Just out of curiousity I did some testing and it looks like the class actually was the culprit.
I submitted a simple strategy in 2 versions, one with a class and the other with a dictionary as state. The class version was rejected (exaclty like the one from my 1st post) and the dictionary version got accepted.

@antinomy In the end we followed your advise and changed a little bit the algorithm for adding data, once a cryptocurrency is in the top10 we include it with its past history and go on with the update (now DASH and XMR are being updated). Of course once the crypto is not among the top 10, the liquidity tag for the filter is "zero". Thank you!

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

@support Thank you very much for the clarification, and once again congratulations for the great job you are doing 😉

@pillocktailor Thank you for the interesting proposal.

Hi @support,

I modified the retraining interval to 1 day and it worked. Thank you for the help.

You guys rock!

@theflyingdutchman

it can help you. See topic 3) Not enough bid information.

https://github.com/quantiacs/strategy-q20-nasdaq100-quick-start/blob/master/strategy.ipynb

def get_enough_bid_for(data_, weights_): time_traded = weights_.time[abs(weights_).fillna(0).sum('asset') > 0] is_strategy_traded = len(time_traded) if is_strategy_traded: return xr.where(weights_.time < time_traded.min(), data_.sel(field="is_liquid"), weights_) return weights_ weights_new = get_enough_bid_for(data, weights) weights_new = weights_new.sel(time=slice("2006-01-01",None))

@magenta-muskrat Yes, we would have said crypto and stocks otherwise.

@carogate Hi, at the moment unfortunately not, sorry...

@buyers_are_back Hi,
the update was related to market_capitalization field availability and correctness, as vyacheslav_b described. Unfortunately, at the moment we cannot provide missing fundamental data, like number of ordinary shares, for some stocks (e.g. META). We are going to investigate potential new data sources in order to improve our datasets.
Regards,

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.

@support Thx for ur reply
I'll try it

@support said in Error found while running analysis:

@alfredaita wordle unlimited Thanks, can it maybe help? https://stackoverflow.com/questions/53421626/valueerror-found-array-with-0-sample-s-shape-0-1-while-a-minimum-of-1-is

Thanks a lot! I'll try that out.

@support said in Submitting strategies:

@aybber mapquest directions 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.

Thank you, all is clear.

@support Thanks!

Hi @support,
no problem.
I didnt check until now, the accepted strategies do not use machine learning 🙂
I will try out some machine learning strategies in the upcoming days and let you know.
Thanks again and Regards

@support
Thank you! I'll try that and let you know.

@magenta-grimer Hi, the lookback period in the backtester function is expressed in calendar days, and the indicators are expressed in trading days. So as far as the lookback period is long enough to include all indicator needed periods, all choices are fine.

If the longest period needed for an indicator is 10 trading days (2 weeks), a lookback period of (for example) 20, 50 or 100 for the backtester function will deliver the same results. The shorter the better for efficiency.

@support Thanks for the opportunity to be reprocessed. I cloned the programs and made the only change as suggested (from “pip install yfinance” to “ pip install git+https://github.com/ranaroussi/yfinance.git@c56e3496dbc6a701c4bcb94787acda7e2928b32d”). I run them successfully and submitted to the contest. The updated strategies are now under Sent Strategies / Filtered (rejected due to the rules). Details were sent to the email as requested. Much appreciated!

@eddiee Hello! Please check here:

https://quantiacs.com/documentation/en/user_guide/local_development.html#updating-the-conda-environment