Merge pull request #101 from auto-differentiation/feature/update-quan…

…tlib-risks-naming

Updated naming of QuantLib-Risks

docs/quantlib/python.md

@@ -1,50 +1,51 @@
 ---
 description: >-
-  quantlib-risks enables high performance risk evaluations with QuantLib Python bindings
+  QuantLib-Risks enables high performance risk evaluations with QuantLib Python bindings
   using XAD automatic differentiation.
 ---
 
-# Python QuantLib Integration: quantlib-risks
+# Python QuantLib Integration: QuantLib-Risks
 
-The Python package [quantlib-risks](https://pypi.org/project/quantlib-risks), a fork of the QuantLib Python bindings, is now available on PyPI. This version integrates automatic differentiation capabilities through its dependency on [XAD's Python bindings](../python.md). This integration significantly boosts the efficiency of performing high-performance risk assessments within QuantLib from Python.
+The Python package [QuantLib-Risks](https://pypi.org/project/QuantLib-Risks), a fork of the QuantLib Python bindings, is now available on PyPI. This version integrates automatic differentiation capabilities through its dependency on [XAD's Python bindings](../python.md). This integration significantly boosts the efficiency of performing high-performance risk assessments within QuantLib from Python.
 
-The key advantage brought by quantlib-risks is its ability to expediently ascertain how the pricing of derivatives is influenced by various input variables, notably market quotes.
+The key advantage brought by QuantLib-Risks is its ability to expediently ascertain how the pricing of derivatives is influenced by various input variables, notably market quotes.
 
 The user interface mimics the official [QuantLib Python package](https://pypi.org/project/QuantLib), with the addition of replacing it's `Real` type with `xad-autodiff.adj_1st.Real`, which can be tracked on an adjoint automatic differentiation tape and derivatives 
 can be calculated [xad-autodiff](../python.md).
 
 
 ## Performance
 
-To gauge the performance impact of calculating sensitivities, we leverage the [multi-curve bootstrapping example](https://github.com/auto-differentiation/quantlib-risks/blob/v1.33/Python/examples/multicurve-bootstrapping.py). This setup incorporates a wide array of quotes to construct a term structure for swap pricing. It prices a forward-starting 5-year swap, set to commence 15 months into the future, with a calculation of 69 sensitivities covering all market quotes used in curve construction and select swap parameters like nominal, fixed rate, and spread.
+To gauge the performance impact of calculating sensitivities, we leverage the [multi-curve bootstrapping example](https://github.com/auto-differentiation/QuantLib-Risks/blob/main/Python/examples/multicurve-bootstrapping.py). This setup incorporates a wide array of quotes to construct a term structure for swap pricing. It prices a forward-starting 5-year swap, set to commence 15 months into the future, with a calculation of 69 sensitivities covering all market quotes used in curve construction and select swap parameters like nominal, fixed rate, and spread.
 
-Performance metrics are drawn from averaging execution times over 20 runs for stability. Initial benchmarks using the standard QuantLib package clock in at 198ms for pricing alone. Switching to `quantlib-risks` for simultaneous pricing and sensitivity analysis results in an execution time of 370ms, demonstrating that *all sensitivities can be obtained within approximately 1.87x of the original pricing time*.
+Performance metrics are drawn from averaging execution times over 20 runs for stability. Initial benchmarks using the standard QuantLib package clock in at 198ms for pricing alone. Switching to `QuantLib-Risks` for simultaneous pricing and sensitivity analysis results in an execution time of 370ms, demonstrating that *all sensitivities can be obtained within approximately 1.87x of the original pricing time*.
 
-Comparatively, a traditional bump-and-reval approach for sensitivities would necessitate 70 pricer code executions (one for valuation and one for each variable bump), translating to 70x the pure pricing time. *Thus, `quantlib-risks` achieves a 37.4x speed advantage over bump and reval*.
+Comparatively, a traditional bump-and-reval approach for sensitivities would necessitate 70 pricer code executions (one for valuation and one for each variable bump), translating to 70x the pure pricing time. *Thus, `QuantLib-Risks` achieves a 37.4x speed advantage over bump and reval*.
 
 Summary of performance benchmarks:
 
-| Sensitivities | Valuation (QuantLib) | AAD (quantlib-risks) | Bumping (estimate) | AAD vs Valuation | Bumping vs AAD |
+| Sensitivities | Valuation (QuantLib) | AAD (QuantLib-Risks) | Bumping (estimate) | AAD vs Valuation | Bumping vs AAD |
 |---:|---:|---:|---:|---:|---:|
 | 69 | 198ms | 370ms | 13,860ms | 1.87x | 37.4x |
 
 Benchmark configuration details include:
-- `QuantLib` and `quantlib-risks` version: 1.33
-- `xad-autodiff` version: 1.5.0
+- `QuantLib` version: 1.33
+- `QuantLib-Risks` version: 1.33.2
+- `xad-autodiff` version: 1.5.1
 - Operating on Ubuntu 22.04 with GCC 11.4.0
 - Hardware specs include 128GB RAM and an Intel(R) Xeon(R) W-2295 CPU @ 3.00GHz.
 
 
 ## Installation
 
 ```
-pip install quantlib-risks
+pip install QuantLib-Risks
 ```
 
 ## Usage Illustration
 
 ```python
-import quantlib_risks as ql
+import QuantLib_Risks as ql
 from xad_autodiff.adj_1st import Tape
 
 with Tape() as t: