@support I have done that twice before my post, but now F_RY looks fine. There are several directories with scripts and notebooks I use with qnt, so maybe I deleted the wrong data-cache before...
Thanks for fixing the data!

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

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

@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.

@support
oh I see now what you mean.
15 strategies PER USER are selected.
At first, I thought you were only going to select 15 strategies total for all users.
Thanks.

@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.

@antinomy Ty

@antinomy thanks!

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? )

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

@iron-tentacruel Sorry for the delay in the answer. We recommend conda as we can better track dependencies. With conda you can create locally an environment which mirrors the one on the Quantiacs server and you can work locally as you would on the server. If you need a specific version of a package, please let us know.

@support, thank you for the clarifications. Regards.

@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.

@antinomy
Thanks for your advice !

@gjhernandezp Thank you for sharing your solution!

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

@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.

@support Great help! Thank you so much!

@xiaolan Ok, but please note that you can work all the time with xarray, the documentation is very good:

http://xarray.pydata.org/en/stable/

@magenta-grimer Hi, we cannot provide the list of strategies we are still trading and the payouts. However, all the statistics are public, the new ones (since Q15) and the old ones at:
https://legacy.quantiacs.com/Systems.aspx