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OpenBB/openbb_terminal/economy/econdb_model.py
Colin Delahunty b27bb59ef6 Added fixes (#5344)
2023-08-17 21:39:34 +00:00

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""" EconDB Model """
__docformat__ = "numpy"
# pylint: disable=no-member
import logging
from datetime import datetime
from typing import Any, Dict, Optional, Tuple, Union
from urllib.error import HTTPError
import pandas as pd
import pandas_datareader.data as web
import yfinance as yf
from openbb_terminal.decorators import log_start_end
from openbb_terminal.helper_funcs import request
from openbb_terminal.helpers_denomination import transform as transform_by_denomination
from openbb_terminal.rich_config import console
logger = logging.getLogger(__name__)
COUNTRY_CODES = {
"Albania": "AL",
"Argentina": "AR",
"Australia": "AU",
"Austria": "AT",
"Azerbaijan": "AZ",
"Bangladesh": "BD",
"Belarus": "BY",
"Belgium": "BE",
"Bhutan": "BT",
"Bosnia_and_Herzegovina": "BA",
"Botswana": "BW",
"Brazil": "BR",
"Bulgaria": "BG",
"Cambodia": "KH",
"Cameroon": "CM",
"Canada": "CA",
"Chile": "CL",
"China": "CN",
"Colombia": "CO",
"Croatia": "HR",
"Cyprus": "CY",
"Czechia": "CZ",
"Denmark": "DK",
"Dominican_Republic": "DO",
"Egypt": "EG",
"Estonia": "EE",
"European_Union": "EU",
"Finland": "FI",
"France": "FR",
"Germany": "DE",
"Greece": "GR",
"Honduras": "HN",
"Hong Kong": "HK",
"Hungary": "HU",
"India": "IN",
"Indonesia": "ID",
"Iran": "IR",
"Ireland": "IE",
"Israel": "IL",
"Italy": "IT",
"Japan": "JP",
"Kazakhstan": "KZ",
"Laos": "LA",
"Latvia": "LV",
"Lebanon": "LB",
"Lithuania": "LT",
"Luxembourg": "LU",
"Macedonia": "MK",
"Malaysia": "MY",
"Malta": "MT",
"Mexico": "MX",
"Mongolia": "MN",
"Netherlands": "NL",
"New_Zealand": "NZ",
"Nigeria": "NG",
"Norway": "NO",
"Oman": "OM",
"Pakistan": "PK",
"Panama": "PA",
"Paraguay": "PYG",
"Peru": "PE",
"Philippines": "PH",
"Poland": "PL",
"Portugal": "PT",
"Qatar": "QA",
"Romania": "RO",
"Russia": "RU",
"Saudi_Arabia": "SA",
"Serbia": "RS",
"Singapore": "SG",
"Slovakia": "SK",
"Slovenia": "SI",
"South_Africa": "ZA",
"South_Korea": "KR",
"Spain": "ES",
"Sweden": "SE",
"Switzerland": "CH",
"Taiwan": "TW",
"Thailand": "TH",
"Tunisia": "TN",
"Turkey": "TR",
"Ukraine": "UA",
"United_Arab_Emirates": "AE",
"United_Kingdom": "UK",
"United_States": "US",
"Uzbekistan": "UZ",
"Venezuela": "VE",
"Vietnam": "VN",
}
COUNTRY_CURRENCIES = {
"Albania": "ALL",
"Argentina": "ARS",
"Australia": "AUD",
"Austria": "EUR",
"Azerbaijan": "AZN",
"Bangladesh": "BDT",
"Belarus": "BYR",
"Belgium": "EUR",
"Bhutan": "BTN",
"Bosnia_and_Herzegovina": "BAM",
"Botswana": "BWP",
"Brazil": "BRL",
"Bulgaria": "BGN",
"Cambodia": "KHR",
"Cameroon": "XAF",
"Canada": "CAD",
"Chile": "CLP",
"China": "CNY",
"Colombia": "COP",
"Croatia": "HRK",
"Cyprus": "EUR",
"Czechia": "CZK",
"Denmark": "DKK",
"Dominican_Republic": "DOP",
"Egypt": "EGP",
"Estonia": "EUR",
"European_Union": "EUR",
"Finland": "EUR",
"France": "EUR",
"Germany": "EUR",
"Greece": "EUR",
"Honduras": "HNL",
"Hong Kong": "HKD",
"Hungary": "HUF",
"India": "INR",
"Indonesia": "IDR",
"Iran": "IRR",
"Ireland": "EUR",
"Israel": "ILS",
"Italy": "EUR",
"Japan": "JPY",
"Kazakhstan": "KZT",
"Laos": "LAK",
"Latvia": "EUR",
"Lebanon": "LBP",
"Lithuania": "EUR",
"Luxembourg": "EUR",
"Macedonia": "MKD",
"Malaysia": "MYR",
"Malta": "EUR",
"Mexico": "MXN",
"Mongolia": "MNT",
"Netherlands": "EUR",
"New_Zealand": "NZD",
"Nigeria": "NGN",
"Norway": "NOK",
"Oman": "OMR",
"Pakistan": "PKR",
"Panama": "PAB",
"Paraguay": "PYG",
"Peru": "PEN",
"Philippines": "PHP",
"Poland": "PLN",
"Portugal": "EUR",
"Qatar": "QAR",
"Romania": "RON",
"Russia": "RUB",
"Saudi_Arabia": "SAR",
"Serbia": "RSD",
"Singapore": "SGD",
"Slovakia": "EUR",
"Slovenia": "EUR",
"South_Africa": "ZAR",
"South_Korea": "KRW",
"Spain": "EUR",
"Sweden": "SEK",
"Switzerland": "CHF",
"Taiwan": "TWD",
"Thailand": "THB",
"Tunisia": "TND",
"Turkey": "TRY",
"Ukraine": "UAH",
"United_Arab_Emirates": "AED",
"United_Kingdom": "GBP",
"United_States": "USD",
"Uzbekistan": "UZS",
"Venezuela": "VEF",
"Vietnam": "VND",
}
PARAMETERS = {
"RGDP": {
"name": "Real gross domestic product",
"period": "Quarterly",
"description": "Inflation-adjusted measure that reflects the value of all goods and services produced by "
"an economy in a given year (chain-linked series).",
},
"RPRC": {
"name": "Real private consumption",
"period": "Quarterly",
"description": "All purchases made by consumers adjusted by inflation (chain-linked series).",
},
"RPUC": {
"name": "Real public consumption",
"period": "Quarterly",
"description": "All purchases made by the government adjusted by inflation (chain-linked series).",
},
"RGFCF": {
"name": "Real gross fixed capital formation",
"period": "Quarterly",
"description": "The acquisition of produced assets adjusted by inflation (chain-linked series).",
},
"REXP": {
"name": "Real exports of goods and services",
"period": "Quarterly",
"description": "Transactions in goods and services from residents to non-residents adjusted for "
"inflation (chain-linked series)",
},
"RIMP": {
"name": "Real imports of goods and services",
"period": "Quarterly",
"description": "Transactions in goods and services to residents from non-residents adjusted for "
"inflation (chain-linked series)",
},
"GDP": {
"name": "Gross domestic product",
"period": "Quarterly",
"description": "Measure that reflects the value of all goods and services produced by "
"an economy in a given year (chain-linked series).",
},
"PRC": {
"name": "Private consumption",
"period": "Quarterly",
"description": "All purchases made by consumers (chain-linked series).",
},
"PUC": {
"name": "Public consumption",
"period": "Quarterly",
"description": "All purchases made by the government (chain-linked series)",
},
"GFCF": {
"name": "Gross fixed capital formation",
"period": "Quarterly",
"description": "The acquisition of produced assets (chain-linked series).",
},
"EXP": {
"name": "Exports of goods and services",
"period": "Quarterly",
"description": "Transactions in goods and services from residents to non-residents (chain-linked series)",
},
"IMP": {
"name": "Imports of goods and services",
"period": "Quarterly",
"description": "Transactions in goods and services to residents from non-residents (chain-linked series)",
},
"CPI": {
"name": "Consumer price index",
"period": "Monthly",
"description": "Purchasing power defined with base 2015 for Europe with varying bases for others. See: "
"https://www.econdb.com/main-indicators",
},
"PPI": {
"name": "Producer price index",
"period": "Monthly",
"description": "Change in selling prices with base 2015 for Europe with varying bases for others. See: "
"https://www.econdb.com/main-indicators",
},
"CORE": {
"name": "Core consumer price index",
"period": "Monthly",
"description": "Purchasing power excluding food and energy defined with base 2015 for Europe with varying "
"bases for others. See: https://www.econdb.com/main-indicators",
},
"URATE": {
"name": "Unemployment",
"period": "Monthly",
"description": "Monthly average % of the working-age population that is unemployed.",
},
"EMP": {
"name": "Employment",
"period": "Quarterly",
"description": "The employed population within a country (in thousands).",
},
"ACOIO": {
"name": "Active population",
"period": "Quarterly",
"description": "The active population, unemployed and employed, in thousands.",
},
"EMRATIO": {
"name": "Employment to working age population",
"period": "Quarterly",
"description": "Unlike the unemployment rate, the employment-to-population ratio includes unemployed "
"people not looking for jobs.",
},
"RETA": {
"name": "Retail trade",
"period": "Monthly",
"description": "Turnover of sales in wholesale and retail trade",
},
"CONF": {
"name": "Consumer confidence index",
"period": "Monthly",
"description": "Measures how optimistic or pessimistic consumers are regarding their expected financial "
"situation.",
},
"IP": {
"name": "Industrial production",
"period": "Monthly",
"description": "Measures monthly changes in the price-adjusted output of industry.",
},
"CP": {
"name": "Construction production",
"period": "Monthly",
"description": "Measures monthly changes in the price-adjusted output of construction.",
},
"GBAL": {
"name": "Government balance",
"period": "Quarterly",
"description": "The government balance (or EMU balance) is the overall difference between government "
"revenues and spending.",
},
"GREV": {
"name": "General government total revenue",
"period": "Quarterly",
"description": "The total amount of revenues collected by governments is determined by past and "
"current political decisions.",
},
"GSPE": {
"name": "General government total expenditure",
"period": "Quarterly",
"description": "Total expenditure consists of total expense and the net acquisition of "
"non-financial assets. ",
},
"GDEBT": {
"name": "Government debt",
"period": "Quarterly",
"description": "The financial liabilities of the government.",
},
"CA": {
"name": "Current account balance",
"period": "Monthly",
"description": "A record of a country's international transactions with the rest of the world",
},
"TB": {
"name": "Trade balance",
"period": "Monthly",
"description": "The difference between the monetary value of a nation's exports and imports over a "
"certain time period.",
},
"NIIP": {
"name": "Net international investment position",
"period": "Quarterly",
"description": "Measures the gap between a nation's stock of foreign assets and a foreigner's stock "
"of that nation's assets",
},
"IIPA": {
"name": "Net international investment position (Assets)",
"period": "Quarterly",
"description": "A nation's stock of foreign assets.",
},
"IIPL": {
"name": "Net international investment position (Liabilities)",
"period": "Quarterly",
"description": "A foreigner's stock of the nation's assets.",
},
"Y10YD": {
"name": "Long term yield (10-year)",
"period": "Monthly",
"description": "The 10-year yield is used as a proxy for mortgage rates. It's also seen as a "
"sign of investor sentiment about the country's economy.",
},
"M3YD": {
"name": "3 month yield",
"period": "Monthly",
"description": "The yield received for investing in a government issued treasury security "
"that has a maturity of 3 months",
},
"HOU": {
"name": "House price index",
"period": "Monthly",
"description": "House price index defined with base 2015 for Europe with varying "
"bases for others. See: https://www.econdb.com/main-indicators",
},
"OILPROD": {
"name": "Oil production",
"period": "Monthly",
"description": "The amount of oil barrels produced per day in a month within a country.",
},
"POP": {
"name": "Population",
"period": "Monthly",
"description": "The population of a country. This can be in thousands or, "
"when relatively small, in actual units.",
},
}
TRANSFORM = {
"": "No transformation",
"TPOP": "Total percentage change on period",
"TOYA": "Total percentage since 1 year ago",
"TUSD": "Level USD",
"TPGP": "Percentage of GDP",
"TNOR": "Start = 100",
}
SCALES = {
"Thousands": 1_000,
"Tens of thousands": 10_000,
"Hundreds of thousands": 100_000,
"Millions": 1_000_000,
"Tens of millions": 10_000_000,
"Hundreds of millions": 100_000_000,
"Billions": 1_000_000_000,
"Tens of billions": 10_000_000_000,
"Units": 1,
}
TREASURIES: Dict = {
"frequencies": {
"annually": 203,
"monthly": 129,
"weekly": 21,
"daily": 9,
},
"instruments": {
"nominal": {
"identifier": "TCMNOM",
"maturities": {
"1m": "1-month",
"3m": "3-month",
"6m": "6-month",
"1y": "1-year",
"2y": "2-year",
"3y": "3-year",
"5y": "5-year",
"7y": "7-year",
"10y": "10-year",
"20y": "20-year",
"30y": "30-year",
},
},
"inflation": {
"identifier": "TCMII",
"maturities": {
"5y": "5-year",
"7y": "7-year",
"10y": "10-year",
"20y": "20-year",
"30y": "30-year",
},
},
"average": {
"identifier": "LTAVG",
"maturities": {
"Longer than 10-year": "Longer than 10-year",
},
},
"secondary": {
"identifier": "TB",
"maturities": {
"4w": "4-week",
"3m": "3-month",
"6m": "6-month",
"1y": "1-year",
},
},
},
}
@log_start_end(log=logger)
def get_macro_data(
parameter: str,
country: str,
transform: str = "",
start_date: str = "1900-01-01",
end_date: Optional[str] = None,
symbol: str = "",
) -> Tuple[pd.Series, Union[str, Any]]:
"""Query the EconDB database to find specific macro data about a company [Source: EconDB]
Parameters
----------
parameter: str
The type of data you wish to display
country : str
the selected country
transform : str
select data transformation from:
'' - no transformation
'TPOP' - total percentage change on period,
'TOYA' - total percentage since 1 year ago,
'TUSD' - level USD,
'TPGP' - Percentage of GDP,
'TNOR' - Start = 100
start_date : str
The starting date, format "YEAR-MONTH-DAY", i.e. 2010-12-31.
end_date : Optional[str]
The end date, format "YEAR-MONTH-DAY", i.e. 2020-06-05.
symbol : str
In what currency you wish to convert all values.
Returns
-------
Tuple[pd.Series, Union[str, Any]]
A series with the requested macro data of the chosen country,
The units of the macro data, e.g. 'Bbl/day" for oil.
"""
if end_date is None:
end_date = datetime.today().strftime("%Y-%m-%d")
df, units = pd.DataFrame(), ""
country = country.replace("_", " ")
country = country.title()
country = country.replace(" ", "_")
parameter = parameter.upper()
if country not in COUNTRY_CODES:
console.print(f"No data available for the country {country}.")
return pd.Series(dtype=float), ""
if parameter not in PARAMETERS:
console.print(f"The parameter {parameter} is not an option for {country}.")
return pd.Series(dtype=float), ""
if transform not in TRANSFORM:
console.print(f"The transform {transform} is not a valid option.")
return pd.Series(dtype=float), ""
country_code = COUNTRY_CODES[country]
country_currency = COUNTRY_CURRENCIES[country]
try:
code = f"{parameter}{country_code}"
if transform:
code += f"~{transform}"
r = request(f"https://www.econdb.com/series/context/?tickers={code}")
res_json = r.json()
if res_json:
data = res_json[0]
scale = data["td"]["scale"]
units = data["td"]["units"]
df = pd.DataFrame(data["dataarray"])
if code not in df.columns:
console.print(
f"No data available for {parameter} of {country} "
f"{f'with transform method {transform}' if transform else ''}"
)
return pd.DataFrame(), "NA/NA"
df = df.set_index(pd.to_datetime(df["date"]))[code] * SCALES[scale]
df = df.sort_index().dropna()
# Since a percentage is done through differences, the first value is NaN
if transform in ["TPOP", "TOYA"]:
df = df.iloc[1:]
if not res_json or df.empty:
console.print(
f"No data available for {parameter} ({PARAMETERS[parameter]['name']}) "
f"of country {country.replace('_', ' ')}"
)
return pd.Series(dtype=float), ""
if start_date or end_date:
try:
dt_start = pd.to_datetime(start_date)
dt_end = pd.to_datetime(end_date)
df = df.loc[dt_start:dt_end]
except TypeError:
console.print("[red]Invalid date sent. Format as YYYY-MM-DD[/red]\n")
return pd.DataFrame(), "NA/NA"
if (
symbol
and country_currency != symbol
and units in COUNTRY_CURRENCIES.values()
):
if units in COUNTRY_CURRENCIES.values():
units = symbol
currency_data = yf.download(
f"{country_currency}{symbol}=X",
start=df.index[0],
end=df.index[-1],
progress=False,
)["Adj Close"]
merged_df = pd.merge_asof(
df, currency_data, left_index=True, right_index=True
)
df = merged_df[code] * merged_df["Adj Close"]
if pd.isna(df).any():
df_old_oldest, df_old_newest = df.index[0].date(), df.index[-1].date()
df = df.dropna()
df_new_oldest, df_new_newest = df.index[0].date(), df.index[-1].date()
console.print(
f"Due to missing exchange values, some data was dropped from {parameter} of {country}. "
f"Consider using the native currency if you want to prevent this. \n"
f"OLD: {df_old_oldest} - {df_old_newest}\n"
f"NEW: {df_new_oldest} - {df_new_newest}"
)
if not df.empty:
df = df.groupby(df.index.strftime("%Y-%m")).head(1)
df.index = df.index.strftime("%Y-%m")
df = pd.to_numeric(df, errors="coerce").dropna()
except HTTPError:
return console.print(
f"There is no data available for the combination {parameter} and {country}."
)
return df, units
@log_start_end(log=logger)
def get_macro_transform() -> Dict[str, str]:
"""This function returns the available macro transform with detail.
Returns
-------
Dict[str, str]
A dictionary with the available macro transforms.
"""
return TRANSFORM
@log_start_end(log=logger)
def get_macro_parameters() -> Dict[str, Dict[str, str]]:
"""This function returns the available macro parameters with detail.
Returns
-------
Dict[str, Dict[str, str]]
A dictionary with the available macro parameters.
"""
return PARAMETERS
@log_start_end(log=logger)
def get_macro_countries() -> Dict[str, str]:
"""This function returns the available countries and respective currencies.
Returns
-------
Dict[str, str]
A dictionary with the available countries and respective currencies.
"""
return COUNTRY_CURRENCIES
@log_start_end(log=logger)
def get_aggregated_macro_data(
parameters: Optional[list] = None,
countries: Optional[list] = None,
transform: str = "",
start_date: str = "1900-01-01",
end_date: Optional[str] = None,
symbol: str = "",
) -> Tuple[pd.DataFrame, Dict[Any, Dict[Any, Any]], str]:
"""This functions groups the data queried from the EconDB database [Source: EconDB]
Parameters
----------
parameters: list
The type of data you wish to download. Available parameters can be accessed through economy.macro_parameters().
countries : list
The selected country or countries. Available countries can be accessed through economy.macro_countries().
transform : str
The selected transform. Available transforms can be accessed through get_macro_transform().
start_date : str
The starting date, format "YEAR-MONTH-DAY", i.e. 2010-12-31.
end_date : Optional[str]
The end date, format "YEAR-MONTH-DAY", i.e. 2020-06-05.
symbol : str
In what currency you wish to convert all values.
Returns
-------
Tuple[pd.DataFrame, Dict[Any, Dict[Any, Any]], str]
A DataFrame with the requested macro data of all chosen countries,
A dictionary containing the units of each country's parameter (e.g. EUR),
A string denomination which can be Trillions, Billions, Millions, Thousands
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> macro_df = openbb.economy.macro()
"""
if end_date is None:
end_date = datetime.today().strftime("%Y-%m-%d")
if parameters is None:
parameters = ["CPI"]
if countries is None:
countries = ["United_States"]
country_data: Dict[Any, Dict[Any, pd.Series]] = {}
units: Dict[Any, Dict[Any, Any]] = {}
for country in countries:
country_data[country] = {}
units[country] = {}
for parameter in parameters:
(
country_data[country][parameter],
units[country][parameter],
) = get_macro_data(
parameter, country, transform, start_date, end_date, symbol
)
if country_data[country][parameter].empty:
del country_data[country][parameter]
del units[country][parameter]
country_data_df = (
pd.DataFrame.from_dict(country_data, orient="index").stack().to_frame()
)
country_data_df = pd.DataFrame(
country_data_df[0].values.tolist(), index=country_data_df.index
).T
(df_rounded, denomination) = transform_by_denomination(country_data_df)
df_rounded.index = pd.DatetimeIndex(df_rounded.index)
if transform:
denomination_string = f" [{TRANSFORM[transform]}]"
else:
denomination_string = f" [in {denomination}]" if denomination != "Units" else ""
df_rounded = df_rounded.dropna()
return (df_rounded, units, denomination_string)
@log_start_end(log=logger)
def get_treasuries(
instruments: Optional[list] = None,
maturities: Optional[list] = None,
frequency: str = "monthly",
start_date: str = "1900-01-01",
end_date: Optional[str] = None,
) -> pd.DataFrame:
"""Get U.S. Treasury rates [Source: EconDB]
Parameters
----------
instruments: list
Type(s) of treasuries, nominal, inflation-adjusted (long term average) or secondary market.
Available options can be accessed through economy.treasury_maturities().
maturities : list
Treasury maturities to get. Available options can be accessed through economy.treasury_maturities().
frequency : str
Frequency of the data, this can be annually, monthly, weekly or daily.
start_date : str
Starting date, format "YEAR-MONTH-DAY", i.e. 2010-12-31.
end_date : Optional[str]
End date, format "YEAR-MONTH-DAY", i.e. 2020-06-05.
Returns
-------
treasury_data: pd.Dataframe
Holds data of the selected types and maturities
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> openbb.economy.treasury()
"""
if end_date is None:
end_date = datetime.today().strftime("%Y-%m-%d")
if instruments is None:
instruments = ["nominal"]
if maturities is None:
maturities = ["10y"]
treasury_data: Dict[Any, Dict[Any, pd.Series]] = {}
for instrument in instruments:
if instrument not in TREASURIES["instruments"]:
console.print(
f"{instrument} is not an option. Please choose between: "
f"{', '.join(TREASURIES['instruments'].keys())}"
)
else:
instrument_identifier = TREASURIES["instruments"][instrument]["identifier"]
frequency_number = TREASURIES["frequencies"][frequency]
df = web.DataReader(
"&".join(
[
"dataset=FRB_H15",
"v=Instrument",
"h=TIME",
f"instrument=[{instrument_identifier}]",
f"from={start_date}",
f"to={end_date}",
f"freq=[{frequency_number}",
"UNIT=[PERCENT:_PER_YEAR]",
]
),
"econdb",
)
if instrument == "average":
maturities_list = ["Longer than 10-year"]
type_string = "Long-term average"
else:
maturities_list = maturities
type_string = instrument.capitalize()
treasury_data[type_string] = {}
for maturity in maturities_list:
if maturity not in TREASURIES["instruments"][instrument]["maturities"]:
console.print(
f"The maturity {maturity} is not an option for {instrument}. Please choose between "
f"{', '.join(TREASURIES['instruments'][instrument]['maturities'].keys())}"
)
else:
maturity_string = TREASURIES["instruments"][instrument][
"maturities"
][maturity]
for column in df.columns:
# check if type inside the name and maturity inside the maturity string
if (
type_string.lower() in column[2].lower()
and maturity_string in column[3]
):
treasury_data[type_string][maturity_string] = df[
column
].dropna()
break
if maturity_string not in treasury_data[type_string]:
console.print(
f"No data found for the combination {instrument} and {maturity}."
)
df = pd.DataFrame.from_dict(treasury_data, orient="index").stack().to_frame()
df = pd.DataFrame(df[0].values.tolist(), index=df.index).T
df.columns = ["_".join(column) for column in df.columns]
return df
@log_start_end(log=logger)
def get_treasury_maturities() -> pd.DataFrame:
"""Get treasury maturity options [Source: EconDB]
Returns
-------
df: pd.DataFrame
Contains the name of the instruments and a string containing all options.
"""
instrument_maturities = {
instrument: ", ".join(values["maturities"].keys())
for instrument, values in TREASURIES["instruments"].items()
}
df = pd.DataFrame.from_dict(instrument_maturities, orient="index")
df.loc["average"] = "Defined by function"
df.index.name = "Instrument"
df.columns = ["Maturities"]
return df
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