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V0.0.17.0 ipo calendar (#25)
* update version * added ipo calendar and tests * update versions
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‎poetry.lock‎

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‎pyproject.toml‎

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[tool.poetry]
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name = "fmp-py"
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version = "0.0.16.0"
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version = "0.0.17.0"
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description = "Python package for Financial Modeling Prep API"
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authors = ["TexasCoding <[email protected]>"]
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readme = "README.md"

‎src/fmp_py/fmp_ipo_calendar.py‎

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import pandas as pd
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from fmp_py.fmp_base import FmpBase
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import os
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import pendulum
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from dotenv import load_dotenv
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load_dotenv()
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"""
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This class provides methods for retrieving IPO calendar data from the Financial Modeling Prep API.
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Ref: https://site.financialmodelingprep.com/developer/docs#ipo-calendar
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def ipo_calendar_by_symbol(self, from_date: str, to_date: str) -> pd.DataFrame:
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Reference: https://site.financialmodelingprep.com/developer/docs#ipo-confirmed-ipo-calendar
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def ipo_prospectus(self, from_date: str, to_date: str) -> pd.DataFrame:
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Reference: https://site.financialmodelingprep.com/developer/docs#ipo-prospectus-ipo-calendar
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def ipo_confirmed(self, from_date: str, to_date: str) -> pd.DataFrame:
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Reference: https://site.financialmodelingprep.com/developer/docs#ipo-calender-by-ipo-calendar
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"""
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class FmpIpoCalendar(FmpBase):
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def __init__(self, api_key: str = os.getenv("FMP_API_KEY")):
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super().__init__(api_key)
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#############################
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# IPO Calendar by Symbol
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#############################
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def ipo_calendar_by_symbol(self, from_date: str, to_date: str) -> pd.DataFrame:
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"""
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Retrieves IPO calendar data for a specific symbol within a given date range.
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Args:
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from_date (str): The starting date of the date range in "YYYY-MM-DD" format.
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to_date (str): The ending date of the date range in "YYYY-MM-DD" format.
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Returns:
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pd.DataFrame: A DataFrame containing IPO calendar data for the specified symbol within the given date range.
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"""
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from_date = pendulum.parse(from_date).format("YYYY-MM-DD")
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to_date = pendulum.parse(to_date).format("YYYY-MM-DD")
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if from_date > to_date:
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raise ValueError("from_date must be less than or equal to to_date")
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url = "v3/ipo_calendar"
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params = {"from": from_date, "to": to_date}
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response = self.get_request(url, params)
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if not response:
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raise ValueError("Error fetching IPO calendar data")
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data_df = (
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pd.DataFrame(response)
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.fillna(0)
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.rename(
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columns={
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"date": "date",
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"company": "company",
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"symbol": "symbol",
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"exchange": "exchange",
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"actions": "actions",
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"shares": "shares",
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"priceRange": "price_range",
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"marketCap": "market_cap",
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}
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)
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.astype(
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{
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"date": "datetime64[ns]",
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"company": "str",
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"symbol": "str",
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"exchange": "str",
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"actions": "str",
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"shares": "int",
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"price_range": "string",
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"market_cap": "int",
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}
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)
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.sort_values(by="date", ascending=True)
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.reset_index(drop=True)
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)
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return data_df
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#############################
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# IPO Prspectus
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#############################
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def ipo_prospectus(self, from_date: str, to_date: str) -> pd.DataFrame:
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"""
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Retrieves IPO prospectus data from the specified date range.
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Args:
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from_date (str): The starting date of the date range in "YYYY-MM-DD" format.
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to_date (str): The ending date of the date range in "YYYY-MM-DD" format.
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Returns:
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pd.DataFrame: A DataFrame containing IPO prospectus data with the following columns:
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- symbol (str): The symbol of the IPO.
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- cik (str): The CIK (Central Index Key) of the IPO.
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- form (str): The form type of the IPO.
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- filing_date (datetime64[ns]): The filing date of the IPO.
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- accepted_date (datetime64[ns]): The accepted date of the IPO.
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- ipo_date (datetime64[ns]): The IPO date.
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- price_public_per_share (float): The price per share for the public offering.
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- price_public_total (float): The total price for the public offering.
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- discounts_and_commissions_per_share (float): The discounts and commissions per share.
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- discounts_and_commissions_total (float): The total discounts and commissions.
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- proceeds_before_expenses_per_share (float): The proceeds per share before expenses.
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- proceeds_before_expenses_total (float): The total proceeds before expenses.
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- url (str): The URL of the IPO prospectus.
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Raises:
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ValueError: If from_date is greater than to_date or if there is an error fetching the IPO calendar data.
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"""
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from_date = pendulum.parse(from_date).format("YYYY-MM-DD")
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to_date = pendulum.parse(to_date).format("YYYY-MM-DD")
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if from_date > to_date:
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raise ValueError("from_date must be less than or equal to to_date")
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url = "v4/ipo-calendar-prospectus"
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params = {"from": from_date, "to": to_date}
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response = self.get_request(url, params)
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if not response:
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raise ValueError("Error fetching IPO calendar data")
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data_df = (
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pd.DataFrame(response)
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.fillna("")
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.rename(
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columns={
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"symbol": "symbol",
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"cik": "cik",
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"form": "form",
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"filingDate": "filing_date",
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"acceptedDate": "accepted_date",
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"ipoDate": "ipo_date",
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"pricePublicPerShare": "price_public_per_share",
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"pricePublicTotal": "price_public_total",
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"discountsAndCommissionsPerShare": "discounts_and_commissions_per_share",
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"discountsAndCommissionsTotal": "discounts_and_commissions_total",
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"proceedsBeforeExpensesPerShare": "proceeds_before_expenses_per_share",
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"proceedsBeforeExpensesTotal": "proceeds_before_expenses_total",
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"url": "url",
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}
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)
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.astype(
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{
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"symbol": "str",
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"cik": "str",
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"form": "str",
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"filing_date": "datetime64[ns]",
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"accepted_date": "datetime64[ns]",
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"ipo_date": "datetime64[ns]",
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"price_public_per_share": "float",
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"price_public_total": "float",
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"discounts_and_commissions_per_share": "float",
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"discounts_and_commissions_total": "float",
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"proceeds_before_expenses_per_share": "float",
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"proceeds_before_expenses_total": "float",
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"url": "str",
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}
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)
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.sort_values(by="filing_date", ascending=True)
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.reset_index(drop=True)
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)
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return data_df
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#############################
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# IPO Confirmed
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#############################
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def ipo_confirmed(self, from_date: str, to_date: str) -> pd.DataFrame:
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"""
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Retrieves the IPO calendar data for confirmed IPOs within the specified date range.
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Args:
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from_date (str): The start date of the date range in the format "YYYY-MM-DD".
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to_date (str): The end date of the date range in the format "YYYY-MM-DD".
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Returns:
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pd.DataFrame: A DataFrame containing the IPO calendar data for confirmed IPOs, sorted by filing date.
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Raises:
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ValueError: If from_date is greater than to_date or if there is an error fetching the IPO calendar data.
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"""
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from_date = pendulum.parse(from_date).format("YYYY-MM-DD")
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to_date = pendulum.parse(to_date).format("YYYY-MM-DD")
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if from_date > to_date:
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raise ValueError("from_date must be less than or equal to to_date")
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url = "v4/ipo-calendar-confirmed"
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params = {"from": from_date, "to": to_date}
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response = self.get_request(url, params)
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if not response:
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raise ValueError("Error fetching IPO calendar data")
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data_df = (
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pd.DataFrame(response)
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.fillna("")
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.rename(
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columns={
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"symbol": "symbol",
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"cik": "cik",
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"form": "form",
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"filingDate": "filing_date",
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"acceptedDate": "accepted_date",
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"effectivenessDate": "effectiveness_date",
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"url": "url",
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}
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)
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.astype(
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{
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"symbol": "string",
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"cik": "string",
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"form": "string",
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"filing_date": "datetime64[ns]",
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"accepted_date": "datetime64[ns]",
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"effectiveness_date": "datetime64[ns]",
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"url": "string",
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}
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)
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.sort_values(by="filing_date", ascending=True)
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.reset_index(drop=True)
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)
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return data_df

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