Stock Historical standardization and UX improvements (#5457)

* polygon add interval standard param

* attempt at standardization

* Alpha Vantage params standardization

* rename `get_intervals` and fixed bug for extracting wrong value

* missed changing it in `extract_data`

* linting

* polygon cleanup

* modified `start_date` & `end_date` type to `datetime` from `dateType`

* standardized intrinio stock historical params

* fixed yfinance

* linting

* revert start and end date to dateType from datetime

* fix alpha vantage

* changed param

* fix intrinio infinite loop

* `validator` is `field_validator`

* revert time params and fixed intervals

* `outputsize` alias set in `Field`

* set interval description using in-build dict

* yfinance stock param standardization

* static

* intrinio cleanup

* Polygon add api params as `PrivateAttr`

* modified yfinance params to `PrivateAttr`

* intrinio remove overriding date params

* fixed description for AV adjusted param

* fix alpha vantage intraday not working

* re-record and pass unit tests

* pass integration tests

* Update test

---------

Co-authored-by: Igor Radovanovic <74266147+IgorWounds@users.noreply.github.com>
This commit is contained in:
Pratyush Shukla
2023-10-16 21:00:10 +05:30
committed by GitHub
parent a5d2e833c4
commit f6eac35f0b
81 changed files with 8843 additions and 11871 deletions

View File

@@ -657,152 +657,150 @@ def test_stocks_options_chains(params, headers):
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
"provider": "yfinance",
}
),
(
{
"period": "intraday",
"interval": "60min",
"adjusted": True,
"extended_hours": True,
"month": "2023-03",
"outputsize": "full",
"month": "2023-01",
"output_size": "full",
"provider": "alpha_vantage",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
"interval": "1m",
}
),
(
{
"adjusted": True,
"extended_hours": False,
"output_size": "full",
"month": "2023-01",
"provider": "alpha_vantage",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"function_": "TIME_SERIES_INTRADAY",
"interval": "1d",
}
),
(
{
"provider": "cboe",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
"interval": "1m",
"provider": "cboe",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
}
),
(
{
"provider": "cboe",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
"provider": "cboe",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
}
),
(
{
"interval": "1min",
"limit": "30",
"provider": "fmp",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
"timeseries": 1,
"interval": "1m",
}
),
(
{
"interval": "1day",
"limit": "30",
"provider": "fmp",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"timeseries": 1,
"interval": "1d",
}
),
(
{
"timezone": "UTC",
"source": "realtime",
"interval_size": "30m",
"provider": "intrinio",
"symbol": "AAPL",
"start_date": "2023-10-10",
"end_date": "2023-10-12",
"start_time": time(5, 30, 0),
"end_time": time(12, 0, 0),
"provider": "intrinio",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
"interval": "1m",
}
),
(
{
"timezone": "UTC",
"source": "realtime",
"interval_size": "60m",
"provider": "intrinio",
"symbol": "AAPL",
"start_date": "2023-10-10",
"end_date": "2023-10-12",
"start_time": time(5, 30, 0),
"end_time": time(12, 0, 0),
"provider": "intrinio",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
}
),
(
{
"multiplier": 1,
"timespan": "minute",
"sort": "desc",
"limit": 49999,
"adjusted": True,
"limit": "49999",
"adjusted": "True",
"provider": "polygon",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-04",
"end_date": "2023-01-02",
"interval": "1m",
}
),
(
{
"multiplier": 1,
"timespan": "day",
"sort": "desc",
"limit": 49999,
"adjusted": True,
"limit": "49999",
"adjusted": "True",
"provider": "polygon",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
}
),
(
{
"interval": "1d",
"period": "max",
"prepost": True,
"actions": True,
"auto_adjust": True,
"back_adjust": True,
"progress": True,
"prepost": False,
"include": True,
"adjusted": False,
"back_adjust": False,
"ignore_tz": True,
"rounding": True,
"repair": True,
"keepna": True,
"group_by": "column",
"provider": "yfinance",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-08",
"end_date": "2023-01-02",
"interval": "1m",
}
),
(
{
"interval": "1d",
"period": "max",
"prepost": True,
"actions": True,
"auto_adjust": True,
"back_adjust": True,
"progress": True,
"prepost": False,
"include": True,
"adjusted": False,
"back_adjust": False,
"ignore_tz": True,
"rounding": True,
"repair": True,
"keepna": True,
"group_by": "column",
"provider": "yfinance",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
}
),
],

View File

@@ -618,151 +618,149 @@ def test_stocks_options_chains(params, obb):
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
}
),
(
{
"period": "intraday",
"interval": "60min",
"adjusted": True,
"extended_hours": True,
"month": "2023-03",
"outputsize": "full",
"month": "2023-01",
"output_size": "full",
"provider": "alpha_vantage",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
"interval": "1m",
}
),
(
{
"adjusted": True,
"extended_hours": False,
"output_size": "full",
"month": "2023-01",
"provider": "alpha_vantage",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"function_": "TIME_SERIES_INTRADAY",
"interval": "1d",
}
),
(
{
"provider": "cboe",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
"interval": "1m",
"provider": "cboe",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
}
),
(
{
"provider": "cboe",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
"provider": "cboe",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
}
),
(
{
"interval": "1min",
"limit": "30",
"provider": "fmp",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
"timeseries": 1,
"interval": "1m",
}
),
(
{
"interval": "1day",
"limit": "30",
"provider": "fmp",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"timeseries": 1,
"interval": "1d",
}
),
(
{
"timezone": "UTC",
"source": "realtime",
"interval_size": "30m",
"provider": "intrinio",
"symbol": "AAPL",
"start_date": "2023-10-10",
"end_date": "2023-10-12",
"start_time": time(5, 30, 0),
"end_time": time(12, 0, 0),
"provider": "intrinio",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-02",
"interval": "1m",
}
),
(
{
"timezone": "UTC",
"source": "realtime",
"interval_size": "60m",
"provider": "intrinio",
"symbol": "AAPL",
"start_date": "2023-10-10",
"end_date": "2023-10-12",
"start_time": time(5, 30, 0),
"end_time": time(12, 0, 0),
"provider": "intrinio",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
}
),
(
{
"multiplier": 1,
"timespan": "minute",
"sort": "desc",
"limit": 49999,
"adjusted": True,
"limit": "49999",
"adjusted": "True",
"provider": "polygon",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-04",
"end_date": "2023-01-02",
"interval": "1m",
}
),
(
{
"multiplier": 1,
"timespan": "day",
"sort": "desc",
"limit": 49999,
"adjusted": True,
"limit": "49999",
"adjusted": "True",
"provider": "polygon",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
}
),
(
{
"interval": "1d",
"period": "max",
"prepost": True,
"actions": True,
"auto_adjust": True,
"back_adjust": True,
"progress": True,
"prepost": False,
"include": True,
"adjusted": False,
"back_adjust": False,
"ignore_tz": True,
"rounding": True,
"repair": True,
"keepna": True,
"group_by": "column",
"provider": "yfinance",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-01-08",
"end_date": "2023-01-02",
"interval": "1m",
}
),
(
{
"interval": "1d",
"period": "max",
"prepost": True,
"actions": True,
"auto_adjust": True,
"back_adjust": True,
"progress": True,
"prepost": False,
"include": True,
"adjusted": False,
"back_adjust": False,
"ignore_tz": True,
"rounding": True,
"repair": True,
"keepna": True,
"group_by": "column",
"provider": "yfinance",
"symbol": "AAPL",
"start_date": "2023-01-01",
"end_date": "2023-06-06",
"interval": "1d",
}
),
],

View File

@@ -42,6 +42,7 @@ class ROUTER_crypto(Container):
),
] = None,
provider: Union[Literal["fmp", "polygon", "yfinance"], None] = None,
provider: Union[Literal["fmp", "polygon", "yfinance"], None] = None,
**kwargs
) -> OBBject[List[Data]]:
"""Crypto Historical Price.
@@ -73,7 +74,7 @@ class ROUTER_crypto(Container):
adjusted : bool
Whether the data is adjusted. (provider: polygon)
period : Optional[Union[Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']]]
Period of the data to return. (provider: yfinance)
Time period of the data to return. (provider: yfinance)
Returns
-------

View File

@@ -7,9 +7,9 @@ import typing_extensions
from annotated_types import Gt
from openbb_core.app.model.obbject import OBBject
from openbb_core.app.static.container import Container
from openbb_core.app.static.decorators import validate
from openbb_core.app.static.filters import filter_inputs
from openbb_provider.abstract.data import Data
from pydantic import validate_call
class ROUTER_econometrics(Container):
@@ -33,7 +33,7 @@ class ROUTER_econometrics(Container):
def __repr__(self) -> str:
return self.__doc__ or ""
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def bgot(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -53,7 +53,6 @@ class ROUTER_econometrics(Container):
List of columns to use as exogenous variables.
lags: PositiveInt
Number of lags to use in the test.
Returns
-------
OBBject[Data]
@@ -72,7 +71,7 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def coint(
self, data: Union[List[Data], pandas.DataFrame], columns: List[str]
) -> OBBject[Data]:
@@ -86,7 +85,6 @@ class ROUTER_econometrics(Container):
Data columns to check cointegration
maxlag: PositiveInt
Number of lags to use in the test.
Returns
-------
OBBject[Data]
@@ -103,7 +101,7 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def corr(self, data: Union[List[Data], pandas.DataFrame]) -> OBBject[List[Data]]:
"""Get the corrlelation matrix of an input dataset.
@@ -127,14 +125,14 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def dwat(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform Durbin-Watson test for autocorrelation.
"""Perform Durbin-Watson test for autocorrelation
Parameters
----------
@@ -162,7 +160,7 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def granger(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -182,7 +180,6 @@ class ROUTER_econometrics(Container):
Columns to use as exogenous variables.
lag: PositiveInt
Number of lags to use in the test.
Returns
-------
OBBject[Data]
@@ -201,7 +198,7 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def ols(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -236,7 +233,7 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def ols_summary(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -271,14 +268,14 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelbols(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform a Between estimator regression on panel data.
"""Perform a Between estimator regression on panel data
Parameters
----------
@@ -306,14 +303,14 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelfd(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform a first-difference estimate for panel data.
"""Perform a first-difference estimate for panel data
Parameters
----------
@@ -341,14 +338,14 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelfmac(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Fama-MacBeth estimator for panel data.
"""Fama-MacBeth estimator for panel data
Parameters
----------
@@ -376,14 +373,14 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelols(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""One- and two-way fixed effects estimator for panel data.
"""One- and two-way fixed effects estimator for panel data
Parameters
----------
@@ -411,14 +408,14 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelpols(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform a Pooled coefficvient estimator regression on panel data.
"""Perform a Pooled coefficvient estimator regression on panel data
Parameters
----------
@@ -446,14 +443,14 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def panelre(
self,
data: Union[List[Data], pandas.DataFrame],
y_column: str,
x_columns: List[str],
) -> OBBject[Dict]:
"""Perform One-way Random Effects model for panel data.
"""Perform One-way Random Effects model for panel data
Parameters
----------
@@ -481,7 +478,7 @@ class ROUTER_econometrics(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def unitroot(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -499,7 +496,6 @@ class ROUTER_econometrics(Container):
regression: str
Regression type to use in the test. Either "c" for constant only, "ct" for constant and trend, or "ctt" for
constant, trend, and trend-squared.
Returns
-------
OBBject[Data]

View File

@@ -791,7 +791,7 @@ class ROUTER_economy(Container):
multiplier : int
Multiplier of the timespan. (provider: polygon)
period : Optional[Union[Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']]]
Period of the data to return. (provider: yfinance)
Time period of the data to return. (provider: yfinance)
prepost : bool
Include Pre and Post market data. (provider: yfinance)
rounding : bool
@@ -1083,7 +1083,7 @@ class ROUTER_economy(Container):
**inputs,
)
@validate
@validate_call
def sp500_multiples(
self,
series_name: typing_extensions.Annotated[

View File

@@ -72,7 +72,7 @@ class ROUTER_forex(Container):
adjusted : bool
Whether the data is adjusted. (provider: polygon)
period : Optional[Union[Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']]]
Period of the data to return. (provider: yfinance)
Time period of the data to return. (provider: yfinance)
Returns
-------

View File

@@ -7,9 +7,9 @@ import typing_extensions
from openbb_core.app.model.custom_parameter import OpenBBCustomParameter
from openbb_core.app.model.obbject import OBBject
from openbb_core.app.static.container import Container
from openbb_core.app.static.decorators import validate
from openbb_core.app.static.filters import filter_inputs
from openbb_provider.abstract.data import Data
from pydantic import validate_call
class ROUTER_futures(Container):
@@ -21,7 +21,7 @@ class ROUTER_futures(Container):
def __repr__(self) -> str:
return self.__doc__ or ""
@validate
@validate_call
def curve(
self,
symbol: typing_extensions.Annotated[
@@ -87,7 +87,7 @@ class ROUTER_futures(Container):
**inputs,
)
@validate
@validate_call
def load(
self,
symbol: typing_extensions.Annotated[
@@ -132,7 +132,7 @@ class ROUTER_futures(Container):
interval : Optional[Union[Literal['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo']]]
Data granularity. (provider: yfinance)
period : Optional[Union[Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']]]
Period of the data to return. (provider: yfinance)
Time period of the data to return. (provider: yfinance)
prepost : bool
Include Pre and Post market data. (provider: yfinance)
adjust : bool

View File

@@ -7,7 +7,6 @@ import typing_extensions
from annotated_types import Ge, Gt
from openbb_core.app.model.obbject import OBBject
from openbb_core.app.static.container import Container
from openbb_core.app.static.decorators import validate
from openbb_core.app.static.filters import filter_inputs
from openbb_provider.abstract.data import Data
from openbb_qa.qa_models import (
@@ -17,6 +16,7 @@ from openbb_qa.qa_models import (
SummaryModel,
UnitRootModel,
)
from pydantic import validate_call
class ROUTER_qa(Container):
@@ -36,11 +36,11 @@ class ROUTER_qa(Container):
def __repr__(self) -> str:
return self.__doc__ or ""
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def capm(
self, data: Union[List[Data], pandas.DataFrame], target: str
) -> OBBject[CAPMModel]:
"""Get Capital Asset Pricing Model.""" # noqa: E501
"""Capital Asset Pricing Model.""" # noqa: E501
inputs = filter_inputs(
data=data,
@@ -52,14 +52,14 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def kurtosis(
self,
data: Union[List[Data], pandas.DataFrame],
target: str,
window: typing_extensions.Annotated[int, Gt(gt=0)],
) -> OBBject[List[Data]]:
"""Get the Kurtosis.
"""Kurtosis.
Parameters
----------
@@ -87,11 +87,12 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def normality(
self, data: Union[List[Data], pandas.DataFrame], target: str
) -> OBBject[NormalityModel]:
"""Get Normality Statistics.
"""
Normality Statistics.
- **Kurtosis**: whether the kurtosis of a sample differs from the normal distribution.
- **Skewness**: whether the skewness of a sample differs from the normal distribution.
@@ -122,7 +123,7 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def om(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -130,7 +131,7 @@ class ROUTER_qa(Container):
threshold_start: float = 0.0,
threshold_end: float = 1.5,
) -> OBBject[List[OmegaModel]]:
"""Calculate the Omega Ratio.
"""Omega Ratio.
Parameters
----------
@@ -161,7 +162,7 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def quantile(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -169,24 +170,7 @@ class ROUTER_qa(Container):
window: typing_extensions.Annotated[int, Gt(gt=0)],
quantile_pct: typing_extensions.Annotated[float, Ge(ge=0)] = 0.5,
) -> OBBject[List[Data]]:
"""Get Quantile.
Parameters
----------
data : List[Data]
Time series data.
target : str
Target column name.
window : PositiveInt
Window size.
quantile_pct : NonNegativeFloat, optional
Quantile percentage, by default 0.5
Returns
-------
OBBject[List[Data]]
Quantile.
""" # noqa: E501
"""Quantile.""" # noqa: E501
inputs = filter_inputs(
data=data,
@@ -200,7 +184,7 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def sh(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -208,7 +192,7 @@ class ROUTER_qa(Container):
rfr: float = 0.0,
window: typing_extensions.Annotated[int, Gt(gt=0)] = 252,
) -> OBBject[List[Data]]:
"""Get Sharpe Ratio.
"""Sharpe Ratio.
Parameters
----------
@@ -239,14 +223,14 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def skew(
self,
data: Union[List[Data], pandas.DataFrame],
target: str,
window: typing_extensions.Annotated[int, Gt(gt=0)],
) -> OBBject[List[Data]]:
"""Get Skewness.
"""Skewness.
Parameters
----------
@@ -274,7 +258,7 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def so(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -283,7 +267,7 @@ class ROUTER_qa(Container):
window: typing_extensions.Annotated[int, Gt(gt=0)] = 252,
adjusted: bool = False,
) -> OBBject[List[Data]]:
"""Get Sortino Ratio.
"""Sortino Ratio.
For method & terminology see: http://www.redrockcapital.com/Sortino__A__Sharper__Ratio_Red_Rock_Capital.pdf
@@ -319,11 +303,11 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def summary(
self, data: Union[List[Data], pandas.DataFrame], target: str
) -> OBBject[SummaryModel]:
"""Get Summary Statistics.
"""Summary.
Parameters
----------
@@ -348,7 +332,7 @@ class ROUTER_qa(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def unitroot(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -356,7 +340,7 @@ class ROUTER_qa(Container):
fuller_reg: Literal["c", "ct", "ctt", "nc", "c"] = "c",
kpss_reg: Literal["c", "ct"] = "c",
) -> OBBject[UnitRootModel]:
"""Get Unit Root Test.
"""Unit Root Test.
Augmented Dickey-Fuller test for unit root.
Kwiatkowski-Phillips-Schmidt-Shin test for unit root.

View File

@@ -163,6 +163,10 @@ class ROUTER_stocks(Container):
Union[str, List[str]],
OpenBBCustomParameter(description="Symbol to get data for."),
],
interval: typing_extensions.Annotated[
Union[str, None],
OpenBBCustomParameter(description="Time interval of the data to return."),
] = "1d",
start_date: typing_extensions.Annotated[
Union[datetime.date, None, str],
OpenBBCustomParameter(
@@ -188,6 +192,8 @@ class ROUTER_stocks(Container):
----------
symbol : str
Symbol to get data for.
interval : Union[str, None]
Time interval of the data to return.
start_date : Union[datetime.date, None]
Start date of the data, in YYYY-MM-DD format.
end_date : Union[datetime.date, None]
@@ -198,60 +204,34 @@ class ROUTER_stocks(Container):
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'alpha_vantage' if there is
no default.
function_ : Literal['TIME_SERIES_INTRADAY', 'TIME_SERIES_DAILY', 'TIME_SERIES_WEEKLY', 'TIME_SERIES_MONTHLY', 'TIME_SERIES_DAILY_ADJUSTED', 'TIME_SERIES_WEEKLY_ADJUSTED', 'TIME_SERIES_MONTHLY_ADJUSTED']
The time series of your choice. (provider: alpha_vantage)
period : Optional[Union[Literal['intraday', 'daily', 'weekly', 'monthly'], Literal['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']]]
Period of the data to return. (provider: alpha_vantage, yfinance)
interval : Optional[Union[Literal['1min', '5min', '15min', '30min', '60min'], Literal['1d', '1m'], Literal['1min', '5min', '15min', '30min', '1hour', '4hour', '1day'], Literal['1m', '2m', '5m', '15m', '30m', '60m', '90m', '1h', '1d', '5d', '1wk', '1mo', '3mo']]]
Data granularity. (provider: alpha_vantage, cboe, fmp, yfinance)
adjusted : Optional[Union[bool]]
Output time series is adjusted by historical split and dividend events. (provider: alpha_vantage, polygon)
Output time series is adjusted by historical split and dividend events.Only available for intraday data. (provider: alpha_vantage, polygon); Adjust all OHLC data automatically. (provider: yfinance)
extended_hours : Optional[Union[bool]]
Extended trading hours during pre-market and after-hours. (provider: alpha_vantage)
Extended trading hours during pre-market and after-hours.Only available for intraday data. (provider: alpha_vantage)
month : Optional[Union[str]]
Query a specific month in history (in YYYY-MM format). (provider: alpha_vantage)
outputsize : Optional[Union[Literal['compact', 'full']]]
Compact returns only the latest 100 data points in the intraday time series; full returns trailing 30 days of the most recent intraday data if the month parameter (see above) is not specified, or the full intraday data for a specific month in history if the month parameter is specified. (provider: alpha_vantage)
timeseries : Optional[Union[typing_extensions.Annotated[int, Ge(ge=0)]]]
Number of days to look back. (provider: fmp)
timezone : Optional[Union[Literal['Africa/Algiers', 'Africa/Cairo', 'Africa/Casablanca', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Monrovia', 'Africa/Nairobi', 'America/Argentina/Buenos_Aires', 'America/Bogota', 'America/Caracas', 'America/Chicago', 'America/Chihuahua', 'America/Denver', 'America/Godthab', 'America/Guatemala', 'America/Guyana', 'America/Halifax', 'America/Indiana/Indianapolis', 'America/Juneau', 'America/La_Paz', 'America/Lima', 'America/Lima', 'America/Los_Angeles', 'America/Mazatlan', 'America/Mexico_City', 'America/Mexico_City', 'America/Monterrey', 'America/Montevideo', 'America/New_York', 'America/Phoenix', 'America/Regina', 'America/Santiago', 'America/Sao_Paulo', 'America/St_Johns', 'America/Tijuana', 'Asia/Almaty', 'Asia/Baghdad', 'Asia/Baku', 'Asia/Bangkok', 'Asia/Bangkok', 'Asia/Chongqing', 'Asia/Colombo', 'Asia/Dhaka', 'Asia/Dhaka', 'Asia/Hong_Kong', 'Asia/Irkutsk', 'Asia/Jakarta', 'Asia/Jerusalem', 'Asia/Kabul', 'Asia/Kamchatka', 'Asia/Karachi', 'Asia/Karachi', 'Asia/Kathmandu', 'Asia/Kolkata', 'Asia/Kolkata', 'Asia/Kolkata', 'Asia/Kolkata', 'Asia/Krasnoyarsk', 'Asia/Kuala_Lumpur', 'Asia/Kuwait', 'Asia/Magadan', 'Asia/Muscat', 'Asia/Muscat', 'Asia/Novosibirsk', 'Asia/Rangoon', 'Asia/Riyadh', 'Asia/Seoul', 'Asia/Shanghai', 'Asia/Singapore', 'Asia/Srednekolymsk', 'Asia/Taipei', 'Asia/Tashkent', 'Asia/Tbilisi', 'Asia/Tehran', 'Asia/Tokyo', 'Asia/Tokyo', 'Asia/Tokyo', 'Asia/Ulaanbaatar', 'Asia/Urumqi', 'Asia/Vladivostok', 'Asia/Yakutsk', 'Asia/Yekaterinburg', 'Asia/Yerevan', 'Atlantic/Azores', 'Atlantic/Cape_Verde', 'Atlantic/South_Georgia', 'Australia/Adelaide', 'Australia/Brisbane', 'Australia/Darwin', 'Australia/Hobart', 'Australia/Melbourne', 'Australia/Melbourne', 'Australia/Perth', 'Australia/Sydney', 'Etc/UTC', 'UTC', 'Europe/Amsterdam', 'Europe/Athens', 'Europe/Belgrade', 'Europe/Berlin', 'Europe/Berlin', 'Europe/Bratislava', 'Europe/Brussels', 'Europe/Bucharest', 'Europe/Budapest', 'Europe/Copenhagen', 'Europe/Dublin', 'Europe/Helsinki', 'Europe/Istanbul', 'Europe/Kaliningrad', 'Europe/Kiev', 'Europe/Lisbon', 'Europe/Ljubljana', 'Europe/London', 'Europe/London', 'Europe/Madrid', 'Europe/Minsk', 'Europe/Moscow', 'Europe/Moscow', 'Europe/Paris', 'Europe/Prague', 'Europe/Riga', 'Europe/Rome', 'Europe/Samara', 'Europe/Sarajevo', 'Europe/Skopje', 'Europe/Sofia', 'Europe/Stockholm', 'Europe/Tallinn', 'Europe/Vienna', 'Europe/Vilnius', 'Europe/Volgograd', 'Europe/Warsaw', 'Europe/Zagreb', 'Pacific/Apia', 'Pacific/Auckland', 'Pacific/Auckland', 'Pacific/Chatham', 'Pacific/Fakaofo', 'Pacific/Fiji', 'Pacific/Guadalcanal', 'Pacific/Guam', 'Pacific/Honolulu', 'Pacific/Majuro', 'Pacific/Midway', 'Pacific/Midway', 'Pacific/Noumea', 'Pacific/Pago_Pago', 'Pacific/Port_Moresby', 'Pacific/Tongatapu']]]
Returns trading times in this timezone. (provider: intrinio)
output_size : Optional[Union[Literal['compact', 'full']]]
Compact returns only the latest 100 data points in the intraday time series; full returns trailing 30 days of the most recent intraday data if the month parameter is not specified, or the full intraday data for aspecific month in history if the month parameter is specified. (provider: alpha_vantage)
limit : Optional[Union[typing_extensions.Annotated[int, Ge(ge=0)], int]]
Number of days to look back (Only for interval 1d). (provider: fmp); The number of data entries to return. (provider: polygon)
start_time : Optional[Union[datetime.time]]
Return intervals starting at the specified time on the `start_date` formatted as 'HH:MM:SS'. (provider: intrinio)
end_time : Optional[Union[datetime.time]]
Return intervals stopping at the specified time on the `end_date` formatted as 'HH:MM:SS'. (provider: intrinio)
timezone : str
Timezone of the data, in the IANA format (Continent/City). (provider: intrinio)
source : Optional[Union[Literal['realtime', 'delayed', 'nasdaq_basic']]]
The source of the data. (provider: intrinio)
start_time : Optional[Union[datetime.time]]
Return intervals starting at the specified time on the `start_date` formatted as 'hh:mm:ss'. (provider: intrinio)
end_time : Optional[Union[datetime.time]]
Return intervals stopping at the specified time on the `end_date` formatted as 'hh:mm:ss'. (provider: intrinio)
interval_size : Optional[Union[Literal['1m', '5m', '10m', '15m', '30m', '60m', '1h']]]
The data time frequency. (provider: intrinio)
multiplier : int
Multiplier of the timespan. (provider: polygon)
timespan : Literal['minute', 'hour', 'day', 'week', 'month', 'quarter', 'year']
Timespan of the data. (provider: polygon)
sort : Literal['asc', 'desc']
Sort order of the data. (provider: polygon)
limit : int
The number of data entries to return. (provider: polygon)
prepost : bool
Include Pre and Post market data. (provider: yfinance)
actions : bool
Include actions. (provider: yfinance)
auto_adjust : bool
Adjust all OHLC data automatically. (provider: yfinance)
include : bool
Include Dividends and Stock Splits in results. (provider: yfinance)
back_adjust : bool
Attempt to adjust all the data automatically. (provider: yfinance)
progress : bool
Show progress bar. (provider: yfinance)
ignore_tz : bool
When combining from different timezones, ignore that part of datetime. (provider: yfinance)
rounding : bool
Round to two decimal places? (provider: yfinance)
repair : bool
Detect currency unit 100x mixups and attempt repair. (provider: yfinance)
keepna : bool
Keep NaN rows returned by Yahoo? (provider: yfinance)
group_by : Literal['ticker', 'column']
Group by ticker or column. (provider: yfinance)
Returns
-------
@@ -284,7 +264,7 @@ class ROUTER_stocks(Container):
vwap : Optional[Union[typing_extensions.Annotated[float, Gt(gt=0)]]]
Volume Weighted Average Price of the symbol.
adj_close : Optional[Union[typing_extensions.Annotated[float, Gt(gt=0)], float]]
The adjusted close price of the symbol. (provider: alpha_vantage, fmp)
The adjusted close price of the symbol. (provider: alpha_vantage, fmp); Adjusted closing price during the period. (provider: intrinio)
dividend_amount : Optional[Union[typing_extensions.Annotated[float, Ge(ge=0)]]]
Dividend amount paid for the corresponding date. (provider: alpha_vantage)
split_coefficient : Optional[Union[typing_extensions.Annotated[float, Ge(ge=0)]]]
@@ -295,14 +275,14 @@ class ROUTER_stocks(Container):
Number of puts traded during the most recent trading period. Only valid if interval is 1m. (provider: cboe)
total_options_volume : Optional[Union[float]]
Total number of options traded during the most recent trading period. Only valid if interval is 1m. (provider: cboe)
label : Optional[Union[str]]
Human readable format of the date. (provider: fmp)
unadjusted_volume : Optional[Union[float]]
Unadjusted volume of the symbol. (provider: fmp)
change : Optional[Union[float]]
Change in the price of the symbol from the previous day. (provider: fmp, intrinio)
change_percent : Optional[Union[float]]
Change % in the price of the symbol. (provider: fmp)
label : Optional[Union[str]]
Human readable format of the date. (provider: fmp)
change_over_time : Optional[Union[float]]
Change % in the price of the symbol over a period of time. (provider: fmp)
close_time : Optional[Union[datetime]]
@@ -311,6 +291,28 @@ class ROUTER_stocks(Container):
The data time frequency. (provider: intrinio)
average : Optional[Union[float]]
Average trade price of an individual stock during the interval. (provider: intrinio)
intra_period : Optional[Union[bool]]
If true, the stock price represents an unfinished period (be it day, week, quarter, month, or year), meaning that the close price is the latest price available, not the official close price for the period (provider: intrinio)
adj_open : Optional[Union[float]]
Adjusted open price during the period. (provider: intrinio)
adj_high : Optional[Union[float]]
Adjusted high price during the period. (provider: intrinio)
adj_low : Optional[Union[float]]
Adjusted low price during the period. (provider: intrinio)
adj_volume : Optional[Union[float]]
Adjusted volume during the period. (provider: intrinio)
factor : Optional[Union[float]]
factor by which to multiply stock prices before this date, in order to calculate historically-adjusted stock prices. (provider: intrinio)
split_ratio : Optional[Union[float]]
Ratio of the stock split, if a stock split occurred. (provider: intrinio)
dividend : Optional[Union[float]]
Dividend amount, if a dividend was paid. (provider: intrinio)
percent_change : Optional[Union[float]]
Percent change in the price of the symbol from the previous day. (provider: intrinio)
fifty_two_week_high : Optional[Union[float]]
52 week high price for the symbol. (provider: intrinio)
fifty_two_week_low : Optional[Union[float]]
52 week low price for the symbol. (provider: intrinio)
transactions : Optional[Union[typing_extensions.Annotated[int, Gt(gt=0)]]]
Number of transactions for the symbol in the time period. (provider: polygon)
""" # noqa: E501
@@ -321,6 +323,7 @@ class ROUTER_stocks(Container):
},
standard_params={
"symbol": ",".join(symbol) if isinstance(symbol, list) else symbol,
"interval": interval,
"start_date": start_date,
"end_date": end_date,
},
@@ -809,13 +812,13 @@ class ROUTER_stocks(Container):
**inputs,
)
@validate
@validate_call
def search(
self,
query: typing_extensions.Annotated[
str, OpenBBCustomParameter(description="Search query.")
] = "",
is_symbol: typing_extensions.Annotated[
ticker: typing_extensions.Annotated[
bool,
OpenBBCustomParameter(description="Whether to search by ticker symbol."),
] = False,
@@ -828,7 +831,7 @@ class ROUTER_stocks(Container):
----------
query : str
Search query.
is_symbol : bool
ticker : bool
Whether to search by ticker symbol.
provider : Union[Literal['cboe'], None]
The provider to use for the query, by default None.
@@ -866,7 +869,7 @@ class ROUTER_stocks(Container):
},
standard_params={
"query": query,
"is_symbol": is_symbol,
"ticker": ticker,
},
extra_params=kwargs,
)

View File

@@ -10,6 +10,7 @@ from openbb_core.app.static.container import Container
from openbb_core.app.static.decorators import validate
from openbb_core.app.static.filters import filter_inputs
from openbb_provider.abstract.data import Data
from pydantic import validate_call
class ROUTER_stocks_fa(Container):
@@ -56,8 +57,9 @@ class ROUTER_stocks_fa(Container):
],
period: typing_extensions.Annotated[
Literal["annual", "quarter"],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
limit: typing_extensions.Annotated[
limit: typing_extensions.Annotated[
int,
OpenBBCustomParameter(description="The number of data entries to return."),
@@ -72,7 +74,7 @@ class ROUTER_stocks_fa(Container):
symbol : str
Symbol to get data for.
period : Literal['annual', 'quarter']
Period of the data to return.
Time period of the data to return.
limit : int
The number of data entries to return.
provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None]
@@ -121,6 +123,7 @@ class ROUTER_stocks_fa(Container):
Returns
-------
OBBject
results : Union[List[BalanceSheet]]
results : Union[List[BalanceSheet]]
Serializable results.
provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None]
@@ -134,6 +137,7 @@ class ROUTER_stocks_fa(Container):
BalanceSheet
------------
symbol : Optional[Union[str]]
symbol : Optional[Union[str]]
Symbol to get data for.
date : date
@@ -499,8 +503,9 @@ class ROUTER_stocks_fa(Container):
],
period: typing_extensions.Annotated[
Literal["annual", "quarter"],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
limit: typing_extensions.Annotated[
limit: typing_extensions.Annotated[
int,
OpenBBCustomParameter(description="The number of data entries to return."),
@@ -515,17 +520,19 @@ class ROUTER_stocks_fa(Container):
symbol : str
Symbol to get data for.
period : Literal['annual', 'quarter']
Period of the data to return.
Time period of the data to return.
limit : int
The number of data entries to return.
provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
no default.
cik : Optional[Union[str]]
cik : Optional[Union[str]]
Central Index Key (CIK) of the company. (provider: fmp)
type : Literal['reported', 'standardized']
Type of the statement to be fetched. (provider: intrinio)
year : Optional[Union[int]]
year : Optional[Union[int]]
Year of the statement to be fetched. (provider: intrinio)
company_name : Optional[Union[str]]
@@ -564,6 +571,7 @@ class ROUTER_stocks_fa(Container):
Returns
-------
OBBject
results : Union[List[CashFlowStatement]]
results : Union[List[CashFlowStatement]]
Serializable results.
provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None]
@@ -1187,19 +1195,22 @@ class ROUTER_stocks_fa(Container):
@validate
def est(
self,
symbol: typing_extensions.Annotated[
symbol: typing_extensions.Annotated[
Union[str, List[str]],
OpenBBCustomParameter(description="Symbol to get data for."),
],
period: typing_extensions.Annotated[
Literal["quarter", "annual"],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
limit: typing_extensions.Annotated[
limit: typing_extensions.Annotated[
int,
OpenBBCustomParameter(description="The number of data entries to return."),
] = 30,
provider: Union[Literal["fmp"], None] = None,
provider: Union[Literal["fmp"], None] = None,
**kwargs
) -> OBBject[List[Data]]:
"""Analyst Estimates.
@@ -1209,7 +1220,7 @@ class ROUTER_stocks_fa(Container):
symbol : str
Symbol to get data for.
period : Literal['quarter', 'annual']
Period of the data to return.
Time period of the data to return.
limit : int
The number of data entries to return.
provider : Union[Literal['fmp'], None]
@@ -1382,8 +1393,9 @@ class ROUTER_stocks_fa(Container):
],
period: typing_extensions.Annotated[
Literal["annual", "quarter"],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
limit: typing_extensions.Annotated[
limit: typing_extensions.Annotated[
int,
OpenBBCustomParameter(description="The number of data entries to return."),
@@ -1398,19 +1410,22 @@ class ROUTER_stocks_fa(Container):
symbol : str
Symbol to get data for.
period : Literal['annual', 'quarter']
Period of the data to return.
Time period of the data to return.
limit : int
The number of data entries to return.
provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
no default.
cik : Optional[Union[str]]
cik : Optional[Union[str]]
The CIK of the company if no symbol is provided. (provider: fmp)
type : Literal['reported', 'standardized']
Type of the statement to be fetched. (provider: intrinio)
year : Optional[Union[int]]
year : Optional[Union[int]]
Year of the statement to be fetched. (provider: intrinio)
company_name : Optional[Union[str]]
company_name : Optional[Union[str]]
Name of the company. (provider: polygon)
company_name_search : Optional[Union[str]]
@@ -1447,6 +1462,7 @@ class ROUTER_stocks_fa(Container):
Returns
-------
OBBject
results : Union[List[IncomeStatement]]
results : Union[List[IncomeStatement]]
Serializable results.
provider : Union[Literal['fmp', 'intrinio', 'polygon', 'yfinance'], None]
@@ -1460,6 +1476,7 @@ class ROUTER_stocks_fa(Container):
IncomeStatement
---------------
symbol : Optional[Union[str]]
symbol : Optional[Union[str]]
Symbol to get data for.
date : date
@@ -1585,7 +1602,7 @@ class ROUTER_stocks_fa(Container):
] = 10,
period: typing_extensions.Annotated[
Literal["annual", "quarter"],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
provider: Union[Literal["fmp"], None] = None,
**kwargs
@@ -1599,7 +1616,7 @@ class ROUTER_stocks_fa(Container):
limit : int
The number of data entries to return.
period : Literal['annual', 'quarter']
Period of the data to return.
Time period of the data to return.
provider : Union[Literal['fmp'], None]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
@@ -1608,6 +1625,7 @@ class ROUTER_stocks_fa(Container):
Returns
-------
OBBject
results : Union[List[IncomeStatementGrowth]]
results : Union[List[IncomeStatementGrowth]]
Serializable results.
provider : Union[Literal['fmp'], None]
@@ -1962,19 +1980,23 @@ class ROUTER_stocks_fa(Container):
@validate
def metrics(
self,
symbol: typing_extensions.Annotated[
symbol: typing_extensions.Annotated[
Union[str, List[str]],
OpenBBCustomParameter(description="Symbol to get data for."),
],
period: typing_extensions.Annotated[
Union[Literal["annual", "quarter"], None],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
limit: typing_extensions.Annotated[
Union[int, None],
limit: typing_extensions.Annotated[
Union[int, None],
OpenBBCustomParameter(description="The number of data entries to return."),
] = 100,
provider: Union[Literal["fmp"], None] = None,
provider: Union[Literal["fmp"], None] = None,
**kwargs
) -> OBBject[List[Data]]:
"""Key Metrics.
@@ -1984,13 +2006,15 @@ class ROUTER_stocks_fa(Container):
symbol : str
Symbol to get data for.
period : Union[Literal['annual', 'quarter'], None]
Period of the data to return.
Time period of the data to return.
limit : Union[int, None]
The number of data entries to return.
provider : Union[Literal['fmp'], None]
provider : Union[Literal['fmp'], None]
The provider to use for the query, by default None.
If None, the provider specified in defaults is selected or 'fmp' if there is
no default.
with_ttm : Optional[Union[bool]]
with_ttm : Optional[Union[bool]]
Include trailing twelve months (TTM) data. (provider: fmp)
@@ -2642,7 +2666,7 @@ class ROUTER_stocks_fa(Container):
],
period: typing_extensions.Annotated[
Literal["annual", "quarter"],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
limit: typing_extensions.Annotated[
int,
@@ -2658,7 +2682,7 @@ class ROUTER_stocks_fa(Container):
symbol : str
Symbol to get data for.
period : Literal['annual', 'quarter']
Period of the data to return.
Time period of the data to return.
limit : int
The number of data entries to return.
provider : Union[Literal['fmp'], None]
@@ -2790,12 +2814,16 @@ class ROUTER_stocks_fa(Container):
Price cash flow ratio.
price_earnings_to_growth_ratio : Optional[Union[float]]
Price earnings to growth ratio.
price_sales_ratio : Optional[Union[float]]
price_sales_ratio : Optional[Union[float]]
Price sales ratio.
dividend_yield : Optional[Union[float]]
dividend_yield : Optional[Union[float]]
Dividend yield.
enterprise_value_multiple : Optional[Union[float]]
enterprise_value_multiple : Optional[Union[float]]
Enterprise value multiple.
price_fair_value : Optional[Union[float]]
price_fair_value : Optional[Union[float]]
Price fair value.""" # noqa: E501
@@ -2825,8 +2853,9 @@ class ROUTER_stocks_fa(Container):
],
period: typing_extensions.Annotated[
Literal["quarter", "annual"],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
structure: typing_extensions.Annotated[
structure: typing_extensions.Annotated[
Literal["hierarchical", "flat"],
OpenBBCustomParameter(description="Structure of the returned data."),
@@ -2841,7 +2870,7 @@ class ROUTER_stocks_fa(Container):
symbol : str
Symbol to get data for.
period : Literal['quarter', 'annual']
Period of the data to return.
Time period of the data to return.
structure : Literal['hierarchical', 'flat']
Structure of the returned data.
provider : Union[Literal['fmp'], None]
@@ -2906,7 +2935,7 @@ class ROUTER_stocks_fa(Container):
],
period: typing_extensions.Annotated[
Literal["quarter", "annual"],
OpenBBCustomParameter(description="Period of the data to return."),
OpenBBCustomParameter(description="Time period of the data to return."),
] = "annual",
structure: typing_extensions.Annotated[
Literal["hierarchical", "flat"],
@@ -2922,7 +2951,7 @@ class ROUTER_stocks_fa(Container):
symbol : str
Symbol to get data for.
period : Literal['quarter', 'annual']
Period of the data to return.
Time period of the data to return.
structure : Literal['hierarchical', 'flat']
Structure of the returned data.
provider : Union[Literal['fmp'], None]

View File

@@ -7,9 +7,9 @@ import typing_extensions
from annotated_types import Ge, Gt
from openbb_core.app.model.obbject import OBBject
from openbb_core.app.static.container import Container
from openbb_core.app.static.decorators import validate
from openbb_core.app.static.filters import filter_inputs
from openbb_provider.abstract.data import Data
from pydantic import validate_call
class ROUTER_ta(Container):
@@ -45,7 +45,7 @@ class ROUTER_ta(Container):
def __repr__(self) -> str:
return self.__doc__ or ""
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def ad(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -97,7 +97,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def adosc(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -150,7 +150,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def adx(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -204,7 +204,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def aroon(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -262,7 +262,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def atr(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -317,7 +317,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def bbands(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -388,7 +388,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def cci(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -432,7 +432,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def cg(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -478,7 +478,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def clenow(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -524,7 +524,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def cones(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -606,7 +606,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def demark(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -660,7 +660,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def donchian(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -715,7 +715,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def ema(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -775,7 +775,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def fib(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -822,7 +822,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def fisher(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -872,7 +872,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def hma(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -927,7 +927,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def ichimoku(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -977,7 +977,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def kc(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -1035,7 +1035,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def macd(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -1099,7 +1099,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def obv(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -1149,7 +1149,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def rsi(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -1209,7 +1209,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def sma(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -1267,7 +1267,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def stoch(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -1322,7 +1322,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def vwap(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -1371,7 +1371,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def wma(
self,
data: Union[List[Data], pandas.DataFrame],
@@ -1426,7 +1426,7 @@ class ROUTER_ta(Container):
**inputs,
)
@validate(config=dict(arbitrary_types_allowed=True))
@validate_call(config=dict(arbitrary_types_allowed=True))
def zlma(
self,
data: Union[List[Data], pandas.DataFrame],

View File

@@ -83,7 +83,9 @@ def test_filter_extra_params_wrong_param(query):
extra_params = StockHistorical()
assert not query.filter_extra_params(extra_params, "fmp")
extra = query.filter_extra_params(extra_params, "fmp")
assert isinstance(extra, dict)
assert len(extra) == 1
@pytest.fixture

View File

@@ -8,7 +8,7 @@ from datetime import (
from typing import List, Optional, Set, Union
from dateutil import parser
from pydantic import Field, PositiveFloat, validator
from pydantic import Field, PositiveFloat, field_validator
from openbb_provider.abstract.data import Data
from openbb_provider.abstract.query_params import QueryParams
@@ -19,14 +19,20 @@ class StockHistoricalQueryParams(QueryParams):
"""Stock end of day Query."""
symbol: str = Field(description=QUERY_DESCRIPTIONS.get("symbol", ""))
interval: Optional[str] = Field(
default="1d",
description=QUERY_DESCRIPTIONS.get("interval", ""),
)
start_date: Optional[dateType] = Field(
description=QUERY_DESCRIPTIONS.get("start_date", ""), default=None
default=None,
description=QUERY_DESCRIPTIONS.get("start_date", ""),
)
end_date: Optional[dateType] = Field(
description=QUERY_DESCRIPTIONS.get("end_date", ""), default=None
default=None,
description=QUERY_DESCRIPTIONS.get("end_date", ""),
)
@validator("symbol", pre=True, check_fields=False, always=True)
@field_validator("symbol", mode="before", check_fields=False)
def upper_symbol(cls, v: Union[str, List[str], Set[str]]):
"""Convert symbol to uppercase."""
if isinstance(v, str):
@@ -47,7 +53,7 @@ class StockHistoricalData(Data):
default=None, description=DATA_DESCRIPTIONS.get("vwap", "")
)
@validator("date", pre=True, check_fields=False)
@field_validator("date", mode="before", check_fields=False)
def date_validate(cls, v): # pylint: disable=E0213
"""Return formatted datetime."""
return parser.isoparse(str(v))

View File

@@ -4,9 +4,10 @@ QUERY_DESCRIPTIONS = {
"symbol": "Symbol to get data for.",
"start_date": "Start date of the data, in YYYY-MM-DD format.",
"end_date": "End date of the data, in YYYY-MM-DD format.",
"weekly": "Whether to return weekly data.",
"monthly": "Whether to return monthly data.",
"period": "Period of the data to return.",
"interval": "Time interval of the data to return.",
# "weekly": "Whether to return weekly data.",
# "monthly": "Whether to return monthly data.",
"period": "Time period of the data to return.",
"date": "A specific date to get data for.",
"limit": "The number of data entries to return.",
"countries": "The country or countries to get data.",

View File

@@ -2,12 +2,10 @@
from datetime import datetime
from io import StringIO
from typing import Any, Dict, List, Literal, Optional, get_args
from typing import Any, Dict, List, Literal, Optional
import pandas as pd
import requests
from dateutil.relativedelta import relativedelta
from openbb_alpha_vantage.utils.helpers import extract_key_name, get_data, get_interval
from openbb_provider.abstract.fetcher import Fetcher
from openbb_provider.standard_models.stock_historical import (
StockHistoricalData,
@@ -19,6 +17,7 @@ from pydantic import (
Field,
NonNegativeFloat,
PositiveFloat,
PrivateAttr,
field_validator,
model_validator,
)
@@ -27,12 +26,36 @@ from pydantic import (
class AVStockHistoricalQueryParams(StockHistoricalQueryParams):
"""Alpha Vantage Stock End of Day Query.
Source: https://www.alphavantage.co/documentation/
Source: https://www.alphavantage.co/documentation/#time-series-data
"""
__alias_dict__ = {"function_": "function"}
interval: Literal["1m", "5m", "15m", "30m", "60m", "1d", "1W", "1M"] = Field(
default="1d",
description=QUERY_DESCRIPTIONS.get("interval", ""),
)
adjusted: Optional[bool] = Field(
description="Output time series is adjusted by historical split and dividend events.",
default=False,
)
extended_hours: Optional[bool] = Field(
description="Extended trading hours during pre-market and after-hours."
"Only available for intraday data.",
default=False,
)
month: Optional[str] = Field(
description="Query a specific month in history (in YYYY-MM format).",
default=None,
)
output_size: Optional[Literal["compact", "full"]] = Field(
description="Compact returns only the latest 100 data points in the intraday "
"time series; full returns trailing 30 days of the most recent intraday data "
"if the month parameter is not specified, or the full intraday data for a"
"specific month in history if the month parameter is specified.",
default="full",
alias="outputsize",
)
function_: Literal[
_function: Literal[
"TIME_SERIES_INTRADAY",
"TIME_SERIES_DAILY",
"TIME_SERIES_WEEKLY",
@@ -40,151 +63,13 @@ class AVStockHistoricalQueryParams(StockHistoricalQueryParams):
"TIME_SERIES_DAILY_ADJUSTED",
"TIME_SERIES_WEEKLY_ADJUSTED",
"TIME_SERIES_MONTHLY_ADJUSTED",
] = Field(
description="The time series of your choice. ",
] = PrivateAttr(
default="TIME_SERIES_DAILY",
)
period: Optional[Literal["intraday", "daily", "weekly", "monthly"]] = Field(
default="daily", description=QUERY_DESCRIPTIONS.get("period", "")
)
interval: Optional[Literal["1min", "5min", "15min", "30min", "60min"]] = Field(
description="Data granularity.",
default="60min",
json_schema_extra=dict(
available_on_functions=["TIME_SERIES_INTRADAY"],
required_on_functions=["TIME_SERIES_INTRADAY"],
),
)
adjusted: Optional[bool] = Field(
description="Output time series is adjusted by historical split and dividend events.",
default=False,
json_schema_extra=dict(
available_on_functions=["TIME_SERIES_INTRADAY"],
),
)
extended_hours: Optional[bool] = Field(
description="Extended trading hours during pre-market and after-hours.",
default=False,
json_schema_extra=dict(
available_on_functions=["TIME_SERIES_INTRADAY"],
),
)
month: Optional[str] = Field(
description="Query a specific month in history (in YYYY-MM format).",
default=None,
json_schema_extra=dict(
available_on_functions=["TIME_SERIES_INTRADAY"],
),
)
outputsize: Optional[Literal["compact", "full"]] = Field(
description="Compact returns only the latest 100 data points in the intraday "
"time series; full returns trailing 30 days of the most recent intraday data "
"if the month parameter (see above) is not specified, or the full intraday "
"data for a specific month in history if the month parameter is specified.",
default="full",
json_schema_extra=dict(
available_on_functions=[
"TIME_SERIES_INTRADAY",
"TIME_SERIES_DAILY",
"TIME_SERIES_DAILY_ADJUSTED",
],
),
)
@model_validator(mode="after")
@classmethod
def setup_function(
cls, values: "AVStockHistoricalQueryParams"
): # pylint: disable=E0213
"""Set the function based on the period."""
functions_based_on_period = {
"intraday": "TIME_SERIES_INTRADAY",
"daily": "TIME_SERIES_DAILY",
"weekly": "TIME_SERIES_WEEKLY",
"monthly": "TIME_SERIES_MONTHLY",
}
values.function_ = functions_based_on_period[values.period]
return values
_datatype: Literal["json", "csv"] = PrivateAttr(default="json")
@model_validator(mode="after")
@classmethod
def adjusted_function_validate(
cls, values: "AVStockHistoricalQueryParams"
): # pylint: disable=E0213
"""
Validate that the function is adjusted if the `adjusted` parameter is set to True.
"""
if values.function_ != "TIME_SERIES_INTRADAY" and values.adjusted:
values.function_ = f"{values.function_}_ADJUSTED"
return values
@model_validator(mode="after")
@classmethod
def on_functions_validate(
cls, values: "AVStockHistoricalQueryParams"
): # pylint: disable=E0213
"""
Validate that the functions used on custom extra Field attributes
`available_on_functions` and `required_on_functions` are valid functions.
"""
custom_attributes = ["available_on_functions", "required_on_functions"]
available_functions = get_args(cls.__annotations__["function_"])
if values.function_ not in available_functions:
raise ValueError(
f"Function {values.function_} must be on of the following: {available_functions}"
)
def validate_functions(functions: List[str]):
for f in functions:
if f not in available_functions:
raise ValueError(
f"Function {f} must be on of the following: {available_functions}"
)
for field in cls.__fields__.values():
if (extra := field.json_schema_extra) is None:
continue
for attr in custom_attributes:
if functions := extra.get(attr, None):
validate_functions(functions)
return values
@model_validator(mode="after")
@classmethod
def on_functions_criteria_validate(
cls, values: "AVStockHistoricalQueryParams"
): # pylint: disable=E0213
"""
Validate that the fields are set to None if the function is not available
and that the required fields are not None if the function is required.
"""
timeseries = values.function_
for name, field in cls.__fields__.items():
if (extra := field.json_schema_extra) is None:
continue
field_value = getattr(values, name)
if (
available_on_functions := extra.get("available_on_functions", None)
) and timeseries not in available_on_functions:
setattr(values, name, None)
if (
(required_on_functions := extra.get("required_on_functions", None))
and timeseries in required_on_functions
and field_value is None
):
raise ValueError(f"Field {name} is required on function {timeseries}")
return values
@field_validator("month")
@field_validator("month", mode="before")
def month_validate(cls, v): # pylint: disable=E0213
"""Validate month, check if the month is in YYYY-MM format."""
if v is not None:
@@ -194,6 +79,32 @@ class AVStockHistoricalQueryParams(StockHistoricalQueryParams):
raise e
return v
@model_validator(mode="after")
@classmethod
def get_function_value(cls, values: "AVStockHistoricalQueryParams"):
"""Get the function from the provided interval for the Alpha Vantage API."""
functions = {
"m": "TIME_SERIES_INTRADAY",
"d": "TIME_SERIES_DAILY",
"W": "TIME_SERIES_WEEKLY",
"M": "TIME_SERIES_MONTHLY",
}
adjusted_value = {
False: "",
True: "_ADJUSTED",
}
if values.interval[-1] == "m":
values._function = f"{functions[values.interval[-1]]}"
else:
values._function = (
f"{functions[values.interval[-1]]}{adjusted_value[values.adjusted]}"
)
return values
class AVStockHistoricalData(StockHistoricalData):
"""Alpha Vantage Stock End of Day Data."""
@@ -240,38 +151,30 @@ class AVStockHistoricalFetcher(
query: AVStockHistoricalQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> dict:
) -> Dict:
"""Return the raw data from the Alpha Vantage endpoint."""
api_key = credentials.get("alpha_vantage_api_key") if credentials else ""
interval = get_interval(query.interval)
query_str = get_querystring(
query.model_dump(by_alias=True), ["start_date", "end_date"]
query.model_dump(by_alias=True), ["start_date", "end_date", "interval"]
)
query_str += f"&function={query._function}&interval={interval}"
url = f"https://www.alphavantage.co/query?{query_str}&apikey={api_key}"
url = f"https://www.alphavantage.co/query?{query_str}&datatype=csv&apikey={api_key}"
get_data = requests.get(url, timeout=10)
data = get_data(url, **kwargs)
dynamic_key = (set(data.keys()) - {"Meta Data"}).pop()
if "Information" in get_data.text:
info = get_data.json()["Information"]
raise ValueError(f"Alpha Vantage API Error: {info}")
data = pd.read_csv(
StringIO(get_data.text),
parse_dates=["timestamp"],
)
data["timestamp"] = pd.to_datetime(data["timestamp"])
data = data[
(data["timestamp"] >= pd.to_datetime(query.start_date))
& (data["timestamp"] <= pd.to_datetime(query.end_date))
]
return data.to_dict("records")
return data[dynamic_key]
@staticmethod
def transform_data(
data: dict,
data: Dict,
) -> List[AVStockHistoricalData]:
"""Transform the data to the standard format."""
data = [
{"date": date, **{extract_key_name(k): v for k, v in values.items()}}
for date, values in data.items()
]
return [AVStockHistoricalData.model_validate(d) for d in data]

View File

@@ -0,0 +1,166 @@
"""Alpha Vantage Helpers Module."""
import json
import re
from io import StringIO
from typing import Any, List, Optional, TypeVar, Union
import requests
from openbb_provider import helpers
from openbb_provider.utils.errors import EmptyDataError
from openbb_provider.utils.helpers import get_querystring
from pydantic import BaseModel
from requests.exceptions import SSLError
T = TypeVar("T", bound=BaseModel)
class BasicResponse:
"""Basic Response class."""
def __init__(self, response: StringIO):
"""Initialize the BasicResponse class."""
# Find a way to get the status code
self.status_code = 200
response.seek(0)
self.text = response.read()
def json(self) -> dict:
"""Return the response as a dictionary."""
return json.loads(self.text)
def request(url: str) -> BasicResponse:
"""Request function for PyScript.
Pass in Method and make sure to await!
Parameters
----------
url: str
URL to make request to
Return
------
response: BasicRequest
BasicRequest object with status_code and text attributes
"""
# pylint: disable=import-outside-toplevel
from pyodide.http import open_url # type: ignore
response = open_url(url)
return BasicResponse(response)
def get_data(url: str, **kwargs: Any) -> Union[list, dict]:
"""Get data from Alpha Vantage endpoint."""
try:
r: Union[requests.Response, BasicResponse] = helpers.make_request(url, **kwargs)
except SSLError:
r = request(url)
if r.status_code == 404:
raise RuntimeError("Alpha Vantage endpoint doesn't exist")
data = r.json()
if "Information" in data:
raise RuntimeError(f"Error in Alpha Vantage request -> {data['Information']}")
if len(data) == 0:
raise EmptyDataError()
return data
def create_url(
version: int,
endpoint: str,
api_key: Optional[str],
query: Optional[BaseModel] = None,
exclude: Optional[List[str]] = None,
) -> str:
"""Return a URL for the Alpha Vantage API.
Parameters
----------
version: int
The version of the API to use.
endpoint: str
The endpoint to use.
api_key: str
The API key to use.
query: Optional[BaseModel]
The dictionary to be turned into a querystring.
exclude: List[str]
The keys to be excluded from the querystring.
Returns
-------
str
The querystring.
"""
the_dict = {} if not query else query.model_dump()
query_string = get_querystring(the_dict, exclude or [])
base_url = f"https://financialmodelingprep.com/api/v{version}/"
return f"{base_url}{endpoint}?{query_string}&apikey={api_key}"
def get_data_many(
url: str, sub_dict: Optional[str] = None, **kwargs: Any
) -> List[dict]:
"""Get data from Alpha Vantage endpoint and convert to list of schemas.
Parameters
----------
url: str
The URL to get the data from.
sub_dict: Optional[str]
The sub-dictionary to use.
Returns
-------
List[dict]
Dictionary of data.
"""
data = get_data(url, **kwargs)
if sub_dict and isinstance(data, dict):
data = data.get(sub_dict, [])
if isinstance(data, dict):
raise ValueError("Expected list of dicts, got dict")
if len(data) == 0:
raise EmptyDataError()
return data
def get_data_one(url: str, **kwargs: Any) -> dict:
"""Get data from Alpha Vantage endpoint and convert to schema."""
data = get_data(url, **kwargs)
if isinstance(data, list):
if len(data) == 0:
raise ValueError("Expected dict, got empty list")
try:
data = {i: data[i] for i in range(len(data))} if len(data) > 1 else data[0]
except TypeError as e:
raise ValueError("Expected dict, got list of dicts") from e
return data
def get_interval(value: str) -> str:
"""Get the intervals for the Alpha Vantage API."""
intervals = {
"m": "min",
"d": "day",
"W": "week",
"M": "month",
}
return f"{value[:-1]}{intervals[value[-1]]}"
def extract_key_name(key):
"""Extract the alphabetical part of the key using regex."""
match = re.search(r"\d+\.\s+([a-z]+)", key, re.I)
return match.group(1) if match else key

View File

@@ -1,3 +1,5 @@
from datetime import date
import pytest
from openbb_alpha_vantage.models.stock_historical import AVStockHistoricalFetcher
from openbb_core.app.service.user_service import UserService
@@ -18,7 +20,12 @@ def vcr_config():
@pytest.mark.record_http
@pytest.mark.skip(reason="This is a premium endpoint.")
def test_av_stock_historical_fetcher(credentials=test_credentials):
params = {"symbol": "AAPL"}
params = params = {
"symbol": "AAPL",
"start_date": date(2023, 1, 1),
"end_date": date(2023, 1, 10),
"interval": "1d",
}
fetcher = AVStockHistoricalFetcher()
result = fetcher.test(params, credentials)

View File

@@ -16,6 +16,7 @@ from openbb_provider.standard_models.stock_historical import (
StockHistoricalData,
StockHistoricalQueryParams,
)
from openbb_provider.utils.descriptions import QUERY_DESCRIPTIONS
from openbb_provider.utils.helpers import make_request
from pydantic import Field
@@ -26,9 +27,9 @@ class CboeStockHistoricalQueryParams(StockHistoricalQueryParams):
Source: https://www.cboe.com/
"""
interval: Optional[Literal["1d", "1m"]] = Field(
description="Data granularity.",
interval: Optional[Literal["1m", "1d"]] = Field(
default="1d",
description=QUERY_DESCRIPTIONS.get("interval", ""),
)

View File

@@ -55,7 +55,12 @@ def test_cboe_options_chains_fetcher(credentials=test_credentials):
@pytest.mark.record_http
def test_cboe_stock_historical_fetcher(credentials=test_credentials):
params = {"symbol": "AAPL"}
params = params = {
"symbol": "AAPL",
"start_date": date(2023, 1, 1),
"end_date": date(2023, 1, 10),
"interval": "1d",
}
fetcher = CboeStockHistoricalFetcher()
result = fetcher.test(params, credentials)

View File

@@ -4,7 +4,7 @@ from datetime import datetime
from typing import Any, Dict, List, Literal, Optional
from dateutil.relativedelta import relativedelta
from openbb_fmp.utils.helpers import get_data_many
from openbb_fmp.utils.helpers import get_data_many, get_interval
from openbb_provider.abstract.fetcher import Fetcher
from openbb_provider.standard_models.stock_historical import (
StockHistoricalData,
@@ -20,17 +20,24 @@ class FMPStockHistoricalQueryParams(StockHistoricalQueryParams):
Source: https://financialmodelingprep.com/developer/docs/#Stock-Historical-Price
"""
timeseries: Optional[NonNegativeInt] = Field(
default=None, description="Number of days to look back."
__alias_dict__ = {"start_date": "from", "end_date": "to"}
limit: Optional[NonNegativeInt] = Field(
default=None,
description="Number of days to look back (Only for interval 1d).",
alias="timeseries",
)
interval: Literal["1m", "5m", "15m", "30m", "1h", "4h", "1d"] = Field(
default="1d", description="Time granularity to fetch data for."
)
interval: Literal[
"1min", "5min", "15min", "30min", "1hour", "4hour", "1day"
] = Field(default="1day", description="Data granularity.")
class FMPStockHistoricalData(StockHistoricalData):
"""FMP Stock end of day Data."""
label: Optional[str] = Field(
default=None, description="Human readable format of the date."
)
adj_close: Optional[float] = Field(
default=None, description="Adjusted Close Price of the symbol."
)
@@ -44,12 +51,6 @@ class FMPStockHistoricalData(StockHistoricalData):
change_percent: Optional[float] = Field(
default=None, description="Change % in the price of the symbol."
)
vwap: Optional[float] = Field(
default=None, description="Volume Weighted Average Price of the symbol."
)
label: Optional[str] = Field(
default=None, description="Human readable format of the date."
)
change_over_time: Optional[float] = Field(
default=None,
description="Change % in the price of the symbol over a period of time.",
@@ -87,17 +88,15 @@ class FMPStockHistoricalFetcher(
"""Return the raw data from the FMP endpoint."""
api_key = credentials.get("fmp_api_key") if credentials else ""
interval = get_interval(query.interval)
base_url = "https://financialmodelingprep.com/api/v3"
query_str = (
get_querystring(query.model_dump(), ["symbol"])
.replace("start_date", "from")
.replace("end_date", "to")
)
query_str = get_querystring(query.model_dump(), ["symbol", "interval"])
url_params = f"{query.symbol}?{query_str}&apikey={api_key}"
url = f"{base_url}/historical-chart/{query.interval}/{url_params}"
url = f"{base_url}/historical-chart/{interval}/{url_params}"
if query.interval == "1day":
if interval == "1day":
url = f"{base_url}/historical-price-full/{url_params}"
return get_data_many(url, "historical", **kwargs)

View File

@@ -165,3 +165,15 @@ def most_recent_quarter(base: dateType = dateType.today()) -> dateType:
if base.month < 10:
return dateType(base.year, 6, 30)
return dateType(base.year, 9, 30)
def get_interval(value: str) -> str:
"""Get the intervals for the FMP API."""
intervals = {
"m": "min",
"h": "hour",
"d": "day",
}
return f"{value[:-1]}{intervals[value[-1]]}"

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -103,7 +103,7 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:07:12 GMT
- Mon, 16 Oct 2023 12:22:19 GMT
ETag:
- W/"4f29-H5Ocbb+eJHecqy6nLuPDFiNISZI"
Server:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -95,7 +95,7 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:06:54 GMT
- Mon, 16 Oct 2023 12:21:57 GMT
ETag:
- W/"6c7b-K8kXXeHLrVV30fAQH6wOrC+0HUs"
Server:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -98,7 +98,7 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:06:51 GMT
- Mon, 16 Oct 2023 12:21:53 GMT
ETag:
- W/"62dc-GEW+YMnPRdFsmji3hMBsdqBcEGY"
Server:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -13,91 +13,92 @@ interactions:
response:
body:
string: !!binary |
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Date:
- Tue, 26 Sep 2023 19:06:58 GMT
- Mon, 16 Oct 2023 12:22:02 GMT
ETag:
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View File

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Date:
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ETag:
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Server:

View File

@@ -5,7 +5,7 @@ interactions:
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headers:
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@@ -104,9 +104,9 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:06:59 GMT
- Mon, 16 Oct 2023 12:22:03 GMT
ETag:
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Server:
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Transfer-Encoding:

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View File

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@@ -42,7 +42,7 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:06:55 GMT
- Mon, 16 Oct 2023 12:21:58 GMT
ETag:
- W/"907-UQ8xzk1F9UpGsOU+gSEsLaJs/nw"
Server:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -13,413 +13,416 @@ interactions:
response:
body:
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headers:
Access-Control-Allow-Credentials:
- 'true'
@@ -439,9 +442,9 @@ interactions:
Content-Type:
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Date:
- Tue, 26 Sep 2023 19:07:18 GMT
- Mon, 16 Oct 2023 12:22:27 GMT
ETag:
- W/"16ef2-bT1fdXj9QZJ32zLe5R2gh4XVGQ8"
- W/"1731a-I+hE3E7KdXImrdUd7/lxEVWlPTY"
Server:
- nginx/1.18.0 (Ubuntu)
Transfer-Encoding:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -60,7 +60,7 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:07:27 GMT
- Mon, 16 Oct 2023 12:22:38 GMT
ETag:
- W/"1e34-0aVhobAWlb7MRpqox67w34WX45s"
Server:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -44,7 +44,7 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:06:48 GMT
- Mon, 16 Oct 2023 12:21:49 GMT
ETag:
- W/"98a-GaBjuPAGbArTPT5nkIcCt0/rPEw"
Server:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -13,7 +13,7 @@ interactions:
response:
body:
string: "[\n {\n \"symbol\": \"AAPL\",\n \"targetHigh\": 240,\n \"targetLow\":
110,\n \"targetConsensus\": 189.18,\n \"targetMedian\": 195\n }\n]"
110,\n \"targetConsensus\": 190.63,\n \"targetMedian\": 195\n }\n]"
headers:
Access-Control-Allow-Credentials:
- 'true'
@@ -33,9 +33,9 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:07:10 GMT
- Mon, 16 Oct 2023 12:22:16 GMT
ETag:
- W/"85-MMRo6eJuj/J+yVk/93Sv2itXKsk"
- W/"85-5KEIQel+kr4cTZVp1ZaJCma4um0"
Server:
- nginx/1.18.0 (Ubuntu)
X-Frame-Options:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
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method: GET
@@ -13,188 +13,190 @@ interactions:
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Date:
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View File

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View File

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View File

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View File

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View File

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3z4z+lsCAAA=
headers:
Access-Control-Allow-Credentials:
- 'true'
@@ -39,9 +39,9 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:07:31 GMT
- Mon, 16 Oct 2023 12:22:43 GMT
ETag:
- W/"259-BJ8N5RkVFoRCRJ2wM/2jVfK7lNw"
- W/"25b-mdm4Ln2JCfkNOpR6goqZDOqjg50"
Server:
- nginx/1.18.0 (Ubuntu)
Transfer-Encoding:

View File

@@ -5,7 +5,7 @@ interactions:
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- '*/*'
Accept-Encoding:
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Connection:
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headers:
Access-Control-Allow-Credentials:
- 'true'
@@ -161,9 +148,9 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:07:16 GMT
- Mon, 16 Oct 2023 12:22:24 GMT
ETag:
- W/"17a29-WbO1lfUMLDFFaVJs4wa6TCzcNWs"
- W/"15b10-bvUhQoYLtKXR7FGHLg9g9qDYfc4"
Server:
- nginx/1.18.0 (Ubuntu)
Transfer-Encoding:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -74,7 +74,7 @@ interactions:
Content-Type:
- application/json; charset=utf-8
Date:
- Tue, 26 Sep 2023 19:07:20 GMT
- Mon, 16 Oct 2023 12:22:29 GMT
ETag:
- W/"42d9-CONZDTIRJG9SPo+N7aMvJpUeXno"
Server:

View File

@@ -105,6 +105,7 @@ def test_fmp_stock_historical_fetcher(credentials=test_credentials):
"symbol": "AAPL",
"start_date": date(2023, 1, 1),
"end_date": date(2023, 1, 10),
"interval": "1d",
}
fetcher = FMPStockHistoricalFetcher()

View File

@@ -5,7 +5,6 @@ from typing import Any, Dict, List, Literal, Optional
from dateutil.relativedelta import relativedelta
from openbb_intrinio.utils.helpers import get_data_one
from openbb_intrinio.utils.references import INTERVALS, TIMEZONES
from openbb_provider.abstract.fetcher import Fetcher
from openbb_provider.standard_models.stock_historical import (
StockHistoricalData,
@@ -13,7 +12,7 @@ from openbb_provider.standard_models.stock_historical import (
)
from openbb_provider.utils.descriptions import QUERY_DESCRIPTIONS
from openbb_provider.utils.helpers import get_querystring
from pydantic import Field
from pydantic import Field, PrivateAttr, model_validator
class IntrinioStockHistoricalQueryParams(StockHistoricalQueryParams):
@@ -22,28 +21,53 @@ class IntrinioStockHistoricalQueryParams(StockHistoricalQueryParams):
Source: https://docs.intrinio.com/documentation/web_api/get_security_interval_prices_v2
"""
__alias_dict__ = {"limit": "page_size"}
symbol: str = Field(
description="A Security identifier (Ticker, FIGI, ISIN, CUSIP, Intrinio ID)."
)
timezone: Optional[TIMEZONES] = Field(
default="UTC", description="Returns trading times in this timezone."
interval: Literal[
"1m", "5m", "10m", "15m", "30m", "60m", "1h", "1d", "1W", "1M", "1Q", "1Y"
] = Field(default="1d", description=QUERY_DESCRIPTIONS.get("interval", ""))
start_time: Optional[time] = Field(
default=None,
description="Return intervals starting at the specified time on the `start_date` formatted as 'HH:MM:SS'.",
)
end_time: Optional[time] = Field(
default=None,
description="Return intervals stopping at the specified time on the `end_date` formatted as 'HH:MM:SS'.",
)
timezone: str = Field(
default="UTC",
description="Timezone of the data, in the IANA format (Continent/City).",
)
source: Optional[Literal["realtime", "delayed", "nasdaq_basic"]] = Field(
default="realtime", description="The source of the data."
)
start_time: Optional[time] = Field(
default=None,
description="Return intervals starting at the specified time on the `start_date` formatted as 'hh:mm:ss'.",
)
end_time: Optional[time] = Field(
default=None,
description="Return intervals stopping at the specified time on the `end_date` formatted as 'hh:mm:ss'.",
)
interval_size: Optional[INTERVALS] = Field(
default="60m", description=QUERY_DESCRIPTIONS.get("frequency", "")
_interval_size: Literal["1m", "5m", "10m", "15m", "30m", "60m", "1h"] = PrivateAttr(
default=None
)
_frequency: Literal[
"daily", "weekly", "monthly", "quarterly", "yearly"
] = PrivateAttr(default=None)
@model_validator(mode="after")
@classmethod
def set_time_params(cls, values: "IntrinioStockHistoricalQueryParams"):
"""Set the default start & end date and time params for Intrinio API."""
frequency_dict = {
"1d": "daily",
"1W": "weekly",
"1M": "monthly",
"1Q": "quarterly",
"1Y": "yearly",
}
if values.interval in ["1m", "5m", "10m", "15m", "30m", "60m", "1h"]:
values._interval_size = values.interval
elif values.interval in ["1d", "1W", "1M", "1Q", "1Y"]:
values._frequency = frequency_dict[values.interval]
return values
class IntrinioStockHistoricalData(StockHistoricalData):
@@ -56,7 +80,9 @@ class IntrinioStockHistoricalData(StockHistoricalData):
description="The timestamp that represents the end of the interval span.",
)
interval: Optional[str] = Field(
default=None, description="The data time frequency."
default=None,
description="The data time frequency.",
alias="frequency",
)
average: Optional[float] = Field(
default=None,
@@ -66,6 +92,59 @@ class IntrinioStockHistoricalData(StockHistoricalData):
default=None,
description="Change in the price of the symbol from the previous day.",
)
intra_period: Optional[bool] = Field(
default=None,
description="If true, the stock price represents an unfinished period "
"(be it day, week, quarter, month, or year), meaning that the close "
"price is the latest price available, not the official close price "
"for the period",
alias="intraperiod",
)
adj_open: Optional[float] = Field(
default=None,
description="Adjusted open price during the period.",
)
adj_high: Optional[float] = Field(
default=None,
description="Adjusted high price during the period.",
)
adj_low: Optional[float] = Field(
default=None,
description="Adjusted low price during the period.",
)
adj_close: Optional[float] = Field(
default=None,
description="Adjusted closing price during the period.",
)
adj_volume: Optional[float] = Field(
default=None,
description="Adjusted volume during the period.",
)
factor: Optional[float] = Field(
default=None,
description="factor by which to multiply stock prices before this "
"date, in order to calculate historically-adjusted stock prices.",
)
split_ratio: Optional[float] = Field(
default=None,
description="Ratio of the stock split, if a stock split occurred.",
)
dividend: Optional[float] = Field(
default=None,
description="Dividend amount, if a dividend was paid.",
)
percent_change: Optional[float] = Field(
default=None,
description="Percent change in the price of the symbol from the previous day.",
)
fifty_two_week_high: Optional[float] = Field(
default=None,
description="52 week high price for the symbol.",
)
fifty_two_week_low: Optional[float] = Field(
default=None,
description="52 week low price for the symbol.",
)
class IntrinioStockHistoricalFetcher(
@@ -105,28 +184,33 @@ class IntrinioStockHistoricalFetcher(
"""Return the raw data from the Intrinio endpoint."""
api_key = credentials.get("intrinio_api_key") if credentials else ""
base_url = "https://api-v2.intrinio.com"
query_str = get_querystring(query.dict(by_alias=True), ["symbol"])
url = (
f"{base_url}/securities/{query.symbol}/prices/intervals"
f"?{query_str}&api_key={api_key}"
base_url = f"https://api-v2.intrinio.com/securities/{query.symbol}/prices"
if query._interval_size:
base_url += f"/intervals?interval_size={query._interval_size}"
data_key = "intervals"
elif query._frequency:
base_url += f"?frequency={query._frequency}"
data_key = "stock_prices"
query_str = get_querystring(
query.model_dump(by_alias=True), ["symbol", "interval"]
)
url = f"{base_url}&{query_str}&api_key={api_key}"
data = get_data_one(url, **kwargs)
next_page = data.get("next_page", None)
data = data.get("intervals", [])
data = data.get(data_key, [])
while next_page:
query_str = get_querystring(query.dict(by_alias=True), ["symbol"])
url = (
f"{base_url}/securities/{query.symbol}/prices/intervals"
f"?{query_str}&next_page={next_page}&api_key={api_key}"
query_str = get_querystring(
query.model_dump(by_alias=True), ["symbol", "interval"]
)
url = f"{base_url}&{query_str}&next_page={next_page}&api_key={api_key}"
temp_data = get_data_one(url, **kwargs)
next_page = temp_data.get("next_page", None)
data.extend(temp_data.get("intervals", []))
data.extend(temp_data.get(data_key, []))
return data

View File

@@ -11,158 +11,3 @@ SOURCES = Literal[
"intrinio_mx_plus",
"delayed_sip",
]
INTERVALS = Literal["1m", "5m", "10m", "15m", "30m", "60m", "1h"]
TIMEZONES = Literal[
"Africa/Algiers",
"Africa/Cairo",
"Africa/Casablanca",
"Africa/Harare",
"Africa/Johannesburg",
"Africa/Monrovia",
"Africa/Nairobi",
"America/Argentina/Buenos_Aires",
"America/Bogota",
"America/Caracas",
"America/Chicago",
"America/Chihuahua",
"America/Denver",
"America/Godthab",
"America/Guatemala",
"America/Guyana",
"America/Halifax",
"America/Indiana/Indianapolis",
"America/Juneau",
"America/La_Paz",
"America/Lima",
"America/Lima",
"America/Los_Angeles",
"America/Mazatlan",
"America/Mexico_City",
"America/Mexico_City",
"America/Monterrey",
"America/Montevideo",
"America/New_York",
"America/Phoenix",
"America/Regina",
"America/Santiago",
"America/Sao_Paulo",
"America/St_Johns",
"America/Tijuana",
"Asia/Almaty",
"Asia/Baghdad",
"Asia/Baku",
"Asia/Bangkok",
"Asia/Bangkok",
"Asia/Chongqing",
"Asia/Colombo",
"Asia/Dhaka",
"Asia/Dhaka",
"Asia/Hong_Kong",
"Asia/Irkutsk",
"Asia/Jakarta",
"Asia/Jerusalem",
"Asia/Kabul",
"Asia/Kamchatka",
"Asia/Karachi",
"Asia/Karachi",
"Asia/Kathmandu",
"Asia/Kolkata",
"Asia/Kolkata",
"Asia/Kolkata",
"Asia/Kolkata",
"Asia/Krasnoyarsk",
"Asia/Kuala_Lumpur",
"Asia/Kuwait",
"Asia/Magadan",
"Asia/Muscat",
"Asia/Muscat",
"Asia/Novosibirsk",
"Asia/Rangoon",
"Asia/Riyadh",
"Asia/Seoul",
"Asia/Shanghai",
"Asia/Singapore",
"Asia/Srednekolymsk",
"Asia/Taipei",
"Asia/Tashkent",
"Asia/Tbilisi",
"Asia/Tehran",
"Asia/Tokyo",
"Asia/Tokyo",
"Asia/Tokyo",
"Asia/Ulaanbaatar",
"Asia/Urumqi",
"Asia/Vladivostok",
"Asia/Yakutsk",
"Asia/Yekaterinburg",
"Asia/Yerevan",
"Atlantic/Azores",
"Atlantic/Cape_Verde",
"Atlantic/South_Georgia",
"Australia/Adelaide",
"Australia/Brisbane",
"Australia/Darwin",
"Australia/Hobart",
"Australia/Melbourne",
"Australia/Melbourne",
"Australia/Perth",
"Australia/Sydney",
"Etc/UTC",
"UTC",
"Europe/Amsterdam",
"Europe/Athens",
"Europe/Belgrade",
"Europe/Berlin",
"Europe/Berlin",
"Europe/Bratislava",
"Europe/Brussels",
"Europe/Bucharest",
"Europe/Budapest",
"Europe/Copenhagen",
"Europe/Dublin",
"Europe/Helsinki",
"Europe/Istanbul",
"Europe/Kaliningrad",
"Europe/Kiev",
"Europe/Lisbon",
"Europe/Ljubljana",
"Europe/London",
"Europe/London",
"Europe/Madrid",
"Europe/Minsk",
"Europe/Moscow",
"Europe/Moscow",
"Europe/Paris",
"Europe/Prague",
"Europe/Riga",
"Europe/Rome",
"Europe/Samara",
"Europe/Sarajevo",
"Europe/Skopje",
"Europe/Sofia",
"Europe/Stockholm",
"Europe/Tallinn",
"Europe/Vienna",
"Europe/Vilnius",
"Europe/Volgograd",
"Europe/Warsaw",
"Europe/Zagreb",
"Pacific/Apia",
"Pacific/Auckland",
"Pacific/Auckland",
"Pacific/Chatham",
"Pacific/Fakaofo",
"Pacific/Fiji",
"Pacific/Guadalcanal",
"Pacific/Guam",
"Pacific/Honolulu",
"Pacific/Majuro",
"Pacific/Midway",
"Pacific/Midway",
"Pacific/Noumea",
"Pacific/Pago_Pago",
"Pacific/Port_Moresby",
"Pacific/Tongatapu",
]

View File

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headers:
Connection:
- keep-alive
@@ -109,7 +112,7 @@ interactions:
Content-Type:
- application/json
Date:
- Tue, 26 Sep 2023 19:07:39 GMT
- Mon, 16 Oct 2023 12:23:15 GMT
Transfer-Encoding:
- chunked
Vary:

View File

@@ -5,7 +5,7 @@ interactions:
Accept:
- '*/*'
Accept-Encoding:
- gzip, deflate
- gzip, deflate, br
Connection:
- keep-alive
method: GET
@@ -13,14 +13,13 @@ interactions:
response:
body:
string: !!binary |
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headers:
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- application/json
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Transfer-Encoding:
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View File

@@ -30,8 +30,9 @@ def vcr_config():
def test_intrinio_stock_historical_fetcher(credentials=test_credentials):
params = {
"symbol": "AAPL",
"start_date": date(2023, 1, 23),
"end_date": date(2023, 5, 23),
"start_date": date(2023, 1, 1),
"end_date": date(2023, 1, 10),
"interval": "1d",
}
fetcher = IntrinioStockHistoricalFetcher()

View File

@@ -17,6 +17,8 @@ from openbb_provider.utils.descriptions import QUERY_DESCRIPTIONS
from pydantic import (
Field,
PositiveInt,
PrivateAttr,
model_validator,
)
@@ -26,12 +28,9 @@ class PolygonStockHistoricalQueryParams(StockHistoricalQueryParams):
Source: https://polygon.io/docs/stocks/getting-started
"""
multiplier: PositiveInt = Field(
default=1, description="Multiplier of the timespan."
interval: str = Field(
default="1d", description=QUERY_DESCRIPTIONS.get("interval", "")
)
timespan: Literal[
"minute", "hour", "day", "week", "month", "quarter", "year"
] = Field(default="day", description="Timespan of the data.")
sort: Literal["asc", "desc"] = Field(
default="desc", description="Sort order of the data."
)
@@ -42,6 +41,29 @@ class PolygonStockHistoricalQueryParams(StockHistoricalQueryParams):
default=True,
description="Output time series is adjusted by historical split and dividend events.",
)
_multiplier: PositiveInt = PrivateAttr(default=None)
_timespan: str = PrivateAttr(default=None)
@model_validator(mode="after")
@classmethod
def get_api_interval_params(cls, values: "PolygonStockHistoricalQueryParams"):
"""Get the multiplier and timespan parameters for the Polygon API."""
intervals = {
"s": "second",
"m": "minute",
"h": "hour",
"d": "day",
"W": "week",
"M": "month",
"Q": "quarter",
"Y": "year",
}
values._multiplier = int(values.interval[:-1])
values._timespan = intervals[values.interval[-1]]
return values
class PolygonStockHistoricalData(StockHistoricalData):
@@ -87,7 +109,7 @@ class PolygonStockHistoricalFetcher(
query: PolygonStockHistoricalQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[dict]:
) -> List[Dict]:
api_key = credentials.get("polygon_api_key") if credentials else ""
data: List = []
@@ -98,11 +120,12 @@ class PolygonStockHistoricalFetcher(
results: List = []
url = (
f"https://api.polygon.io/v2/aggs/ticker/"
f"{symbol.upper()}/range/{query.multiplier}/{query.timespan}/"
"https://api.polygon.io/v2/aggs/ticker/"
f"{symbol.upper()}/range/{query._multiplier}/{query._timespan}/"
f"{query.start_date}/{query.end_date}?adjusted={query.adjusted}"
f"&sort={query.sort}&limit={query.limit}&apiKey={api_key}"
)
response = get_data(url, **kwargs)
next_url = response.get("next_url", None)
@@ -116,7 +139,7 @@ class PolygonStockHistoricalFetcher(
for r in results:
r["t"] = datetime.fromtimestamp(r["t"] / 1000)
if query.timespan not in ["minute", "hour"]:
if query._timespan not in ["second", "minute", "hour"]:
r["t"] = r["t"].date()
if "," in query.symbol:
r["symbol"] = symbol
@@ -129,7 +152,5 @@ class PolygonStockHistoricalFetcher(
return data
@staticmethod
def transform_data(data: List[dict]) -> List[PolygonStockHistoricalData]:
transformed_data = [PolygonStockHistoricalData.model_validate(d) for d in data]
transformed_data.sort(key=lambda x: x.date)
return transformed_data
def transform_data(data: List[Dict]) -> List[PolygonStockHistoricalData]:
return [PolygonStockHistoricalData.model_validate(d) for d in data]

View File

@@ -33,6 +33,7 @@ def test_polygon_stock_historical_fetcher(credentials=test_credentials):
"symbol": "AAPL",
"start_date": date(2023, 1, 1),
"end_date": date(2023, 1, 10),
"interval": "1d",
}
fetcher = PolygonStockHistoricalFetcher()

View File

@@ -13,9 +13,9 @@ from openbb_provider.standard_models.stock_historical import (
from openbb_provider.utils.descriptions import QUERY_DESCRIPTIONS
from openbb_provider.utils.errors import EmptyDataError
from openbb_yfinance.utils.helpers import yf_download
from openbb_yfinance.utils.references import INTERVALS, PERIODS
from openbb_yfinance.utils.references import PERIODS
from pandas import Timestamp, to_datetime
from pydantic import Field, validator
from pydantic import Field, PrivateAttr, field_validator
class YFinanceStockHistoricalQueryParams(StockHistoricalQueryParams):
@@ -24,40 +24,55 @@ class YFinanceStockHistoricalQueryParams(StockHistoricalQueryParams):
Source: https://finance.yahoo.com/
"""
interval: Optional[INTERVALS] = Field(default="1d", description="Data granularity.")
period: Optional[PERIODS] = Field(
default="max", description=QUERY_DESCRIPTIONS.get("period", "")
interval: Optional[
Literal[
"1m",
"2m",
"5m",
"15m",
"30m",
"60m",
"90m",
"1h",
"1d",
"5d",
"1W",
"1M",
"3M",
]
] = Field(
default="1d",
description=QUERY_DESCRIPTIONS.get("interval", ""),
)
prepost: bool = Field(
default=False, description="Include Pre and Post market data."
)
actions: bool = Field(default=True, description="Include actions.")
auto_adjust: bool = Field(
default=False, description="Adjust all OHLC data automatically."
include: bool = Field(
default=True, description="Include Dividends and Stock Splits in results."
)
adjusted: bool = Field(
default=False,
description="Adjust all OHLC data automatically.",
)
back_adjust: bool = Field(
default=False, description="Attempt to adjust all the data automatically."
)
progress: bool = Field(default=False, description="Show progress bar.")
ignore_tz: bool = Field(
default=True,
description="When combining from different timezones, ignore that part of datetime.",
)
rounding: bool = Field(default=True, description="Round to two decimal places?")
repair: bool = Field(
default=False,
description="Detect currency unit 100x mixups and attempt repair.",
)
keepna: bool = Field(default=False, description="Keep NaN rows returned by Yahoo?")
group_by: Literal["ticker", "column"] = Field(
default="column", description="Group by ticker or column."
)
_progress: bool = PrivateAttr(default=False)
_keepna: bool = PrivateAttr(default=False)
_period: Optional[PERIODS] = PrivateAttr(default="max")
_rounding: bool = PrivateAttr(default=True)
_repair: bool = PrivateAttr(default=False)
_group_by: Literal["ticker", "column"] = PrivateAttr(default="ticker")
class YFinanceStockHistoricalData(StockHistoricalData):
"""YFinance Stock End of Day Data."""
@validator("date", pre=True, check_fields=False)
@field_validator("date", mode="before", check_fields=False)
def date_validate(cls, v): # pylint: disable=E0213
"""Return formatted datetime."""
if isinstance(v, Timestamp):
@@ -90,26 +105,32 @@ class YFinanceStockHistoricalFetcher(
@staticmethod
def extract_data(
query: YFinanceStockHistoricalQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[Dict]:
"""Return the raw data from the yfinance endpoint."""
if query.interval == "1W":
query.interval = "1wk"
elif query.interval == "1M":
query.interval = "1mo"
elif query.interval == "3M":
query.interval = "3mo"
data = yf_download(
symbol=query.symbol,
start_date=query.start_date,
end_date=query.end_date,
interval=query.interval,
period=query.period,
period=query._period,
prepost=query.prepost,
actions=query.actions,
auto_adjust=query.auto_adjust,
actions=query.include,
auto_adjust=query.adjusted,
back_adjust=query.back_adjust,
progress=query.progress,
progress=query._progress,
ignore_tz=query.ignore_tz,
keepna=query.keepna,
repair=query.repair,
rounding=query.rounding,
group_by=query.group_by,
keepna=query._keepna,
repair=query._repair,
rounding=query._rounding,
group_by=query._group_by,
)
if data.empty:

View File

@@ -75,6 +75,7 @@ def test_y_finance_stock_historical_fetcher(credentials=test_credentials):
"symbol": "AAPL",
"start_date": date(2023, 1, 1),
"end_date": date(2023, 1, 10),
"interval": "1d",
}
fetcher = YFinanceStockHistoricalFetcher()