, pageSize, searchText, filters, sorts, doNotLoadAdditionalInfo)

GME Public Offer

Artesian support Query over GME Public Offers which comes in a custom and dedicated format.

Extract GME Public Offer

from Artesian.GMEPublicOffers import GMEPublicOfferService, Market, Purpose, Status, Zone, Scope, UnitType, GenerationType, BAType

qs = GMEPublicOfferService(cfg)

data = qs.createQuery() \
    .forDate("2020-04-01") \
    .forMarket([Market.MGP]) \
    .forStatus(Status.ACC) \
    .forPurpose(Purpose.BID) \
    .forZone([Zone.NORD]) \
    .withPagination(1,100) \
    .execute()

print(data)

To construct a GME Public Offer Extraction the following must be provided.

GME Public Offer Query	Description
Time Extraction Window	An extraction time window for data to be queried
Market	Provide a market or set of markets to query
Status	Provide a status or set of statuses to query
Purpose	Provide a purpose or set of purposes to query
Zone	Provide a zone to query

Unit of Measure Conversion Functionality

Overview

The unit of measure conversion functionality allows users to request a conversion of units for Market Data that was registered using a different unit. This feature is supported only for Actual and Versioned Time Series. Supported units are defined in the CommonUnitOfMeasure object and conform to ISO/IEC 80000 (i.e., kW, MW, kWh, MWh, m, km, day, min, h, s, mo, yr).

Note: Duration-based units are interpreted with the following fixed assumptions: 1 day = 24 hours 1 mo = 30 days 1 yr = 365 days

Additional supported units include currency codes in 3-letter format as per ISO 4217:2015 (e.g., EUR, USD, JPY). These are not part of CommonUnitOfMeasure and must be specified as regular strings. Units of measure can also be composite, using the {a}/{b} syntax, where both {a} and {b} are either units from CommonUnitOfMeasure or ISO 4217 currency codes.

Conversion Logic

Unit conversion is based on the assumption that each unit of measure can be decomposed into a "BaseDimension", which represents a polynomial of base SI units (m, s, kg, etc.) and currencies (EUR, USD, etc.). A unit of measure is represented as a value in BaseDimension UnitOdMeasure. Example: 10 Wh = 10 kg·m²·s⁻³ Conversion is allowed when the BaseDimensions match exactly, i.e., the same set of base units raised to the same exponents. In Artesian, units that differ only in the time dimension are also potentially convertible, as the time dimension can be inferred from the data’s time interval.

Example: Power to Energy Conversion

Converting W to Wh: • W → BaseDimension: k·m²·s⁻³ • Wh → BaseDimension: kg·m²·s⁻² • 1 h = 3600 s Conversion Steps: 10 W = 10 kg·m²/s³ 1 h = 3600 s 10 kg·m²/s³ × 3600 s = 36000 kg·m²/s² = 10 Wh

MarketData Registration with UnitOfMeasure

The UnitOfMeasure is defined during registration:

mkd = MarketData.MarketDataEntityInput(
      providerName = "TestProviderName",
      marketDataName = "TestMarketDataName",
      originalGranularity=Granularity.Day,
      type=MarketData.MarketDataType.ActualTimeSerie,
      originalTimezone="CET",
      aggregationRule=AggregationRule.SumAndDivide,
    UnitOfMeasure = CommonUnitOfMeasure.kW
  )

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

UnitOfMeasure Conversion and Aggregation Rule Override

In the QueryService, there are two supported methods related to unit of measure handling during extraction:

UnitOfMeasure Conversion
Aggregation Rule Override

UnitOfMeasure Conversion

To convert a UnitOfMeasure during data extraction, use the .inUnitOfMeasure() method. This function converts the data from the unit defined at MarketData registration to the target unit you specify in the query.

qs = QueryService(cfg)
data = qs.createActual() \
    .forMarketData([100011484]) \
    .inAbsoluteDateRange("2024-01-01","2024-01-02") \
    .inTimeZone("UTC") \
    .inGranularity(Granularity.Day) \
    .inUnitOfMeasure(CommonUnitOfMeasure.MW) \
    .execute()

By default, the aggregation rule used during extraction is the one defined at registration. However, you can override it if needed. The conversion is always applied before aggregation.

Aggregation Rule Override

AggregationRule can be overrided using the .withAggregationRule() method in QueryService.

qs = QueryService(cfg)
data = qs.createActual() \
    .forMarketData([100011484]) \
    .inAbsoluteDateRange("2024-01-01","2024-01-02") \
    .inTimeZone("UTC") \
    .inGranularity(Granularity.Day) \
    .withAggregationRule(AggregationRule.AverageAndReplicate) \
    .execute()

Sometimes, especially when converting from a consumption unit (e.g., MWh) to a power unit (e.g., MW), the registered aggregation rule (e.g., SumAndDivide) may not make sense for the new unit.

If you don’t override the aggregation rule, the conversion may produce invalid or misleading results.

Example: Convert power (`MW`) to energy (`MWh`):

data = qs.createActual() \
    .forMarketData([100011484]) \
    .inAbsoluteDateRange("2024-01-01","2024-01-02") \
    .inTimeZone("UTC") \
    .inGranularity(Granularity.Day) \
    .inUnitOfMeasure(CommonUnitOfMeasure.MWh) \
    .withAggregationRule(AggregationRule.AverageAndReplicate) \
    .execute()

Composite Unit Example: `MWh/day`

data = qs.createActual() \
    .forMarketData([100011484]) \
    .inAbsoluteDateRange("2024-01-01","2024-01-02") \
    .inTimeZone("UTC") \
    .inGranularity(Granularity.Day) \
    .inUnitOfMeasure(CommonUnitOfMeasure.MWh / CommonUnitOfMeasure.day) \
    .withAggregationRule(AggregationRule.AverageAndReplicate) \
    .execute()

CheckConversion: Validate Unit Compatibility

Use the CheckConversion method to verify whether a list of input units can be converted into a specifified target unit:

from Artesian import ArtesianConfig, MarketData
from Artesian.MarketData import CommonUnitOfMeasure

cfg = ArtesianConfg()

mkservice = MarketData.MarketDataService(cfg)

inputUnitsOfMeasure = [CommonUnitOfMeasure.kW, CommonUnitOfMeasure.kWh, "EUR/MWh"]
targetUnitOfMeasure = CommonUnitOfMeasure.MW

checkConversionResult = mkservice.checkConversion(inputUnitsOfMeasure , targetUnitOfMeasure)

Returned Object: CheckConversionResult

TargetUnitOfMeasure: "kW"
ConvertibleInputUnitsOfMeasure: [ "MW", "kW/s" ]
NotConvertibleInputUnitsOfMeasure: [ "s" ]

Extraction Options

Extraction options for GME Public Offer queries.

Date

 .forDate("2020-04-01")

Purpose

 .forPurpose(Purpose.BID)

Status

 .forStatus(Status.ACC)

Operator

 .forOperator(["Operator_1", "Operator_2"])

Unit

 .forUnit(["UP_1", "UP_2"])

Market

 .forMarket([Market.MGP])

Scope

 .forScope([Scope.ACC, Scope.RS])

BAType

 .forBAType([BAType.NETT, BAType.NERV])

Zone

 .forZone([Zone.NORD])

UnitType

 .forUnitType([UnitType.UCV, UnitType.UPV])

Generation Type

 .forGenerationType(GenerationType.GAS)

Pagination

 .withPagination(1,10)

UnitOfMeasure (for Actual and Versioned Time Series)

 .inUnitOfMeasure(CommonUnitOfMeasure.kWh)

AggregationRule (for Actual and Versioned Time Series)

 .withAggregationRule(AggregationRule.SumAndDivide)

Write Data in Artesian

Using the MarketDataService is possible to register MarketData and write curves into it using the UpsertData method.

Depending on the Type of the MarketData, the UpsertData should be composed as per example below.

Write Data in an Actual Time Series

from Artesian import ArtesianConfig, Granularity, MarketData
from Artesian.MarketData import AggregationRule
from datetime import datetime
from dateutil import tz

cfg = ArtesianConfg()

mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
mkd = MarketData.MarketDataEntityInput(
      providerName = mkdid.provider,
      marketDataName = mkdid.name,
      originalGranularity=Granularity.Day,
      type=MarketData.MarketDataType.ActualTimeSerie,
      originalTimezone="CET",
      aggregationRule=AggregationRule.AverageAndReplicate,
      tags={
        'TestSDKPython': ['PythonValue2']
      },
      derivedCfg=DerivedCfg(
                version=1,
                derivedAlgorithm=DerivedAlgorithm.Coalesce,
                orderedReferencedMarketDataIds=[10000, 10001, 10002],
            ),
  )

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

data = MarketData.UpsertData(mkdid, 'CET',
  rows=
  {
      datetime(2020,1,1): 42.0,
      datetime(2020,1,2): 43.0,
  },
  downloadedAt=datetime(2020,1,3).replace(tzinfo=tz.UTC)
  )

mkservice.upsertData(data)

DerivedCfg can be of algorithm type: Coalesce, Sum, Muv.

Updating the DerivedCfg can be performed with updateDerivedConfiguration on MarketDataService. A validation will be done on the existing DerivedCfg of the MarketData, that should be not null and with same type as the one used for the update.

derivedCfgUpdate = DerivedCfg(
    version=1,
    derivedAlgorithm=DerivedAlgorithm.Coalesce,
    orderedReferencedMarketDataIds=[10002, 10001, 10000],
)

marketDataUpdated = mkdservice.updateDerivedConfiguration(
                        registeredDerived.marketDataId,
                        derivedCfgUpdate,
                        False)

In case we want to write an hourly (or lower) time series the timezone for the upsert data must be UTC:

mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
mkd = MarketData.MarketDataEntityInput(
      providerName = mkdid.provider,
      marketDataName = mkdid.name,
      originalGranularity=Granularity.Hour,
      type=MarketData.MarketDataType.ActualTimeSerie,
      originalTimezone="CET",
      aggregationRule=AggregationRule.AverageAndReplicate,
      tags={
        'TestSDKPython': ['PythonValue2']
      }
  )

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

data = MarketData.UpsertData(mkdid, 'UTC',
  rows=
  {
      datetime(2020,1,1,5,0,0): 42.0,
      datetime(2020,1,2,6,0,0): 43.0,
      datetime(2020,1,2,7,0,0): 44.0,
      datetime(2020,1,2,8,0,0): 45.0,
      datetime(2020,1,2,9,0,0): 46.0,
  },
  downloadedAt=datetime(2020,1,3).replace(tzinfo=tz.UTC)
  )

mkservice.upsertData(data)

Write Data in a Versioned Time Series

from Artesian import ArtesianConfig, Granularity, MarketData
from Artesian.MarketData import AggregationRule
from datetime import datetime
from dateutil import tz

cfg = ArtesianConfg()

mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
mkd = MarketData.MarketDataEntityInput(
      providerName = mkdid.provider,
      marketDataName = mkdid.name,
      originalGranularity=Granularity.Day,
      type=MarketData.MarketDataType.VersionedTimeSerie,
      originalTimezone="CET",
      aggregationRule=AggregationRule.AverageAndReplicate,
      tags={
        'TestSDKPython': ['PythonValue2']
      }
  )

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

data = MarketData.UpsertData(mkdid, 'CET',
  rows=
  {
      datetime(2020,1,1): 42.0,
      datetime(2020,1,2): 43.0,
  },
  version= datetime(2020,1,3,12,0),
  downloadedAt=datetime(2020,1,3).replace(tzinfo=tz.UTC)
  )

mkservice.upsertData(data)

Write Data in a Market Assessment Time Series

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

cfg = ArtesianConfg()
mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
mkd = MarketData.MarketDataEntityInput(
      providerName = mkdid.provider,
      marketDataName = mkdid.name,
      originalGranularity=Granularity.Day,
      type=MarketData.MarketDataType.MarketAssessment,
      originalTimezone="CET",
      tags={
        'TestSDKPython': ['PythonValue2']
      }
  )

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

marketAssessment = MarketData.UpsertData(MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME'), 'CET',
  marketAssessment=
  {
      datetime(2020,1,1):
      {
         "Feb-20": MarketData.MarketAssessmentValue(open=10.0, close=11.0),
         "Mar-20": MarketData.MarketAssessmentValue(open=20.0, close=21.0)
      },
          datetime(2020,1,2):
          {
              "Feb-20": MarketData.MarketAssessmentValue(open=11.0, close=12.0),
              "Mar-20": MarketData.MarketAssessmentValue(open=21.0, close=22.0)
          }
  },
  downloadedAt=datetime(2020,1,3).replace(tzinfo=tz.UTC)
  )

mkservice.upsertData(marketAssessment)

Write Data in a Bid Ask Time Series

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

cfg = ArtesianConfg()
mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
mkd = MarketData.MarketDataEntityInput(
      providerName = mkdid.provider,
      marketDataName = mkdid.name,
      originalGranularity=Granularity.Day,
      type=MarketData.MarketDataType.BidAsk,
      originalTimezone="CET",
      tags={
        'TestSDKPython': ['PythonValue2']
      }
  )

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

bidAsk = MarketData.UpsertData(MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME'), 'CET',
  bidAsk={
      datetime(2020,1,1):
      {
          "Feb-20":MarketData.BidAskValue(bestBidPrice=15.0, lastQuantity=14.0),
          "Mar-20":MarketData.BidAskValue(bestBidPrice=25.0, lastQuantity=24.0)
      },
      datetime(2020,1,2):
      {
          "Feb-20":MarketData.BidAskValue(bestBidPrice=15.0, lastQuantity=14.0),
          "Mar-20":MarketData.BidAskValue(bestBidPrice=25.0, lastQuantity=24.0)
      }

  },
  downloadedAt=datetime(2020,1,3).replace(tzinfo=tz.UTC)
  )

mkservice.upsertData(bidAsk)

Write Data in an Auction Time Series

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

cfg = ArtesianConfg()
mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
mkd = MarketData.MarketDataEntityInput(
      providerName = mkdid.provider,
      marketDataName = mkdid.name,
      originalGranularity=Granularity.Day,
      type=MarketData.MarketDataType.Auction,
      originalTimezone="CET",
      tags={
        'TestSDKPython': ['PythonValue2']
      }
  )

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

auctionRows = MarketData.UpsertData(MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME'), 'CET',
  auctionRows={
      datetime(2020,1,1): MarketData.AuctionBids(datetime(2020,1,1),
          bid=[
              MarketData.AuctionBidValue(11.0, 12.0),
              MarketData.AuctionBidValue(13.0, 14.0),
          ],
          offer=[
              MarketData.AuctionBidValue(21.0, 22.0),
              MarketData.AuctionBidValue(23.0, 24.0),
          ]
      )
  },
  downloadedAt=datetime(2020,1,3).replace(tzinfo=tz.UTC)
  )

  mkservice.upsertData(auctionRows)

Delete Data in Artesian

Using the MarketDataService is possible to delete MarketData and its curves.

Delete MarketData in Artesian

Using the MarketDataService is possible to delete MarketData and its curves.

from Artesian import ArtesianConfig
from Artesian.MarketData import MarketDataService

cfg = ArtesianConfg()
mkservice = MarketDataService(cfg)

mkservice.deleteMarketData(100042422)

Depending on the Type of the MarketData, the DeletData should be composed as per example below. The timezone is optional: for DateSeries if provided must be equal to MarketData OriginalTimezone Default:MarketData OriginalTimezone. For TimeSeries Default:CET

Delete Data in an Actual Time Series

from Artesian import ArtesianConfig, Granularity, MarketData
from Artesian.MarketData import AggregationRule
from datetime import datetime
from dateutil import tz

cfg = ArtesianConfg()

mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
deleteData = MarketData.DeleteData(
    ID=mkdid,
    timezone='CET',
    rangeStart=datetime(2020, 1, 1, 6),
    rangeEnd=datetime(2020, 1, 1, 18),
)

mkdservice.deleteData(deleteData)

Delete Data in an Versioned Time Series

from Artesian import ArtesianConfig, Granularity, MarketData
from Artesian.MarketData import AggregationRule
from datetime import datetime
from dateutil import tz

cfg = ArtesianConfg()

mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
deleteData = MarketData.DeleteData(
    ID=mkdid,
    timezone='CET',
    rangeStart=datetime(2020, 1, 1, 0),
    rangeEnd=datetime(2020, 1, 7, 0),
    version=datetime(2020, 1, 1, 0)
)

mkdservice.deleteData(deleteData)

Delte Data in a Market Assessment Time Series

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

cfg = ArtesianConfg()
mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
deleteData = MarketData.DeleteData(
    ID= mkdid,
    timezone='CET',
    rangeStart=datetime(2020, 1, 1, 0),
    rangeEnd=datetime(2020, 1, 3, 0),
    product=["Feb-20"]
)

mkdservice.deleteData(deleteData)

Delte Data in a Bid Ask Time Series

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

cfg = ArtesianConfg()
mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
deleteData = MarketData.DeleteData(
    ID= mkdid,
    timezone='CET',
    rangeStart=datetime(2020, 1, 1, 0),
    rangeEnd=datetime(2020, 1, 3, 0),
    product=["Feb-20"]
)

mkdservice.deleteData(deleteData)

Delete Data in an Auction Time Series

from Artesian import ArtesianConfig, Granularity, MarketData
from Artesian.MarketData import AggregationRule
from datetime import datetime
from dateutil import tz

cfg = ArtesianConfg()

mkservice = MarketData.MarketDataService(cfg)

mkdid = MarketData.MarketDataIdentifier('PROVIDER', 'MARKETDATANAME')
deleteData = MarketData.DeleteData(
    ID=mkdid,
    timezone='CET',
    rangeStart=datetime(2020, 1, 1, 6),
    rangeEnd=datetime(2020, 1, 1, 18),
)

mkdservice.deleteData(deleteData)

Jupyter Support

Artesian SDK uses asyncio internally, this causes a conflict with Jupyter. You can work around this issue by add the following at the beginning of the notebook.

!pip install nest_asyncio

import nest_asyncio
nest_asyncio.apply()

Issue #3397 with workaround

Name		Name	Last commit message	Last commit date
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.github		.github
.vscode		.vscode
samples		samples
src/Artesian		src/Artesian
tests		tests
.flake8		.flake8
.gitignore		.gitignore
CODEOWNERS		CODEOWNERS
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml
renovate.json		renovate.json

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ARKlab/Artesian.SDK-Python

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Artesian.SDK

Getting Started

Installation

How to use

BREAKING CHANGES: Upgrade v2->v3

Python Version >=3.8

SubPackaging

Enum entries Casing

MarketData QueryService

Actual Time Series Extraction

Versioned Time Series Extraction

Market Assessment Time Series Extraction

Bid Ask Time Series Extraction

Auction Time Series Extraction

Extraction Windows

Filler Strategy

Query written Versions or Products

Search the MarketData collection with faceted results

GME Public Offer

Extract GME Public Offer

Unit of Measure Conversion Functionality

Overview

Conversion Logic

Example: Power to Energy Conversion

MarketData Registration with UnitOfMeasure

UnitOfMeasure Conversion and Aggregation Rule Override

UnitOfMeasure Conversion

Aggregation Rule Override

Example: Convert power (MW) to energy (MWh):

Composite Unit Example: MWh/day

CheckConversion: Validate Unit Compatibility

Extraction Options

Date

Purpose

Status

Operator

Unit

Market

Scope

BAType

Zone

UnitType

Generation Type

Pagination

UnitOfMeasure (for Actual and Versioned Time Series)

AggregationRule (for Actual and Versioned Time Series)

Write Data in Artesian

Write Data in an Actual Time Series

Write Data in a Versioned Time Series

Write Data in a Market Assessment Time Series

Write Data in a Bid Ask Time Series

Write Data in an Auction Time Series

Delete Data in Artesian

Delete MarketData in Artesian

Delete Data in an Actual Time Series

Delete Data in an Versioned Time Series

Delte Data in a Market Assessment Time Series

Delte Data in a Bid Ask Time Series

Delete Data in an Auction Time Series

Jupyter Support

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Example: Convert power (`MW`) to energy (`MWh`):

Composite Unit Example: `MWh/day`

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