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 one of the benchmark models, which CFDL validates against an independent reference to the penny.
# On Colab, install the SDK and fetch the models this notebook reads.
# Inside a checkout both are already present and this cell does nothing.
import subprocess, sys
from pathlib import Path
REPO = "https://github.com/cfdl-dev/cfdl"
def repo_root() -> Path:
"""The checkout holding benchmarks/ and packs/, cloning it if need be.
Searching a bounded set of ancestors means a plain `python` run outside a
checkout fails with an explanation rather than walking to the filesystem
root. On a hosted runtime there is no checkout to find, so fetch one.
"""
here = Path.cwd().resolve()
for candidate in (here, *here.parents):
if (candidate / "Cargo.toml").exists() and (candidate / "packs").is_dir():
return candidate
if "google.colab" not in sys.modules:
raise RuntimeError(
f"No CFDL checkout found above {here}. This notebook reads a model "
f"from benchmarks/ and pack definitions from packs/, so run it "
f"inside a clone of {REPO}."
)
subprocess.run([sys.executable, "-m", "pip", "install", "-q", "cfdl-sdk[viz]"], check=True)
# Packs and benchmark models track the engine, so take the checkout at the
# tag matching the wheel pip just resolved. `main` runs ahead of the last
# release and its packs may use metric ops the released engine rejects.
import importlib
from importlib.metadata import PackageNotFoundError, version
importlib.invalidate_caches()
try:
tag = f"v{version('cfdl-sdk')}"
except PackageNotFoundError:
tag = None
clone = ["git", "clone", "--depth", "1", "-q", REPO]
target = Path("/content/cfdl")
if not target.exists():
pinned = tag is not None and not subprocess.run(
clone + ["--branch", tag, str(target)]
).returncode
if not pinned:
# A dev or pre-release wheel has no matching tag; main is the best
# available, and the notebook may fail if the two have diverged.
print(f"warning: no {tag} tag for this SDK build; falling back to main.")
subprocess.run(clone + [str(target)], check=True)
return target
ROOT = repo_root()
PACKS = ROOT / "packs"
import cfdl_sdkCompile
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: 8
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, 22)
account.asset.buyer.balance asset.buyer.credit_loan_balance_lag_auto_a \
period
2026-01 2.463766e+07 25000000.0
2026-02 2.427892e+07 25000000.0
2026-03 2.392374e+07 25000000.0
2026-04 2.357208e+07 25000000.0
2026-05 2.322393e+07 25000000.0
domain.credit.balance_outstanding domain.credit.gross_collections \
period
2026-01 2.442971e+07 457194.867943
2026-02 2.411241e+07 452225.142236
2026-03 2.379807e+07 447301.537920
2026-04 2.348667e+07 442423.647210
2026-05 2.317816e+07 437591.065867
domain.credit.net_collections domain.credit.original_balance domain.credit.pool_factor \
period
2026-01 446795.723595 24750000.0 0.987059
2026-02 441976.718401 24750000.0 0.974239
2026-03 437202.339282 24750000.0 0.961538
2026-04 432472.191964 24750000.0 0.948956
2026-05 427785.885597 24750000.0 0.936491
domain.credit.principal_collections domain.credit.principal_paid_negated \
period
2026-01 320285.175229 -3.202852e+05
2026-02 317299.872656 -6.375850e+05
2026-03 314341.003541 -9.519261e+05
2026-04 311408.337988 -1.263334e+06
2026-05 308501.648088 -1.571836e+06
domain.credit.principal_paid_to_date ... entity.asset.buyer.net_cash_flow \
period ...
2026-01 3.202852e+05 ... -2.430320e+07
2026-02 6.375850e+05 ... 4.419767e+05
2026-03 9.519261e+05 ... 4.372023e+05
2026-04 1.263334e+06 ... 4.324722e+05
2026-05 1.571836e+06 ... 4.277859e+05
model.net_cash_flow stream.credit.loan.defaults.auto_a \
period
2026-01 -2.430320e+07 -42053.563818
2026-02 4.419767e+05 -41444.058407
2026-03 4.372023e+05 -40840.599975
2026-04 4.324722e+05 -40243.133886
2026-05 4.277859e+05 -39651.605981
stream.credit.loan.interest.auto_a stream.credit.loan.penalty.auto_a \
period
2026-01 135188.876529 1720.816184
2026-02 133229.509847 1695.759733
2026-03 131289.582287 1670.952092
2026-04 129368.918209 1646.391013
2026-05 127467.343513 1622.074267
stream.credit.loan.prepay.auto_a stream.credit.loan.recoveries.auto_a \
period
2026-01 172081.618419 0.0
2026-02 169575.973277 0.0
2026-03 167095.209193 0.0
2026-04 164639.101296 0.0
2026-05 162207.426682 0.0
stream.credit.loan.sched_principal.auto_a stream.credit.loan.servicing.auto_a \
period
2026-01 148203.556810 -10399.144348
2026-02 147723.899379 -10248.423834
2026-03 147245.794348 -10099.198637
2026-04 146769.236692 -9951.455247
2026-05 146294.221405 -9805.180270
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
[5 rows x 22 columns]
# 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 labeled.
results.metrics_frame() metric value currency source
0 domain.credit.collections 3.072748e+07 USD domain:credit
1 domain.credit.collections_multiple 1.241514e+00 domain:credit
2 domain.credit.interest 6.499895e+06 USD domain:credit
3 domain.credit.penalties 8.220768e+04 USD domain:credit
4 domain.credit.principal 2.297806e+07 USD domain:credit
5 domain.credit.purchase 2.475000e+07 USD domain:credit
6 domain.credit.recoveries 1.167320e+06 USD domain:credit
7 domain.credit.servicing 4.999919e+05 USD domain:credit
8 domain.credit.wal_years 4.056967e+00 domain:credit
9 entity.asset.buyer.total 5.477491e+06 USD core
10 model.irr 5.630900e-02 core
11 model.moic 1.225381e+00 core
12 model.npv -2.959752e+05 USD core
13 model.payback_periods 8.000000e+01 core
14 model.payback_years 6.750000e+00 core
15 model.total 5.477491e+06 USD core
16 model.wal_years 3.843940e+00 core
17 run.annual_discount_rate 6.000000e-02 core
18 run.periods_per_year 1.200000e+01 core
19 stream.credit.loan.defaults.auto_a.t... -2.021940e+06 USD core
20 stream.credit.loan.interest.auto_a.t... 6.499895e+06 USD core
21 stream.credit.loan.penalty.auto_a.total 8.220768e+04 USD core
22 stream.credit.loan.prepay.auto_a.total 8.220768e+06 USD core
23 stream.credit.loan.recoveries.auto_a... 1.167320e+06 USD core
24 stream.credit.loan.sched_principal.a... 1.475729e+07 USD core
25 stream.credit.loan.servicing.auto_a.... -4.999919e+05 USD core
26 stream.credit.purchase.price.auto_a.... -2.475000e+07 USD 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: 1.2415
WAL (years): 4.057