Hi,
I would like to know how the classification is done in context. you only look at the shape ratio over the out-of-sample period?

@support Hi,
I would like to know how the classification is done in context. you only look at the shape ratio over the out-o-sample period?

Hi,

Is there a way to speed up optimization running ?

thank for your help

@dark-pidgeot Hi, i have the same probleme with the version of pandas

@support

@support Hi,
is it necessary to manually modify the qnt module before using it in colab?

@theflyingdutchman it seemed to me that strategies should be able to open long and short positions

@support said in Q20 contest results:

That is also a good observation. In general, we would like a long-only system with low turnover to beat the Nasdaq index for example. But still, we see a lot of problems with hand-picking stocks and survivorship-bias affected systems.

if I understand correctly

if I initially select 10 stocks, and even if I have a good sharpe ratio, my strategy can make long and short positions. I could be disqualified

@support I always ask myself the question, when we look at the winners of other competitions, there are some who trade just 5 stocks. can we consider this to be hand-picking?

@support I don't really understand, if I have a strategy that works on 10 stocks for example, to be eligible for the price, I absolutely have to trade all the stocks?

@support Am I disqualified if I do this?

data = qndata.stocks.load_ndx_data(min_date="2005-06-01", assets=["NAS:AAPL", "NAS:AMZN", "NAS:MRNA", "NAS:MSFT"])

I select a few stocks, it's not a dynamic selection

@support Hello,

got it, thanks for the reply,

@support said in Q20 contest results:

W
Hi,
thank you for these answers. So the disqualification will depend on how the user selected the actions?

Hi,
The progress check has frozen for all my strategies since March 11, 2024. is this normal?
Thank you for your help

@support said in Q20 contest results:

Hand-picking of stocks allows to easily game the contest, for example by submitting 15 long only trading systems on single stocks chosen in a discretionary way. The purpose of these contests is to develop systems which are working on stocks and are free from survivo

Hello,

if there's a hint of manual stock selection,
how do you rule on disqualification?
thanks in advance

@support Thank you for your advise, it's ok

Hello everyone,

I get errors when I save the isssus parameters of my optimization in the json file

I've created a program that takes two parameters param1 and param2. I have otpimized them and I would like to save the parameters in a json file.

here's a snippet of the command used :

#DEBUG#

evaluator will remove cells with such marks before evaluation

result_long = qno.optimize_strategy(
data,
strategy_long,
qno.full_range_args_generator(param1=np.arange(10, 20, 1, dtype=int),param2=np.arange(1, 20, 2, dtype=int),
asset=data.asset.values.tolist()),
workers=10 # you can set more workers on your local PC to speed up
)

config = find_best_parameters(result=result_long, asset_count=15, parameter_set_count=3)

If you change the asset_count and/or parameters_count, you will get a new strategy.

json.dump(config, open('config.json', 'w'), indent=2)

display(config)

I get the following errors

TypeError Traceback (most recent call last)
Cell In[10], line 30
27 config = find_best_parameters(result=result_long, asset_count=15, parameter_set_count=3)
28 # If you change the asset_count and/or parameters_count, you will get a new strategy.
---> 30 json.dump(config, open('config.json', 'w'), indent=2)
32 display(config)

File /usr/local/lib/python3.10/json/init.py:179, in dump(obj, fp, skipkeys, ensure_ascii, check_circular, allow_nan, cls, indent, separators, default, sort_keys, **kw)
173 iterable = cls(skipkeys=skipkeys, ensure_ascii=ensure_ascii,
174 check_circular=check_circular, allow_nan=allow_nan, indent=indent,
175 separators=separators,
176 default=default, sort_keys=sort_keys, **kw).iterencode(obj)
177 # could accelerate with writelines in some versions of Python, at
178 # a debuggability cost
--> 179 for chunk in iterable:
180 fp.write(chunk)

File /usr/local/lib/python3.10/json/encoder.py:429, in _make_iterencode.<locals>._iterencode(o, _current_indent_level)
427 yield _floatstr(o)
428 elif isinstance(o, (list, tuple)):
--> 429 yield from _iterencode_list(o, _current_indent_level)
430 elif isinstance(o, dict):
431 yield from _iterencode_dict(o, _current_indent_level)

File /usr/local/lib/python3.10/json/encoder.py:325, in _make_iterencode.<locals>._iterencode_list(lst, _current_indent_level)
323 else:
324 chunks = _iterencode(value, _current_indent_level)
--> 325 yield from chunks
326 if newline_indent is not None:
327 _current_indent_level -= 1

File /usr/local/lib/python3.10/json/encoder.py:405, in _make_iterencode.<locals>._iterencode_dict(dct, _current_indent_level)
403 else:
404 chunks = _iterencode(value, _current_indent_level)
--> 405 yield from chunks
406 if newline_indent is not None:
407 _current_indent_level -= 1

File /usr/local/lib/python3.10/json/encoder.py:438, in _make_iterencode.<locals>._iterencode(o, _current_indent_level)
436 raise ValueError("Circular reference detected")
437 markers[markerid] = o
--> 438 o = _default(o)
439 yield from _iterencode(o, _current_indent_level)
440 if markers is not None:

File /usr/local/lib/python3.10/json/encoder.py:179, in JSONEncoder.default(self, o)
160 def default(self, o):
161 """Implement this method in a subclass such that it returns
162 a serializable object for o, or calls the base implementation
163 (to raise a TypeError).
(...)
177
178 """
--> 179 raise TypeError(f'Object of type {o.class.name} '
180 f'is not JSON serializable')

TypeError: Object of type int64 is not JSON serializable

thank you in advance for your help

@jeppe_and Thanks for the reply, i'll check my code

@support

Hi, here the screenshoot

@support

On the picture, there are indeed generated positions, so I don't really understand