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Use joblib.Parallel for Backtest.optimize(method='grid') #1206

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Feb 4, 2025
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Use joblib.Parallel for Backtest.optimize(method='grid') #1206

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58 changes: 14 additions & 44 deletions backtesting/backtesting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,12 +8,9 @@

from __future__ import annotations

import multiprocessing as mp
import os
import sys
import warnings
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 chain, product, repeat
Expand All @@ -23,6 +20,7 @@

import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from numpy.random import default_rng

try:
Expand Down Expand Up @@ -1495,41 +1493,15 @@ 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):
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]

# Save necessary objects into "global" state; pass into concurrent executor
# (and thus pickle) nothing but two numbers; receive nothing but numbers.
# With start method "fork", children processes will inherit parent address space
# in a copy-on-write manner, achieving better performance/RAM benefit.
backtest_uuid = np.random.random()
param_batches = list(_batch(param_combos))
Backtest._mp_backtests[backtest_uuid] = (self, param_batches, maximize)
try:
# If multiprocessing start method is 'fork' (i.e. on POSIX), use
# a pool of processes to compute results in parallel.
# Otherwise (i.e. on Windos), sequential computation will be "faster".
if mp.get_start_method(allow_none=False) == 'fork':
with ProcessPoolExecutor() as executor:
futures = [executor.submit(Backtest._mp_task, backtest_uuid, i)
for i in range(len(param_batches))]
for future in _tqdm(as_completed(futures), total=len(futures),
desc='Backtest.optimize'):
batch_index, values = future.result()
for value, params in zip(values, param_batches[batch_index]):
heatmap[tuple(params.values())] = value
else:
if os.name == 'posix':
warnings.warn("For multiprocessing support in `Backtest.optimize()` "
"set multiprocessing start method to 'fork'.")
for batch_index in _tqdm(range(len(param_batches))):
_, values = Backtest._mp_task(backtest_uuid, batch_index)
for value, params in zip(values, param_batches[batch_index]):
heatmap[tuple(params.values())] = value
finally:
del Backtest._mp_backtests[backtest_uuid]
with Parallel(prefer='threads', require='sharedmem', max_nbytes='50M',
n_jobs=-2, return_as='generator') as parallel:
results = _tqdm(
parallel(delayed(self._mp_task)(self, params, maximize=maximize)
for params in param_combos),
total=len(param_combos),
desc='Backtest.optimize')
for value, params in zip(results, param_combos):
heatmap[tuple(params.values())] = value

if pd.isnull(heatmap).all():
# No trade was made in any of the runs. Just make a random
Expand Down Expand Up @@ -1578,7 +1550,7 @@ def memoized_run(tup):
stats = self.run(**dict(tup))
return -maximize(stats)

progress = iter(_tqdm(repeat(None), total=max_tries, leave=False, desc='Backtest.optimize'))
progress = iter(_tqdm(repeat(None), total=max_tries, desc='Backtest.optimize'))
_names = tuple(kwargs.keys())

def objective_function(x):
Expand Down Expand Up @@ -1623,11 +1595,9 @@ def cons(x):
return output

@staticmethod
def _mp_task(backtest_uuid, batch_index):
bt, param_batches, maximize_func = Backtest._mp_backtests[backtest_uuid]
return batch_index, [maximize_func(stats) if stats['# Trades'] else np.nan
for stats in (bt.run(**params)
for params in param_batches[batch_index])]
def _mp_task(bt, params, *, maximize):
stats = bt.run(**params)
return maximize(stats) if stats['# Trades'] else np.nan

_mp_backtests: Dict[float, Tuple['Backtest', List, Callable]] = {}

Expand Down
14 changes: 1 addition & 13 deletions backtesting/test/_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -621,18 +621,6 @@ def test_max_tries(self):
**OPT_PARAMS)
self.assertEqual(len(heatmap), 6)

def test_multiprocessing_windows_spawn(self):
df = GOOG.iloc[:100]
kw = {'fast': [10]}

stats1 = Backtest(df, SmaCross).optimize(**kw)
with patch('multiprocessing.get_start_method', lambda **_: 'spawn'):
with self.assertWarns(UserWarning) as cm:
stats2 = Backtest(df, SmaCross).optimize(**kw)

self.assertIn('multiprocessing support', cm.warning.args[0])
assert stats1.filter(chars := tuple('[^_]')).equals(stats2.filter(chars)), (stats1, stats2)

def test_optimize_invalid_param(self):
bt = Backtest(GOOG.iloc[:100], SmaCross)
self.assertRaises(AttributeError, bt.optimize, foo=range(3))
Expand All @@ -648,7 +636,7 @@ def test_optimize_speed(self):
start = time.process_time()
bt.optimize(fast=(2, 5, 7), slow=[10, 15, 20, 30])
end = time.process_time()
self.assertLess(end - start, .2)
self.assertLess(end - start, 1)


class TestPlot(TestCase):
Expand Down
1 change: 1 addition & 0 deletions setup.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,7 @@
'numpy >= 1.17.0',
'pandas >= 0.25.0, != 0.25.0',
'bokeh >= 1.4.0, != 3.0.*',
'joblib',
],
extras_require={
'doc': [
Expand Down