Executable reading route

The example collection supplies complete market inputs and valuations for the workflows developed in this book. Run the Rust packages from the repository root so their data paths resolve. Python notebooks have companion loaders and data files in their own directories.

From one valuation to a constructed market

Begin with cargo run -p valuation-swaps. It isolates one flat-curve regular swap and gives a numerical reference for compounding, sign and cashflow-table interpretation. cargo run -p bootstrap then replaces the hand-built market with calibrated SOFR, Term SOFR, ICP, collateralized CLP and bond-anchored curves. cargo run -p sensitivity follows those dependencies into quote-level trade risk.

The product examples develop valuation and quotation choices for each family. valuation-caps connects optionlet quotes to volatility risk. valuation-bonds compares tree and backward-Fourier callable valuation. valuation-loans separates contractual principal from market observations, prepayment, credit and fair-rate schedule choices.

From market targets to future states

cargo run -p volatilitysurface inspects the implied-volatility grid. cargo run -p hullwhite calibrates model volatility, reports instrument-level quality and generates numerical paths. cargo run -p pfe develops claim-based exposure, while cargo run -p cva adds netting-set credit/funding inputs and the high-level XVA engine.

The scripting package has explicit binaries:

cargo run -p scripting-examples --bin valuation
cargo run -p scripting-examples --bin xva
cargo run -p scripting-examples --bin stepped-coupon-bond

These compare native and scripted representations under specified models and payoff rules. Their assertions test those setups; stochastic comparisons also require a sampling tolerance and matching exercise assumptions.

Python and Excel

The Python notebook collection follows the same scopes under bindings/python/examples: bootstrap; valuation/swaps, bonds, caps and loans; volatility surfaces; Hull–White; sensitivities; scripting; and XVA. Each can run in a fresh kernel. The repository’s existing runner executes a selected scope or the whole collection:

python bindings/python/examples/run_notebooks.py valuation/swaps
python bindings/python/examples/run_notebooks.py

The optional rendered studies present the same canonical notebooks and saved outputs without requiring a local kernel.

The notebook directory contains local loaders and illustrative market/model files. Read the problem-setting narrative and units alongside the code; do not copy helper defaults into an unrelated market without reviewing them.

Excel supplies swap and historical-pricing workbooks. Load the released XLL before opening them and reconstruct native handles with full recalculation. The workbook chapter explains revisioned dependencies; the historical chapter explains snapshots. Their formulas, not saved handle strings, are the reproducible workflow.

Checking examples and prose

cargo test -p quantsupport runs the native tests and doctests. The book’s self-contained Rust fences can be built and checked with:

RUSTUP_TOOLCHAIN=stable python tools/book/check_rust.py
python -m unittest discover -s tools/tests -p 'test_book.py' -v

The runner builds the library and selects the artifacts reported by Cargo, then gives mdBook a temporary library search directory. Cached builds with different features cannot introduce duplicate crate candidates. The build and rustdoc harness use the same compiler toolchain, selected explicitly above.

Runnable book snippets include the hidden crate declaration required by mdBook’s rustdoc harness. Fences marked rust,ignore are explicitly described fragments requiring surrounding inputs, not a substitute for testing complete examples. Structural tests check chapter coverage, local links and this snippet distinction.