@support
Hello I resolved the issue

1. I moved the function and copied pasted the appropriate import s to a ,py file and named it strategy .py

2. i commented out the strategy_long(...) function from my notebook

3. then :... inserted in the notebook: from strategy import * before running the optimizer.

4 ) and it works FYI for asset by asset 20 cores about 2,5 times faster counting wall time

NOTE : I think this procedure gets around Windows Python GIL. however, it does not appear to be explained in the python Docs

@support
Hello I resolved the issue

1. I moved the function and copied pasted the appropriate import s to a ,py file and named it strategy .py

2. i commented out the strategy_long(...) function from my notebook

3. then :... inserted in the notebook: from strategy import * before running the optimizer.

4 ) and it works FYI for asset by asset 20 cores about 2,5 times faster counting wall time

NOTE : I think this procedure gets around Windows Python GIL. however, it does not appear to be explained in the python Docs

@support
No just the complaint indicated on my second comment.

additional information after trying to optimize THIS IS ON WINDOWS

KeyboardInterrupt Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_9644\3607707927.py in <module>
6 strategy_long,
7 qno.full_range_args_generator(ma_period=range(10, 200, 10)),
----> 8 workers = 20 #cpu_count # you can set more workers on your local PC to speed up default =1
9 )
10 end = time.time()

~\anaconda3\envs\qntdev\lib\site-packages\qnt\optimizer.py in optimize_strategy(data, output_function, argument_generator, stats_function, stats_to_weight, workers)
38 argument_generator,
39 stats_to_weight,
---> 40 workers
41 )
42

~\anaconda3\envs\qntdev\lib\site-packages\qnt\optimizer.py in optimize(target_function, argument_generator, result_to_weight, workers)
61 for w in workers:
62 w.daemon = True
---> 63 w.start()
64
65 with progressbar.ProgressBar(max_value=length, poll_interval=1) as bar:

~\anaconda3\envs\qntdev\lib\multiprocessing\process.py in start(self)
110 'daemonic processes are not allowed to have children'
111 _cleanup()
--> 112 self._popen = self._Popen(self)
113 self._sentinel = self._popen.sentinel
114 # Avoid a refcycle if the target function holds an indirect

~\anaconda3\envs\qntdev\lib\multiprocessing\context.py in _Popen(process_obj)
221 @staticmethod
222 def _Popen(process_obj):
--> 223 return _default_context.get_context().Process._Popen(process_obj)
224
225 class DefaultContext(BaseContext):

~\anaconda3\envs\qntdev\lib\multiprocessing\context.py in _Popen(process_obj)
320 def _Popen(process_obj):
321 from .popen_spawn_win32 import Popen
--> 322 return Popen(process_obj)
323
324 class SpawnContext(BaseContext):

~\anaconda3\envs\qntdev\lib\multiprocessing\popen_spawn_win32.py in init(self, process_obj)
87 try:
88 reduction.dump(prep_data, to_child)
---> 89 reduction.dump(process_obj, to_child)
90 finally:
91 set_spawning_popen(None)

~\anaconda3\envs\qntdev\lib\multiprocessing\reduction.py in dump(obj, file, protocol)
58 def dump(obj, file, protocol=None):
59 '''Replacement for pickle.dump() using ForkingPickler.'''
---> 60 ForkingPickler(file, protocol).dump(obj)
61
62 #

KeyboardInterrupt:

additionally
the following complaint

The environment variable DATA_BASE_URL was not specified. The default value is 'https://data-api.quantiacs.io/'
NOTICE: The environment variable CACHE_RETENTION was not specified. The default value is '7'
NOTICE: The environment variable CACHE_DIR was not specified. The default value is 'data-cache'

optimizer does not work on my local platform.

whether i set workers to 1 or to os.cpu_count which is 20.