start np20 update

pyproject.toml

@@ -31,7 +31,7 @@ classifiers = [
 
 license = { file = "LICENSE" }
 
-requires-python = '>=3.8'
+requires-python = '>=3.9'
 dependencies = [
     'alembic >=0.7.7',
     'bcolz-zipline >=1.2.6',
@@ -46,7 +46,7 @@ dependencies = [
     'multipledispatch >=0.6.0',
     'networkx >=2.0',
     'numexpr >=2.6.1',
-    'numpy >=1.14.5,<2.0.0',
+    'numpy >=1.14.5',
     'pandas >=1.3',
     'patsy >=0.4.0',
     'python-dateutil >=2.4.2',
@@ -75,7 +75,8 @@ requires = [
     'wheel>=0.36.0',
     'Cython>=0.29.21',
     #    'Cython>=3',
-    'oldest-supported-numpy; python_version>="3.8"',
+    'numpy>=2.1.0rc1'
+    #    'oldest-supported-numpy; python_version>="3.9"',
 ]
 build-backend = 'setuptools.build_meta'
 
@@ -105,6 +106,7 @@ dev = [
     'pre-commit >=2.12.1',
     #    'Cython>=0.29.21,<3',
     'Cython>=0.29.21',
+    'ruff'
 ]
 docs = [
     'Cython',
@@ -114,7 +116,6 @@ docs = [
     'pydata-sphinx-theme',
     'sphinx_markdown_tables',
     'm2r2'
-
 ]
 
 [project.scripts]
@@ -161,7 +162,7 @@ test-skip = ["*"]
 
 [tool.black]
 line-length = 88
-target-version = ['py38', 'py39', 'py310', 'py311']
+target-version = ['py39', 'py39', 'py310', 'py311', 'py312']
 exclude = '''
 (
     asv_bench/env
@@ -175,6 +176,10 @@ exclude = '''
 )
 '''
 
+[tool.ruff.lint]
+select = ["NPY201"]
+
+
 [tool.tox]
 legacy_tox_ini = """
 [tox]

src/zipline/lib/quantiles.py

@@ -1,7 +1,8 @@
 """
 Algorithms for computing quantiles on numpy arrays.
 """
-from numpy.lib import apply_along_axis
+# from numpy.lib import apply_along_axis
+from numpy import apply_along_axis
 from pandas import qcut
 
 

src/zipline/pipeline/factors/basic.py

@@ -11,12 +11,13 @@
     full,
     isnan,
     log,
-    NINF,
+    # NINF,
     sqrt,
     sum as np_sum,
     unique,
     errstate as np_errstate,
 )
+import numpy as np
 
 from zipline.pipeline.data import EquityPricing
 from zipline.utils.input_validation import expect_types
@@ -157,7 +158,7 @@ class MaxDrawdown(SingleInputMixin, CustomFactor):
 
     def compute(self, today, assets, out, data):
         drawdowns = fmax.accumulate(data, axis=0) - data
-        drawdowns[isnan(drawdowns)] = NINF
+        drawdowns[isnan(drawdowns)] = -np.inf
         drawdown_ends = nanargmax(drawdowns, axis=0)
 
         # TODO: Accelerate this loop in Cython or Numba.