release

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "quantflow"
-version = "0.2.8"
+version = "0.2.9"
 description = "quantitative analysis"
 authors = ["Luca <luca@quantmind.com>"]
 license = "BSD-3-Clause"
@@ -30,7 +30,7 @@ pytest-cov = "^6.0.0"
 mypy = "^1.13.0"
 ghp-import = "^2.0.2"
 ruff = "^0.8.1"
-pytest-asyncio = "^0.24.0"
+pytest-asyncio = "^0.25.0"
 
 
 [tool.poetry.extras]

quantflow/__init__.py

@@ -1,3 +1,3 @@
 """Quantitative analysis and pricing"""
 
-__version__ = "0.2.8"
+__version__ = "0.2.9"

quantflow/options/pricer.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Any, Generic, NamedTuple, TypeVar
+from typing import Any, Generic, NamedTuple, TypeVar, cast
 
 import numpy as np
 import pandas as pd
@@ -156,7 +156,7 @@ def plot3d(
         moneyness_ttm = np.linspace(-max_moneyness_ttm, max_moneyness_ttm, support)
         implied = np.zeros((len(ttm), len(moneyness_ttm)))
         for i, t in enumerate(ttm):
-            maturity = self.maturity(t)
+            maturity = self.maturity(cast(float, t))
             implied[i, :] = maturity.interp(moneyness_ttm * np.sqrt(t)).implied_vols
         properties: dict = dict(
             xaxis_title="moneyness_ttm",

quantflow/sp/ou.py

@@ -9,7 +9,7 @@
 
 from ..utils.distributions import Exponential
 from ..utils.paths import Paths
-from ..utils.types import FloatArrayLike, Vector
+from ..utils.types import FloatArrayLike, Vector, Float
 from .base import Im, IntensityProcess
 from .poisson import CompoundPoissonProcess, D
 from .weiner import WeinerProcess
@@ -139,7 +139,12 @@ def sample(self, n: int, time_horizon: float = 1, time_steps: int = 100) -> Path
         return Paths(t=time_horizon, data=paths)
 
     def _advance(
-        self, i: int, pp: np.ndarray, dt: float, arrival: float = 0, jump: float = 0
+        self,
+        i: int,
+        pp: np.ndarray,
+        dt: Float,
+        arrival: Float = 0,
+        jump: Float = 0,
     ) -> int:
         x = pp[i - 1]
         kappa = self.kappa

quantflow/utils/bins.py

@@ -1,4 +1,4 @@
-from typing import Dict, Sequence
+from typing import Sequence, cast, Any
 
 import numpy as np
 from pandas import DataFrame
@@ -13,9 +13,9 @@ def pdf(
     symmetric: float | None = None,
     precision: int = 6,
 ) -> DataFrame:
-    max_value = np.max(data)
-    min_value = np.min(data)
-    domain = max(abs(data)) if symmetric is not None else max_value - min_value
+    max_value = cast(float, np.max(data))
+    min_value = cast(float, np.min(data))
+    domain: float = max(abs(data)) if symmetric is not None else max_value - min_value  # type: ignore
     if num_bins is None:
         if not delta:
             num_bins = 50
@@ -39,7 +39,9 @@ def pdf(
     return DataFrame(dict(pdf=pdf), index=x[1:-1])
 
 
-def event_density(df: DataFrame, columns: Sequence, num: int = 10) -> Dict:
+def event_density(
+    df: DataFrame, columns: Sequence[str], num: int = 10
+) -> dict[str, Any]:
     """Calculate the probability density of the number of events
     in the dataframe columns
     """
@@ -48,5 +50,5 @@ def event_density(df: DataFrame, columns: Sequence, num: int = 10) -> Dict:
     for col in columns:
         counts, _ = np.histogram(df[col], bins=bins)
         counts = counts / np.sum(counts)
-        data[col] = counts[:num]
+        data[col] = counts[:num]  # type: ignore
     return data

quantflow/utils/paths.py

@@ -51,7 +51,7 @@ def xs(self) -> list[np.ndarray]:
     @property
     def ys(self) -> list[list[float]]:
         """Paths as list of list (for visualization tools)"""
-        return self.data.transpose().tolist()
+        return self.data.transpose().tolist()  # type: ignore
 
     def mean(self) -> FloatArray:
         """Mean of paths"""

quantflow/utils/types.py

@@ -1,16 +1,17 @@
 from decimal import Decimal
-from typing import Optional, Union
+from typing import Optional, Union, Any
 
 import pandas as pd
 import numpy as np
 import numpy.typing as npt
 
 Number = Decimal
-Numbers = Union[int, float, np.number]
+Float = float | np.floating[Any]
+Numbers = Union[int, Float, np.number]
 NumberType = Union[float, int, str, Number]
 Vector = Union[int, float, complex, np.ndarray, pd.Series]
-FloatArray = npt.NDArray[np.float64]
-IntArray = npt.NDArray[np.int_]
+FloatArray = npt.NDArray[np.floating[Any]]
+IntArray = npt.NDArray[np.signedinteger[Any]]
 FloatArrayLike = FloatArray | float