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3d84247e5f
* Fix treasury * Update oecd_model.py --------- Co-authored-by: James Maslek <jmaslek11@gmail.com>
304 lines
11 KiB
Python
304 lines
11 KiB
Python
""" OECD model """
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__docformat__ = "numpy"
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import logging
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from datetime import datetime
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from typing import List, Optional
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import pandas as pd
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from openbb_terminal.decorators import log_start_end
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logger = logging.getLogger(__name__)
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COUNTRY_TO_CODE_SHORT = {
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"australia": "AUS",
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"austria": "AUT",
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"belgium": "BEL",
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"bulgaria": "BGR",
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"canada": "CAN",
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"switzerland": "CHE",
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"chile": "CHL",
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"china": "CHN",
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"colombia": "COL",
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"costa_rica": "CRI",
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"czech_republic": "CZE",
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"germany": "DEU",
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"denmark": "DNK",
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"euro_area": "EA19",
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"spain": "ESP",
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"estonia": "EST",
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"finland": "FIN",
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"france": "FRA",
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"united_kingdom": "GBR",
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"greece": "GRC",
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"croatia": "HRV",
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"hungary": "HUN",
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"indonesia": "IDN",
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"india": "IND",
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"ireland": "IRL",
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"iceland": "ISL",
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"israel": "ISR",
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"italy": "ITA",
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"japan": "JPN",
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"korea": "KOR",
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"lithuania": "LTU",
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"luxembourg": "LUX",
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"latvia": "LVA",
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"mexico": "MEX",
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"netherlands": "NLD",
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"norway": "NOR",
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"new_zealand": "NZL",
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"poland": "POL",
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"portugal": "PRT",
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"romania": "ROU",
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"russia": "RUS",
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"slovak_republic": "SVK",
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"slovenia": "SVN",
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"sweden": "SWE",
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"united_states": "USA",
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"south_africa": "ZAF",
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}
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COUNTRY_TO_CODE_LONG = {
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"australia": "AUS",
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"austria": "AUT",
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"belgium": "BEL",
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"bulgaria": "BGR",
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"brazil": "BRA",
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"canada": "CAN",
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"switzerland": "CHE",
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"chile": "CHL",
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"china": "CHN",
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"colombia": "COL",
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"costa_rica": "CRI",
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"czech_republic": "CZE",
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"germany": "DEU",
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"denmark": "DNK",
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"euro_area": "EA19",
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"spain": "ESP",
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"estonia": "EST",
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"finland": "FIN",
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"france": "FRA",
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"united_kingdom": "GBR",
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"greece": "GRC",
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"croatia": "HRV",
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"hungary": "HUN",
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"indonesia": "IDN",
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"india": "IND",
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"ireland": "IRL",
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"iceland": "ISL",
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"israel": "ISR",
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"italy": "ITA",
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"japan": "JPN",
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"korea": "KOR",
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"lithuania": "LTU",
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"luxembourg": "LUX",
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"latvia": "LVA",
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"mexico": "MEX",
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"netherlands": "NLD",
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"norway": "NOR",
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"new_zealand": "NZL",
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"poland": "POL",
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"portugal": "PRT",
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"romania": "ROU",
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"russia": "RUS",
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"slovak_republic": "SVK",
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"slovenia": "SVN",
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"sweden": "SWE",
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"united_states": "USA",
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"south_africa": "ZAF",
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}
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@log_start_end(log=logger)
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def get_interest_rate_data(
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data: str = "short",
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countries: Optional[List[str]] = None,
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start_date: str = "",
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end_date: str = "",
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) -> pd.DataFrame:
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"""Gets interest rate data from selected countries.
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Long-term interest rates refer to government bonds maturing in ten years. Rates are mainly determined by the
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price charged by the lender, the risk from the borrower and the fall in the capital value. Long-term interest
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rates are generally averages of daily rates, measured as a percentage. These interest rates are implied by the
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prices at which the government bonds are traded on financial markets, not the interest rates at which the loans
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were issued. In all cases, they refer to bonds whose capital repayment is guaranteed by governments. Long-term
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interest rates are one of the determinants of business investment. Low long-term interest rates encourage
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investment in new equipment and high interest rates discourage it. Investment is, in turn, a major source of
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economic growth.
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Short-term interest rates are the rates at which short-term borrowings are effected between financial
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institutions or the rate at which short-term government paper is issued or traded in the market. Short-term
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interest rates are generally averages of daily rates, measured as a percentage. Short-term interest rates are
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based on three-month money market rates where available. Typical standardised names are "money market rate" and
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"treasury bill rate".
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Parameters
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----------
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data: str
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Type of data to getz, options ['short', 'short_forecast', 'long', 'long_forecast']
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countries: list
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List of countries to get data for
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start_date: str
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Start date of data, in YYYY-MM-DD format
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end_date: str
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End date of data, in YYYY-MM-DD format
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Returns
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-------
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pd.DataFrame
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Dataframe with the interest rate data
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"""
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if data == "short":
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if isinstance(start_date, datetime):
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start_date = start_date.strftime("%Y-%m")
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elif len(start_date) > 8:
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start_date = start_date[:8]
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if isinstance(end_date, datetime):
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end_date = end_date.strftime("%Y-%m")
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elif len(end_date) > 8:
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end_date = end_date[:8]
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df = pd.read_csv(
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f"https://stats.oecd.org/sdmx-json/data/DP_LIVE/.STINT.TOT.PC_PA.M/OECD?contentType=csv&detail=code"
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f"&separator=comma&csv-lang=en&startPeriod={start_date}&endPeriod={end_date}",
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index_col=5,
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)
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data_name = "3 month"
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elif data == "short_forecast":
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if isinstance(start_date, datetime):
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month = start_date.month
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start_date = start_date.strftime("%Y") + "-Q" + str((month - 1) // 3 + 1)
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elif len(start_date) > 7:
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month = int(start_date[5:7])
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start_date = start_date[:4] + "-Q" + str((month - 1) // 3 + 1)
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if isinstance(end_date, datetime):
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month = end_date.month
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end_date = end_date.strftime("%Y") + "-Q" + str((month - 1) // 3 + 1)
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elif len(end_date) > 7:
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month = int(end_date[5:7])
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end_date = end_date[:4] + "-Q" + str((month - 1) // 3 + 1)
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df = pd.read_csv(
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f"https://stats.oecd.org/sdmx-json/data/DP_LIVE/.STINTFORECAST.TOT.PC_PA.Q/OECD?contentType=csv&detail"
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f"=code&separator=comma&csv-lang=en&startPeriod={start_date}&endPeriod={end_date}",
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index_col=5,
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)
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data_name = "3 month with forecast"
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elif data == "long":
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if isinstance(start_date, datetime):
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start_date = start_date.strftime("%Y-%m")
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elif len(start_date) > 8:
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start_date = start_date[:8]
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if isinstance(end_date, datetime):
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end_date = end_date.strftime("%Y-%m")
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elif len(end_date) > 8:
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end_date = end_date[:8]
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df = pd.read_csv(
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f"https://stats.oecd.org/sdmx-json/data/DP_LIVE/.LTINT.TOT.PC_PA.M/OECD?contentType=csv&detail=code"
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f"&separator=comma&csv-lang=en&startPeriod={start_date}&endPeriod={end_date}",
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index_col=5,
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)
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data_name = "10 year"
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elif data == "long_forecast":
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if isinstance(start_date, datetime):
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month = start_date.month
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start_date = start_date.strftime("%Y") + "-Q" + str((month - 1) // 3 + 1)
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elif len(start_date) > 7:
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month = int(start_date[5:7])
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start_date = start_date[:4] + "-Q" + str((month - 1) // 3 + 1)
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if isinstance(end_date, datetime):
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month = end_date.month
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end_date = end_date.strftime("%Y") + "-Q" + str((month - 1) // 3 + 1)
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elif len(end_date) > 7:
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month = int(end_date[5:7])
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end_date = end_date[:4] + "-Q" + str((month - 1) // 3 + 1)
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df = pd.read_csv(
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f"https://stats.oecd.org/sdmx-json/data/DP_LIVE/.LTINTFORECAST.TOT.PC_PA.Q/OECD?contentType=csv&detail"
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f"=code&separator=comma&csv-lang=en&startPeriod={start_date}&endPeriod={end_date}",
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index_col=5,
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)
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data_name = "10 year with forecast"
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else:
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return pd.DataFrame()
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df = df.iloc[:, [0, 5]]
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result = pd.DataFrame()
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for country in countries: # type: ignore
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if data in ["short", "short_forecast"]:
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temp = pd.DataFrame(
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df[df["LOCATION"] == COUNTRY_TO_CODE_SHORT[country]]["Value"]
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)
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else:
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temp = pd.DataFrame(
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df[df["LOCATION"] == COUNTRY_TO_CODE_LONG[country]]["Value"]
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)
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temp.columns = [f"{country} ({data_name})"]
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result = pd.concat([result, temp], axis=1)
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result.index = pd.to_datetime(result.index).date
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result.sort_index(inplace=True)
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return result
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def get_treasury(
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short_term: Optional[list] = None,
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long_term: Optional[list] = None,
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forecast: bool = False,
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start_date: Optional[str] = None,
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end_date: Optional[str] = None,
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) -> pd.DataFrame:
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"""Gets interest rates data from selected countries (3 month and 10 year)
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Short-term interest rates are the rates at which short-term borrowings are effected between financial
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institutions or the rate at which short-term government paper is issued or traded in the market. Short-term
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interest rates are generally averages of daily rates, measured as a percentage. Short-term interest rates are
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based on three-month money market rates where available. Typical standardised names are "money market rate" and
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"treasury bill rate".
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Long-term interest rates refer to government bonds maturing in ten years. Rates are mainly determined by the
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price charged by the lender, the risk from the borrower and the fall in the capital value. Long-term interest
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rates are generally averages of daily rates, measured as a percentage. These interest rates are implied by the
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prices at which the government bonds are traded on financial markets, not the interest rates at which the loans
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were issued. In all cases, they refer to bonds whose capital repayment is guaranteed by governments. Long-term
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interest rates are one of the determinants of business investment. Low long-term interest rates encourage
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investment in new equipment and high interest rates discourage it. Investment is, in turn, a major source of
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economic growth.
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Parameters
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----------
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short_term: list
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Countries you wish to plot the 3-month interest rate for
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long_term: list
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Countries you wish to plot the 10-year interest rate for
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forecast: bool
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If True, plot forecasts for short term interest rates
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start_date: str
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Start date of data, in YYYY-MM-DD format
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end_date: str
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End date of data, in YYYY-MM-DD format
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"""
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df_short, df_long = pd.DataFrame(), pd.DataFrame()
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if short_term:
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df_short = get_interest_rate_data(
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f"short{'_forecast' if forecast else ''}",
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short_term,
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start_date if start_date is not None else "",
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end_date if end_date is not None else "",
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)
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if long_term:
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df_long = get_interest_rate_data(
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f"long{'_forecast' if forecast else ''}",
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long_term,
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start_date if start_date is not None else "",
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end_date if end_date is not None else "",
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)
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df = pd.concat([df_short, df_long], axis=1)
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return df
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