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

Hi @support
Thank you, I'll try that.

@antinomy great! Sorry, there were many submissions on the last day.

@stefanm Thank you!

@magenta-grimer Hello, the 34 M USD have been allocated to the winning strategies according to the contest rules.

Other strategies have been funded, and agreements are in place between quantiacs, investors and quants. We cannot disclose more details now, sorry.

5M USD is a reasonable capacity a strategy could handle, yes.

@sheikh Hi,

it simply means that your system should make a new global high before you are entitled for a payment. If your system makes 1000 at the end of January, 800 at the end of Februray, 900 at the end of March and 1100 at the end of April, your profit will be generated at the end of April and they will amount to 1100-100=100;

no, once the system starts being traded, it will be traded in the form it was at submission time, i.e. the quant will not be allowed to update parameters/change details. Of course a submitted system can have an adaptive logic, by changing parameters according to the value of some meta-indicator. If the quant believes there is some big change to be made, it is ok to re-submit the changed system, but it will need again to accumulate a track record before being traded.

@vyacheslav_b
Thank you! 🎉 🎉

@vyacheslav_b thank you!

@support
ok I did what @antinomy said below and the 'state' strategy worked.
https://quantiacs.com/community/topic/46/macroeconomic-data-with-quantiacs/3?_=1619554449031

However, it broke my old strategies. So he further suggested to:
https://quantiacs.com/community/topic/46/macroeconomic-data-with-quantiacs/5?_=1619556376479
And my old strategies are running again.

Ok, so looks like for now for all strategies without a state I have to output and pass None to weights for the state & pass a state variable to the strategy.
Will keep you updated. Thanks for looking into the issue.

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

@pillocktailor Thank you for the interesting proposal.

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 Hi, sorry for the late answer. The problem is the size of files, we are working on a solution for avoiding this issue in the future.

@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!

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

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

Hi @support,

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

You guys rock!

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

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.

@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.