feat(ci): add codespeedHQ performance analysis

.github/workflows/tests.yaml

@@ -33,3 +33,8 @@ jobs:
         env_vars: OS,PYTHON
         fail_ci_if_error: true
         verbose: true
+    - name: Run benchmarks
+      uses: CodSpeedHQ/action@v2
+      with:
+        token: ${{ secrets.CODSPEED_TOKEN }}
+        run: pixi run test --codspeed

pixi.toml

@@ -8,7 +8,8 @@ channels = ["conda-forge"]
 platforms = ["win-64", "linux-64"]
 
 [tasks]
-postinstall = "pip install --upgrade --no-build-isolation --no-deps --disable-pip-version-check -e ."
+# install packages not on conda-forge
+postinstall = "pip install --upgrade --no-build-isolation --no-deps --disable-pip-version-check -e .; pip install pytest-codspeed mkdocs-bibtex"
 doc = "mkdocs serve"
 test = "pytest -v tests/ --cov=src --cov-report=xml"
 lint = "pre-commit run --all"

tests/test_classical_classifier.py

@@ -304,6 +304,7 @@ def _apply_rule(
         )
         assert (y_pred == y_test).all()
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["all", "ex"])
     def test_tick_rule(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if tick rule is correctly applied.
@@ -323,6 +324,7 @@ def test_tick_rule(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([-1, 1, 1, -1])
         self._apply_rule(x_train, x_test, y_test, [("tick", subset)], 7)
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["all", "ex"])
     def test_rev_tick_rule(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if rev. tick rule is correctly applied.
@@ -341,6 +343,7 @@ def test_rev_tick_rule(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([-1, 1, 1, -1])
         self._apply_rule(x_train, x_test, y_test, [("rev_tick", subset)], 7)
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["best", "ex"])
     def test_quote_rule(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if quote rule is correctly applied.
@@ -366,6 +369,7 @@ def test_quote_rule(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([-1, 1, 1, -1, -1, 1])
         self._apply_rule(x_train, x_test, y_test, [("quote", subset)], 45)
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["best", "ex"])
     def test_lr(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if the lr algorithm is correctly applied.
@@ -389,6 +393,7 @@ def test_lr(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([-1, 1, 1, -1])
         self._apply_rule(x_train, x_test, y_test, [("lr", subset)], 7)
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["best", "ex"])
     def test_rev_lr(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if the rev. lr algorithm is correctly applied.
@@ -419,6 +424,7 @@ def test_rev_lr(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([-1, 1, 1, -1, -1, 1])
         self._apply_rule(x_train, x_test, y_test, [("rev_lr", subset)], 42)
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["best", "ex"])
     def test_emo(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if the emo algorithm is correctly applied.
@@ -449,6 +455,7 @@ def test_emo(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([-1, 1, 1, -1, -1, 1])
         self._apply_rule(x_train, x_test, y_test, [("emo", subset)], 42)
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["best", "ex"])
     def test_rev_emo(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if the rev. emo algorithm is correctly applied.
@@ -479,6 +486,7 @@ def test_rev_emo(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([-1, 1, 1, -1, -1, 1])
         self._apply_rule(x_train, x_test, y_test, [("rev_emo", subset)], 42)
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["best", "ex"])
     def test_clnv(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if the clnv algorithm is correctly applied.
@@ -509,6 +517,7 @@ def test_clnv(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([1, -1, 1, -1, 1, -1])
         self._apply_rule(x_train, x_test, y_test, [("clnv", subset)], 42)
 
+    @pytest.mark.benchmark()
     @pytest.mark.parametrize("subset", ["best", "ex"])
     def test_rev_clnv(self, x_train: pd.DataFrame, subset: str) -> None:
         """Test, if the rev. clnv algorithm is correctly applied.
@@ -538,6 +547,7 @@ def test_rev_clnv(self, x_train: pd.DataFrame, subset: str) -> None:
         y_test = pd.Series([1, -1, 1, -1, 1, -1])
         self._apply_rule(x_train, x_test, y_test, [("rev_clnv", subset)], 5)
 
+    @pytest.mark.benchmark()
     def test_trade_size(self, x_train: pd.DataFrame) -> None:
         """Test, if the trade size algorithm is correctly applied.
 
@@ -558,6 +568,7 @@ def test_trade_size(self, x_train: pd.DataFrame) -> None:
         y_test = pd.Series([-1, 1, -1, 1, -1, 1])
         self._apply_rule(x_train, x_test, y_test, [("trade_size", "ex")], 42)
 
+    @pytest.mark.benchmark()
     def test_depth(self, x_train: pd.DataFrame) -> None:
         """Test, if the depth rule is correctly applied.