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"""Seeking Alpha Forward EPS Estimates Model."""
# pylint: disable=unused-argument
from typing import Any, Dict, List, Literal, Optional
from warnings import warn
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.forward_eps_estimates import (
ForwardEpsEstimatesData,
ForwardEpsEstimatesQueryParams,
)
from openbb_core.provider.utils.helpers import amake_request
from openbb_seeking_alpha.utils.helpers import HEADERS, get_seekingalpha_id
from pydantic import Field, field_validator
class SAForwardEpsEstimatesQueryParams(ForwardEpsEstimatesQueryParams):
"""Seeking Alpha Forward EPS Estimates Query.
Source: https://seekingalpha.com/earnings/earnings-calendar
"""
__json_schema_extra__ = {"symbol": {"multiple_items_allowed": True}}
period: Literal["annual", "quarter"] = Field(
default="quarter",
description="The reporting period.",
json_schema_extra={"choices": ["annual", "quarter"]},
)
@field_validator("symbol", mode="before", check_fields=False)
@classmethod
def check_symbol(cls, value):
"""Check the symbol."""
if not value:
raise RuntimeError("Error: Symbol is a required field for Seeking Alpha.")
return value
class SAForwardEpsEstimatesData(ForwardEpsEstimatesData):
"""Seeking Alpha Forward EPS Estimates Data."""
normalized_actual: Optional[float] = Field(
default=None,
description="Actual normalized EPS.",
)
period_growth: Optional[float] = Field(
default=None,
description="Estimated (or actual if reported) EPS growth for the period.",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
low_estimate_gaap: Optional[float] = Field(
default=None,
description="Estimated GAAP EPS low for the period.",
)
high_estimate_gaap: Optional[float] = Field(
default=None,
description="Estimated GAAP EPS high for the period.",
)
mean_gaap: Optional[float] = Field(
default=None,
description="Estimated GAAP EPS mean for the period.",
)
gaap_actual: Optional[float] = Field(
default=None,
description="Actual GAAP EPS.",
)
class SAForwardEpsEstimatesFetcher(
Fetcher[
SAForwardEpsEstimatesQueryParams,
List[SAForwardEpsEstimatesData],
]
):
"""Seeking Alpha Forward EPS Estimates Fetcher."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> SAForwardEpsEstimatesQueryParams:
"""Transform the query."""
return SAForwardEpsEstimatesQueryParams(**params)
@staticmethod
async def aextract_data(
query: SAForwardEpsEstimatesQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> Dict:
"""Return the raw data from the Seeking Alpha endpoint."""
tickers = query.symbol.split(",")
fp = query.period if query.period == "annual" else "quarterly"
url = "https://seekingalpha.com/api/v3/symbol_data/estimates"
querystring: Dict = {
"estimates_data_items": "eps_normalized_actual,eps_normalized_consensus_low,eps_normalized_consensus_mean,"
"eps_normalized_consensus_high,eps_normalized_num_of_estimates,"
"eps_gaap_actual,eps_gaap_consensus_low,eps_gaap_consensus_mean,eps_gaap_consensus_high,",
"period_type": fp,
"relative_periods": "-4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11,12",
}
ids: Dict = {ticker: await get_seekingalpha_id(ticker) for ticker in tickers}
querystring["ticker_ids"] = (",").join(list(ids.values()))
payload: str = ""
response = await amake_request(
url, data=payload, headers=HEADERS, params=querystring
)
estimates: Dict = response.get("estimates", {}) # type: ignore
if not estimates:
raise RuntimeError(f"No estimates data was returned for: {query.symbol}")
output: Dict = {"ids": ids, "estimates": estimates}
return output
@staticmethod
def transform_data(
query: SAForwardEpsEstimatesQueryParams,
data: Dict,
**kwargs: Any,
) -> List[SAForwardEpsEstimatesData]:
"""Transform the data to the standard format."""
tickers = query.symbol.split(",")
ids = data.get("ids")
estimates = data.get("estimates")
results: List[SAForwardEpsEstimatesData] = []
for ticker in tickers:
sa_id = str(ids.get(ticker, ""))
if sa_id == "" or sa_id not in estimates:
warn(f"Symbol Error: No data found for, {ticker}")
seek_object = estimates.get(sa_id)
items = len(seek_object["eps_normalized_num_of_estimates"])
for i in range(0, items - 4):
eps_estimates: Dict = {}
eps_estimates["symbol"] = ticker
num_estimates = seek_object["eps_normalized_num_of_estimates"].get(str(i))
if not num_estimates:
continue
period = num_estimates[0].get("period", {})
if period:
period_type = period.get("periodtypeid")
eps_estimates["calendar_year"] = period.get("calendaryear")
eps_estimates["calendar_period"] = (
"Q" + str(period.get("calendarquarter", ""))
if period_type == "quarterly"
else "FY"
)
eps_estimates["date"] = period.get("periodenddate").split("T")[0]
eps_estimates["fiscal_year"] = period.get("fiscalyear")
eps_estimates["fiscal_period"] = (
"Q" + str(period.get("fiscalquarter", ""))
if period_type == "quarterly"
else "FY"
)
eps_estimates["number_of_analysts"] = num_estimates[0].get(
"dataitemvalue"
)
actual = seek_object["eps_normalized_actual"].get(str(i))
if actual:
eps_estimates["normalized_actual"] = actual[0].get("dataitemvalue")
gaap_actual = seek_object["eps_gaap_actual"].get(str(i))
if gaap_actual:
eps_estimates["gaap_actual"] = gaap_actual[0].get("dataitemvalue")
low = seek_object["eps_normalized_consensus_low"].get(str(i))
if low:
eps_estimates["low_estimate"] = low[0].get("dataitemvalue")
gaap_low = seek_object["eps_gaap_consensus_low"].get(str(i))
if gaap_low:
eps_estimates["low_estimate_gaap"] = gaap_low[0].get(
"dataitemvalue"
)
high = seek_object["eps_normalized_consensus_high"].get(str(i))
if high:
eps_estimates["high_estimate"] = high[0].get("dataitemvalue")
gaap_high = seek_object["eps_gaap_consensus_high"].get(str(i))
if gaap_high:
eps_estimates["high_estimate_gaap"] = gaap_high[0].get(
"dataitemvalue"
)
mean = seek_object["eps_normalized_consensus_mean"].get(str(i))
if mean:
mean = mean[0].get("dataitemvalue")
eps_estimates["mean"] = mean
gaap_mean = seek_object["eps_gaap_consensus_mean"].get(str(i))
if gaap_mean:
eps_estimates["mean_gaap"] = gaap_mean[0].get("dataitemvalue")
# Calculate the estimated growth percent.
this = float(mean) if mean else None
prev = None
percent = None
try:
prev = float(
seek_object["eps_normalized_actual"][str(i - 1)][0].get(
"dataitemvalue"
)
)
except KeyError:
prev = float(
seek_object["eps_normalized_consensus_mean"][str(i - 1)][0].get(
"dataitemvalue"
)
)
if this and prev:
percent = (this - prev) / prev
eps_estimates["period_growth"] = percent
results.append(SAForwardEpsEstimatesData.model_validate(eps_estimates))
return results
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