ci(codspeed): setup codspeed benchmarking

.github/workflows/codspeed.yml

@@ -0,0 +1,36 @@
+name: codspeed-benchmarks
+
+on:
+  push:
+    branches:
+      - "main" # or "master"
+  pull_request:
+  # `workflow_dispatch` allows CodSpeed to trigger backtest
+  # performance analysis in order to generate initial data.
+  workflow_dispatch:
+
+env:
+  UV_SYSTEM_PYTHON: 1
+
+jobs:
+  benchmarks:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Install uv
+        uses: astral-sh/setup-uv@v2
+
+      - name: "Set up Python"
+        uses: actions/setup-python@v5
+        with:
+          python-version-file: "pyproject.toml"
+
+      - name: Install the project
+        run: uv sync --all-extras --dev
+
+      - name: Run benchmarks
+        uses: CodSpeedHQ/action@v3
+        with:
+          token: ${{ secrets.CODSPEED_TOKEN }}
+          run: uv run pytest --codspeed

.gitignore

@@ -12,3 +12,4 @@ venv
 __pycache__/
 data/
 .DS_Store
+scratchpad.py

README.md

@@ -1 +1,2 @@
 # Polars Trading
+[![CodSpeed Badge](https://img.shields.io/endpoint?url=https://codspeed.io/badge.json)](https://codspeed.io/ngriffiths13/polars-trading)

polars_trading/_testing/labels.py

@@ -12,3 +12,12 @@ def get_daily_vol(close: pd.Series, span0: int = 100) -> pd.Series:
     )
     df0 = close.loc[df0.index] / close.loc[df0.values].values - 1
     return df0.ewm(span=span0).std().fillna(0.0)
+
+
+def apply_pt_sl_on_t1(
+    close: pd.Series, events: pd.DataFrame, pt_sl: tuple[float, float]
+) -> None:
+    """Apply stop loss and profit taking.
+
+    AFML pg. 45
+    """

polars_trading/labels/dynamic_labels.py

@@ -22,6 +22,10 @@ def daily_vol(
     in the last 24 hour period to the current price. It then calculates the exponential
     weighted standard deviation of the returns.
 
+    This currently fails to account for weekend returns when there is no trading.
+
+    Marco Lopez de Prado, Advances in Financial Machine Learning, pg. 44
+
     Args:
     ----
         df (DataFrame): The DataFrame containing the price series.

pyproject.toml

@@ -12,6 +12,7 @@ classifiers = [
 ]
 dependencies = ["polars[plot]>=1.3"]
 
+
 [tool.ruff.format]
 docstring-code-format = true
 
@@ -83,21 +84,21 @@ ignore_missing_imports = true
 dev-dependencies = [
     "build>=1.2.1",
     "commitizen>=3.29.0",
-    "hypothesis>=6.111.2",
     "marimo>=0.8.8",
     "maturin>=1.7.1",
-    "mimesis>=12.1.0",
     "mkdocs-gen-files>=0.5.0",
     "mkdocstrings[python]>=0.26.1",
-    "pandas>=2.2.2",
     "pip>=24.2",
     "pre-commit>=3.8.0",
-    "pyarrow>=17.0.0",
-    "pytest-benchmark>=4.0.0",
-    "pytest>=8.3.2",
     "ruff>=0.6.3",
     "setuptools>=74.1.0",
     "wheel>=0.44.0",
+    "pandas>=2.2.2",
+    "pyarrow>=17.0.0",
+    "pytest>=8.3.2",
+    "pytest-benchmark[histogram]>=4.0.0",
+    "pytest-codspeed>=2.2.1",
+    "mimesis>=12.1.0",
 ]
 
 [tool.commitizen]
@@ -109,7 +110,9 @@ update_changelog_on_bump = true
 major_version_zero = true
 
 [tool.pytest.ini_options]
+markers = ["pandas"]
 addopts = [
     "--benchmark-disable",
     "--benchmark-columns=min,mean,median,max,stddev",
+    "-m not pandas",
 ]

tests/labels/test_dynamic_labels.py

@@ -64,6 +64,7 @@ def test__daily_vol__polars_benchmark(benchmark, trade_data):
     benchmark(daily_vol(trade_data.lazy(), "ts_event", "price", "symbol", 100).collect)
 
 
+@pytest.mark.pandas
 @pytest.mark.benchmark(group="daily_vol")
 @pytest.mark.parametrize(
     "trade_data",

tests/test_bars.py

@@ -91,6 +91,7 @@ def test__time_bars__polars_benchmark(benchmark, trade_data):
     benchmark(time_bars, trade_data, timestamp_col="ts_event", bar_size="1d")
 
 
+@pytest.mark.pandas
 @pytest.mark.benchmark(group="time_bars")
 @pytest.mark.parametrize(
     "trade_data",
@@ -135,6 +136,7 @@ def test__tick_bars__polars_benchmark(benchmark, trade_data):
     benchmark(tick_bars, trade_data, timestamp_col="ts_event", bar_size=100)
 
 
+@pytest.mark.pandas
 @pytest.mark.benchmark(group="tick_bars")
 @pytest.mark.parametrize(
     "trade_data",