@joshua408 We allow development on our cloud or local development on user's machines. No need to open any port.

@mwalimudan Sorry for the issue, but do not worry, we are extending the submission deadline to end of October as there were several issues. Please do not hesitate to report problems.

@aybber Hello, that is just due to the fact that there is a long queue of systems to be processed, we will work on improving the processing time. Submission time will be extended to end of October as there were some issues.

@support no worries, I've been able to recover the strategy thank you!

@joshua408 Yes, you can. You can use conda or pip for installing custom packages in your own environment by editing the init.py file

More info can be found at:

https://quantiacs.com/documentation/en/user_guide/code.html

@antinomy Ty

@eddiee Dear eddiee, no, please, for the moment do not resubmit. The timed out submissions are stored as timed out submissions and we can reprocess them. In case you need resubmission, we will let you know.

@support Thanks a lot!

@alphastar Sorry for the issue, it has been fixed.

It's safe to ignore these notices but if they bother you, you can set the variables together with your API key using the defaults and the messages go away:

import os os.environ['API_KEY'] = 'YOUR-API-KEY' os.environ['DATA_BASE_URL'] = 'https://data-api.quantiacs.io/' os.environ['CACHE_RETENTION'] = '7' os.environ['CACHE_DIR'] = 'data-cache'

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

@mwalimudan Yes, we are really sorry. We did not think about these corner cases, but with cryptos, they can take place.

@eddiee It is fixed, sorry for the problem.

Thanks @dark-yellowjacket for post your solution for the optimizer, it works fine for me but could you please go deeper explaining how do you implement the optimized obtained parameter values in the final strategy. I have tried several methods (even in several computers) , obtaining always errors.
For example using this template provided by Quantiacs (where i call to "Strategy" I use the same previouslly optimized, and the config.json is the output asset by asset obtained after optimization):

import json import xarray as xr import qnt.backtester as qnbk from Strategy import * def optmized_strategy(data, config): results = [] for c in config: results.append(strategy_long(data, **c)) # align and join results results = xr.align(*results, join='outer') results = [r.fillna(0) for r in results] output = sum(results) / len(results) return output config = json.load(open('config.json', 'r')) # multi-pass # It may look slow, but it is ok. The evaluator will run only one iteration per day. qnbk.backtest( competition_type='stocks_nasdaq100', lookback_period=365, strategy=lambda d: optmized_strategy(d, config), # strategy=strategy_long, # you can check the base strategy too start_date='2006-01-01')

It rises the following error:

Reloaded modules: Estrategia fetched chunk 1/5 0s fetched chunk 2/5 0s fetched chunk 3/5 0s fetched chunk 4/5 0s fetched chunk 5/5 0s Data loaded 1s Run last pass... Load data... fetched chunk 1/1 0s Data loaded 0s Run strategy... Traceback (most recent call last): File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\spyder_kernels\py3compat.py", line 356, in compat_exec exec(code, globals, locals) File "c:\users\luispc\desktop\quantiacs\q18\prueba.py", line 39, in <module> start_date='2006-01-01') File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\qnt\backtester.py", line 291, in backtest result = strategy_wrap(data, state) File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\qnt\backtester.py", line 270, in <lambda> strategy_wrap = (lambda d, s: strategy(d)) if args_count < 2 else strategy File "c:\users\luispc\desktop\quantiacs\q18\prueba.py", line 37, in <lambda> strategy=lambda d: optmized_strategy(d, config), File "c:\users\luispc\desktop\quantiacs\q18\prueba.py", line 22, in optmized_strategy results.append(strategy_long(data, **c)) File "C:\Users\LuisPC\Desktop\quantiacs\Q18\Estrategia.py", line 16, in strategy_long data = data.sel(asset=[asset]) File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\dataarray.py", line 1337, in sel **indexers_kwargs, File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\dataset.py", line 2505, in sel self, indexers=indexers, method=method, tolerance=tolerance File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\coordinates.py", line 422, in remap_label_indexers obj, v_indexers, method=method, tolerance=tolerance File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\indexing.py", line 120, in remap_label_indexers idxr, new_idx = index.query(labels, method=method, tolerance=tolerance) File "C:\Users\LuisPC\.conda\envs\qntdev\lib\site-packages\xarray\core\indexes.py", line 242, in query raise KeyError(f"not all values found in index {coord_name!r}") KeyError: "not all values found in index 'asset'"

I tried to explain this problem about the implementation in the Backtester in another forum thread time ago, without getting a valid answer, therefore I would appreciate any idea you can give me on this matter.
...Otherwise I am afraid that I will not present algorithms for this contest (Sadly 😢 )
Regards.
Luis G.

@theflyingdutchman Hello, before the end of the week the update will be ready, sorry for the delay

Dear Eddie, the training takes place on a rolling basis. The prediction at time "t" uses the defined training period (until "t-1"). If you choose the backtest to start at "2006-01-01", then this will be the first "predicted" date.

As the training can be computationally expensive, the retraining option offers the option to freeze the model and to perform the interval every "retrain_interval" days. As you correctly say, the rolling window is still the one of "train_period".

If by "expanding window" you mean a retraining which uses more and more data as time goes on, no, this is not currently implemented, we use a fixed-size rolling window.

@support Thanks. Works fine now.

@captain-nidoran Not fully conclusive yet, please check the related topic:

https://quantiacs.com/community/topic/249/strategy-optimization-in-local-development-environment-is-not-working/5