FinanceToolkit v1.9.0

[JerBouma]

The release of the Finance Toolkit v1.9.0 includes an entirely new Fixed Income module. This module contains a wide variety of fixed income related calculations such as the Effective Yield, the Macaulay Duration, the Modified Duration Convexity, the Yield to Maturity and models such as Black and Bachelier to valuate derivative instruments such as Swaptions.

For example, it is possible to perform Bond Calculations with collect_bond_statistics which can valuate a variety of bonds based on used input:

	Bond 1	Bond 2	Bond 3	Bond 4	Bond 5	Bond 6
Par Value	100	250	50	1000	85	320
Coupon Rate	0.05	0.02	0.075	0	0.15	0.015
Years to Maturity	5	10	2	10	3	1
Yield to Maturity	0.08	0.021	0.03	0	0.16	0.04
Frequency	1	1	4	1	2	12
Present Value	88.0219	247.766	54.3518	1000	83.0353	312.171
Current Yield	0.0568	0.0202	0.069	0	0.1535	0.0154
Effective Yield	0.05	0.02	0.0771	0	0.1556	0.0151
Macaulay's Duration	4.5116	9.1576	1.8849	10	2.5667	0.9932
Modified Duration	4.1774	8.9693	1.8709	10	2.3766	0.9899
Effective Duration	4.0677	8.5181	1.8477	9.4713	2.2952	0.9844
Dollar Duration	3.677	22.2228	1.0168	100	1.9734	3.0902
DV01	0.0004	0.0022	0	0.01	0.0001	0
Convexity	22.4017	93.7509	4.0849	110	7.0923	1.0662

This can be done with the following code:

from financetoolkit import FixedIncome

fixedincome = FixedIncome()

# Valuate a specific bond, for example Bond 1
fixedincome.collect_bond_statistics(
    par_value=100,
    coupon_rate=0.05,
    years_to_maturity=5,
    yield_to_maturity=0.08,
    frequency=1,
)

Not only Bonds can be valuated, the module can also be used to valudate derivatives such as Swaptions utilizing the Black and Bachelier models. For example, the Black model can be used to valuate a Swaption:

from financetoolkit import FixedIncome

fixedincome = FixedIncome()

# You can also provide lists of values for the strike rate and years to maturity
# to define your own strike rates and years to maturity to display in the DataFrame
fixedincome.get_derivative_price(model_type='black', forward_rate=0.0325)

This returns a large DataFrame that shows the present value of such a swaption contract at a variety of strike rates and maturities.

Strike Rate	2025-04-21	2026-04-21	2027-04-21	2028-04-20	2029-04-20	2030-04-20	2031-04-20	2032-04-19	2033-04-19	2034-04-19
0.005	0	0	0	0	0	0	0	0	0	0
0.01	0	0	0	0	0	0	0	0	0	0
0.015	0	0	0	0	0	0	0	0	0	0
0.02	0	0	0	0	0	0	0	0	0	0
0.025	0	0	0	0	0	0	0	0	0	0
0.03	0	0	0	0.04	0.25	0.9	2.3	4.68	8.22	12.98
0.035	24200.6	23426.7	22677.6	21952.5	21251.2	20573.2	19918.5	19286.4	18676.5	18088
0.04	72601.7	70280.1	68032.7	65857.2	63751.2	61712.6	59739.2	57828.9	55979.6	54189.6
0.045	121003	117133	113388	109762	106252	102854	99565.3	96381.4	93299.4	90315.9
0.05	169404	163987	158743	153667	148753	143996	139391	134934	130619	126442
0.055	217805	210840	204098	197571	191254	185138	179218	173487	167939	162569
0.06	266206	257694	249453	241476	233754	226280	219044	212039	205259	198695
0.065	314607	304547	294808	285381	276255	267421	258870	250592	242578	234821
0.07	363008	351400	340163	329286	318756	308563	298696	289144	279898	270948
0.075	411410	398254	385518	373191	361257	349705	338522	327697	317218	307074
0.08	459811	445107	430874	417095	403758	390846	378348	366250	354538	343200
0.085	508212	491960	476229	461000	446258	431988	418174	404802	391858	379327
0.09	556613	538814	521584	504905	488759	473130	458000	443355	429177	415453
0.095	605014	585667	566939	548810	531260	514272	497827	481907	466497	451580
0.1	653415	632521	612294	592714	573761	555413	537653	520460	503817	487706
0.105	701816	679374	657649	636619	616262	596555	577479	559012	541137	523832
0.11	750217	726227	703004	680524	658762	637697	617305	597565	578456	559959
0.115	798619	773081	748359	724429	701263	678839	657131	636118	615776	596085
0.12	847020	819934	793715	768334	743764	719980	696957	674670	653096	632211
0.125	895421	866787	839070	812238	786265	761122	736783	713223	690416	668338
0.13	943822	913641	884425	856143	828766	802264	776609	751775	727735	704464

It is also possible to access Government Bond rates from 30+ countries. For example, using get_government_bond_yields(short_term=False, period='monthly'):

	Australia	Austria	Belgium	Brazil	Bulgaria	Canada	Chile	China	Colombia	Costa Rica	Croatia	Czech Republic	Denmark	Estonia	Euro Area 19	Finland	France	Germany	Greece	Hungary	Iceland	India	Indonesia	Ireland	Israel	Italy	Japan	Latvia	Lithuania	Luxembourg	Mexico	Netherlands	New Zealand	Norway	Poland	Portugal	Romania	Russia	Slovakia	Slovenia	South Africa	South Korea	Spain	Sweden	Switzerland	United Kingdom	United States
2023-05	0.035	0.0303	0.0304	0.0728	0.0403	0.0305	0.0555	0.0269	0.1111	nan	0.0371	0.045	0.0254	0.0394	0.0321	0.0298	0.0294	0.0234	0.0398	0.0778	nan	0.0701	0.0636	0.0279	0.0379	0.0431	0.0043	0.0383	0.0288	0.0295	0.0876	0.0273	0.0423	0.0323	0.0593	0.0316	0.063	nan	0.0359	0.0315	0.1194	0.034	0.0341	0.0232	0.0085	0.0396	0.0357
2023-06	0.0392	0.0305	0.0305	0.0728	0.0403	0.0334	0.0531	0.0264	0.1015	nan	0.0373	0.0435	0.0257	0.0394	0.0316	0.03	0.0293	0.0238	0.037	0.072	nan	0.0705	0.0622	0.0278	0.0374	0.0407	0.004	0.0379	0.0288	0.0295	0.0857	0.0275	0.0448	0.0348	0.0591	0.0309	0.0611	nan	0.0362	0.0338	0.1194	0.0361	0.034	0.0246	0.0097	0.0437	0.0375
2023-07	0.0403	0.0312	0.0314	0.07	0.0403	0.0345	0.0533	0.0266	0.1022	nan	0.0374	0.0415	0.027	0.0409	0.0327	0.0307	0.0304	0.0246	0.0385	0.071	nan	0.0711	0.0633	0.029	0.0377	0.0416	0.0059	0.0381	0.0288	0.0303	nan	0.0284	0.0465	0.0364	0.0556	0.0323	0.062	nan	0.0369	0.034	0.1172	0.0368	0.035	0.0252	0.0098	0.0444	0.039
2023-08	0.0413	0.0318	0.0322	0.07	0.0403	0.0365	0.0563	0.0256	0.107	nan	0.0393	0.0426	0.0277	0.0415	0.0334	0.0315	0.0311	0.0255	0.0388	0.074	nan	0.0719	0.0636	0.0296	0.0391	0.0422	0.0064	0.0385	0.0288	0.0308	0.091	0.0292	0.049	0.0381	0.0559	0.0331	0.0634	nan	0.038	0.0348	0.1172	0.0386	0.0359	0.0275	0.0095	0.0453	0.0417
2023-09	0.0421	0.0329	0.0332	0.07	0.0403	0.0382	0.0597	0.0268	0.1187	nan	0.0395	0.0449	0.0283	0.0425	0.0351	0.0325	0.0324	0.0266	0.0408	0.0708	nan	0.0717	0.0691	0.0309	0.04	0.0451	0.0076	0.0388	0.0288	0.0316	0.0968	0.0304	0.0505	0.0382	0.0568	0.0345	0.0748	nan	0.0391	0.0347	0.1206	0.0395	0.0372	0.0289	0.0109	0.0442	0.0438
2023-10	0.0463	0.0349	0.0352	0.0655	0.0403	0.0406	0.0653	0.0269	0.1181	nan	0.0418	0.0472	0.0305	0.0409	0.0372	0.0347	0.0345	0.0282	0.043	0.0753	nan	0.0735	0.0705	0.0327	0.0438	0.0488	0.0095	0.0414	0.0288	0.0332	nan	0.0321	0.0546	0.0395	0.0584	0.0359	0.0681	nan	0.0412	0.0367	0.1236	0.0427	0.0395	0.0302	0.0105	0.0457	0.048
2023-11	0.0458	0.0321	0.0323	0.0655	0.0403	0.0371	0.0598	nan	0.1089	nan	0.0393	0.0443	0.0279	0.0367	0.0342	0.032	0.0319	0.026	0.0388	0.0701	nan	nan	nan	0.0301	0.0427	0.0443	0.0066	0.0398	0.0288	0.0303	0.1023	0.0295	0.051	0.0368	0.0554	nan	0.0675	nan	0.0386	0.0353	0.1179	0.0389	0.0366	0.0276	0.0082	0.0427	0.045

Next to that, the well-known Corporate Bond indices from ICE BofA are also available. For example this shows the Corporate Bond Yield and Option-Adjust Spread (OAS) for a variety of maturities. You can access this data with get_ice_bofa_effective_yield and get_ice_bofa_option_adjusted_spread.

Central Bank Rates such as the EURIBOR and SOFR rates can be accessed that serve as the reference (or benchmark) rate of a large variety of derivative contracts such as swaps and futures. For example:

from financetoolkit import FixedIncome

fixedincome = FixedIncome(start_date='2023-12-01')

euribor_rates = fixedincome.get_euribor_rates()

Which returns:

	1-Month	3-Month	6-Month	12-Month
2023-12	0.0386	0.0393	0.0392	0.0367
2024-01	0.0387	0.0393	0.0389	0.0361
2024-02	0.0387	0.0392	0.039	0.0367

Next to that, I've added in the European Central Banks which include:

• The main refinancing operations (MRO) rate is the interest rate banks pay when they borrow money from the ECB for one week. When they do this, they have to provide collateral to guarantee that the money will be paid back.
• The marginal lending facility rate is the interest rate banks pay when they borrow from the ECB overnight. When they do this, they have to provide collateral, for example securities, to guarantee that the money will be paid back.
• The deposit facility rate which is one of the three interest rates the ECB sets every six weeks as part of its monetary policy. The rate defines the interest banks receive for depositing money with the central bank overnight.

These can be shown by using:

from financetoolkit import FixedIncome

fixedincome = FixedIncome(start_date='2000-01-01')

fixedincome.get_european_central_bank_rates()

Which returns (when plotted):

I've also added in the Federal Funds rates which include:

• The effective federal funds rate (EFFR) which is calculated as a volume-weighted median of overnight federal funds transactions reported in the FR 2420 Report of Selected Money Market Rates.
• The overnight bank funding rate (OBFR) which is calculated as a volume-weighted median of overnight federal funds transactions, Eurodollar transactions, and the domestic deposits reported as “Selected Deposits” in the FR 2420 Report.
• The TGCR which is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon.
• The BGCR which is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon as well as GCF Repo transaction data obtained from the U.S. Department of the Treasury’s Office of Financial Research (OFR).
• The SOFR which is calculated as a volume-weighted median of transaction-level tri-party repo data collected from the Bank of New York Mellon as well as GCF Repo transaction data and data on bilateral Treasury repo transactions cleared through FICC's DVP service, which are obtained from the U.S. Department of the Treasury’s Office of Financial Research (OFR).

These can be shown by using (and defining the rate):

from financetoolkit import FixedIncome

fixedincome = FixedIncome(start_date='2016-01-01')

# Effective Federal Funds Rate
fixedincome.get_federal_reserve_rates(rate='EFFR')

# Secured Overnight Financing Rate
fixedincome.get_federal_reserve_rates(rate='SOFR')

Which returns (when plotted):