fmp_py.fmp_historical_data

Classes

class fmp_py.fmp_historical_data.FmpHistoricalData(api_key: str = os.getenv('FMP_API_KEY'))

daily_history(symbol: str, from_date: str, to_date: str) → pandas.DataFrame

Retrieves daily historical data for a given symbol within a specified date range.

Parameters:

symbol (str) – The symbol of the stock or asset.
from_date (str) – The starting date of the historical data in the format ‘YYYY-MM-DD’.
to_date (str) – The ending date of the historical data in the format ‘YYYY-MM-DD’.

Returns:

A DataFrame containing the daily historical data for the specified symbol.

Return type:

pd.DataFrame

intraday_history(symbol: str, interval: str, from_date: str, to_date: str) → pandas.DataFrame

Retrieves intraday historical data for a given symbol within a specified time interval.

Parameters:

symbol (str) – The stock or asset symbol.
interval (str) – The time interval for the data. Must be one of: [‘1min’, ‘5min’, ‘15min’, ‘30min’, ‘1hour’, ‘4hour’].
from_date (str) – The starting date for the data in the format ‘YYYY-MM-DD’.
to_date (str) – The ending date for the data in the format ‘YYYY-MM-DD’.

Returns:

A DataFrame containing the intraday historical data for the specified symbol and time interval.

Return type:

pd.DataFrame

_prepare_data(data_df: pandas.DataFrame) → pandas.DataFrame

Prepare data by calculating VWAP and converting data types.

_round_prices(data_df: pandas.DataFrame) → pandas.DataFrame

Round prices to 2 decimal places.

_calc_vwap(data_df: pandas.DataFrame) → pandas.Series

Calculate the Volume Weighted Average Price (VWAP).

Parameters:: data_df (pd.DataFrame) – DataFrame with price and volume data.
Returns:: VWAP values.
Return type:: pd.Series