MNT: Replace flake8 with ruff

.github/workflows/ci.yml

@@ -39,7 +39,7 @@ jobs:
       - run: pip install -U .[test]
 
       - if: matrix.test-type == 'lint'
-        run: flake8
+        run: ruff backtesting
       - if: matrix.test-type == 'lint'
         run: mypy backtesting
       - if: matrix.test-type == 'lint'

backtesting/__init__.py

@@ -53,10 +53,10 @@ itself find their way back to the community.
 # API Reference Documentation
 """
 try:
-    from ._version import version as __version__  # noqa: F401
+    from ._version import version as __version__
 except ImportError:
     __version__ = '?.?.?'  # Package not installed
 
-from .backtesting import Backtest, Strategy  # noqa: F401
 from . import lib  # noqa: F401
 from ._plotting import set_bokeh_output  # noqa: F401
+from .backtesting import Backtest, Strategy  # noqa: F401

backtesting/_plotting.py

@@ -257,7 +257,7 @@ this.labels = this.labels || formatter.doFormat(ticks
 return this.labels[index] || "";
         ''')
 
-    NBSP = '\N{NBSP}' * 4
+    NBSP = '\N{NBSP}' * 4  # noqa: E999
     ohlc_extreme_values = df[['High', 'Low']].copy(deep=False)
     ohlc_tooltips = [
         ('x, y', NBSP.join(('$index',

backtesting/_stats.py

@@ -1,4 +1,4 @@
-from typing import List, TYPE_CHECKING, Union
+from typing import TYPE_CHECKING, List, Union
 
 import numpy as np
 import pandas as pd

backtesting/_util.py

@@ -1,6 +1,6 @@
 import warnings
-from typing import Dict, List, Optional, Sequence, Union, cast
 from numbers import Number
+from typing import Dict, List, Optional, Sequence, Union, cast
 
 import numpy as np
 import pandas as pd

backtesting/backtesting.py

@@ -9,11 +9,11 @@ import multiprocessing as mp
 import os
 import sys
 import warnings
-from abc import abstractmethod, ABCMeta
+from abc import ABCMeta, abstractmethod
 from concurrent.futures import ProcessPoolExecutor, as_completed
 from copy import copy
 from functools import lru_cache, partial
-from itertools import repeat, product, chain, compress
+from itertools import chain, compress, product, repeat
 from math import copysign
 from numbers import Number
 from typing import Callable, Dict, List, Optional, Sequence, Tuple, Type, Union
@@ -29,7 +29,7 @@ except ImportError:
     def _tqdm(seq, **_):
         return seq
 
-from ._plotting import plot
+from ._plotting import plot  # noqa: I001
 from ._stats import compute_stats
 from ._util import _as_str, _Indicator, _Data, try_
 
@@ -75,7 +75,7 @@ class Strategy(metaclass=ABCMeta):
             setattr(self, k, v)
         return params
 
-    def I(self,  # noqa: E741, E743
+    def I(self,  # noqa: E743
           func: Callable, *args,
           name=None, plot=True, overlay=None, color=None, scatter=False,
           **kwargs) -> np.ndarray:
@@ -126,7 +126,7 @@ class Strategy(metaclass=ABCMeta):
         try:
             value = func(*args, **kwargs)
         except Exception as e:
-            raise RuntimeError(f'Indicator "{name}" errored with exception: {e}')
+            raise RuntimeError(f'Indicator "{name}" error') from e
 
         if isinstance(value, pd.DataFrame):
             value = value.values.T
@@ -190,7 +190,7 @@ class Strategy(metaclass=ABCMeta):
             super().next()
         """
 
-    class __FULL_EQUITY(float):
+    class __FULL_EQUITY(float):  # noqa: N801
         def __repr__(self): return '.9999'
     _FULL_EQUITY = __FULL_EQUITY(1 - sys.float_info.epsilon)
 
@@ -664,7 +664,7 @@ class Trade:
         if order:
             order.cancel()
         if price:
-            kwargs = dict(stop=price) if type == 'sl' else dict(limit=price)
+            kwargs = {'stop': price} if type == 'sl' else {'limit': price}
             order = self.__broker.new_order(-self.size, trade=self, **kwargs)
             setattr(self, attr, order)
 
@@ -1409,13 +1409,13 @@ class Backtest:
                                        Tuple[pd.Series, pd.Series, dict]]:
             try:
                 from skopt import forest_minimize
-                from skopt.space import Integer, Real, Categorical
-                from skopt.utils import use_named_args
                 from skopt.callbacks import DeltaXStopper
                 from skopt.learning import ExtraTreesRegressor
+                from skopt.space import Categorical, Integer, Real
+                from skopt.utils import use_named_args
             except ImportError:
                 raise ImportError("Need package 'scikit-optimize' for method='skopt'. "
-                                  "pip install scikit-optimize")
+                                  "pip install scikit-optimize") from None
 
             nonlocal max_tries
             max_tries = (200 if max_tries is None else

backtesting/lib.py

@@ -12,18 +12,18 @@ Please raise ideas for additions to this collection on the [issue tracker].
 """
 
 from collections import OrderedDict
+from inspect import currentframe
 from itertools import compress
 from numbers import Number
-from inspect import currentframe
-from typing import Sequence, Optional, Union, Callable
+from typing import Callable, Optional, Sequence, Union
 
 import numpy as np
 import pandas as pd
 
-from .backtesting import Strategy
 from ._plotting import plot_heatmaps as _plot_heatmaps
 from ._stats import compute_stats as _compute_stats
 from ._util import _Array, _as_str
+from .backtesting import Strategy
 
 __pdoc__ = {}
 
@@ -461,8 +461,8 @@ class TrailingStrategy(Strategy):
         Set the lookback period for computing ATR. The default value
         of 100 ensures a _stable_ ATR.
         """
-        h, l, c_prev = self.data.High, self.data.Low, pd.Series(self.data.Close).shift(1)
-        tr = np.max([h - l, (c_prev - h).abs(), (c_prev - l).abs()], axis=0)
+        hi, lo, c_prev = self.data.High, self.data.Low, pd.Series(self.data.Close).shift(1)
+        tr = np.max([hi - lo, (c_prev - hi).abs(), (c_prev - lo).abs()], axis=0)
         atr = pd.Series(tr).rolling(periods).mean().bfill().values
         self.__atr = atr
 

backtesting/test/_test.py

@@ -18,21 +18,21 @@ from pandas.testing import assert_frame_equal
 
 from backtesting import Backtest, Strategy
 from backtesting._stats import compute_drawdown_duration_peaks
+from backtesting._util import _Array, _as_str, _Indicator, try_
 from backtesting.lib import (
     OHLCV_AGG,
+    SignalStrategy,
+    TrailingStrategy,
     barssince,
     compute_stats,
     cross,
     crossover,
-    quantile,
-    SignalStrategy,
-    TrailingStrategy,
-    resample_apply,
     plot_heatmaps,
+    quantile,
     random_ohlc_data,
+    resample_apply,
 )
-from backtesting.test import GOOG, EURUSD, SMA
-from backtesting._util import _Indicator, _as_str, _Array, try_
+from backtesting.test import EURUSD, GOOG, SMA
 
 SHORT_DATA = GOOG.iloc[:20]  # Short data for fast tests with no indicator lag
 
@@ -146,7 +146,7 @@ class TestBacktest(TestCase):
 
                 assert float(self.data.Close) == self.data.Close[-1]
 
-            def next(self, FIVE_DAYS=pd.Timedelta('3 days')):
+            def next(self, _FEW_DAYS=pd.Timedelta('3 days')):  # noqa: N803
                 assert self.equity >= 0
 
                 assert isinstance(self.sma, _Indicator)
@@ -193,7 +193,7 @@ class TestBacktest(TestCase):
                     assert self.position.size < 0
 
                     trade = self.trades[0]
-                    if self.data.index[-1] - self.data.index[trade.entry_bar] > FIVE_DAYS:
+                    if self.data.index[-1] - self.data.index[trade.entry_bar] > _FEW_DAYS:
                         assert not trade.is_long
                         assert trade.is_short
                         assert trade.size < 0
@@ -290,7 +290,7 @@ class TestBacktest(TestCase):
             except TypeError:
                 return a == b
 
-        diff = {key: print(key) or value
+        diff = {key: print(key) or value  # noqa: T201
                 for key, value in stats.filter(regex='^[^_]').items()
                 if not almost_equal(value, expected[key])}
         self.assertDictEqual(diff, {})
@@ -510,7 +510,7 @@ class TestStrategy(TestCase):
 class TestOptimize(TestCase):
     def test_optimize(self):
         bt = Backtest(GOOG.iloc[:100], SmaCross)
-        OPT_PARAMS = dict(fast=range(2, 5, 2), slow=[2, 5, 7, 9])
+        OPT_PARAMS = {'fast': range(2, 5, 2), 'slow': [2, 5, 7, 9]}
 
         self.assertRaises(ValueError, bt.optimize)
         self.assertRaises(ValueError, bt.optimize, maximize='missing key', **OPT_PARAMS)
@@ -556,7 +556,7 @@ class TestOptimize(TestCase):
 
     def test_max_tries(self):
         bt = Backtest(GOOG.iloc[:100], SmaCross)
-        OPT_PARAMS = dict(fast=range(2, 10, 2), slow=[2, 5, 7, 9])
+        OPT_PARAMS = {'fast': range(2, 10, 2), 'slow': [2, 5, 7, 9]}
         for method, max_tries, random_state in (('grid', 5, 0),
                                                 ('grid', .3, 0),
                                                 ('skopt', 7, 0),
@@ -589,7 +589,7 @@ class TestOptimize(TestCase):
 
     def test_multiprocessing_windows_spawn(self):
         df = GOOG.iloc[:100]
-        kw = dict(fast=[10])
+        kw = {'fast': [10]}
 
         stats1 = Backtest(df, SmaCross).optimize(**kw)
         with patch('multiprocessing.get_start_method', lambda **_: 'spawn'):
@@ -633,7 +633,7 @@ class TestPlot(TestCase):
         bt = Backtest(GOOG.iloc[:100], SmaCross)
         bt.run()
         with _tempfile() as f:
-            for p in dict(plot_volume=False,
+            for p in dict(plot_volume=False,  # noqa: C408
                           plot_equity=False,
                           plot_return=True,
                           plot_pl=False,
@@ -722,8 +722,8 @@ class TestPlot(TestCase):
         self.assertEqual(stats['Equity Final [$]'], 0)
         self.assertEqual(len(trades), 2)
         assert trades[['EntryTime', 'ExitTime']].equals(
-            pd.DataFrame(dict(EntryTime=pd.to_datetime(['2006-11-01', '2008-11-14']),
-                              ExitTime=pd.to_datetime(['2007-10-31', '2009-09-21']))))
+            pd.DataFrame({'EntryTime': pd.to_datetime(['2006-11-01', '2008-11-14']),
+                          'ExitTime': pd.to_datetime(['2007-10-31', '2009-09-21'])}))
         assert trades['PnL'].round().equals(pd.Series([23469., -34420.]))
 
         with _tempfile() as f:

pyproject.toml

@@ -0,0 +1,37 @@
+[tool.ruff]
+exclude = [
+    '.git',
+    '.eggs',
+    '__pycache__',
+    'doc/examples',
+]
+ignore = [
+    'U006',
+    'U007',
+    'U009',
+    'N802',
+    'N806',
+    'C901',
+    'B008',
+    'B011',
+]
+line-length = 100
+select = [
+    'I',
+    'E',
+    'F',
+    'W',
+    'U',
+    'N',
+    'C',
+    'B',
+    'T',
+    'M',
+    'YTT',
+]
+
+[tool.ruff.pep8-naming]
+ignore-names = [
+    'l',
+    'h',
+]

setup.py

@@ -50,7 +50,7 @@ if __name__ == '__main__':
                 'scikit-optimize',
             ],
             'dev': [
-                'flake8',
+                'ruff',
                 'coverage',
                 'mypy',
             ],