Hi @support,
Thanks for getting back. No worries, I was able to get 6 strategies into the Q16 competition so far.
qnt3.PNG

@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.

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

@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? )

@vyacheslav_b Thank you so much

@support Yes, I can load now the stocks data. Thank you once again!

@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

@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.

@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.

@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.

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

@antinomy Ty

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

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

@buyers_are_back We reprocessed the submission, it is formally correct and passes all the filters. Sorry for the issue, evidently on our side.

@gjhernandezp Thank you for sharing your solution!

@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.

@eddiee

This code works for me. I can give you ideas on what to try.

Update the qnt library or reinstall.

If it doesn't help, clone the repository

https://github.com/quantiacs/toolbox

git clone https://github.com/quantiacs/toolbox.git

run
qnt/examples/005-01-optimizer.py
and other examples.

You may need to specify API_KEY

You might be able to see exactly where the error occurs in the code.
And you can modify the library code by adding logging for optimize_strategy

@support no worries, I've been able to recover the strategy thank you!