A C++17 quantitative finance library with built-in Automatic Adjoint Differentiation (AAD). Features include yield curve construction, cross-currency pricing and calibration, Monte Carlo simulation, finite difference PDE solvers, a scripting engine for exotic payoffs with tree-walk and compiled evaluators, and parallel model evaluation.
Pull requests with source, configuration, or build changes build and test the
full compiler × AAD-backend matrix below. Documentation-only changes run the
documentation integrity check and stable Linux/Windows gates without starting
the compile, sanitizer, or benchmark jobs. Pushes to master (merges and direct
master pushes) run a lean GCC 14 + Clang 20 subset across all four backends,
because the pull request already covered every combination. GitHub publishes
one status badge per workflow; open a workflow run for per-job results.
| Platform | Compiler | AADet | XAD | CoDiPack | Adept |
|---|---|---|---|---|---|
Ubuntu (ubuntu-latest) |
GCC 13 | ✓ | ✓ | ✓ | ✓ |
Ubuntu (ubuntu-latest) |
GCC 14 | ✓ | ✓ | ✓ | ✓ |
Ubuntu (ubuntu-latest) |
GCC 15 | ✓ | ✓ | ✓ | ✓ |
Ubuntu (ubuntu-latest) |
Clang 18 | ✓ | ✓ | ✓ | ✓ |
Ubuntu (ubuntu-latest) |
Clang 19 | ✓ | ✓ | ✓ | ✓ |
Ubuntu (ubuntu-latest) |
Clang 20 | ✓ | ✓ | ✓ | ✓ |
Windows (windows-latest) |
MSVC | ✓ | ✓ | — | ✓ |
- The GCC 14 + AADet leg additionally runs gcov coverage, tracked by Coveralls.
- Windows legs additionally build the
dal-pythonbindings and thedal-exceladd-in. - Separate Linux jobs cover CoDiPack thread isolation, Python bindings with generated-source verification, documentation integrity, a warning-clean build, ASan/UBSan/TSan spot tests, and benchmark regression gating.
git clone --recursive git@github.com:wegamekinglc/Derivatives-Algorithms-Lib.git
cd Derivatives-Algorithms-Lib
bash build_linux.shThe Linux default builds/tests core and public C++ and stages the install under
build/stage/Release-linux; use --full for Python plus benchmarks. For the
supported profiles, Windows workflow, Python bindings, and troubleshooting,
see the installation guide.
dal-cpp (DAL::cpp)
└─ dal-public (DAL::public)
├─ dal-python
└─ dal-excel
The native dependency graph is dal-cpp ← dal-public ← {dal-python, dal-excel}.
dal-public is a developer-facing convenience facade over core DAL types; it is
not an ABI-isolated compatibility boundary.
| Sub-project | Purpose |
|---|---|
dal-cpp/ |
Core library: math, curves, models, scripting, AAD |
dal-public/ |
Public C++ convenience facade over DAL::cpp |
dal-python/ |
pybind11 Python bindings |
dal-excel/ |
Excel .xll add-in (Windows-only) |
Core domains in dal-cpp/dal/:
- math/ — Interpolation, optimization, PDE solvers, random numbers, matrix ops
- math/aad/ — Automatic Adjoint Differentiation (native, XAD, Adept, CoDiPack backends)
- curve/ — Yield curve construction, piecewise forward rates, calibration
- script/ — Expression scripting engine for exotic payoffs, with tree-walk and compiled evaluation modes
- model/ — Financial models (Black-Scholes, Dupire local volatility, etc.)
- time/ — Dates, calendars, schedules, and day-count bases
- protocol/, currency/, indice/ — Market/contract conventions, currency data, and index/fixing management
- risk/ — Risk report types and aggregation
- storage/ — Storable objects, archives, and repository integration
- concurrency/ — Thread pool for parallel Monte Carlo
- platform/, io/, string/, utilities/ — Infrastructure: configuration, host init, Excel I/O helpers, strings, dictionaries
- auto/ — Machinist-generated enums and serialization (do not hand-edit)
from dal import *
today = Date_(2022, 9, 15)
EvaluationDate_Set(today)
spot, vol, rate, div = 100.0, 0.15, 0.0, 0.0
strike = 120.0
maturity = Date_(2025, 9, 15)
events = [f"call pays MAX(spot() - {strike}, 0.0)"]
product = Product_New([maturity], events)
model = BSModelData_New(spot, vol, rate, div)
res = MonteCarlo_Value(
product,
model,
2**20,
method="sobol",
enable_aad=True,
compiled=True,
)
for k, v in res.items():
print(f"{k:<8}: {v:>10.4f}")Output:
PV : 4.0389
d_div : -85.2290
d_rate : 73.1011
d_spot : 0.2838
d_vol : 58.7140
More examples: Python, Excel, C++. The C++ Monte Carlo script examples show both tree-walk and compiled evaluator output where applicable.
Cross-currency examples:
- reset-aware pricing
- staged basis calibration
- joint domestic/foreign/basis calibration
- Python joint calibration
Quote-space DV01 examples:
The quote-risk workflow freezes exact single-curve, joint-XCCY, or staged-XCCY- basis calibration provenance and aggregates true portfolio price-per-decimal quote sensitivity plus DV01. Fingerprints reject stale curve state, and results remain separated by actual PV currency without FX conversion.
Monte Carlo script valuation defaults to the tree-walk evaluator (compiled=false).
Pass compiled=True in Python or compiled=true in C++ to select the flat-stream
evaluator. The compiled mode is a performance option; payoff values and AAD risks
are expected to match tree-walk results up to normal floating-point noise.
For implementation details and parity coverage, see Script Engine methodology. To compare runtime locally, build and run the script_mc_perf benchmark target:
bash ./build_linux.sh --benchmarks
./build/Release-linux/dal-cpp/benchmarks/script_mc_perf/script_mc_perfScript products dump three ways: the legacy s-expression listing
(DebugScriptProduct in C++, Product_Debug in Python), a versioned JSON AST
for machine consumers (DebugScriptProductJson / Product_DebugJson, schema
dal.script-product/1), and a width-aware Unicode tree with an ASCII fallback
(DebugScriptProductTree / Product_DebugTree). See
Product Debug Outputs
for the formats.
=PRODUCT.NEW("my_product", A2, B2)
=BSMODELDATA.NEW("model", 100, 0.15, 0.0, 0.0)
=MONTECARLO.VALUE(A5, C7, 2^20, "sobol", FALSE, TRUE, 0.01)
The portfolio management web UI moved to its own repository: wegamekinglc/dal-web.
- Installation Guide — Canonical setup workflows
- Architecture Guide — Components, ownership, and execution flows
- Public API Guide — C++, Python, and Excel entry points
- Contributing Guide — Development and review workflow
- Documentation Index — All methodology and component guides
Methodology notes (see the index above for the full list):
- AAD — Automatic adjoint differentiation: expression templates, tape, propagation
- Yield Curve and Yield-Curve Jacobian — discount curves, calibration, inverse-Jacobian transforms, and production quote-space DV01
- Cross-Currency Pricing and Calibration — fixed, resettable, and MTM swaps; immutable fixing snapshots; staged basis and simultaneous domestic/foreign/basis calibration
- Interpolation — linear, log-linear, cubic interpolators
- PDE — PDE framework, grid construction, and coordinate maps
- Script Engine — expression scripting, fuzzy AAD evaluation, and compiled evaluator parity
- Random — random number generation and path construction
- Black / Bachelier — vanilla option pricing
- Matrix — matrix and linear algebra
MIT License — see LICENSE
- Tom Hyer, Derivatives Algorithms: Volume 1: Bones (repo)
- Antoine Savine, Modern Computational Finance: AAD and Parallel Simulations (repo)
- Antoine Savine, Modern Computational Finance: Scripting for Derivatives and xVA (repo)
- Brian Huge and Jesper Andreasen, Finite Difference Methods for Financial PDEs (repo)