""" ECB view """ __docformat__ = "numpy" # IMPORTATION STANDARD import logging import os from typing import Optional # IMPORTATION THIRDPARTY import pandas as pd from openbb_terminal import OpenBBFigure from openbb_terminal.decorators import log_start_end from openbb_terminal.fixedincome import ecb_model from openbb_terminal.helper_funcs import export_data, print_rich_table from openbb_terminal.rich_config import console logger = logging.getLogger(__name__) # pylint: disable=too-many-function-args ID_TO_NAME = { "EST.B.EU000A2X2A25.WT": "Euro Short-Term Rate: Volume-Weighted Trimmed Mean Rate [Percent]", "EST.B.EU000A2X2A25.TT": "Euro Short-Term Rate: Total Volume [Millions of EUR]", "EST.B.EU000A2X2A25.NT": "Euro Short-Term Rate: Number of Transactions", "EST.B.EU000A2X2A25.R75": "Euro Short-Term Rate: Rate at 75th Percentile of Volume [Percent]", "EST.B.EU000A2X2A25.NB": "Euro Short-Term Rate: Number of Active Banks", "EST.B.EU000A2X2A25.VL": "Euro Short-Term Rate: Share of Volume of the 5 Largest Active Banks [Percent]", "EST.B.EU000A2X2A25.R25": "Euro Short-Term Rate: Rate at 25th Percentile of Volume [Percent]", } ESTR_PARAMETER_TO_ECB_ID = { "volume_weighted_trimmed_mean_rate": "EST.B.EU000A2X2A25.WT", "number_of_transactions": "EST.B.EU000A2X2A25.NT", "number_of_active_banks": "EST.B.EU000A2X2A25.NB", "total_volume": "EST.B.EU000A2X2A25.TT", "share_of_volume_of_the_5_largest_active_banks": "EST.B.EU000A2X2A25.VL", "rate_at_75th_percentile_of_volume": "EST.B.EU000A2X2A25.R75", "rate_at_25th_percentile_of_volume": "EST.B.EU000A2X2A25.R25", } @log_start_end(log=logger) def plot_estr( parameter: str = "EST.B.EU000A2X2A25.WT", start_date: Optional[str] = None, end_date: Optional[str] = None, export: str = "", sheet_name: str = "", external_axes: bool = False, ): """Plot Euro Short-Term Rate (ESTR) Parameters ---------- series_id: str ECB ID of ESTR data to plot, options: ['EST.B.EU000A2X2A25.WT', 'EST.B.EU000A2X2A25.TT', 'EST.B.EU000A2X2A25.NT', 'EST.B.EU000A2X2A25.R75', 'EST.B.EU000A2X2A25.NB', 'EST.B.EU000A2X2A25.VL', 'EST.B.EU000A2X2A25.R25'] start_date: Optional[str] Start date, formatted YYYY-MM-DD end_date: Optional[str] End date, formatted YYYY-MM-DD export: str Export data to csv or excel file external_axes : bool, optional Whether to return the figure object or not, by default False """ series_id = ESTR_PARAMETER_TO_ECB_ID.get(parameter, "") df = ecb_model.get_series_data( series_id, start_date if start_date else "", end_date if end_date else "" ) fig = OpenBBFigure() fig.set_title(ID_TO_NAME[series_id]) fig.add_scatter(x=df.index, y=df.values, name=series_id, mode="lines") if export: if "[Percent]" in ID_TO_NAME[series_id]: # Check whether it is a percentage, relevant for exporting df_transformed = pd.DataFrame(df, columns=[series_id]) / 100 else: df_transformed = pd.DataFrame(df, columns=[series_id]) export_data( export, os.path.dirname(os.path.abspath(__file__)), series_id, df_transformed, sheet_name, fig, ) return fig.show(external=external_axes) @log_start_end(log=logger) def display_ecb_yield_curve( date: str = "", yield_type: str = "spot_rate", detailed: bool = False, any_rating: bool = True, external_axes: bool = False, raw: bool = False, export: str = "", sheet_name: str = "", ): """Display yield curve based on ECB Treasury rates for a specified date. The graphic depiction of the relationship between the yield on bonds of the same credit quality but different maturities is known as the yield curve. In the past, most market participants have constructed yield curves from the observations of prices and yields in the Treasury market. Two reasons account for this tendency. First, Treasury securities are viewed as free of default risk, and differences in creditworthiness do not affect yield estimates. Second, as the most active bond market, the Treasury market offers the fewest problems of illiquidity or infrequent trading. The key function of the Treasury yield curve is to serve as a benchmark for pricing bonds and setting yields in other sectors of the debt market. It is clear that the market’s expectations of future rate changes are one important determinant of the yield-curve shape. For example, a steeply upward-sloping curve may indicate market expectations of near-term Fed tightening or of rising inflation. However, it may be too restrictive to assume that the yield differences across bonds with different maturities only reflect the market’s rate expectations. The well-known pure expectations hypothesis has such an extreme implication. The pure expectations hypothesis asserts that all government bonds have the same near-term expected return (as the nominally riskless short-term bond) because the return-seeking activity of risk-neutral traders removes all expected return differentials across bonds. Parameters ---------- date: str Date to get curve for. If None, gets most recent date (format yyyy-mm-dd) yield_type: str What type of yield curve to get, options: ['spot_rate', 'instantaneous_forward', 'par_yield'] detailed: bool If True, returns detailed data. Note that this is very slow. aaa_only: bool If True, it only returns rates for AAA rated bonds. If False, it returns rates for all bonds external_axes : bool, optional Whether to return the figure object or not, by default False raw : bool Output only raw data export : str Export data to csv,json,xlsx or png,jpg,pdf,svg file """ rates, date_of_yield = ecb_model.get_ecb_yield_curve( date, yield_type, True, detailed, any_rating ) if rates.empty: return console.print(f"[red]Yield data not found for {date_of_yield}.[/red]\n") fig = OpenBBFigure(xaxis_title="Maturity", yaxis_title="Rate (%)") fig.set_title( f"Euro Area{' AAA Bonds' if not any_rating else ' All Bonds'} {yield_type.replace('_', ' ').title()} " f"Yield Curve for {date_of_yield}", ) fig.add_scatter(x=rates["Maturity"], y=rates["Rate"], name="Yield") if raw: print_rich_table( rates, headers=list(rates.columns), show_index=False, title=f"Euro Area{' AAA Bonds' if not any_rating else ' All Bonds'} {yield_type.replace('_', ' ').title()} " f"Yield Curve for {date_of_yield}", floatfmt=".3f", export=bool(export), ) export_data( export, os.path.dirname(os.path.abspath(__file__)), "ecbycrv", rates / 100, sheet_name, fig, ) return fig.show(external=raw or external_axes)