@algotime Dear Algotime, all three strategies will participate in the contest. We will update the contest leaderboard once all eligible strategies have finished processing. Thank you for your patience.

@cyan-gloom

Hello. The provided code is insufficient to understand the problem.

I assume that a certain function might not be returning the required value (for instance, the function where your model is being created).

I recommend that you check all return values of functions, using tools like display or print. Then, compare them with what is returned in properly working examples.

The state allows you to use data from previous iterations. You can find an example here:
https://github.com/quantiacs/toolbox/blob/2f4c42e33c7ce789dfad5d170444fd542e28c8ae/qnt/examples/004-strategy-futures-multipass-stateful.py

@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))

@eddiee

Hello.

This code looks to the future.
It is needed to train the model.
Pay attention to the name of the variable.

@support
Thanks a lot !

@support
Thanks
I got it !

@stefanm Thank you!

@johback

Hello

More examples are here https://github.com/quantiacs/toolbox/blob/main/qnt/tests/test_fundamental_data.py

This is a simple example.

import qnt.data as qndata import datetime as dt import qnt.data.secgov_indicators import qnt.data as qndata import qnt.stats as qns assets = qndata.stocks.load_ndx_list(tail=dt.timedelta(days=5 * 365)) assets_names = [i["id"] for i in assets] data = qndata.stocks.load_ndx_data(tail=dt.timedelta(days=5 * 365), dims=("time", "field", "asset"), assets=assets_names, forward_order=True) facts_names = ['operating_expense'] # 'assets', 'liabilities', 'ivestment_short_term' and other fundamental_data = qnt.data.secgov_load_indicators(assets, time_coord=data.time, standard_indicators=facts_names) # Operating expenses include marketing, noncapitalized R&D, # travel and entertainment, office supply, rent, salary, cogs... weights = fundamental_data.sel(field='operating_expense') is_liquid = data.sel(field="is_liquid") weights = weights * is_liquid # calc stats stats = qns.calc_stat(data, weights.sel(time=slice("2006-01-01", None))) display(stats.to_pandas().tail()) # graph performance = stats.to_pandas()["equity"] import qnt.graph as qngraph qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log")

@gjhernandezp yes, correct, 2/(n+1), sorry for the typo, thanks for correcting

@gjhernandezp Hello, you can use them for local development. Unfortunately, we do not support yet external datafeeds after submission...it is on our to-do list.

@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.

@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

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

@eddiee It is fixed, sorry for the problem.

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.