spgci 0.0.22

SPGCI is an API Client for the S&P Commodity Insights REST API

SPGCI

Python Client for the S&P Global Commodity Insights API.

Installation

Requires Python >= 3.8.10.

pip install spgci

Getting Started

    import spgci as ci

    ci.set_credentials(<username>, <password>, <appkey>)
    mdd = ci.MarketData()

    symbols = ["PCAAS00", "PCAAT00"]
    mdd.get_assessments_by_symbol_current(symbol=symbols)

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Alternatively, you can set your credentials via Environment Variables and omit the set_credentials call:

Environment Variable	Description
SPGCI_USERNAME	Your Username
SPGCI_PASSWORD	Your Password
SPGCI_APPKEY	Your AppKey

Features

• Automatically generates token prior to making request.
• Returns data as a pandas DataFrame (set raw=False to get the raw request.response object).
• Can auto-paginate response and concatenates into a single DataFrame (set paginate=True to enable).
• Sets datatype for date and datetime fields in DataFrame.
• Composes nicely with native python/pandas types. Arguments support lists and pd.Series which are automatically converted into filter expressions.

Datasets Supported

Market Data

import spgci as ci

mdd = ci.MarketData()

mdd.get_symbols(commodity="Crude oil")
# DataFrame of symbols with commodity = "Crude oil".

mdd.get_mdcs(subscribed_only=True)
# DataFrame of all Market Data Categories you are subscribed to.

mdd.get_assessments_by_mdc_current(mdc="ET")
# DataFrame of current assessments for all symbols in the Market Data Category "ET".

Forward Curves

import spgci as ci

fc = ci.ForwardCurves()

fc.get_curves(
    commodity=["Benzene", "Crude oil"],
    derivative_maturity_frequency="Month"
    )
# DataFrame of all curves with commodity in ("Benzene", "Crude Oil") and have a Monthly frequency.

fc.get_assessments(curve_code=["CN003", "CN006"])
# DataFrame of the latest assessments for all symbols in the curves ("CN003", "CN006").

Energy Price Forecast

import spgci as ci

epf = ci.EnergyPriceForecast()

epf.get_prices_shortterm(symbol="PCAAS00", month=[10, 11, 12])
# DataFrame of monthly forecasts for the symbol "PCAAS00" in the last 3 months of the year.

epf.get_prices_longterm(year=[2020, 2021], sector="Energy Transition", delivery_region="Europe")
# DataFrame of the annual forecasts for the years in ("2020", "2021"), where the sector is "Energy Transition" and the delivery region is "Europe".

EWindow Market Data

import spgci as ci
from datetime import date

ewmd = ci.EWindowMarketData()

ewmd.get_markets()
# DataFrame of Markets.

d = date(2023,2,13)
ewmd.get_botes(market=["EU BFOE", "US MidWest"], order_time=d)
# DataFrame of all BOTes in the markets ("EU BFOE", "US MidWest") on Feb 13, 2023.

World Oil Supply

import spgci as ci

wos = ci.WorldOilSupply()

countries = wos.get_reference_data(type=wos.RefTypes.Countries)
# DataFrame of all countries.

wos.get_ownership(country=countries['countryName'][:3], year=2040)
# DataFrame of Ownership for the first three countries from the countries endpoint and year "2040".

World Refinery Database

import spgci as ci

wrd = ci.WorldRefineryData()

wrd.get_yields(year=2020, owner="BP")
# DataFrame of yields for the year "2020" where "BP" is the refinery owner.

ref = wrd.get_reference_data(type=wrd.RefTypes.Refineries)
# DataFrame of all refineries.

az = ref[ref['Name'].str.contains("Al-Zour")]
wrd.get_runs(refinery_id=az["Id"])
# DataFrame of runs for the refineries with "Al-Zour" in the name.

wrd.get_outages(refinery_id=245)
# DataFrame of outages for refineryId 245.

Insights

import spgci as ci

ni = ci.Insights()

ni.get_stories(q="Suez", content_type=ni.ContentType.MarketCommentary)
# DataFrame of articles related to "Suez" where the content type is "Market Commentary".

ni.get_subscriber_notes(q="Naptha")
# DataFrame of all subscriber notes related to "Naptha".

ni.get_heards(q="Steel", content_type=ni.HeardsContentType.Heard, geography=['Europe', 'Middle East'], strip_html=True)
# DataFrame of all Heards related to "Steel" where the geography is in ("Europe", "Middle East") with HTML Tags removed from the headline and body.

Global Oil Demand

import spgci as ci

od = ci.GlobalOilDemand()

od.get_demand(country="Cambodia", product=["Naphtha", "Ethane"])
# DataFrame of forecast monthly demand for ("Naphtha", "Ethane") for Cambodia.

products = od.get_reference_data(type=od.RefTypes.Products)
# DataFrame of all "products" covered by Global Oil Demand dataset.

od.get_demand(product=products["productName"][:3], year_gte=2023)
# DataFrame of forecast monthly demand for the first 3 products in the previous DataFrame and the year >= 2023.

od.get_demand_archive(scenario_id=150, country="Norway")
# DataFrame of an archived (March 2023) forecast of monthly oil demand for Norway.

Disclaimer

Please note that by using this Alpha offering, you agree that you are participating in a prototype to develop these software development kits (“Alpha SDKs”) and that your participation and use of the Alpha SDKs is at your own risk. You will be liable for all activities by anyone using your application in connection with the Alpha SDKs.

If you are dissatisfied with the Alpha SDKs, your sole remedy is to stop using it, and S&P Global Commodity Insights (“SPGCI”) will not pay you any damages (direct, indirect, special, or consequential) in connection with your use of the Alpha SDKs. Commodity Insights SPGCI disclaims any and all representations and warranties, express or implied, including but not limited to any warranties of merchantability or fitness for a particular purpose or use as to the Alpha SDKs, and does not guarantee the adequacy, accuracy, timeliness or completeness of the Alpha SDKs or any component of them.