RLS: v1.0.0 !

docs/source/_static/badges.json

@@ -1,9 +1,9 @@
 {
   "coverage": "99%",
   "licence": "CC-BY-NC-ND-4.0",
-  "pypi": "v0.7.0",
-  "conda": "v0.7.0",
+  "pypi": "v1.0.0",
+  "conda": "v1.0.0",
   "python": "3.9 | 3.10 | 3.11",
   "style": "black",
-  "status": "beta"
+  "status": "stable"
 }

docs/source/_static/switcher.json

@@ -35,8 +35,13 @@
         "url": "https://rateslib.readthedocs.io/en/0.6.x/"
     },
     {
-        "name": "stable",
+        "name": "0.7.x",
         "version": "0.7.x",
+        "url": "https://rateslib.readthedocs.io/en/0.7.x/"
+    },
+    {
+        "name": "stable",
+        "version": "1.0.0",
         "url": "https://rateslib.readthedocs.io/en/stable/"
     }
 ]

docs/source/i_whatsnew.rst

@@ -22,14 +22,6 @@ email contact through **[email protected]**.
      - Description
      - Consideration
      - Timeframe
-   * - Coding Interest Rates
-     - Officially document this library's algorithms and release the book.
-     - Planned
-     - End 2023
-   * - Version 1.0
-     - Release the official first non-beta version of this library.
-     - Planned
-     - End 2023
    * - Vanilla FX options and volatility products
      - Adding option instruments and benchmark trades such as risk-reversals.
      - Highly likely (v2.0?)
@@ -53,7 +45,7 @@ email contact through **[email protected]**.
      - no ETA
 
 
-1.0.0 (Not released)
+1.0.0 (1st Feb 2024)
 **********************
 
 .. list-table::

pyproject.toml

@@ -6,7 +6,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "rateslib"
-version = "0.7.0"
+version = "1.0.0"
 description = "A fixed income library for trading interest rates"
 readme = "README.md"
 authors = [{ name = "J H M Darbyshire"}]

rateslib/periods.py

@@ -1465,10 +1465,13 @@ def _rfr_fixings_array(
                 rate = self._isda_compounded_rate_with_spread(rates_dual, dcf_vals)
             notional_exposure = Series(
                 [rate.gradient(f"fixing_{i}")[0] for i in range(len(dcf_dates.index) - 1)]
-            )
+            ).astype(float)
             v = disc_curve[self.payment]
             mask = ~fixed.to_numpy()  # exclude fixings that are already fixed
-            notional_exposure[mask] *= (-self.notional * (self.dcf / dcf_of_r[mask]) * v / v_with_r[mask])
+
+            notional_exposure[mask] *= -self.notional * (self.dcf / dcf_of_r[mask]) * float(v)
+            notional_exposure[mask] /= v_with_r[mask].astype(float)
+            # notional_exposure[mask] *= (-self.notional * (self.dcf / dcf_of_r[mask]) * v / v_with_r[mask])
             # notional_exposure[fixed.drop_index(drop=True)] = 0.0
             notional_exposure[fixed.to_numpy()] = 0.0
             extra_cols = {