MultiBacktest multi-dataset backtesting wrapper

.github/workflows/ci.yml

@@ -34,7 +34,12 @@ jobs:
     timeout-minutes: 3
     strategy:
       matrix:
-        python-version: [3.11, 3.12, 3.13]
+        python-version: [3.12, 3.13]
+        experimental: [false]
+        include:
+          - python-version: '3.*'
+            experimental: true
+    continue-on-error: ${{ matrix.experimental }}
     steps:
       - uses: actions/setup-python@v5
         with:

backtesting/_util.py

@@ -1,8 +1,11 @@
 from __future__ import annotations
 
+import os
 import sys
 import warnings
 from contextlib import contextmanager
+from functools import partial
+from itertools import chain
 from multiprocessing import resource_tracker as _mprt
 from multiprocessing import shared_memory as _mpshm
 from numbers import Number
@@ -12,6 +15,13 @@
 import numpy as np
 import pandas as pd
 
+try:
+    from tqdm.auto import tqdm as _tqdm
+    _tqdm = partial(_tqdm, leave=False)
+except ImportError:
+    def _tqdm(seq, **_):
+        return seq
+
 
 def try_(lazy_func, default=None, exception=Exception):
     try:
@@ -55,6 +65,13 @@ def _as_list(value) -> List:
     return [value]
 
 
+def _batch(seq):
+    # XXX: Replace with itertools.batched
+    n = np.clip(int(len(seq) // (os.cpu_count() or 1)), 1, 300)
+    for i in range(0, len(seq), n):
+        yield seq[i:i + n]
+
+
 def _data_period(index) -> Union[pd.Timedelta, Number]:
     """Return data index period as pd.Timedelta"""
     values = pd.Series(index[-100:])
@@ -233,7 +250,6 @@ def __setstate__(self, state):
 
 if sys.version_info >= (3, 13):
     SharedMemory = _mpshm.SharedMemory
-    from multiprocessing.managers import SharedMemoryManager  # noqa: F401
 else:
     class SharedMemory(_mpshm.SharedMemory):
         # From https://github.com/python/cpython/issues/82300#issuecomment-2169035092
@@ -244,7 +260,7 @@ def __init__(self, *args, track: bool = True, **kwargs):
             if track:
                 return super().__init__(*args, **kwargs)
             with self.__lock:
-                with patch(_mprt, 'register', lambda *a, **kw: None):  # TODO lambda
+                with patch(_mprt, 'register', lambda *a, **kw: None):
                     super().__init__(*args, **kwargs)
 
         def unlink(self):
@@ -253,23 +269,66 @@ def unlink(self):
                 if self._track:
                     _mprt.unregister(self._name, "shared_memory")
 
-    class SharedMemoryManager:
-        def __init__(self) -> None:
-            self._shms: list[SharedMemory] = []
-
-        def SharedMemory(self, size):
-            shm = SharedMemory(create=True, size=size, track=True)
-            self._shms.append(shm)
-            return shm
-
-        def __enter__(self):
-            return self
 
-        def __exit__(self, *args, **kwargs):
-            for shm in self._shms:
-                try:
-                    shm.close()
+class SharedMemoryManager:
+    """
+    A simple shared memory contextmanager based on
+    https://docs.python.org/3/library/multiprocessing.shared_memory.html#multiprocessing.shared_memory.SharedMemory
+    """
+    def __init__(self, create=False) -> None:
+        self._shms: list[SharedMemory] = []
+        self.__create = create
+
+    def SharedMemory(self, *, name=None, create=False, size=0, track=True):
+        shm = SharedMemory(name=name, create=create, size=size, track=track)
+        shm._create = create
+        # Essential to keep refs on Windows
+        # https://stackoverflow.com/questions/74193377/filenotfounderror-when-passing-a-shared-memory-to-a-new-process#comment130999060_74194875  # noqa: E501
+        self._shms.append(shm)
+        return shm
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, *args, **kwargs):
+        for shm in self._shms:
+            try:
+                shm.close()
+                if shm._create:
                     shm.unlink()
-                except Exception:
-                    warnings.warn(f'Failed to unlink shared memory {shm.name!r}',
-                                  category=ResourceWarning, stacklevel=2)
+            except Exception:
+                warnings.warn(f'Failed to unlink shared memory {shm.name!r}',
+                              category=ResourceWarning, stacklevel=2)
+                raise
+
+    def arr2shm(self, vals):
+        """Array to shared memory. Returns (shm_name, shape, dtype) used for restore."""
+        assert vals.ndim == 1, (vals.ndim, vals.shape, vals)
+        shm = self.SharedMemory(size=vals.nbytes, create=True)
+        buf = np.ndarray(vals.shape, dtype=vals.dtype, buffer=shm.buf)
+        buf[:] = vals[:]  # Copy into shared memory
+        return shm.name, vals.shape, vals.dtype
+
+    def df2shm(self, df):
+        return tuple((
+            (column, *self.arr2shm(values))
+            for column, values in chain([(self._DF_INDEX_COL, df.index)], df.items())
+        ))
+
+    @staticmethod
+    def shm2arr(shm, shape, dtype):
+        arr = np.ndarray(shape, dtype=dtype, buffer=shm.buf)
+        arr.setflags(write=False)
+        return arr
+
+    _DF_INDEX_COL = '__bt_index'
+
+    @staticmethod
+    def shm2df(data_shm):
+        shm = [SharedMemory(name=name, create=False, track=False) for _, name, _, _ in data_shm]
+        df = pd.DataFrame({
+            col: SharedMemoryManager.shm2arr(shm, shape, dtype)
+            for shm, (col, _, shape, dtype) in zip(shm, data_shm)})
+        df.set_index(SharedMemoryManager._DF_INDEX_COL, drop=True, inplace=True)
+        df.index.name = None
+        return df, shm

backtesting/backtesting.py

@@ -9,7 +9,6 @@
 from __future__ import annotations
 
 import multiprocessing as mp
-import os
 import sys
 import warnings
 from abc import ABCMeta, abstractmethod
@@ -24,18 +23,11 @@
 import pandas as pd
 from numpy.random import default_rng
 
-try:
-    from tqdm.auto import tqdm as _tqdm
-    _tqdm = partial(_tqdm, leave=False)
-except ImportError:
-    def _tqdm(seq, **_):
-        return seq
-
 from ._plotting import plot  # noqa: I001
 from ._stats import compute_stats
 from ._util import (
-    SharedMemory, SharedMemoryManager, _as_str, _Indicator, _Data, _indicator_warmup_nbars,
-    _strategy_indicators, patch, try_,
+    SharedMemoryManager, _as_str, _Indicator, _Data, _batch, _indicator_warmup_nbars,
+    _strategy_indicators, patch, try_, _tqdm,
 )
 
 __pdoc__ = {
@@ -1507,36 +1499,14 @@ def _optimize_grid() -> Union[pd.Series, Tuple[pd.Series, pd.Series]]:
                                     [p.values() for p in param_combos],
                                     names=next(iter(param_combos)).keys()))
 
-            def _batch(seq):
-                # XXX: Replace with itertools.batched
-                n = np.clip(int(len(seq) // (os.cpu_count() or 1)), 1, 300)
-                for i in range(0, len(seq), n):
-                    yield seq[i:i + n]
-
             with mp.Pool() as pool, \
                     SharedMemoryManager() as smm:
 
-                shm_refs = []  # https://stackoverflow.com/questions/74193377/filenotfounderror-when-passing-a-shared-memory-to-a-new-process#comment130999060_74194875  # noqa: E501
-
-                def arr2shm(vals):
-                    nonlocal smm
-                    shm = smm.SharedMemory(size=vals.nbytes)
-                    buf = np.ndarray(vals.shape, dtype=vals.dtype, buffer=shm.buf)
-                    buf[:] = vals[:]  # Copy into shared memory
-                    assert vals.ndim == 1, (vals.ndim, vals.shape, vals)
-                    shm_refs.append(shm)
-                    return shm.name, vals.shape, vals.dtype
-
-                data_shm = tuple((
-                    (column, *arr2shm(values))
-                    for column, values in chain([(Backtest._mp_task_INDEX_COL, self._data.index)],
-                                                self._data.items())
-                ))
                 with patch(self, '_data', None):
                     bt = copy(self)  # bt._data will be reassigned in _mp_task worker
                 results = _tqdm(
                     pool.imap(Backtest._mp_task,
-                              ((bt, data_shm, params_batch)
+                              ((bt, smm.df2shm(self._data), params_batch)
                                for params_batch in _batch(param_combos))),
                     total=len(param_combos),
                     desc='Backtest.optimize'
@@ -1640,27 +1610,15 @@ def cons(x):
     @staticmethod
     def _mp_task(arg):
         bt, data_shm, params_batch = arg
-        shm = [SharedMemory(name=shm_name, create=False, track=False)
-               for _, shm_name, *_ in data_shm]
+        bt._data, shm = SharedMemoryManager.shm2df(data_shm)
         try:
-            def shm2arr(shm, shape, dtype):
-                arr = np.ndarray(shape, dtype=dtype, buffer=shm.buf)
-                arr.setflags(write=False)
-                return arr
-
-            bt._data = df = pd.DataFrame({
-                col: shm2arr(shm, shape, dtype)
-                for shm, (col, _, shape, dtype) in zip(shm, data_shm)})
-            df.set_index(Backtest._mp_task_INDEX_COL, drop=True, inplace=True)
             return [stats.filter(regex='^[^_]') if stats['# Trades'] else None
                     for stats in (bt.run(**params)
                                   for params in params_batch)]
         finally:
             for shmem in shm:
                 shmem.close()
 
-    _mp_task_INDEX_COL = '__bt_index'
-
     def plot(self, *, results: pd.Series = None, filename=None, plot_width=None,
              plot_equity=True, plot_return=False, plot_pl=True,
              plot_volume=True, plot_drawdown=False, plot_trades=True,

backtesting/lib.py

@@ -13,9 +13,10 @@
 
 from __future__ import annotations
 
+import multiprocessing as mp
 from collections import OrderedDict
 from inspect import currentframe
-from itertools import compress
+from itertools import chain, compress, count
 from numbers import Number
 from typing import Callable, Generator, Optional, Sequence, Union
 
@@ -24,7 +25,7 @@
 
 from ._plotting import plot_heatmaps as _plot_heatmaps
 from ._stats import compute_stats as _compute_stats
-from ._util import _Array, _as_str
+from ._util import SharedMemoryManager, _Array, _as_str, _batch, _tqdm
 from .backtesting import Backtest, Strategy
 
 __pdoc__ = {}
@@ -474,11 +475,24 @@ def set_atr_periods(self, periods: int = 100):
 
     def set_trailing_sl(self, n_atr: float = 6):
         """
-        Sets the future trailing stop-loss as some multiple (`n_atr`)
+        Set the future trailing stop-loss as some multiple (`n_atr`)
         average true bar ranges away from the current price.
         """
         self.__n_atr = n_atr
 
+    def set_trailing_pct(self, pct: float = .05):
+        """
+        Set the future trailing stop-loss as some percent (`0 < pct < 1`)
+        below the current price (default 5% below).
+
+        .. note:: Stop-loss set by `pct` is inexact
+            Stop-loss set by `set_trailing_pct` is converted to units of ATR
+            with `mean(Close * pct / atr)` and set with `set_trailing_sl`.
+        """
+        assert 0 < pct < 1, 'Need pct= as rate, i.e. 5% == 0.05'
+        pct_in_atr = np.mean(self.data.Close * pct / self.__atr)  # type: ignore
+        self.set_trailing_sl(pct_in_atr)
+
     def next(self):
         super().next()
         # Can't use index=-1 because self.__atr is not an Indicator type
@@ -522,6 +536,74 @@ def __init__(self,
         __pdoc__[f'{cls.__name__}.__init__'] = False
 
 
+class MultiBacktest:
+    """
+    Multi-dataset `backtesting.backtesting.Backtest` wrapper.
+
+    Run supplied `backtesting.backtesting.Strategy` on several instruments,
+    in parallel.  Used for comparing strategy runs across many instruments
+    or classes of instruments. Example:
+
+        from backtesting.test import EURUSD, BTCUSD, SmaCross
+        btm = MultiBacktest([EURUSD, BTCUSD], SmaCross)
+        stats_per_ticker: pd.DataFrame = btm.run(fast=10, slow=20)
+        heatmap_per_ticker: pd.DataFrame = btm.optimize(...)
+    """
+    def __init__(self, df_list, strategy_cls, **kwargs):
+        self._dfs = df_list
+        self._strategy = strategy_cls
+        self._bt_kwargs = kwargs
+
+    def run(self, **kwargs):
+        """
+        Wraps `backtesting.backtesting.Backtest.run`. Returns `pd.DataFrame` with
+        currency indexes in columns.
+        """
+        with mp.Pool() as pool, \
+                SharedMemoryManager() as smm:
+            shm = [smm.df2shm(df) for df in self._dfs]
+            results = _tqdm(
+                pool.imap(self._mp_task_run,
+                          ((df_batch, self._strategy, self._bt_kwargs, kwargs)
+                           for df_batch in _batch(shm))),
+                total=len(shm),
+                desc=self.__class__.__name__,
+            )
+            df = pd.DataFrame(list(chain(*results))).transpose()
+        return df
+
+    @staticmethod
+    def _mp_task_run(args):
+        data_shm, strategy, bt_kwargs, run_kwargs = args
+        dfs, shms = zip(*(SharedMemoryManager.shm2df(i) for i in data_shm))
+        try:
+            return [stats.filter(regex='^[^_]') if stats['# Trades'] else None
+                    for stats in (Backtest(df, strategy, **bt_kwargs).run(**run_kwargs)
+                                  for df in dfs)]
+        finally:
+            for shmem in chain(*shms):
+                shmem.close()
+
+    def optimize(self, **kwargs) -> pd.DataFrame:
+        """
+        Wraps `backtesting.backtesting.Backtest.optimize`, but returns `pd.DataFrame` with
+        currency indexes in columns.
+
+            heamap: pd.DataFrame = btm.optimize(...)
+            from backtesting.plot import plot_heatmaps
+            plot_heatmaps(heatmap.mean(axis=1))
+        """
+        heatmaps = []
+        # Simple loop since bt.optimize already does its own multiprocessing
+        for df in _tqdm(self._dfs, desc=self.__class__.__name__):
+            bt = Backtest(df, self._strategy, **self._bt_kwargs)
+            _best_stats, heatmap = bt.optimize(  # type: ignore
+                return_heatmap=True, return_optimization=False, **kwargs)
+            heatmaps.append(heatmap)
+        heatmap = pd.DataFrame(dict(zip(count(), heatmaps)))
+        return heatmap
+
+
 # NOTE: Don't put anything below this __all__ list
 
 __all__ = [getattr(v, '__name__', k)