Credit loan pool (level-pay)
Outputs below are real: the notebook runs against the
creditpack's benchmark model, which CFDL validates against an independent reference. To run it yourself, see the Python SDK guide.
A homogeneous level-pay loan pool with CPR prepayments, CDR defaults, loss severity, a recovery lag, a servicing strip and prepayment penalties — priced at a discount to par.
This notebook uses the benchmark model that CFDL validates against an independent reference to the penny (see benchmarks/).
from pathlib import Path
import cfdl_sdk
# This notebook reads a benchmark model and the pack definitions, both of which
# live in the repository, so locate its root. Searching a bounded set of
# ancestors means running from outside a checkout fails with an explanation
# rather than looping forever at the filesystem root.
def repo_root() -> Path:
here = Path.cwd().resolve()
for candidate in (here, *here.parents):
if (candidate / "Cargo.toml").exists() and (candidate / "packs").is_dir():
return candidate
raise RuntimeError(
"No CFDL checkout found above "
f"{here}. This notebook loads a model from benchmarks/ and pack "
"definitions from packs/, so it needs to run inside a clone of "
"https://github.com/bizarc/cfdl."
)
ROOT = repo_root()
PACKS = ROOT / "packs"Compile
Compile the model directory to IR.
model_dir = ROOT / "benchmarks/credit/level_pay_pool"
model = cfdl_sdk.compile(model_dir, packs_dir=PACKS)
print("streams:", len(model.ir["streams"]))streams: 7
Run
Run with the benchmark's configuration and apply the credit pack's domain metrics.
results = model.run(
config=str(model_dir / "run.json"),
pack="credit",
)
print("status:", results.status, "| warnings:", len(results.warnings))status: ok | warnings: 0
Cash flows
The engine returns per-period signed cash flows; cashflows() gives a wide DataFrame indexed by period.
cf = results.cashflows()
print('shape:', cf.shape)
cf.head()shape: (126, 8)
model.net_cash_flow stream.credit.pool.interest.auto_a \
period
2026-01 -2.430350e+07 135188.876529
2026-02 4.416938e+05 133231.075149
2026-03 4.369287e+05 131292.667326
2026-04 4.322077e+05 129373.478097
2026-05 4.275304e+05 127473.334031
stream.credit.pool.penalty.auto_a stream.credit.pool.prepay.auto_a \
period
2026-01 1717.921526 171792.152606
2026-02 1692.927113 169292.711299
2026-03 1668.180510 166818.051025
2026-04 1643.679482 164367.948157
2026-05 1619.421810 161942.181025
stream.credit.pool.recoveries.auto_a \
period
2026-01 0.0
2026-02 0.0
2026-03 0.0
2026-04 0.0
2026-05 0.0
stream.credit.pool.sched_principal.auto_a \
period
2026-01 148203.556810
2026-02 147725.634974
2026-03 147249.254325
2026-04 146774.409892
2026-05 146301.096721
stream.credit.pool.servicing.auto_a \
period
2026-01 -10399.144348
2026-02 -10248.544242
2026-03 -10099.435948
2026-04 -9951.806007
2026-05 -9805.641079
stream.credit.purchase.price.auto_a
period
2026-01 -24750000.0
2026-02 0.0
2026-03 0.0
2026-04 0.0
2026-05 0.0
# Requires the [viz] extra (pip install cfdl-sdk[viz]).
results.plot.cumulative()<Axes: xlabel='period', ylabel='cumulative amount'>
Metrics
Core metrics (NPV/IRR/MOIC/...) plus the pack's domain metrics, with their source labelled.
results.metrics_frame() metric value currency \
0 domain.credit.collections 3.087670e+07 USD
1 domain.credit.collections_multiple 1.247543e+00 None
2 domain.credit.interest 6.502567e+06 USD
3 domain.credit.penalties 8.210289e+04 USD
4 domain.credit.principal 2.297723e+07 USD
5 domain.credit.purchase 2.475000e+07 USD
6 domain.credit.recoveries 1.314801e+06 USD
7 domain.credit.servicing 5.001975e+05 USD
8 domain.credit.wal_years 3.981460e+00 None
9 entity.fund.buyer.total 5.626503e+06 USD
10 model.irr 5.916000e-02 None
11 model.moic 1.231510e+00 None
12 model.npv -6.589882e+04 USD
13 model.payback_periods 7.900000e+01 None
14 model.payback_years 6.583333e+00 None
15 model.total 5.626503e+06 USD
16 model.wal_years 3.824182e+00 None
17 run.annual_discount_rate 6.000000e-02 None
18 run.periods_per_year 1.200000e+01 None
19 stream.credit.pool.interest.auto_a.total 6.502567e+06 USD
20 stream.credit.pool.penalty.auto_a.total 8.210289e+04 USD
21 stream.credit.pool.prepay.auto_a.total 8.210289e+06 USD
22 stream.credit.pool.recoveries.auto_a.total 1.314801e+06 USD
23 stream.credit.pool.sched_principal.auto_a.total 1.476694e+07 USD
24 stream.credit.pool.servicing.auto_a.total -5.001975e+05 USD
25 stream.credit.purchase.price.auto_a.total -2.475000e+07 USD
source
0 domain:credit
1 domain:credit
2 domain:credit
3 domain:credit
4 domain:credit
5 domain:credit
6 domain:credit
7 domain:credit
8 domain:credit
9 core
10 core
11 core
12 core
13 core
14 core
15 core
16 core
17 core
18 core
19 core
20 core
21 core
22 core
23 core
24 core
25 core
What-if
Show the collections multiple and the principal-weighted WAL.
m = results.metrics()
print("collections multiple:", round(m["domain.credit.collections_multiple"], 4))
print("WAL (years):", round(m["domain.credit.wal_years"], 3))collections multiple: 1.2475
WAL (years): 3.981