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"""FMP Index Historical Model."""
# pylint: disable=unused-argument
import asyncio
from datetime import datetime
from typing import Any, Dict, List, Literal, Optional
from warnings import warn
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 (
amake_request,
get_querystring,
)
from openbb_fmp.utils.helpers import get_interval
from pydantic import Field
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
symbols = query.symbol.split(",")
results = []
messages = []
async def get_one(symbol):
"""Get data for one symbol."""
url = get_url_params(symbol)
data = []
response = await amake_request(url, **kwargs)
if isinstance(response, dict) and response.get("Error Message"):
message = f"Error fetching data for {symbol}: {response.get('Error Message', '')}"
warn(message)
messages.append(message)
if not response:
message = f"No data found for {symbol}."
warn(message)
messages.append(message)
if isinstance(response, list) and len(response) > 0:
data = response
if len(symbols) > 1:
for d in data:
d["symbol"] = symbol
if isinstance(response, dict) and response.get("historical"):
data = response["historical"]
if len(symbols) > 1:
for d in data:
d["symbol"] = symbol
if data:
results.extend(data)
tasks = [get_one(symbol) for symbol in symbols]
await asyncio.gather(*tasks)
if not results:
raise EmptyDataError(
f"{str(','.join(messages)).replace(',',' ') if messages else 'No data found'}"
)
return results
@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"], x["symbol"])
if len(query.symbol.split(",")) > 1
else 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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