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Add OECD unemployment (+more) + data cache idea (#5917)
* Add OECD unemployment + data cache idea * Docstring edits * Tests * Add composite leading indicator * i truested ruff --fix too much * Add short + long term interest rates * pylint * pylint2 * was not committing. my b * add csv cache + make it default. remove pyarrow from poetry * Clean up * Didnt run on this file. My bad * Why was this ignored /shrug * pylint --------- Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com>
This commit is contained in:
James Maslek
@@ -0,0 +1,37 @@
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"""Composite Leading Indicator Standard Model."""
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from datetime import date as dateType
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from typing import Optional
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from pydantic import Field
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from openbb_core.provider.abstract.data import Data
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from openbb_core.provider.abstract.query_params import QueryParams
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from openbb_core.provider.utils.descriptions import (
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DATA_DESCRIPTIONS,
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QUERY_DESCRIPTIONS,
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)
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class CLIQueryParams(QueryParams):
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"""Composite Leading Indicator Query."""
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start_date: Optional[dateType] = Field(
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default=None, description=QUERY_DESCRIPTIONS.get("start_date")
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)
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end_date: Optional[dateType] = Field(
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default=None, description=QUERY_DESCRIPTIONS.get("end_date")
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)
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class CLIData(Data):
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"""Composite Leading Indicator Data."""
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date: Optional[dateType] = Field(
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default=None, description=DATA_DESCRIPTIONS.get("date")
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)
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value: Optional[float] = Field(default=None, description="CLI value")
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country: Optional[str] = Field(
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default=None,
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description="Country for which CLI is given",
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)
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@@ -0,0 +1,40 @@
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"""Long Term Interest Rates Standard Model."""
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from datetime import date as dateType
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from typing import Optional
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from pydantic import Field
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from openbb_core.provider.abstract.data import Data
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from openbb_core.provider.abstract.query_params import QueryParams
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from openbb_core.provider.utils.descriptions import (
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DATA_DESCRIPTIONS,
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QUERY_DESCRIPTIONS,
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)
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class LTIRQueryParams(QueryParams):
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"""Long Term Interest Rates Query."""
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start_date: Optional[dateType] = Field(
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default=None, description=QUERY_DESCRIPTIONS.get("start_date")
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)
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end_date: Optional[dateType] = Field(
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default=None, description=QUERY_DESCRIPTIONS.get("end_date")
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)
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class LTIRData(Data):
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"""Long Term Interest Rates Data."""
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date: Optional[dateType] = Field(
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default=None, description=DATA_DESCRIPTIONS.get("date")
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)
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value: Optional[float] = Field(
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default=None,
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description="Interest rate (given as a whole number, i.e 10=10%)",
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)
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country: Optional[str] = Field(
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default=None,
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description="Country for which interest rate is given",
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)
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@@ -0,0 +1,40 @@
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"""Short Term Interest Rates Standard Model."""
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from datetime import date as dateType
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from typing import Optional
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from pydantic import Field
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from openbb_core.provider.abstract.data import Data
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from openbb_core.provider.abstract.query_params import QueryParams
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from openbb_core.provider.utils.descriptions import (
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DATA_DESCRIPTIONS,
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QUERY_DESCRIPTIONS,
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)
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class STIRQueryParams(QueryParams):
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"""Short Term Interest Rates Query."""
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start_date: Optional[dateType] = Field(
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default=None, description=QUERY_DESCRIPTIONS.get("start_date")
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)
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end_date: Optional[dateType] = Field(
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default=None, description=QUERY_DESCRIPTIONS.get("end_date")
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)
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class STIRData(Data):
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"""Short Term Interest Rates Data."""
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date: Optional[dateType] = Field(
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default=None, description=DATA_DESCRIPTIONS.get("date")
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)
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value: Optional[float] = Field(
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default=None,
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description="Interest rate (given as a whole number, i.e 10=10%)",
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)
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country: Optional[str] = Field(
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default=None,
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description="Country for which interest rate is given",
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)
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@@ -0,0 +1,40 @@
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"""Unemployment Standard Model."""
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from datetime import date as dateType
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from typing import Optional
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from pydantic import Field
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from openbb_core.provider.abstract.data import Data
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from openbb_core.provider.abstract.query_params import QueryParams
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from openbb_core.provider.utils.descriptions import (
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DATA_DESCRIPTIONS,
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QUERY_DESCRIPTIONS,
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)
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class UnemploymentQueryParams(QueryParams):
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"""Unemployment Query."""
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start_date: Optional[dateType] = Field(
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default=None, description=QUERY_DESCRIPTIONS.get("start_date")
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)
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end_date: Optional[dateType] = Field(
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default=None, description=QUERY_DESCRIPTIONS.get("end_date")
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)
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class UnemploymentData(Data):
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"""Unemployment Data."""
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date: Optional[dateType] = Field(
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default=None, description=DATA_DESCRIPTIONS.get("date")
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)
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value: Optional[float] = Field(
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default=None,
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description="Unemployment rate (given as a whole number, i.e 10=10%)",
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)
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country: Optional[str] = Field(
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default=None,
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description="Country for which unemployment rate is given",
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)
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@@ -372,3 +372,109 @@ def test_economy_money_measures(params, headers):
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result = requests.get(url, headers=headers, timeout=10)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params",
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[
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({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
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(
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{
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"country": "united_states",
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"sex": "total",
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"frequency": "monthly",
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"age": "total",
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"seasonal_adjustment": True,
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"provider": "oecd",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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}
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),
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],
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)
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@pytest.mark.integration
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def test_economy_unemployment(params, headers):
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params = {p: v for p, v in params.items() if v}
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/economy/unemployment?{query_str}"
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result = requests.get(url, headers=headers, timeout=10)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params",
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[
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({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
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(
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{
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"country": "united_states",
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"provider": "oecd",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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}
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),
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],
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)
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@pytest.mark.integration
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def test_economy_composite_leading_indicator(params, headers):
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params = {p: v for p, v in params.items() if v}
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/economy/composite_leading_indicator?{query_str}"
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result = requests.get(url, headers=headers, timeout=10)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params",
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[
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({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
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(
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{
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"country": "united_states",
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"frequency": "monthly",
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"provider": "oecd",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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}
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),
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],
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)
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@pytest.mark.integration
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def test_economy_short_term_interest_rate(params, headers):
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params = {p: v for p, v in params.items() if v}
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/economy/short_term_interest_rate?{query_str}"
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result = requests.get(url, headers=headers, timeout=10)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@parametrize(
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"params",
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[
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({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
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(
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{
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"country": "united_states",
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"frequency": "monthly",
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"provider": "oecd",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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}
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),
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],
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)
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@pytest.mark.integration
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def test_economy_long_term_interest_rate(params, headers):
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params = {p: v for p, v in params.items() if v}
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query_str = get_querystring(params, [])
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url = f"http://0.0.0.0:8000/api/v1/economy/long_term_interest_rate?{query_str}"
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result = requests.get(url, headers=headers, timeout=10)
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assert isinstance(result, requests.Response)
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assert result.status_code == 200
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@@ -328,3 +328,105 @@ def test_economy_money_measures(params, obb):
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params",
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[
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({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
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(
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{
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"country": "united_states",
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"sex": "total",
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"frequency": "monthly",
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"age": "total",
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"seasonal_adjustment": True,
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"provider": "oecd",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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}
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),
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],
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)
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@pytest.mark.integration
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def test_economy_unemployment(params, obb):
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params = {p: v for p, v in params.items() if v}
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result = obb.economy.unemployment(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params",
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[
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({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
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(
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{
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"country": "united_states",
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"provider": "oecd",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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}
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),
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],
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)
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@pytest.mark.integration
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def test_economy_composite_leading_indicator(params, obb):
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params = {p: v for p, v in params.items() if v}
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result = obb.economy.composite_leading_indicator(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params",
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[
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({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
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(
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{
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"country": "united_states",
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"frequency": "monthly",
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"provider": "oecd",
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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}
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),
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],
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)
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@pytest.mark.integration
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def test_economy_short_term_interest_rate(params, obb):
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params = {p: v for p, v in params.items() if v}
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result = obb.economy.short_term_interest_rate(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@parametrize(
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"params",
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[
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({"start_date": "2023-01-01", "end_date": "2023-06-06"}),
|
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(
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{
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"country": "united_states",
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"frequency": "monthly",
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"provider": "oecd",
|
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"start_date": "2023-01-01",
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"end_date": "2023-06-06",
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}
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),
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],
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)
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@pytest.mark.integration
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def test_economy_long_term_interest_rate(params, obb):
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params = {p: v for p, v in params.items() if v}
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result = obb.economy.long_term_interest_rate(**params)
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assert result
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assert isinstance(result, OBBject)
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assert len(result.results) > 0
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@@ -96,5 +96,67 @@ async def money_measures(
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standard_params: StandardParams,
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extra_params: ExtraParams,
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) -> OBBject[BaseModel]:
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"""Money Measures."""
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"""Money Measures (M1/M2 and components)."""
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return await OBBject.from_query(Query(**locals()))
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@router.command(model="Unemployment")
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async def unemployment(
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cc: CommandContext,
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provider_choices: ProviderChoices,
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standard_params: StandardParams,
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extra_params: ExtraParams,
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) -> OBBject[BaseModel]:
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"""Global unemployment data."""
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return await OBBject.from_query(Query(**locals()))
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@router.command(model="CLI")
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async def composite_leading_indicator(
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cc: CommandContext,
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provider_choices: ProviderChoices,
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standard_params: StandardParams,
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extra_params: ExtraParams,
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) -> OBBject[BaseModel]:
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"""The composite leading indicator (CLI) is designed to provide early signals of turning points
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in business cycles showing fluctuation of the economic activity around its long term potential level.
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CLIs show short-term economic movements in qualitative rather than quantitative terms.
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"""
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return await OBBject.from_query(Query(**locals()))
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@router.command(model="STIR")
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async def short_term_interest_rate(
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cc: CommandContext,
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provider_choices: ProviderChoices,
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standard_params: StandardParams,
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extra_params: ExtraParams,
|
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) -> OBBject[BaseModel]:
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"""
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Short-term interest rates are the rates at which short-term borrowings are effected between
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financial institutions or the rate at which short-term government paper is issued or traded in the market.
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Short-term interest rates are generally averages of daily rates, measured as a percentage.
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Short-term interest rates are based on three-month money market rates where available.
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Typical standardised names are "money market rate" and "treasury bill rate".
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"""
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return await OBBject.from_query(Query(**locals()))
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|
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@router.command(model="STIR")
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async def long_term_interest_rate(
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cc: CommandContext,
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provider_choices: ProviderChoices,
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standard_params: StandardParams,
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extra_params: ExtraParams,
|
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) -> OBBject[BaseModel]:
|
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"""
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Long-term interest rates refer to government bonds maturing in ten years.
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Rates are mainly determined by the price charged by the lender, the risk from the borrower and the
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fall in the capital value. Long-term interest rates are generally averages of daily rates,
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measured as a percentage. These interest rates are implied by the prices at which the government bonds are
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traded on financial markets, not the interest rates at which the loans were issued.
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In all cases, they refer to bonds whose capital repayment is guaranteed by governments.
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Long-term interest rates are one of the determinants of business investment.
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Low long-term interest rates encourage investment in new equipment and high interest rates discourage it.
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Investment is, in turn, a major source of economic growth."""
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return await OBBject.from_query(Query(**locals()))
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||||
@@ -20,6 +20,7 @@ class ROUTER_economy(Container):
|
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/gdp
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money_measures
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risk_premium
|
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unemployment
|
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"""
|
||||
|
||||
def __repr__(self) -> str:
|
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@@ -571,7 +572,7 @@ class ROUTER_economy(Container):
|
||||
provider: Optional[Literal["federal_reserve"]] = None,
|
||||
**kwargs
|
||||
) -> OBBject:
|
||||
"""Money Measures.
|
||||
"""Money Measures (M1/M2 and components.
|
||||
|
||||
Parameters
|
||||
----------
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||||
@@ -694,3 +695,87 @@ class ROUTER_economy(Container):
|
||||
extra_params=kwargs,
|
||||
)
|
||||
)
|
||||
|
||||
@validate
|
||||
def unemployment(
|
||||
self,
|
||||
start_date: Annotated[
|
||||
Union[datetime.date, None, str],
|
||||
OpenBBCustomParameter(
|
||||
description="Start date of the data, in YYYY-MM-DD format."
|
||||
),
|
||||
] = None,
|
||||
end_date: Annotated[
|
||||
Union[datetime.date, None, str],
|
||||
OpenBBCustomParameter(
|
||||
description="End date of the data, in YYYY-MM-DD format."
|
||||
),
|
||||
] = None,
|
||||
provider: Optional[Literal["oecd"]] = None,
|
||||
**kwargs
|
||||
) -> OBBject:
|
||||
"""Global unemployment data.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
start_date : Optional[datetime.date]
|
||||
Start date of the data, in YYYY-MM-DD format.
|
||||
end_date : Optional[datetime.date]
|
||||
End date of the data, in YYYY-MM-DD format.
|
||||
provider : Optional[Literal['oecd']]
|
||||
The provider to use for the query, by default None.
|
||||
If None, the provider specified in defaults is selected or 'oecd' if there is
|
||||
no default.
|
||||
country : Literal['colombia', 'new_zealand', 'united_kingdom', 'italy', 'luxembourg', 'euro_area19', 'sweden', 'oecd', 'south_africa', 'denmark', 'canada', 'switzerland', 'slovakia', 'hungary', 'portugal', 'spain', 'france', 'czech_republic', 'costa_rica', 'japan', 'slovenia', 'russia', 'austria', 'latvia', 'netherlands', 'israel', 'iceland', 'united_states', 'ireland', 'mexico', 'germany', 'greece', 'turkey', 'australia', 'poland', 'south_korea', 'chile', 'finland', 'european_union27_2020', 'norway', 'lithuania', 'euro_area20', 'estonia', 'belgium', 'brazil', 'indonesia', 'all']
|
||||
Country to get GDP for. (provider: oecd)
|
||||
sex : Literal['total', 'male', 'female']
|
||||
Sex to get unemployment for. (provider: oecd)
|
||||
frequency : Literal['monthly', 'quarterly', 'annual']
|
||||
Frequency to get unemployment for. (provider: oecd)
|
||||
age : Literal['total', '15-24', '15-64', '25-54', '55-64']
|
||||
Age group to get unemployment for. Total indicates 15 years or over (provider: oecd)
|
||||
seasonal_adjustment : bool
|
||||
Whether to get seasonally adjusted unemployment. Defaults to False. (provider: oecd)
|
||||
|
||||
Returns
|
||||
-------
|
||||
OBBject
|
||||
results : List[Unemployment]
|
||||
Serializable results.
|
||||
provider : Optional[Literal['oecd']]
|
||||
Provider name.
|
||||
warnings : Optional[List[Warning_]]
|
||||
List of warnings.
|
||||
chart : Optional[Chart]
|
||||
Chart object.
|
||||
extra: Dict[str, Any]
|
||||
Extra info.
|
||||
|
||||
Unemployment
|
||||
------------
|
||||
date : Optional[date]
|
||||
The date of the data.
|
||||
value : Optional[float]
|
||||
Unemployment rate (given as a whole number, i.e 10=10%)
|
||||
country : Optional[str]
|
||||
Country for which unemployment rate is given
|
||||
|
||||
Example
|
||||
-------
|
||||
>>> from openbb import obb
|
||||
>>> obb.economy.unemployment()
|
||||
""" # noqa: E501
|
||||
|
||||
return self._run(
|
||||
"/economy/unemployment",
|
||||
**filter_inputs(
|
||||
provider_choices={
|
||||
"provider": provider,
|
||||
},
|
||||
standard_params={
|
||||
"start_date": start_date,
|
||||
"end_date": end_date,
|
||||
},
|
||||
extra_params=kwargs,
|
||||
)
|
||||
)
|
||||
|
||||
@@ -1,9 +1,13 @@
|
||||
"""FRED provider module."""
|
||||
"""OECD provider module."""
|
||||
|
||||
from openbb_core.provider.abstract.provider import Provider
|
||||
from openbb_oecd.models.composite_leading_indicator import OECDCLIFetcher
|
||||
from openbb_oecd.models.gdp_forecast import OECDGdpForecastFetcher
|
||||
from openbb_oecd.models.gdp_nominal import OECDGdpNominalFetcher
|
||||
from openbb_oecd.models.gdp_real import OECDGdpRealFetcher
|
||||
from openbb_oecd.models.long_term_interest_rate import OECDLTIRFetcher
|
||||
from openbb_oecd.models.short_term_interest_rate import OECDSTIRFetcher
|
||||
from openbb_oecd.models.unemployment import OECDUnemploymentFetcher
|
||||
|
||||
oecd_provider = Provider(
|
||||
name="oecd",
|
||||
@@ -13,5 +17,9 @@ oecd_provider = Provider(
|
||||
"GdpNominal": OECDGdpNominalFetcher,
|
||||
"GdpReal": OECDGdpRealFetcher,
|
||||
"GdpForecast": OECDGdpForecastFetcher,
|
||||
"Unemployment": OECDUnemploymentFetcher,
|
||||
"CLI": OECDCLIFetcher,
|
||||
"STIR": OECDSTIRFetcher,
|
||||
"LTIR": OECDLTIRFetcher,
|
||||
},
|
||||
)
|
||||
|
||||
@@ -0,0 +1,130 @@
|
||||
"""OECD CLI Data."""
|
||||
|
||||
import re
|
||||
from datetime import date, timedelta
|
||||
from typing import Any, Dict, List, Literal, Optional, Union
|
||||
|
||||
from openbb_core.provider.abstract.fetcher import Fetcher
|
||||
from openbb_core.provider.standard_models.composite_leading_indicator import (
|
||||
CLIData,
|
||||
CLIQueryParams,
|
||||
)
|
||||
from openbb_oecd.utils import helpers
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
cli_mapping = {
|
||||
"USA": "united_states",
|
||||
"GBR": "united_kingdom",
|
||||
"JPN": "japan",
|
||||
"MEX": "mexico",
|
||||
"IDN": "indonesia",
|
||||
"AUS": "australia",
|
||||
"BRA": "brazil",
|
||||
"CAN": "canada",
|
||||
"ITA": "italy",
|
||||
"DEU": "germany",
|
||||
"TUR": "turkey",
|
||||
"FRA": "france",
|
||||
"ZAF": "south_africa",
|
||||
"KOR": "south_korea",
|
||||
"ESP": "spain",
|
||||
"IND": "india",
|
||||
"CHN": "china",
|
||||
"G7": "g7",
|
||||
"G20": "g20",
|
||||
}
|
||||
|
||||
|
||||
countries = tuple(cli_mapping.values()) + ("all",)
|
||||
CountriesLiteral = Literal[countries] # type: ignore
|
||||
country_to_code = {v: k for k, v in cli_mapping.items()}
|
||||
|
||||
|
||||
class OECDCLIQueryParams(CLIQueryParams):
|
||||
"""OECD CLI Query."""
|
||||
|
||||
country: CountriesLiteral = Field(
|
||||
description="Country to get GDP for.", default="united_states"
|
||||
)
|
||||
|
||||
|
||||
class OECDCLIData(CLIData):
|
||||
"""OECD CLI Data."""
|
||||
|
||||
@field_validator("date", mode="before")
|
||||
@classmethod
|
||||
def date_validate(cls, in_date: Union[date, str]): # pylint: disable=E0213
|
||||
"""Validate value."""
|
||||
if isinstance(in_date, str):
|
||||
# i.e 2022-Q1
|
||||
if re.match(r"\d{4}-Q[1-4]$", in_date):
|
||||
year, quarter = in_date.split("-")
|
||||
_year = int(year)
|
||||
if quarter == "Q1":
|
||||
return date(_year, 3, 31)
|
||||
if quarter == "Q2":
|
||||
return date(_year, 6, 30)
|
||||
if quarter == "Q3":
|
||||
return date(_year, 9, 30)
|
||||
if quarter == "Q4":
|
||||
return date(_year, 12, 31)
|
||||
# Now match if it is monthly, i.e 2022-01
|
||||
elif re.match(r"\d{4}-\d{2}$", in_date):
|
||||
year, month = map(int, in_date.split("-"))
|
||||
if month == 12:
|
||||
return date(year, month, 31)
|
||||
next_month = date(year, month + 1, 1)
|
||||
return date(next_month.year, next_month.month, 1) - timedelta(days=1)
|
||||
# Now match if it is yearly, i.e 2022
|
||||
elif re.match(r"\d{4}$", in_date):
|
||||
return date(int(in_date), 12, 31)
|
||||
# If the input date is a year
|
||||
if isinstance(in_date, int):
|
||||
return date(in_date, 12, 31)
|
||||
|
||||
return in_date
|
||||
|
||||
|
||||
class OECDCLIFetcher(Fetcher[OECDCLIQueryParams, List[OECDCLIData]]):
|
||||
"""Transform the query, extract and transform the data from the OECD endpoints."""
|
||||
|
||||
@staticmethod
|
||||
def transform_query(params: Dict[str, Any]) -> OECDCLIQueryParams:
|
||||
"""Transform the query."""
|
||||
transformed_params = params.copy()
|
||||
if transformed_params["start_date"] is None:
|
||||
transformed_params["start_date"] = date(1950, 1, 1)
|
||||
if transformed_params["end_date"] is None:
|
||||
transformed_params["end_date"] = date(date.today().year, 12, 31)
|
||||
|
||||
return OECDCLIQueryParams(**transformed_params)
|
||||
|
||||
@staticmethod
|
||||
def extract_data(
|
||||
query: OECDCLIQueryParams, # pylint: disable=W0613
|
||||
credentials: Optional[Dict[str, str]],
|
||||
**kwargs: Any,
|
||||
) -> Dict:
|
||||
"""Return the raw data from the OECD endpoint."""
|
||||
url = "https://sdmx.oecd.org/public/rest/data/OECD.SDD.STES,DSD_KEI@DF_KEI,4.0/..LI...."
|
||||
data = helpers.get_possibly_cached_data(
|
||||
url, function="economy_composite_leading_indicator"
|
||||
)
|
||||
|
||||
if query.country != "all":
|
||||
data = data.query(f"REF_AREA == '{country_to_code[query.country]}'")
|
||||
|
||||
# Filter down
|
||||
data = data.reset_index(drop=True)[["REF_AREA", "TIME_PERIOD", "VALUE"]].rename(
|
||||
columns={"REF_AREA": "country", "TIME_PERIOD": "date", "VALUE": "value"}
|
||||
)
|
||||
data["country"] = data["country"].map(cli_mapping)
|
||||
|
||||
return data.to_dict(orient="records")
|
||||
|
||||
@staticmethod
|
||||
def transform_data(
|
||||
query: OECDCLIQueryParams, data: Dict, **kwargs: Any
|
||||
) -> List[OECDCLIData]:
|
||||
"""Transform the data from the OECD endpoint."""
|
||||
return [OECDCLIData.model_validate(d) for d in data]
|
||||
@@ -0,0 +1,163 @@
|
||||
"""OECD Long Term Interest Rate Rate Data."""
|
||||
|
||||
import re
|
||||
from datetime import date, timedelta
|
||||
from typing import Any, Dict, List, Literal, Optional, Union
|
||||
|
||||
from openbb_core.provider.abstract.fetcher import Fetcher
|
||||
from openbb_core.provider.standard_models.long_term_interest_rate import (
|
||||
LTIRData,
|
||||
LTIRQueryParams,
|
||||
)
|
||||
from openbb_oecd.utils import helpers
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
ltir_mapping = {
|
||||
"BEL": "belgium",
|
||||
"IRL": "ireland",
|
||||
"MEX": "mexico",
|
||||
"IDN": "indonesia",
|
||||
"NZL": "new_zealand",
|
||||
"JPN": "japan",
|
||||
"GBR": "united_kingdom",
|
||||
"FRA": "france",
|
||||
"CHL": "chile",
|
||||
"CAN": "canada",
|
||||
"NLD": "netherlands",
|
||||
"USA": "united_states",
|
||||
"KOR": "south_korea",
|
||||
"NOR": "norway",
|
||||
"AUT": "austria",
|
||||
"ZAF": "south_africa",
|
||||
"DNK": "denmark",
|
||||
"CHE": "switzerland",
|
||||
"HUN": "hungary",
|
||||
"LUX": "luxembourg",
|
||||
"AUS": "australia",
|
||||
"DEU": "germany",
|
||||
"SWE": "sweden",
|
||||
"ISL": "iceland",
|
||||
"TUR": "turkey",
|
||||
"GRC": "greece",
|
||||
"ISR": "israel",
|
||||
"CZE": "czech_republic",
|
||||
"LVA": "latvia",
|
||||
"SVN": "slovenia",
|
||||
"POL": "poland",
|
||||
"EST": "estonia",
|
||||
"LTU": "lithuania",
|
||||
"PRT": "portugal",
|
||||
"CRI": "costa_rica",
|
||||
"SVK": "slovakia",
|
||||
"FIN": "finland",
|
||||
"ESP": "spain",
|
||||
"RUS": "russia",
|
||||
"EA19": "euro_area19",
|
||||
"COL": "colombia",
|
||||
"ITA": "italy",
|
||||
"IND": "india",
|
||||
"CHN": "china",
|
||||
"HRV": "croatia",
|
||||
}
|
||||
|
||||
countries = tuple(ltir_mapping.values()) + ("all",)
|
||||
CountriesLiteral = Literal[countries] # type: ignore
|
||||
country_to_code = {v: k for k, v in ltir_mapping.items()}
|
||||
|
||||
|
||||
class OECDLTIRQueryParams(LTIRQueryParams):
|
||||
"""OECD Short Term Interest Rate Query."""
|
||||
|
||||
country: CountriesLiteral = Field(
|
||||
description="Country to get GDP for.", default="united_states"
|
||||
)
|
||||
|
||||
frequency: Literal["monthly", "quarterly", "annual"] = Field(
|
||||
description="Frequency to get interest rate for for.", default="monthly"
|
||||
)
|
||||
|
||||
|
||||
class OECDLTIRData(LTIRData):
|
||||
"""OECD Long Term Interest Rate Data."""
|
||||
|
||||
@field_validator("date", mode="before")
|
||||
@classmethod
|
||||
def date_validate(cls, in_date: Union[date, str]): # pylint: disable=E0213
|
||||
"""Validate value."""
|
||||
if isinstance(in_date, str):
|
||||
# i.e 2022-Q1
|
||||
if re.match(r"\d{4}-Q[1-4]$", in_date):
|
||||
year, quarter = in_date.split("-")
|
||||
_year = int(year)
|
||||
if quarter == "Q1":
|
||||
return date(_year, 3, 31)
|
||||
if quarter == "Q2":
|
||||
return date(_year, 6, 30)
|
||||
if quarter == "Q3":
|
||||
return date(_year, 9, 30)
|
||||
if quarter == "Q4":
|
||||
return date(_year, 12, 31)
|
||||
# Now match if it is monthly, i.e 2022-01
|
||||
elif re.match(r"\d{4}-\d{2}$", in_date):
|
||||
year, month = map(int, in_date.split("-"))
|
||||
if month == 12:
|
||||
return date(year, month, 31)
|
||||
next_month = date(year, month + 1, 1)
|
||||
return date(next_month.year, next_month.month, 1) - timedelta(days=1)
|
||||
# Now match if it is yearly, i.e 2022
|
||||
elif re.match(r"\d{4}$", in_date):
|
||||
return date(int(in_date), 12, 31)
|
||||
# If the input date is a year
|
||||
if isinstance(in_date, int):
|
||||
return date(in_date, 12, 31)
|
||||
|
||||
return in_date
|
||||
|
||||
|
||||
class OECDLTIRFetcher(Fetcher[OECDLTIRQueryParams, List[OECDLTIRData]]):
|
||||
"""Transform the query, extract and transform the data from the OECD endpoints."""
|
||||
|
||||
@staticmethod
|
||||
def transform_query(params: Dict[str, Any]) -> OECDLTIRQueryParams:
|
||||
"""Transform the query."""
|
||||
transformed_params = params.copy()
|
||||
if transformed_params["start_date"] is None:
|
||||
transformed_params["start_date"] = date(1950, 1, 1)
|
||||
if transformed_params["end_date"] is None:
|
||||
transformed_params["end_date"] = date(date.today().year, 12, 31)
|
||||
|
||||
return OECDLTIRQueryParams(**transformed_params)
|
||||
|
||||
@staticmethod
|
||||
def extract_data(
|
||||
query: OECDLTIRQueryParams, # pylint: disable=W0613
|
||||
credentials: Optional[Dict[str, str]],
|
||||
**kwargs: Any,
|
||||
) -> Dict:
|
||||
"""Return the raw data from the OECD endpoint."""
|
||||
frequency = query.frequency[0].upper()
|
||||
country = "" if query.country == "all" else country_to_code[query.country]
|
||||
url = "https://sdmx.oecd.org/public/rest/data/OECD.SDD.STES,DSD_KEI@DF_KEI,4.0/..IRLT...."
|
||||
data = helpers.get_possibly_cached_data(
|
||||
url, function="economy_long_term_interest_rate"
|
||||
)
|
||||
query = f"FREQ=='{frequency}'"
|
||||
query = query + f" & REF_AREA=='{country}'" if country else query
|
||||
# Filter down
|
||||
data = (
|
||||
data.query(query)
|
||||
.reset_index(drop=True)[["REF_AREA", "TIME_PERIOD", "VALUE"]]
|
||||
.rename(
|
||||
columns={"REF_AREA": "country", "TIME_PERIOD": "date", "VALUE": "value"}
|
||||
)
|
||||
)
|
||||
data["country"] = data["country"].map(ltir_mapping)
|
||||
|
||||
return data.to_dict(orient="records")
|
||||
|
||||
@staticmethod
|
||||
def transform_data(
|
||||
query: OECDLTIRQueryParams, data: Dict, **kwargs: Any
|
||||
) -> List[OECDLTIRData]:
|
||||
"""Transform the data from the OECD endpoint."""
|
||||
return [OECDLTIRData.model_validate(d) for d in data]
|
||||
@@ -0,0 +1,163 @@
|
||||
"""OECD Short Term Interest Rate Rate Data."""
|
||||
|
||||
import re
|
||||
from datetime import date, timedelta
|
||||
from typing import Any, Dict, List, Literal, Optional, Union
|
||||
|
||||
from openbb_core.provider.abstract.fetcher import Fetcher
|
||||
from openbb_core.provider.standard_models.short_term_interest_rate import (
|
||||
STIRData,
|
||||
STIRQueryParams,
|
||||
)
|
||||
from openbb_oecd.utils import helpers
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
stir_mapping = {
|
||||
"BEL": "belgium",
|
||||
"IRL": "ireland",
|
||||
"MEX": "mexico",
|
||||
"IDN": "indonesia",
|
||||
"NZL": "new_zealand",
|
||||
"JPN": "japan",
|
||||
"GBR": "united_kingdom",
|
||||
"FRA": "france",
|
||||
"CHL": "chile",
|
||||
"CAN": "canada",
|
||||
"NLD": "netherlands",
|
||||
"USA": "united_states",
|
||||
"KOR": "south_korea",
|
||||
"NOR": "norway",
|
||||
"AUT": "austria",
|
||||
"ZAF": "south_africa",
|
||||
"DNK": "denmark",
|
||||
"CHE": "switzerland",
|
||||
"HUN": "hungary",
|
||||
"LUX": "luxembourg",
|
||||
"AUS": "australia",
|
||||
"DEU": "germany",
|
||||
"SWE": "sweden",
|
||||
"ISL": "iceland",
|
||||
"TUR": "turkey",
|
||||
"GRC": "greece",
|
||||
"ISR": "israel",
|
||||
"CZE": "czech_republic",
|
||||
"LVA": "latvia",
|
||||
"SVN": "slovenia",
|
||||
"POL": "poland",
|
||||
"EST": "estonia",
|
||||
"LTU": "lithuania",
|
||||
"PRT": "portugal",
|
||||
"CRI": "costa_rica",
|
||||
"SVK": "slovakia",
|
||||
"FIN": "finland",
|
||||
"ESP": "spain",
|
||||
"RUS": "russia",
|
||||
"EA19": "euro_area19",
|
||||
"COL": "colombia",
|
||||
"ITA": "italy",
|
||||
"IND": "india",
|
||||
"CHN": "china",
|
||||
"HRV": "croatia",
|
||||
}
|
||||
|
||||
countries = tuple(stir_mapping.values()) + ("all",)
|
||||
CountriesLiteral = Literal[countries] # type: ignore
|
||||
country_to_code = {v: k for k, v in stir_mapping.items()}
|
||||
|
||||
|
||||
class OECDSTIRQueryParams(STIRQueryParams):
|
||||
"""OECD Short Term Interest Rate Query."""
|
||||
|
||||
country: CountriesLiteral = Field(
|
||||
description="Country to get GDP for.", default="united_states"
|
||||
)
|
||||
|
||||
frequency: Literal["monthly", "quarterly", "annual"] = Field(
|
||||
description="Frequency to get interest rate for for.", default="monthly"
|
||||
)
|
||||
|
||||
|
||||
class OECDSTIRData(STIRData):
|
||||
"""OECD Short Term Interest Rate Data."""
|
||||
|
||||
@field_validator("date", mode="before")
|
||||
@classmethod
|
||||
def date_validate(cls, in_date: Union[date, str]): # pylint: disable=E0213
|
||||
"""Validate value."""
|
||||
if isinstance(in_date, str):
|
||||
# i.e 2022-Q1
|
||||
if re.match(r"\d{4}-Q[1-4]$", in_date):
|
||||
year, quarter = in_date.split("-")
|
||||
_year = int(year)
|
||||
if quarter == "Q1":
|
||||
return date(_year, 3, 31)
|
||||
if quarter == "Q2":
|
||||
return date(_year, 6, 30)
|
||||
if quarter == "Q3":
|
||||
return date(_year, 9, 30)
|
||||
if quarter == "Q4":
|
||||
return date(_year, 12, 31)
|
||||
# Now match if it is monthly, i.e 2022-01
|
||||
elif re.match(r"\d{4}-\d{2}$", in_date):
|
||||
year, month = map(int, in_date.split("-"))
|
||||
if month == 12:
|
||||
return date(year, month, 31)
|
||||
next_month = date(year, month + 1, 1)
|
||||
return date(next_month.year, next_month.month, 1) - timedelta(days=1)
|
||||
# Now match if it is yearly, i.e 2022
|
||||
elif re.match(r"\d{4}$", in_date):
|
||||
return date(int(in_date), 12, 31)
|
||||
# If the input date is a year
|
||||
if isinstance(in_date, int):
|
||||
return date(in_date, 12, 31)
|
||||
|
||||
return in_date
|
||||
|
||||
|
||||
class OECDSTIRFetcher(Fetcher[OECDSTIRQueryParams, List[OECDSTIRData]]):
|
||||
"""Transform the query, extract and transform the data from the OECD endpoints."""
|
||||
|
||||
@staticmethod
|
||||
def transform_query(params: Dict[str, Any]) -> OECDSTIRQueryParams:
|
||||
"""Transform the query."""
|
||||
transformed_params = params.copy()
|
||||
if transformed_params["start_date"] is None:
|
||||
transformed_params["start_date"] = date(1950, 1, 1)
|
||||
if transformed_params["end_date"] is None:
|
||||
transformed_params["end_date"] = date(date.today().year, 12, 31)
|
||||
|
||||
return OECDSTIRQueryParams(**transformed_params)
|
||||
|
||||
@staticmethod
|
||||
def extract_data(
|
||||
query: OECDSTIRQueryParams, # pylint: disable=W0613
|
||||
credentials: Optional[Dict[str, str]],
|
||||
**kwargs: Any,
|
||||
) -> Dict:
|
||||
"""Return the raw data from the OECD endpoint."""
|
||||
frequency = query.frequency[0].upper()
|
||||
country = "" if query.country == "all" else country_to_code[query.country]
|
||||
url = "https://sdmx.oecd.org/public/rest/data/OECD.SDD.STES,DSD_KEI@DF_KEI,4.0/..IR3TIB...."
|
||||
data = helpers.get_possibly_cached_data(
|
||||
url, function="economy_short_term_interest_rate"
|
||||
)
|
||||
query = f"FREQ=='{frequency}'"
|
||||
query = query + f" & REF_AREA=='{country}'" if country else query
|
||||
# Filter down
|
||||
data = (
|
||||
data.query(query)
|
||||
.reset_index(drop=True)[["REF_AREA", "TIME_PERIOD", "VALUE"]]
|
||||
.rename(
|
||||
columns={"REF_AREA": "country", "TIME_PERIOD": "date", "VALUE": "value"}
|
||||
)
|
||||
)
|
||||
data["country"] = data["country"].map(stir_mapping)
|
||||
|
||||
return data.to_dict(orient="records")
|
||||
|
||||
@staticmethod
|
||||
def transform_data(
|
||||
query: OECDSTIRQueryParams, data: Dict, **kwargs: Any
|
||||
) -> List[OECDSTIRData]:
|
||||
"""Transform the data from the OECD endpoint."""
|
||||
return [OECDSTIRData.model_validate(d) for d in data]
|
||||
@@ -0,0 +1,182 @@
|
||||
"""OECD Unemployment Data."""
|
||||
|
||||
import re
|
||||
from datetime import date, timedelta
|
||||
from typing import Any, Dict, List, Literal, Optional, Union
|
||||
|
||||
from openbb_core.provider.abstract.fetcher import Fetcher
|
||||
from openbb_core.provider.standard_models.unemployment import (
|
||||
UnemploymentData,
|
||||
UnemploymentQueryParams,
|
||||
)
|
||||
from openbb_oecd.utils import helpers
|
||||
from pydantic import Field, field_validator
|
||||
|
||||
country_mapping = {
|
||||
"COL": "colombia",
|
||||
"NZL": "new_zealand",
|
||||
"GBR": "united_kingdom",
|
||||
"ITA": "italy",
|
||||
"LUX": "luxembourg",
|
||||
"EA19": "euro_area19",
|
||||
"SWE": "sweden",
|
||||
"OECD": "oecd",
|
||||
"ZAF": "south_africa",
|
||||
"DNK": "denmark",
|
||||
"CAN": "canada",
|
||||
"CHE": "switzerland",
|
||||
"SVK": "slovakia",
|
||||
"HUN": "hungary",
|
||||
"PRT": "portugal",
|
||||
"ESP": "spain",
|
||||
"FRA": "france",
|
||||
"CZE": "czech_republic",
|
||||
"CRI": "costa_rica",
|
||||
"JPN": "japan",
|
||||
"SVN": "slovenia",
|
||||
"RUS": "russia",
|
||||
"AUT": "austria",
|
||||
"LVA": "latvia",
|
||||
"NLD": "netherlands",
|
||||
"ISR": "israel",
|
||||
"ISL": "iceland",
|
||||
"USA": "united_states",
|
||||
"IRL": "ireland",
|
||||
"MEX": "mexico",
|
||||
"DEU": "germany",
|
||||
"GRC": "greece",
|
||||
"TUR": "turkey",
|
||||
"AUS": "australia",
|
||||
"POL": "poland",
|
||||
"KOR": "south_korea",
|
||||
"CHL": "chile",
|
||||
"FIN": "finland",
|
||||
"EU27_2020": "european_union27_2020",
|
||||
"NOR": "norway",
|
||||
"LTU": "lithuania",
|
||||
"EA20": "euro_area20",
|
||||
"EST": "estonia",
|
||||
"BEL": "belgium",
|
||||
"BRA": "brazil",
|
||||
"IDN": "indonesia",
|
||||
}
|
||||
countries = tuple(country_mapping.values()) + ("all",)
|
||||
CountriesLiteral = Literal[countries] # type: ignore
|
||||
country_to_code = {v: k for k, v in country_mapping.items()}
|
||||
|
||||
|
||||
class OECDUnemploymentQueryParams(UnemploymentQueryParams):
|
||||
"""OECD Unemployment Query."""
|
||||
|
||||
country: CountriesLiteral = Field(
|
||||
description="Country to get GDP for.", default="united_states"
|
||||
)
|
||||
sex: Literal["total", "male", "female"] = Field(
|
||||
description="Sex to get unemployment for.", default="total"
|
||||
)
|
||||
frequency: Literal["monthly", "quarterly", "annual"] = Field(
|
||||
description="Frequency to get unemployment for.", default="monthly"
|
||||
)
|
||||
age: Literal["total", "15-24", "15-64", "25-54", "55-64"] = Field(
|
||||
description="Age group to get unemployment for. Total indicates 15 years or over",
|
||||
default="total",
|
||||
)
|
||||
seasonal_adjustment: bool = Field(
|
||||
description="Whether to get seasonally adjusted unemployment. Defaults to False.",
|
||||
default=False,
|
||||
)
|
||||
|
||||
|
||||
class OECDUnemploymentData(UnemploymentData):
|
||||
"""OECD Unemployment Data."""
|
||||
|
||||
@field_validator("date", mode="before")
|
||||
@classmethod
|
||||
def date_validate(cls, in_date: Union[date, str]): # pylint: disable=E0213
|
||||
"""Validate value."""
|
||||
if isinstance(in_date, str):
|
||||
# i.e 2022-Q1
|
||||
if re.match(r"\d{4}-Q[1-4]$", in_date):
|
||||
year, quarter = in_date.split("-")
|
||||
_year = int(year)
|
||||
if quarter == "Q1":
|
||||
return date(_year, 3, 31)
|
||||
if quarter == "Q2":
|
||||
return date(_year, 6, 30)
|
||||
if quarter == "Q3":
|
||||
return date(_year, 9, 30)
|
||||
if quarter == "Q4":
|
||||
return date(_year, 12, 31)
|
||||
# Now match if it is monthly, i.e 2022-01
|
||||
elif re.match(r"\d{4}-\d{2}$", in_date):
|
||||
year, month = map(int, in_date.split("-"))
|
||||
if month == 12:
|
||||
return date(year, month, 31)
|
||||
next_month = date(year, month + 1, 1)
|
||||
return date(next_month.year, next_month.month, 1) - timedelta(days=1)
|
||||
# Now match if it is yearly, i.e 2022
|
||||
elif re.match(r"\d{4}$", in_date):
|
||||
return date(int(in_date), 12, 31)
|
||||
# If the input date is a year
|
||||
if isinstance(in_date, int):
|
||||
return date(in_date, 12, 31)
|
||||
|
||||
return in_date
|
||||
|
||||
|
||||
class OECDUnemploymentFetcher(
|
||||
Fetcher[OECDUnemploymentQueryParams, List[OECDUnemploymentData]]
|
||||
):
|
||||
"""Transform the query, extract and transform the data from the OECD endpoints."""
|
||||
|
||||
@staticmethod
|
||||
def transform_query(params: Dict[str, Any]) -> OECDUnemploymentQueryParams:
|
||||
"""Transform the query."""
|
||||
transformed_params = params.copy()
|
||||
if transformed_params["start_date"] is None:
|
||||
transformed_params["start_date"] = date(1950, 1, 1)
|
||||
if transformed_params["end_date"] is None:
|
||||
transformed_params["end_date"] = date(date.today().year, 12, 31)
|
||||
|
||||
return OECDUnemploymentQueryParams(**transformed_params)
|
||||
|
||||
@staticmethod
|
||||
def extract_data(
|
||||
query: OECDUnemploymentQueryParams, # pylint: disable=W0613
|
||||
credentials: Optional[Dict[str, str]],
|
||||
**kwargs: Any,
|
||||
) -> Dict:
|
||||
"""Return the raw data from the OECD endpoint."""
|
||||
sex = {"total": "_T", "male": "M", "female": "F"}[query.sex]
|
||||
frequency = query.frequency[0].upper()
|
||||
age = {
|
||||
"total": "Y_GE15",
|
||||
"15-24": "Y15T24",
|
||||
"15-64": "Y15T64",
|
||||
"25-54": "Y25T54",
|
||||
"55-64": "Y55T64",
|
||||
}[query.age]
|
||||
seasonal_adjustment = "Y" if query.seasonal_adjustment else "N"
|
||||
country = "" if query.country == "all" else country_to_code[query.country]
|
||||
url = "https://sdmx.oecd.org/public/rest/data/OECD.SDD.TPS,DSD_LFS@DF_IALFS_INDIC,1.0/.UNE_LF........"
|
||||
data = helpers.get_possibly_cached_data(url, function="economy_unemployment")
|
||||
query = f"AGE=='{age}' & SEX=='{sex}' & FREQ=='{frequency}' & ADJUSTMENT=='{seasonal_adjustment}'"
|
||||
query = query + f" & REF_AREA=='{country}'" if country else query
|
||||
# Filter down
|
||||
data = (
|
||||
data.query(query)
|
||||
.reset_index(drop=True)[["REF_AREA", "TIME_PERIOD", "VALUE"]]
|
||||
.rename(
|
||||
columns={"REF_AREA": "country", "TIME_PERIOD": "date", "VALUE": "value"}
|
||||
)
|
||||
)
|
||||
data["country"] = data["country"].map(country_mapping)
|
||||
|
||||
return data.to_dict(orient="records")
|
||||
|
||||
@staticmethod
|
||||
def transform_data(
|
||||
query: OECDUnemploymentQueryParams, data: Dict, **kwargs: Any
|
||||
) -> List[OECDUnemploymentData]:
|
||||
"""Transform the data from the OECD endpoint."""
|
||||
return [OECDUnemploymentData.model_validate(d) for d in data]
|
||||
@@ -1,11 +1,19 @@
|
||||
import ssl
|
||||
from datetime import date
|
||||
from io import StringIO
|
||||
from pathlib import Path
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import requests
|
||||
import urllib3
|
||||
from defusedxml.ElementTree import fromstring
|
||||
from openbb_core.app.utils import get_user_cache_directory
|
||||
from openbb_core.provider import helpers
|
||||
from pandas import DataFrame, read_csv
|
||||
from pandas import DataFrame, read_csv, read_parquet
|
||||
|
||||
cache = get_user_cache_directory() + "/oecd"
|
||||
# Create the cache directory if it does not exist
|
||||
Path(cache).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# OECD does not play well with newer python. This code block from stackoverflow helps
|
||||
# to create a custom session:
|
||||
@@ -18,8 +26,9 @@ class CustomHttpAdapter(requests.adapters.HTTPAdapter):
|
||||
self.ssl_context = ssl_context
|
||||
super().__init__(**kwargs)
|
||||
|
||||
# pylint: disable=arguments-differ
|
||||
def init_poolmanager(self, connections, maxsize, block=False):
|
||||
self.poolmanager = urllib3.poolmanager.PoolManager(
|
||||
self.poolmanager = urllib3.poolmanager.PoolManager( # pylint: disable=attribute-defined-outside-init
|
||||
num_pools=connections,
|
||||
maxsize=maxsize,
|
||||
block=block,
|
||||
@@ -27,6 +36,9 @@ class CustomHttpAdapter(requests.adapters.HTTPAdapter):
|
||||
)
|
||||
|
||||
|
||||
# pylint: enable=arguments-differ
|
||||
|
||||
|
||||
def get_legacy_session():
|
||||
"""Stackoverflow code to create a custom session."""
|
||||
ctx = ssl.create_default_context(ssl.Purpose.SERVER_AUTH)
|
||||
@@ -46,3 +58,174 @@ def fetch_data(url: str, csv_kwargs: Optional[Dict] = None, **kwargs: Any) -> Da
|
||||
# or a delimiter.
|
||||
data = read_csv(StringIO(response.text), **csv_kwargs)
|
||||
return data
|
||||
|
||||
|
||||
### The functions below are for using the new oecd data-explorer instead of the stats.oecd
|
||||
|
||||
|
||||
def oecd_xml_to_df(xml_string: str) -> DataFrame:
|
||||
"""Helper function to parse the OECD XML and return a dataframe.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
xml_string : str
|
||||
A string containing the OECD XML data.
|
||||
|
||||
Returns
|
||||
-------
|
||||
DataFrame
|
||||
A Pandas DataFrame containing the parsed data from the XML string.
|
||||
"""
|
||||
root = fromstring(xml_string)
|
||||
|
||||
namespaces = {
|
||||
"message": "http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message",
|
||||
"generic": "http://www.sdmx.org/resources/sdmxml/schemas/v2_1/data/generic",
|
||||
}
|
||||
|
||||
# Prepare a list to hold your extracted data
|
||||
data = []
|
||||
|
||||
# Iterate through each 'Series' in the XML
|
||||
for series in root.findall(".//generic:Series", namespaces=namespaces):
|
||||
series_data = {}
|
||||
# Extract series key values
|
||||
for value in series.findall(".//generic:Value", namespaces=namespaces):
|
||||
series_data[value.get("id")] = value.get("value")
|
||||
# Extract observation values
|
||||
for obs in series.findall("./generic:Obs", namespaces=namespaces):
|
||||
obs_data = series_data.copy()
|
||||
obs_data["TIME_PERIOD"] = obs.find(
|
||||
"./generic:ObsDimension", namespaces=namespaces
|
||||
).get("value")
|
||||
obs_data["VALUE"] = obs.find(
|
||||
"./generic:ObsValue", namespaces=namespaces
|
||||
).get("value")
|
||||
data.append(obs_data)
|
||||
|
||||
# Create a DataFrame
|
||||
return DataFrame(data)
|
||||
|
||||
|
||||
def parse_url(url: str) -> DataFrame:
|
||||
"""Helper function to parse the SDMX url and return a dataframe.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
url:str
|
||||
URL to parse
|
||||
|
||||
Returns
|
||||
-------
|
||||
DataFrame
|
||||
Pandas dataframe containing URL data
|
||||
"""
|
||||
response = helpers.make_request(url)
|
||||
response.raise_for_status()
|
||||
return oecd_xml_to_df(response.text)
|
||||
|
||||
|
||||
def check_cache_exists_and_valid(function: str, cache_method: str = "csv") -> bool:
|
||||
"""Check if the cache exists and is valid.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
function : str
|
||||
The name of the function for which the cache is being checked.
|
||||
cache_method : str, optional
|
||||
The method used for caching (default is 'csv').
|
||||
|
||||
Returns
|
||||
-------
|
||||
bool
|
||||
True if the cache exists and is valid for the current day, False otherwise.
|
||||
"""
|
||||
# TODO: add setting to disable cache for tests
|
||||
|
||||
if cache_method not in ["csv", "parquet"]:
|
||||
raise NotImplementedError("Currently only working with parquet or csv")
|
||||
# First check that the cache exists. This will be a parquet/csv and a timestamp
|
||||
cache_path = f"{cache}/{function}.{cache_method}"
|
||||
time_cache_path = f"{cache}/{function}.timestamp"
|
||||
if Path(cache_path).exists() and Path(time_cache_path).exists():
|
||||
# Now check that the cache is valid. I am going to check that we write to a file the date the cache was made
|
||||
# Read the timestamp
|
||||
with open(time_cache_path) as f:
|
||||
cached_date = f.read().strip()
|
||||
# TODO: More robust caching logic
|
||||
if cached_date == str(date.today()):
|
||||
return True
|
||||
return False
|
||||
return False
|
||||
|
||||
|
||||
def write_to_cache(function: str, data: DataFrame, cache_method: str) -> None:
|
||||
"""Write data to the cache.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
function : str
|
||||
The name of the function for which data is being cached.
|
||||
data : DataFrame
|
||||
The DataFrame to be cached.
|
||||
cache_method : str
|
||||
The method used for caching the data.
|
||||
|
||||
Raises
|
||||
------
|
||||
NotImplementedError
|
||||
If the cache_method is not 'parquet'.
|
||||
"""
|
||||
if cache_method == "parquet":
|
||||
cache_path = f"{cache}/{function}.parquet"
|
||||
data.to_parquet(cache_path, engine="pyarrow")
|
||||
# Write the current date to a file called cache/function.timestamp
|
||||
with open(f"{cache}/{function}.timestamp", "w") as f:
|
||||
f.write(str(date.today()))
|
||||
elif cache_method == "csv":
|
||||
cache_path = f"{cache}/{function}.csv"
|
||||
data.to_csv(cache_path)
|
||||
# Write the current date to a file called cache/function.timestamp
|
||||
with open(f"{cache}/{function}.timestamp", "w") as f:
|
||||
f.write(str(date.today()))
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def get_possibly_cached_data(
|
||||
url: str, function: Optional[str] = None, cache_method: str = "csv"
|
||||
) -> DataFrame:
|
||||
"""
|
||||
Retrieve data from a given URL or from the cache if available and valid.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
url : str
|
||||
The URL from which to fetch the data if it's not available in the cache.
|
||||
function : Optional[str], optional
|
||||
The name of the function for which data is being fetched or cached.
|
||||
cache_method : str, optional
|
||||
The method used for caching the data (default is 'csv').
|
||||
|
||||
Returns
|
||||
-------
|
||||
DataFrame
|
||||
A Pandas DataFrame containing the fetched or cached data.
|
||||
"""
|
||||
if cache_method == "parquet":
|
||||
cache_path = f"{cache}/{function}.parquet"
|
||||
elif cache_method == "csv":
|
||||
cache_path = f"{cache}/{function}.csv"
|
||||
|
||||
use_cache = check_cache_exists_and_valid(
|
||||
function=function, cache_method=cache_method
|
||||
)
|
||||
if use_cache:
|
||||
if cache_method == "csv":
|
||||
data = read_csv(cache_path)
|
||||
elif cache_method == "parquet":
|
||||
data = read_parquet(cache_path, engine="pyarrow")
|
||||
else:
|
||||
data = parse_url(url)
|
||||
write_to_cache(function=function, data=data, cache_method=cache_method)
|
||||
return data
|
||||
|
||||
45
openbb_platform/providers/oecd/poetry.lock
generated
vendored
45
openbb_platform/providers/oecd/poetry.lock
generated
vendored
@@ -320,6 +320,17 @@ files = [
|
||||
{file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "defusedxml"
|
||||
version = "0.8.0rc2"
|
||||
description = "XML bomb protection for Python stdlib modules"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "defusedxml-0.8.0rc2-py2.py3-none-any.whl", hash = "sha256:1c812964311154c3bf4aaf3bc1443b31ee13530b7f255eaaa062c0553c76103d"},
|
||||
{file = "defusedxml-0.8.0rc2.tar.gz", hash = "sha256:138c7d540a78775182206c7c97fe65b246a2f40b29471e1a2f1b0da76e7a3942"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "ecdsa"
|
||||
version = "0.18.0"
|
||||
@@ -635,40 +646,6 @@ files = [
|
||||
{file = "numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "1.25.2"
|
||||
description = "Fundamental package for array computing in Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "numpy-1.25.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:db3ccc4e37a6873045580d413fe79b68e47a681af8db2e046f1dacfa11f86eb3"},
|
||||
{file = "numpy-1.25.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:90319e4f002795ccfc9050110bbbaa16c944b1c37c0baeea43c5fb881693ae1f"},
|
||||
{file = "numpy-1.25.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dfe4a913e29b418d096e696ddd422d8a5d13ffba4ea91f9f60440a3b759b0187"},
|
||||
{file = "numpy-1.25.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:f08f2e037bba04e707eebf4bc934f1972a315c883a9e0ebfa8a7756eabf9e357"},
|
||||
{file = "numpy-1.25.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:bec1e7213c7cb00d67093247f8c4db156fd03075f49876957dca4711306d39c9"},
|
||||
{file = "numpy-1.25.2-cp310-cp310-win32.whl", hash = "sha256:7dc869c0c75988e1c693d0e2d5b26034644399dd929bc049db55395b1379e044"},
|
||||
{file = "numpy-1.25.2-cp310-cp310-win_amd64.whl", hash = "sha256:834b386f2b8210dca38c71a6e0f4fd6922f7d3fcff935dbe3a570945acb1b545"},
|
||||
{file = "numpy-1.25.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c5462d19336db4560041517dbb7759c21d181a67cb01b36ca109b2ae37d32418"},
|
||||
{file = "numpy-1.25.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c5652ea24d33585ea39eb6a6a15dac87a1206a692719ff45d53c5282e66d4a8f"},
|
||||
{file = "numpy-1.25.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0d60fbae8e0019865fc4784745814cff1c421df5afee233db6d88ab4f14655a2"},
|
||||
{file = "numpy-1.25.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:60e7f0f7f6d0eee8364b9a6304c2845b9c491ac706048c7e8cf47b83123b8dbf"},
|
||||
{file = "numpy-1.25.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:bb33d5a1cf360304754913a350edda36d5b8c5331a8237268c48f91253c3a364"},
|
||||
{file = "numpy-1.25.2-cp311-cp311-win32.whl", hash = "sha256:5883c06bb92f2e6c8181df7b39971a5fb436288db58b5a1c3967702d4278691d"},
|
||||
{file = "numpy-1.25.2-cp311-cp311-win_amd64.whl", hash = "sha256:5c97325a0ba6f9d041feb9390924614b60b99209a71a69c876f71052521d42a4"},
|
||||
{file = "numpy-1.25.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b79e513d7aac42ae918db3ad1341a015488530d0bb2a6abcbdd10a3a829ccfd3"},
|
||||
{file = "numpy-1.25.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:eb942bfb6f84df5ce05dbf4b46673ffed0d3da59f13635ea9b926af3deb76926"},
|
||||
{file = "numpy-1.25.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e0746410e73384e70d286f93abf2520035250aad8c5714240b0492a7302fdca"},
|
||||
{file = "numpy-1.25.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d7806500e4f5bdd04095e849265e55de20d8cc4b661b038957354327f6d9b295"},
|
||||
{file = "numpy-1.25.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8b77775f4b7df768967a7c8b3567e309f617dd5e99aeb886fa14dc1a0791141f"},
|
||||
{file = "numpy-1.25.2-cp39-cp39-win32.whl", hash = "sha256:2792d23d62ec51e50ce4d4b7d73de8f67a2fd3ea710dcbc8563a51a03fb07b01"},
|
||||
{file = "numpy-1.25.2-cp39-cp39-win_amd64.whl", hash = "sha256:76b4115d42a7dfc5d485d358728cdd8719be33cc5ec6ec08632a5d6fca2ed380"},
|
||||
{file = "numpy-1.25.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:1a1329e26f46230bf77b02cc19e900db9b52f398d6722ca853349a782d4cff55"},
|
||||
{file = "numpy-1.25.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c3abc71e8b6edba80a01a52e66d83c5d14433cbcd26a40c329ec7ed09f37901"},
|
||||
{file = "numpy-1.25.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:1b9735c27cea5d995496f46a8b1cd7b408b3f34b6d50459d9ac8fe3a20cc17bf"},
|
||||
{file = "numpy-1.25.2.tar.gz", hash = "sha256:fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "openbb-core"
|
||||
version = "1.1.1"
|
||||
|
||||
@@ -10,6 +10,7 @@ packages = [{ include = "openbb_oecd" }]
|
||||
python = "^3.8"
|
||||
openbb-core = "^1.1.1"
|
||||
urllib3 = ">1.26.16"
|
||||
defusedxml = "^0.8.0rc2"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
|
||||
@@ -17,61 +17,60 @@ interactions:
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||||
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||||
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||||
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||||
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|
||||
AA==
|
||||
headers:
|
||||
Access-Control-Allow-Headers:
|
||||
- Content-Type
|
||||
@@ -207,13 +205,13 @@ interactions:
|
||||
Cache-Control:
|
||||
- private
|
||||
Content-Disposition:
|
||||
- attachment;filename=DP_LIVE_10112023143408467.csv
|
||||
- attachment;filename=DP_LIVE_26012024223439918.csv
|
||||
Content-Encoding:
|
||||
- gzip
|
||||
Content-Type:
|
||||
- text/csv
|
||||
Date:
|
||||
- Fri, 10 Nov 2023 13:34:08 GMT
|
||||
- Fri, 26 Jan 2024 21:34:39 GMT
|
||||
Transfer-Encoding:
|
||||
- chunked
|
||||
Vary:
|
||||
|
||||
100203
openbb_platform/providers/oecd/tests/record/http/test_oecd_fetchers/test_oecd_unemployment_fetcher.yaml
vendored
Normal file
100203
openbb_platform/providers/oecd/tests/record/http/test_oecd_fetchers/test_oecd_unemployment_fetcher.yaml
vendored
Normal file
File diff suppressed because it is too large
Load Diff
5594
openbb_platform/providers/oecd/tests/record/http/test_oecd_fetchers/test_oecdcli_fetcher.yaml
vendored
Normal file
5594
openbb_platform/providers/oecd/tests/record/http/test_oecd_fetchers/test_oecdcli_fetcher.yaml
vendored
Normal file
File diff suppressed because it is too large
Load Diff
8775
openbb_platform/providers/oecd/tests/record/http/test_oecd_fetchers/test_oecdltir_fetcher.yaml
vendored
Normal file
8775
openbb_platform/providers/oecd/tests/record/http/test_oecd_fetchers/test_oecdltir_fetcher.yaml
vendored
Normal file
File diff suppressed because it is too large
Load Diff
8526
openbb_platform/providers/oecd/tests/record/http/test_oecd_fetchers/test_oecdstir_fetcher.yaml
vendored
Normal file
8526
openbb_platform/providers/oecd/tests/record/http/test_oecd_fetchers/test_oecdstir_fetcher.yaml
vendored
Normal file
File diff suppressed because it is too large
Load Diff
@@ -2,9 +2,13 @@ import datetime
|
||||
|
||||
import pytest
|
||||
from openbb_core.app.service.user_service import UserService
|
||||
from openbb_oecd.models.composite_leading_indicator import OECDCLIFetcher
|
||||
from openbb_oecd.models.gdp_forecast import OECDGdpForecastFetcher
|
||||
from openbb_oecd.models.gdp_nominal import OECDGdpNominalFetcher
|
||||
from openbb_oecd.models.gdp_real import OECDGdpRealFetcher
|
||||
from openbb_oecd.models.long_term_interest_rate import OECDLTIRFetcher
|
||||
from openbb_oecd.models.short_term_interest_rate import OECDSTIRFetcher
|
||||
from openbb_oecd.models.unemployment import OECDUnemploymentFetcher
|
||||
|
||||
test_credentials = UserService().default_user_settings.credentials.model_dump(
|
||||
mode="json"
|
||||
@@ -54,3 +58,51 @@ def test_oecd_forecast_gdp_fetcher(credentials=test_credentials):
|
||||
fetcher = OECDGdpForecastFetcher()
|
||||
result = fetcher.test(params, credentials)
|
||||
assert result is None
|
||||
|
||||
|
||||
@pytest.mark.record_http
|
||||
def test_oecd_unemployment_fetcher(credentials=test_credentials):
|
||||
params = {
|
||||
"start_date": datetime.date(2023, 1, 1),
|
||||
"end_date": datetime.date(2023, 6, 6),
|
||||
}
|
||||
|
||||
fetcher = OECDUnemploymentFetcher()
|
||||
result = fetcher.test(params, credentials)
|
||||
assert result is None
|
||||
|
||||
|
||||
@pytest.mark.record_http
|
||||
def test_oecdcli_fetcher(credentials=test_credentials):
|
||||
params = {
|
||||
"start_date": datetime.date(2023, 1, 1),
|
||||
"end_date": datetime.date(2023, 6, 6),
|
||||
}
|
||||
|
||||
fetcher = OECDCLIFetcher()
|
||||
result = fetcher.test(params, credentials)
|
||||
assert result is None
|
||||
|
||||
|
||||
@pytest.mark.record_http
|
||||
def test_oecdstir_fetcher(credentials=test_credentials):
|
||||
params = {
|
||||
"start_date": datetime.date(2023, 1, 1),
|
||||
"end_date": datetime.date(2023, 6, 6),
|
||||
}
|
||||
|
||||
fetcher = OECDSTIRFetcher()
|
||||
result = fetcher.test(params, credentials)
|
||||
assert result is None
|
||||
|
||||
|
||||
@pytest.mark.record_http
|
||||
def test_oecdltir_fetcher(credentials=test_credentials):
|
||||
params = {
|
||||
"start_date": datetime.date(2023, 1, 1),
|
||||
"end_date": datetime.date(2023, 6, 6),
|
||||
}
|
||||
|
||||
fetcher = OECDLTIRFetcher()
|
||||
result = fetcher.test(params, credentials)
|
||||
assert result is None
|
||||
|
||||
Reference in New Issue
Block a user