Hello.

I checked this problem. The script which cut "###DEBUG###" cells was incorrect. I fixed this and resent your strategies (filtered by time out) to checking.

Regards.

@theflyingdutchman Hi, sorry for the delay, yes, all fine, more details by e-mail

@wool-dewgong Hello! We added one template which should address your issue and allow you to perform a rolling fast ML training with retraining. It is available in your user space in the Examples section and you can read it here also in the public docs:

https://quantiacs.com/documentation/en/examples/machine_learning_with_a_voting_classifier.html

@antinomy

I got it !
Thanks a lot !!

@antinomy thanks!

@magenta-grimer Hello, you can have at most 50 running submissions in your user area. You can stop any of them any moment and replace it with another one.

Before the end of the Q17 submission phase, you should select at most 15 of them. These will take part to the live contest.

@sun-73 Yes, we will, sorry for the issue.

@magenta-kabuto We support only Python 3.7 right now. But it can coexist with Python 3.9:

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

Basically you can use Python 3.7 inside a conda environment.

@alexeigor Hello. Version 0.0.501 of the qnt library works correctly in Colab. Python version support has been extended from 3.10 to 3.13. The basic functionality of the library should work without issues.

To install, use the following command:

!pip install git+https://github.com/quantiacs/toolbox.git 2>/dev/null

Note: Installing ta-lib in Colab is not working for me at the moment.

@scalpingaf Correct, all trades (buy or sell) are taken at the open of the next day you take the decision.

@xiaolan That is correct, but the logic can be easily re-used. The only novel element will be the introduction of the liquidity filter at intermediate stages/at the final stage for the selection of the weights.

@vyacheslav_b Thank you so much

Hello colleagues.

The solution in case of predicting one financial instrument can be the following (train_period changed)

def load_data(period): return qndata.cryptodaily_load_data(tail=period, assets=['BTC']) def train_model(data): """ train the LSTM network """ asset_name = 'BTC' features_all = get_features(data) target_all = get_target_classes(data) model = get_model() # drop missing values: target_cur = target_all.sel(asset=asset_name).dropna('time', 'any') features_cur = features_all.sel(asset=asset_name).dropna('time', 'any') # align features and targets: target_for_learn_df, feature_for_learn_df = xr.align(target_cur, features_cur, join='inner') criterion = nn.MSELoss() # define loss function optimiser = optim.LBFGS(model.parameters(), lr=0.08) # we use an LBFGS solver as optimiser epochs = 1 # how many epochs for i in range(epochs): def closure(): # reevaluates the model and returns the loss (forward pass) optimiser.zero_grad() # input tensor in_ = torch.zeros(1, len(feature_for_learn_df.values)) in_[0, :] = torch.tensor(np.array(feature_for_learn_df.values)) # output out = model(in_) # target tensor target = torch.zeros(1, len(target_for_learn_df.values)) target[0, :] = torch.tensor(np.array(target_for_learn_df.values)) # evaluate loss loss = criterion(out, target) loss.backward() return loss optimiser.step(closure) # updates weights return model weights = qnbt.backtest_ml( load_data=load_data, train=train_model, predict=predict, train_period=1 * 365, # the data length for training in calendar days retrain_interval=365, # how often we have to retrain models (calendar days) retrain_interval_after_submit=1, # how often retrain models after submission during evaluation (calendar days) predict_each_day=False, # Is it necessary to call prediction for every day during backtesting? # Set it to true if you suspect that get_features is looking forward. competition_type='crypto_daily_long_short', # competition type lookback_period=365, # how many calendar days are needed by the predict function to generate the output start_date='2014-01-01', # backtest start date build_plots=True # do you need the chart? )

@illustrious-felice Hi, just insist and test other ideas, it is not easy but you will manage!

@support Hi again guys, I think the leaderboard is not updating again 😳

@yonasbo Hi, sorry for delay, we will start soon a new contest, in the next 2 weeks

@sheikh It is fine, please just submit, check the result and let us know if you see any issue. It should work fine.

@green-flareon Thanks. The live phase of the Q23 is running. Quants can join any contest during the submission phase. Q24 is on.

@support Great help! Thank you so much!

@illustrious-felice Hello. The reason you're still seeing a large number of tickers (e.g., around 300) even after applying the filter is that the "best" instrument by Sharpe ratio changes over time. The rank_assets_by function returns a time-dependent mask, selecting the top N assets at each time step. So the total number of unique assets that were selected at any point in time may be much larger than top_assets.

This is expected behavior.

To illustrate this more clearly, let's consider a minimal working example that selects only 1 top asset at each point in time and shows all the intermediate steps:

import qnt.data as qndata import qnt.ta as qnta import qnt.stats as qnstats import qnt.output as qnout import qnt.filter as qnfilter import xarray as xr import pandas as pd top_assets = 1 data = qndata.stocks.load_spx_data(min_date="2005-06-01") weights = data.sel(field="is_liquid") stats_per_asset = qnstats.calc_stat(data, weights, per_asset=True) sharpe_ratio = stats_per_asset.sel(field="sharpe_ratio") asset_filter = qnfilter.rank_assets_by(data, sharpe_ratio, top_assets, ascending=False) weights = weights * asset_filter stats = qnstats.calc_stat(data, weights.sel(time=slice("2005-06-01", None))) display(asset_filter.to_pandas().tail()) display(stats.to_pandas().tail()) display(sharpe_ratio.to_pandas().tail()) display(weights.to_pandas().tail())

If you want to see which asset was the best on specific dates, you can do something like this:

dates = ["2015-01-15", "2020-01-15", "2025-01-15"] records = [] for date_str in dates: best_mask = asset_filter.sel(time=date_str) assets = best_mask.where(best_mask > 0, drop=True).asset.values srs = sharpe_ratio.sel(time=date_str, asset=assets).values for a, s in zip(assets, srs): records.append({"time": date_str, "asset": a.item(), "sharpe_ratio": float(s)}) df = pd.DataFrame(records).set_index("time") display(df) asset sharpe_ratio time 2025-05-22 NYS:HRL 1.084683 2025-05-22 NAS:KDP 1.093528 2025-05-22 NAS:AAPL 0.968039

Or simply for a single date:

date = "2020-05-22" best_mask = asset_filter.sel(time=date) best_assets = best_mask.where(best_mask > 0, drop=True).asset best_sr = sharpe_ratio.sel(time=date, asset=best_assets) print(best_sr.to_pandas())

This shows clearly that only one asset is selected at each time step, but over the full time range, many different assets can appear in the top list depending on how their Sharpe ratios change.