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"""FMP Index Historical Model."""
# pylint: disable=unused-argument
import warnings
from datetime import datetime
from typing import Any, Dict, List, Literal, Optional
from dateutil.relativedelta import relativedelta
from openbb_core.provider.abstract.fetcher import Fetcher
from openbb_core.provider.standard_models.index_historical import (
IndexHistoricalData,
IndexHistoricalQueryParams,
)
from openbb_core.provider.utils.descriptions import (
DATA_DESCRIPTIONS,
QUERY_DESCRIPTIONS,
)
from openbb_core.provider.utils.errors import EmptyDataError
from openbb_core.provider.utils.helpers import (
ClientResponse,
amake_requests,
get_querystring,
)
from openbb_fmp.utils.helpers import get_interval
from pydantic import Field
_warn = warnings.warn
class FMPIndexHistoricalQueryParams(IndexHistoricalQueryParams):
"""FMP Index Historical Query.
Source: https://site.financialmodelingprep.com/developer/docs/historical-index-price-api/
"""
__alias_dict__ = {"start_date": "from", "end_date": "to"}
__json_schema_extra__ = {"symbol": ["multiple_items_allowed"]}
interval: Literal["1m", "5m", "15m", "30m", "1h", "4h", "1d"] = Field(
default="1d", description=QUERY_DESCRIPTIONS.get("interval", "")
)
class FMPIndexHistoricalData(IndexHistoricalData):
"""FMP Index Historical Data."""
vwap: Optional[float] = Field(
default=None, description=DATA_DESCRIPTIONS.get("vwap", "")
)
change: Optional[float] = Field(
default=None,
description="Change in the price from the previous close.",
)
change_percent: Optional[float] = Field(
default=None,
description="Change in the price from the previous close, as a normalized percent.",
alias="changeOverTime",
json_schema_extra={"x-unit_measurement": "percent", "x-frontend_multiply": 100},
)
class FMPIndexHistoricalFetcher(
Fetcher[
FMPIndexHistoricalQueryParams,
List[FMPIndexHistoricalData],
]
):
"""Transform the query, extract and transform the data from the FMP endpoints."""
@staticmethod
def transform_query(params: Dict[str, Any]) -> FMPIndexHistoricalQueryParams:
"""Transform the query params."""
transformed_params = params
now = datetime.now().date()
if params.get("start_date") is None:
transformed_params["start_date"] = now - relativedelta(years=1)
if params.get("end_date") is None:
transformed_params["end_date"] = now
return FMPIndexHistoricalQueryParams.model_validate(transformed_params)
@staticmethod
async def aextract_data(
query: FMPIndexHistoricalQueryParams,
credentials: Optional[Dict[str, str]],
**kwargs: Any,
) -> List[Dict]:
"""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"])
def get_url_params(symbol: str) -> str:
url_params = f"{symbol}?{query_str}&apikey={api_key}"
url = f"{base_url}/historical-chart/{interval}/{url_params}"
if interval == "1day":
url = f"{base_url}/historical-price-full/{url_params}"
return url
# if there are more than 20 symbols, we need to increase the timeout
if len(query.symbol.split(",")) > 20:
kwargs.update({"preferences": {"request_timeout": 30}})
async def callback(response: ClientResponse, _: Any) -> List[Dict]:
data = await response.json()
symbol = response.url.parts[-1]
results = []
if not data:
_warn(f"No data found the the symbol: {symbol}")
return results
if isinstance(data, dict):
results = data.get("historical", [])
if isinstance(data, list):
results = data
if "," in query.symbol:
for d in results:
d["symbol"] = symbol
return results
urls = [get_url_params(symbol) for symbol in query.symbol.split(",")]
return await amake_requests(urls, callback, **kwargs)
@staticmethod
def transform_data(
query: FMPIndexHistoricalQueryParams,
data: List[Dict],
**kwargs: Any,
) -> List[FMPIndexHistoricalData]:
"""Return the transformed data."""
if not data:
raise EmptyDataError()
# Get rid of duplicate fields.
to_pop = ["label", "changePercent", "unadjustedVolume", "adjClose"]
results: List[FMPIndexHistoricalData] = []
for d in sorted(data, key=lambda x: x["date"], reverse=False):
_ = [d.pop(pop) for pop in to_pop if pop in d]
if d.get("volume") == 0:
_ = d.pop("volume")
results.append(FMPIndexHistoricalData.model_validate(d))
return results
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