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Bonds
Primer on bonds.
The purpose of this notebook is to show functionality related to bonds on the platform.
A notebook containing all the code used in this page can be accessed in the research environment: Example notebooks.
Setting up your environment takes three steps:
- Import the relevant internal and external libraries
- Configure the environment parameters
- Initialise the environment
import sigtech.framework as sig
import QuantLib as ql
import datetime as dtm
import seaborn as sns
sns.set(rc={'figure.figsize': (18, 6)})
sig.config.init();
Government bonds are objects that can be retrieved by the object name constructed by the Country code, coupon, maturity date and 'GOVT' key.
bond_sig = sig.obj.get('US 2.625 2029/02/15 GOVT')
Each bond has various data elements accessible through their objects. The available time series for bond instruments are stored in the properties
.history_fields and .extra_fields and are the following:Python
Output
bond_sig.history_fields, bond_sig.extra_fields
(['AskPrice',
'BidPrice',
'LastPrice',
'data_point',
'HighPrice',
'LowPrice',
'MidPrice',
'open',
'open_interest',
'Volume'],
['ModDuration', 'DV01', 'YTM'])
For querying the listed time series, the name of the time series can be passed in as the
field parameter in the method .history(field = ) as shown below:Python
Output
bond_sig.history(field='YTM').tail(5)
2021-04-26 0.013621
2021-04-27 0.014046
2021-04-28 0.014022
2021-04-29 0.014157
2021-04-30 0.014128
Name: (YTM, EOD, US 2.625 2029/02/15 GOVT), dtype: float64
Furthermore, the bond also holds relevant static data as seen below:
Python
Output
bond_sig.data_dict()
{'instrument_id': None,
'data_source_all': [],
'available_data_points': ['EOD'],
'default_data_point': 'EOD',
'price_factor': 1.0,
'use_price_factor': True,
'intraday_times': [],
'intraday_tz_str': 'UTC',
'currency': 'USD',
'db_ticker': 'US 2.625 2029/02/15',
'db_sector': None,
'exchange_code': 'Unknown',
'activity_fields': ['Volume'],
'group_name': 'US GOVT BOND GROUP',
'description': "2.625% NTS 15/02/2029 USD 'B-2029'",
'coupon': 2.625,
'coupon_type': 'FIXED',
'coupon_frequency': 'SEMI_ANNUAL',
'issue_date': datetime.date(2019, 2, 15),
'issuer': 'United States Treasury Notes',
'maturity_date': datetime.date(2029, 2, 15),
'first_coupon_date': datetime.date(2019, 8, 15),
'int_acc_date': datetime.date(2019, 2, 15),
'redemption_amount': 100.0,
'day_count': 'ACT/ACT',
'isin': 'US9128286B18',
'days_to_settle': 1,
'first_settle_date': datetime.date(2019, 2, 15),
'series_number': 'B-2029',
'calc_type': 1,
'db_history_end_date': datetime.date(9999, 12, 31),
'country': 'US'}
Every single bond is part of a bond group, where a bond group contains all bonds issued by an issuer.
Python
Output
bond_group = bond_sig.group()
bond_group
US GOVT BOND GROUP <class 'sigtech.framework.instruments.bonds.BondGroup'>[5481449408]
As shown above, the bond group also acts as an object, therefore it holds functionality and data on its own. An example of the functionality is to query all bonds in a bond group which is shown below:
Python
Output
bond_group.query_instrument_names()[:5]
['US 0 2022/12/31 GOVT',
'US 0.125 2013/08/31 GOVT',
'US 0.125 2013/09/30 GOVT',
'US 0.125 2013/12/31 GOVT',
'US 0.125 2014/07/31 GOVT']
All coupons can be retrieved by the method
.cashflows() as shown below:Python
Output
bond_sig.cashflows(dtm.date(2021, 2, 1))
array([[datetime.date(2021, 2, 16), 1.3125000000000053, 0.0],
[datetime.date(2021, 8, 16), 1.3125000000000053, 0.0],
[datetime.date(2022, 2, 15), 1.3125000000000053, 0.0],
[datetime.date(2022, 8, 15), 1.3125000000000053, 0.0],
[datetime.date(2023, 2, 15), 1.3125000000000053, 0.0],
[datetime.date(2023, 8, 15), 1.3125000000000053, 0.0],
[datetime.date(2024, 2, 15), 1.3125000000000053, 0.0],
[datetime.date(2024, 8, 15), 1.3125000000000053, 0.0],
[datetime.date(2025, 2, 17), 1.3125000000000053, 0.0],
[datetime.date(2025, 8, 15), 1.3125000000000053, 0.0],
[datetime.date(2026, 2, 16), 1.3125000000000053, 0.0],
[datetime.date(2026, 8, 17), 1.3125000000000053, 0.0],
[datetime.date(2027, 2, 15), 1.3125000000000053, 0.0],
[datetime.date(2027, 8, 16), 1.3125000000000053, 0.0],
[datetime.date(2028, 2, 15), 1.3125000000000053, 0.0],
[datetime.date(2028, 8, 15), 1.3125000000000053, 0.0],
[datetime.date(2029, 2, 15), 1.3125000000000053, 100.0]],
dtype=object)
The z-spread is calculated using the Python QuantLib library. Below is an example of when calculating the z-spread on a given date. For more information on the Python QuantLib library, see the Python QuantLib documentation.
bond_sig.z_spread(dtm.date(2020, 2, 28))
The z-spread can also be retrieved as a time series as shown below:
Python
Output
bond_sig.z_spreads().plot();
The asset swap spread is also calculated using the Python QuantLib library. The asset swap spread can be retrieved for a given date with the following method:
Python
Output
bond_sig.asw_spread(dtm.date(2020, 2, 28))
4.808005964045284
Python
Output
ql_bond = bond_sig._quant_library_bond
ql.BondFunctions.convexity(
ql_bond,
0.05,
ql_bond.dayCounter(),
ql.Annual,
ql_bond.frequency()
)
78.7683778608369
Python
Output
bond_sig.modified_duration(
dtm.date(2020, 1, 4),
6.
)
0.07241386265496533
To create strategies of forward contract, users can easily make use of the building block
RollingBondStrategy object. For further information on RollingBondStrategy, see Rolling Bond Strategy.Last modified 10mo ago