"""The main Space in the :mod:`~assets.BasicBonds` model.
.. rubric:: Parameters and References
(In all the sample code below,
the global variable ``Bonds`` refers to the
:mod:`~assets.BasicBonds.Bonds` space.)
Attributes:
ql: The `QuantLib <https://www.quantlib.org/>`_ module.
date_init: Valuation date as a string in the form of 'YYYY-MM-DD'.
date_end: Projection end date as a string in the form of 'YYYY-MM-DD'.
zero_curve: Zero curve at the valuation date as a pandas Series
indexed with strings indicating various durations.
This data is used by :func:`riskfree_curve` to create
QuantLib's ZeroCurve object::
>>> Bonds.zero_curve
Duration
1M 0.0004
2M 0.0015
3M 0.0026
6M 0.0057
1Y 0.0091
2Y 0.0136
3Y 0.0161
5Y 0.0182
7Y 0.0192
10Y 0.0194
20Y 0.0231
30Y 0.0225
Name: Rate, dtype: float64
The data is saved as an Excel file named "zero_curve.xlsx" in the
model.
bond_data: Bond data as a pandas DataFrame.
By default, a sample table generated by the
*generate_bond_data.ipynb* notebook included in the library::
>>> Bonds.bond_data
settlement_days face_value issue_date ... tenor coupon_rate z_spread
bond_id ...
1 0 235000 2017-12-12 ... 1Y 0.07 0.0304
2 0 324000 2021-11-29 ... 1Y 0.08 0.0304
3 0 799000 2017-02-03 ... 6M 0.03 0.0155
4 0 679000 2017-11-19 ... 1Y 0.08 0.0229
5 0 397000 2018-07-01 ... 6M 0.06 0.0142
... ... ... ... ... ... ...
996 0 560000 2019-02-16 ... 1Y 0.06 0.0261
997 0 161000 2020-03-12 ... 6M 0.05 0.0199
998 0 375000 2019-05-05 ... 1Y 0.03 0.0138
999 0 498000 2019-02-21 ... 1Y 0.03 0.0230
1000 0 438000 2019-03-14 ... 1Y 0.06 0.0256
[1000 rows x 8 columns]
The column names and their data types are as follows::
>>> Bonds.bond_data.dtypes
settlement_days int64
face_value int64
issue_date datetime64[ns]
bond_term int64
maturity_date datetime64[ns]
tenor object
coupon_rate float64
z_spread float64
dtype: object
The data is saved as an Excel file named "bond_data.xlsx" in the
model.
"""
from modelx.serialize.jsonvalues import *
_formula = None
_bases = []
_allow_none = None
_spaces = []
# ---------------------------------------------------------------------------
# Cells
[docs]
def cashflows(bond_id):
"""Returns the cashflows of the selected bond.
Returns the cashflows of the selected bond as a list.
Each element of the list is the total
cashflows falling in each projection period defined by :func:`date_`.
"""
result = [0] * step_size()
leg = fixed_rate_bond(bond_id).cashflows()
i = 0 # cashflow index
for t in range(step_size()):
while i < len(leg):
if i > 0:
# Check if cashflow dates are in order.
assert leg[i-1].date() <= leg[i].date()
if date_(t) <= leg[i].date() < date_(t+1):
result[t] += leg[i].amount()
elif date_(t+1) <= leg[i].date():
break
i += 1
return result
[docs]
def cashflows_total():
"""Returns the aggregated cashflows of the entire bond portfolio.
Takes the sum of :func:`cashflows` across ``bond_id`` and
returns as a list the aggregated cashflows of all the bonds
in :attr:`bond_data`.
"""
result = [0] * step_size()
for t in range(step_size()):
for i in bond_data.index:
result[t] += cashflows(i)[t]
return result
[docs]
def date_(i):
"""Date at each projection step
Defines projection time steps by returning QuantLib's `Date`_ object
that corresponds to the value of the integer index ``i``.
By default, ``date_(i)`` starts from the valuation date specified
by :attr:`date_init`, and increments annually.
.. _Date:
https://www.quantlib.org/reference/class_quant_lib_1_1_date.html
"""
if i == 0:
return ql.Date(date_init, "%Y-%m-%d")
else:
return date_(i-1) + ql.Period('1Y')
[docs]
def fixed_rate_bond(bond_id):
"""Returns QuantLib’s `FixedRateBond`_ object
Create QuantLib’s `FixedRateBond`_ object
representing a bond specified by the given bond ID.
The bond object is created from the attributes in :attr:`bond_data`
and :func:`schedule`.
A pricing engine for the bond object is created as a `DiscountingBondEngine`_ object
from :func:`riskfree_curve` and the ``z_spread`` attribute in :attr:`bond_data`,
and associated with the bond object through a `ZeroSpreadedTermStructure`_ object.
.. _FixedRateBond:
https://www.quantlib.org/reference/class_quant_lib_1_1_fixed_rate_bond.html
.. _DiscountingBondEngine:
https://quantlib-python-docs.readthedocs.io/en/latest/pricing_engines/bonds.html
.. _ZeroSpreadedTermStructure:
https://www.quantlib.org/reference/class_quant_lib_1_1_zero_spreaded_term_structure.html
"""
settlement_days = bond_data.loc[bond_id]['settlement_days']
face_value = bond_data.loc[bond_id]['face_value']
coupons = [bond_data.loc[bond_id]['coupon_rate']]
bond = ql.FixedRateBond(
int(settlement_days),
float(face_value),
schedule(bond_id),
coupons,
ql.Actual360(), # DayCount
ql.Unadjusted)
spread = bond_data.loc[bond_id]['z_spread']
spread = ql.QuoteHandle(ql.SimpleQuote(spread))
disc_curve = ql.ZeroSpreadedTermStructure(
ql.YieldTermStructureHandle(riskfree_curve()), spread,
ql.Compounded, ql.Annual)
# Set discount curve
bondEngine = ql.DiscountingBondEngine(
ql.YieldTermStructureHandle(disc_curve))
bond.setPricingEngine(bondEngine)
return bond
[docs]
def redemptions(bond_id):
"""Returns cashflows of redemptions
For the specified bond, returns a list of redemptions cashflows.
Since the redemption cashflow occurs only once,
all but one element are zero.
"""
result = [0] * step_size()
leg = fixed_rate_bond(bond_id).redemptions()
i = 0 # cashflow index
for t in range(step_size()):
while i < len(leg):
if date_(t) <= leg[i].date() < date_(t+1):
result[t] += leg[i].amount()
elif date_(t+1) <= leg[i].date():
break
i += 1
return result
[docs]
def redemptions_total():
"""Returns all redemption cashflows
Returns a list of redemptions of all the bonds in :attr:`bond_data`.
"""
result = [0] * step_size()
for t in range(step_size()):
for i in bond_data.index:
result[t] += redemptions(i)[t]
return result
[docs]
def riskfree_curve():
"""Returns `ZeroCurve`_ object
Creates QuantLib's `ZeroCurve`_ object from :attr:`zero_curve` and returns it.
The `ZeroCurve`_ object is used by :func:`fixed_rate_bond` to
construct a discount curve for calculating the market value of the specified bond.
.. _ZeroCurve:
https://www.quantlib.org/reference/group__yieldtermstructures.html
"""
ql.Settings.instance().evaluationDate = date_(0)
spot_dates = [date_(0)] + list(date_(0) + ql.Period(dur) for dur in zero_curve.index)
spot_rates = [0] + list(zero_curve)
return ql.ZeroCurve(
spot_dates,
spot_rates,
ql.Actual360(), # dayCount
ql.UnitedStates(ql.UnitedStates.Settlement), # calendar
ql.Linear(), # Interpolator
ql.Compounded, # compounding
ql.Annual # frequency
)
[docs]
def schedule(bond_id):
"""Returns a `Schedule`_ object
Create QuantLib's `Schedule`_ object for the specified bond and returns it.
The returned `Schedule`_ object is used to by :func:`fixed_rate_bond`
to construct `FixedRateBond`_ object.
.. _Schedule:
https://www.quantlib.org/reference/class_quant_lib_1_1_schedule.html
.. _FixedRateBond:
https://www.quantlib.org/reference/class_quant_lib_1_1_fixed_rate_bond.html
"""
d = bond_data.loc[bond_id]['issue_date']
issue_date = ql.Date(d.day, d.month, d.year)
d = bond_data.loc[bond_id]['maturity_date']
maturity_date = ql.Date(d.day, d.month, d.year)
tenor = ql.Period(
ql.Semiannual if bond_data.loc[bond_id]['tenor'] == '6Y' else ql.Annual)
return ql.Schedule(
issue_date,
maturity_date,
tenor,
ql.UnitedStates(ql.UnitedStates.Settlement), # calendar
ql.Unadjusted, # convention
ql.Unadjusted , # terminationDateConvention
ql.DateGeneration.Backward, # rule
False # endOfMonth
)
[docs]
def step_size():
"""Returns the number of time steps
Calculates the number of time steps from :attr:`date_end`
and :func:`date_` ren returns it.
"""
d_end = ql.Date(date_end, "%Y-%m-%d")
t = 0
while True:
if date_(t) < d_end:
t += 1
else:
return t
[docs]
def z_spread_recalc(bond_id):
"""Calculate Z-spread
For the bond specified by ``bond_id``,
Calculate the Z-spread of the bond specified by ``bond_id`` from
the bond's market value and :func:`riskfree_curve`.
This is for testing that the calculated Z-spread matches the input in :attr:`bond_data`.
"""
return ql.BondFunctions.zSpread(
fixed_rate_bond(bond_id),
fixed_rate_bond(bond_id).cleanPrice(),
riskfree_curve(),
ql.Thirty360(), ql.Compounded, ql.Annual)
[docs]
def market_values():
"""Returns the market values of the entire bonds
Calculates and Returns a list of the market values of :func:`fixed_rate_bond`
for all bonds input in :attr:`bond_data`.
"""
bond = fixed_rate_bond
return list(
bond(i).notional() * bond(i).cleanPrice() / 100
for i in bond_data.index)
# ---------------------------------------------------------------------------
# References
date_end = "2053-01-01"
date_init = "2022-01-01"
bond_data = ("DataSpec", 2323236992288, 2323228088304)
ql = ("Module", "QuantLib")
zero_curve = ("DataSpec", 2323237349888, 2323227835552)
