finished frac diff

Cargo.toml

@@ -13,4 +13,4 @@ pyo3-polars = { version = "*", features = ["derive", "dtype-struct"] }
 serde = { version = "1", features = ["derive"] }
 polars = { version = "*", features = ["dtype-struct"] }
 num = "0.4.3"
-polars-arrow = "0.44.2"
+polars-arrow = "*"

polars_trading/_testing/features.py

@@ -48,30 +48,3 @@ def frac_diff_ffd(series: pd.DataFrame, d: float, thresh: float = 1e-5) -> pd.Da
             df_[loc1] = np.dot(w.T, series_f.loc[loc0:loc1])[0, 0]
         df[name] = df_.copy(deep=True)
     return pd.concat(df, axis=1)
-
-
-if __name__ == "__main__":
-    import polars as pl
-
-    from polars_trading._testing.data import generate_trade_data
-    from polars_trading.features.frac_diff import frac_diff
-
-    pl.Config.set_verbose(True)
-    data = generate_trade_data(10_000, n_companies=3).sort("ts_event")
-    print(
-        data.with_columns(
-            frac_diff("price", 0.5, 1e-3).over("symbol").alias("frac_diff")
-        ).sort("symbol")
-    )
-
-    print(
-        pl.from_pandas(
-            data.to_pandas()
-            .set_index("ts_event")
-            .groupby("symbol")[["price"]]
-            .apply(frac_diff_ffd, 0.5, 1e-3),
-            include_index=True,
-        ).sort("symbol")
-    )
-
-    # print(frac_diff_ffd(data.to_pandas().set_index("ts_event")[["price"]], 0.5, 1e-3))

pyproject.toml

@@ -20,7 +20,6 @@ docstring-code-format = true
 lint.select = ["ALL"]
 lint.ignore = [
     'A003',
-    'ANN101',
     'ANN401',
     'ARG002',  # todo: enable
     'ARG003',  # todo: enable
@@ -85,7 +84,6 @@ dev-dependencies = [
     "build>=1.2.1",
     "commitizen>=3.29.0",
     "marimo>=0.8.8",
-    "maturin[patchelf]>=1.7.1",
     "mkdocs-gen-files>=0.5.0",
     "mkdocstrings[python]>=0.26.1",
     "pip>=24.2",
@@ -100,6 +98,7 @@ dev-dependencies = [
     "pytest-codspeed>=2.2.1",
     "mimesis>=12.1.0",
     "pytest-cov>=5.0.0",
+    "maturin>=1.7.4",
 ]
 
 [tool.commitizen]

tests/features/test_frac_diff.py

@@ -39,7 +39,7 @@ def test__get_weights_ffd__benchmark_pandas(benchmark):
 def test__frac_diff__matches_pandas(trade_data):
     trade_data = trade_data.sort("ts_event")
     out = trade_data.select(
-        "ts_event", "symbol", frac_diff("price", 0.5, 1e-3).alias("frac_diff")
+        "ts_event", "symbol", frac_diff("price", 0.5, 1e-3).over("symbol").alias("frac_diff")
     )
     out2 = pl.DataFrame(apply_pd_frac_diff(trade_data.to_pandas(), 0.5, 1e-3)).rename(
         {"level_1": "ts_event", "price": "frac_diff"}