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artesian-sdk-3.0.5.dev0
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مشاهده بیشترتوضیحات
Library provides read access to the Artesian API
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | artesian-sdk-3.0.5.dev0 |
| نام | artesian-sdk |
| نسخه کتابخانه | 3.0.5.dev0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Ark S.r.l. |
| ایمیل نویسنده | arklab@ark-energy.eu |
| آدرس صفحه اصلی | https://github.com/ARKlab/Artesian.SDK-Python |
| آدرس اینترنتی | https://pypi.org/project/artesian-sdk/ |
| مجوز | MIT |
# Artesian.SDK
This Library provides read access to the Artesian API
## Getting Started
### Installation
You can install the package directly from [pip](https://pypi.org/project/artesian-sdk/).
```Python
pip install artesian-sdk
```
Alternatively, to install this package go to the [release page](https://github.com/ARKlab/Artesian.SDK-Python/releases) .
### How to use
The Artesian.SDK instance can be configured using API-Key authentication
```Python
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:
```Python
from Artesian import *
cfg = ArtesianConfig("https://arkive.artesian.cloud/{tenantName}/", "{api-key}")
qs = QueryService(cfg)
```
now you have to:
```Python
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
```Python
.inGranularity(Granularity.HOUR) \
```
now is
```Python
.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
```Python
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.
<table>
<tr><th>Actual Query</th><th>Description</th></tr>
<tr><td>Market Data ID</td><td>Provide a market data id or set of market data id's to query</td></tr>
<tr><td>Time Granularity</td><td>Specify the granularity type</td></tr>
<tr><td>Time Extraction Window</td><td>An extraction time window for data to be queried</td></tr>
</table>
[Go to Time Extraction window section](#artesian-sdk-extraction-windows)
## Versioned Time Series Extraction
```Python
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.
<table>
<tr><th>Versioned Query</th><th>Description</th></tr>
<tr><td>Market Data ID</td><td>Provide a market data id or set of market data id's to query</td></tr>
<tr><td>Time Granularity</td><td>Specify the granularity type</td></tr>
<tr><td>Versioned Time Extraction Window</td><td>Versioned extraction time window</td></tr>
<tr><td>Time Extraction Window</td><td>An extraction time window for data to be queried</td></tr>
</table>
[Go to Time Extraction window section](#artesian-sdk-extraction-windows)
## Market Assessment Time Series Extraction
```Python
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.
<table>
<tr><th>Mas Query</th><th>Description</th></tr>
<tr><td>Market Data ID</td><td>Provide a market data id or set of market data id's to query</td></tr>
<tr><td>Product</td><td>Provide a product or set of products</td></tr>
<tr><td>Time Extraction Window</td><td>An extraction time window for data to be queried </td></tr>
</table>
[Go to Time Extraction window section](#artesian-sdk-extraction-windows)
## Bid Ask Time Series Extraction
```Python
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.
<table>
<tr><th>Mas Query</th><th>Description</th></tr>
<tr><td>Market Data ID</td><td>Provide a market data id or set of market data id's to query</td></tr>
<tr><td>Product</td><td>Provide a product or set of products</td></tr>
<tr><td>Time Extraction Window</td><td>An extraction time window for data to be queried </td></tr>
</table>
[Go to Time Extraction window section](#artesian-sdk-extraction-windows)
## Auction Time Series Extraction
```Python
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.
<table>
<tr><th>Auction Query</th><th>Description</th></tr>
<tr><td>Market Data ID</td><td>Provide a market data id or set of market data id's to query</td></tr>
<tr><td>Time Extraction Window</td><td>An extraction time window for data to be queried</td></tr>
</table>
[Go to Time Extraction window section](#artesian-sdk-extraction-windows)
## Extraction Windows
Extraction window types for queries.
Date Range
```Python
.inAbsoluteDateRange("2018-08-01", "2018-08-10")
```
Relative Interval
```Python
.inRelativeInterval(RelativeInterval.RollingWeek)
```
Period
```Python
.inRelativePeriod("P5D")
```
Period Range
```Python
.inRelativePeriodRange("P-3D", "P10D")
```
## Filler Strategy
All extraction types (Actual,Versioned, Market Assessment and BidAsk) have an optional filler strategy.
```python
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.
```python
.withFillNull()
```
Use 'None' to not fill at all: timepoints are not returned if not present.
```python
.withFillNone()
```
Custom Value can be provided for each MarketDataType.
Custom Value for Actual extraction type.
```python
.withFillCustomValue(123)
```
Custom Value for BidAsk extraction type.
```python
.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.
```python
.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.
```python
.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.
```python
.withFillLatestValue("P5D")
```
```python
.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.
```python
.withFillLatestValue("P5D", "True")
```
# GME Public Offer
Artesian support Query over GME Public Offers which comes in a custom and dedicated format.
## Extract GME Public Offer
```Python
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.
<table>
<tr><th>GME Public Offer Query</th><th>Description</th></tr>
<tr><td>Time Extraction Window</td><td>An extraction time window for data to be queried</td></tr>
<tr><td>Market</td><td>Provide a market or set of markets to query</td></tr>
<tr><td>Status</td><td>Provide a status or set of statuses to query</td></tr>
<tr><td>Purpose</td><td>Provide a purpose or set of purposes to query</td></tr>
<tr><td>Zone</td><td>Provide a zone to query</td></tr>
</table>
## Extraction Options
Extraction options for GME Public Offer queries.
### Date
```Python
.forDate("2020-04-01")
```
### Purpose
```Python
.forPurpose(Purpose.BID)
```
### Status
```Python
.forStatus(Status.ACC)
```
### Operator
```Python
.forOperator(["Operator_1", "Operator_2"])
```
### Unit
```Python
.forUnit(["UP_1", "UP_2"])
```
### Market
```Python
.forMarket([Market.MGP])
```
### Scope
```Python
.forScope([Scope.ACC, Scope.RS])
```
### BAType
```Python
.forBAType([BAType.NETT, BAType.NERV])
```
### Zone
```Python
.forZone([Zone.NORD])
```
### UnitType
```Python
.forUnitType([UnitType.UCV, UnitType.UPV])
```
### Generation Type
```Python
.forGenerationType(GenerationType.GAS)
```
### Pagination
```Python
.withPagination(1,10)
```
# 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
```Python
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']
}
)
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)
```
In case we want to write an hourly (or lower) time series the timezone for the upsert data must be UTC:
```Python
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
```Python
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
```Python
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
```Python
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
```Python
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 MarketData in Artesian
Using the MarketDataService is possible to delete MarketData and its curves.
```Python
from Artesian import ArtesianConfig
from Artesian.MarketData import MarketDataService
cfg = ArtesianConfg()
mkservice = MarketDataService(cfg)
mkservice.deleteMarketData(100042422)
```
## 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.
```Python
from Artesian.MarketData import MarketDataService
mds = MarketDataService(cfg)
```
To list MarketData curves
```Python
page = 1
pageSize = 100
res = mds.readCurveRange(100042422, page, pageSize, versionFrom="2016-12-20" , versionTo="2019-03-12")
```
# 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.
```python
!pip install nest_asyncio
import nest_asyncio
nest_asyncio.apply()
```
[Issue #3397 with workaround](https://github.com/jupyter/notebook/issues/3397#issuecomment-419386811)
## Links
* [Github](https://github.com/ARKlab/Artesian.SDK-Python)
* [Ark Energy](http://www.ark-energy.eu/)
* [Artesian Portal](https://portal.artesian.cloud)
نیازمندی
| مقدار | نام |
|---|---|
| - | requests |
| - | six |
| >=1.6.1 | jsons |
| - | python-dateutil |
| - | twine |
| - | wheel |
| >=0.18 | responses |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl artesian-sdk-3.0.5.dev0:
pip install artesian-sdk-3.0.5.dev0.whl
نصب پکیج tar.gz artesian-sdk-3.0.5.dev0:
pip install artesian-sdk-3.0.5.dev0.tar.gz