Generating sample bond data

This notebook generates the sample bond data used by the BasicBond model. Numpy and QuantLib is required to run this notebook.

Columns:

• bond_id: Bond identifier

• settlement_days: 0

• face_value: Face value. Uniformly distributed from 10,000 to 1,000,000.

• issue_date: Issue date. Uniformly distributed between 1 Jan 2017 to 31 Dec 2021.

• bond_term: Bond term in years. Evenly distributed among 5, 10, 15, 20, 25, 30 years.

• maturity_date: Matuirty date. issue_date + bond_term.

• tenor: “6M” or “1Y” to indicate length of time between each coupon payment. The samples are evenry distributed.

• coupon_rate: Coupon rate. Uniformly distributed between 0 and 8% by 1%

• z_spread: Z-spread. Uniformly distributed from 0% to 4%.

Number of data:

• 1000

Click the badge below to run this notebook online on Google Colab. You need a Google account and need to be logged in to it to run this notebook on Google Colab.

The next code cell below is relevant only when you run this notebook on Google Colab. It installs lifelib and creates a copy of the library for this notebook.

import sys, os

if 'google.colab' in sys.modules:
    lib = 'assets'; lib_dir = '/content/'+ lib
    if not os.path.exists(lib_dir):
        !pip install lifelib
        !pip install QuantLib
        import lifelib; lifelib.create(lib, lib_dir)

    %cd $lib_dir

import numpy as np
from numpy.random import default_rng  # Requires NumPy 1.17 or newer

rng = default_rng(12345)

# Number of Data
DataSize = 1000

# Settlement Days: 0
settlement_days = np.array([0] * DataSize)

# Face Value (Float): 10000 - 1000000

face_value = np.round((1000000 - 10000) * rng.random(size=DataSize) + 10000, -3)

# Issue Date (datetime64): 1 Jan 2017 to 31 Dec 2021

import datetime
date_begin = datetime.date(2017,1,1)
date_end = datetime.date(2022,1,1)

issue_date = (date_end - date_begin) * rng.random(size=DataSize) + date_begin

# Bond Term: 5,..,40 years
import QuantLib as ql

terms = [ql.Period(y, ql.Years) for y in [5, 10, 15, 20, 25, 30]]
bond_term = [terms[i] for i in rng.integers(low=0, high=len(terms), size=DataSize)]

# Maturity Date (datetime64): Issue date + Bond Term
maturity_date = [ql.Date(issue.day, issue.month, issue.year) + term for issue, term in zip(issue_date, bond_term)]


# Coupon Tenor: "6M" or "1Y"
_tenor = ["6M", "1Y"]
tenor = np.fromiter(map(lambda i: _tenor[i], rng.integers(low=0, high=len(_tenor), size=DataSize)), np.dtype('<U2'))

# Coupon Rate: 0% - 8%
coupon_rate = rng.integers(low=0, high=9, size=DataSize) / 100

# Z-spread: 0% - 4%
z_spread = np.round(rng.random(size=DataSize) * 0.04, 4)

import pandas as pd

attrs = [
    "settlement_days",
    "face_value",
    "issue_date",
    "bond_term",
    "maturity_date",
    "tenor",
    "coupon_rate",
    "z_spread"
]

data = [
    settlement_days,
    face_value,
    issue_date,
    [y.length() for y in bond_term],
    [d.to_date() for d in maturity_date],
    tenor,
    coupon_rate,
    z_spread
]

bond_data = pd.DataFrame(dict(zip(attrs, data)), index=range(1, DataSize+1))
bond_data.index.name = "bond_id"
bond_data

	settlement_days	face_value	issue_date	bond_term	maturity_date	tenor	coupon_rate	z_spread
bond_id
1	0	235000.0	2017-12-12	10	2027-12-12	1Y	0.07	0.0304
2	0	324000.0	2021-11-29	25	2046-11-29	1Y	0.08	0.0304
3	0	799000.0	2017-02-03	10	2027-02-03	6M	0.03	0.0155
4	0	679000.0	2017-11-19	10	2027-11-19	1Y	0.08	0.0229
5	0	397000.0	2018-07-01	5	2023-07-01	6M	0.06	0.0142
...	...	...	...	...	...	...	...	...
996	0	560000.0	2019-02-16	10	2029-02-16	1Y	0.06	0.0261
997	0	161000.0	2020-03-12	30	2050-03-12	6M	0.05	0.0199
998	0	375000.0	2019-05-05	5	2024-05-05	1Y	0.03	0.0138
999	0	498000.0	2019-02-21	15	2034-02-21	1Y	0.03	0.0230
1000	0	438000.0	2019-03-14	30	2049-03-14	1Y	0.06	0.0256

1000 rows × 8 columns

Uncomment the command below to save the data to an Excel file.

# bond_data.to_excel("bond_data_sample.xlsx")