@theflyingdutchman Yes, we are integrating new data sources for a new asset class, once we are done (next week) the data and leaderboard updates will start again.

@sun-73 yes

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

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

@vyacheslav_b Thank you so much.

I have one more question for you to answer. I ran the precheck and the result was nan value the first time, but I set the min_date to 2005 - 01 - 01. I would like to ask, why is there a nan value problem? Is it because the ticker I chose had some companies that weren't listed at that time? My strategy id code is # 16767242. Thank you so much

Screenshot 2024-04-09 173002.png
Screenshot 2024-04-09 173012.png

@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

@omohyoid Dear omohyoid,

Yes, that's right. After submitting your strategy shouldn't override environment variables.

Regards

@support Thank you !

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

@support Great help! Thank you so much!

@support
Thanks a lot !

@nosaai Hello

Spyder should be run under conda environment

conda activate qntdev conda install spyder spyder

an alternative way is to clone the library from https://github.com/quantiacs/toolbox
and develop strategies inside qnt. But I recommend using the approach from the documentation.

@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

@vg2001 Hello, the Q19 is a replica of the Q18, you ccan use the same examples.

@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")

@pink-seel Super that you found it, please do not hesitate to ask for support!

It's safe to ignore these notices but if they bother you, you can set the variables together with your API key using the defaults and the messages go away:

import os os.environ['API_KEY'] = 'YOUR-API-KEY' os.environ['DATA_BASE_URL'] = 'https://data-api.quantiacs.io/' os.environ['CACHE_RETENTION'] = '7' os.environ['CACHE_DIR'] = 'data-cache'

@multi_byte-wildebeest Hello. I don't use machine learning models in trading.

@support

This is a possible fix, but no gurantee. You have to adjust also the prediction function.

def train_model(data):
"""Create and train the models working on an asset-by-asset basis."""

models = dict()

asset_name_all = data.coords['asset'].values

data = data.sel(time=slice('2013-05-01',None)) # cut the noisy data head before 2013-05-01

features_all = get_features(data)
target_all = get_target_classes(data)

model = create_model()

for asset_name in asset_name_all:

# 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') if len(features_cur.time) < 10: # not enough points for training continue try: model.fit(feature_for_learn_df.values, target_for_learn_df) models[asset_name] = model except KeyboardInterrupt as e: raise e except: logging.exception('model training failed')

return models

@cespadilla Hello.

The reason is in "train_model" function.

def train_model(data): asset_name_all = data.coords['asset'].values features_all = get_features(data) target_all = get_target_classes(data) models = dict() for asset_name in asset_name_all: # 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') target_for_learn_df, feature_for_learn_df = xr.align(target_cur, features_cur, join='inner') if len(features_cur.time) < 10: continue model = get_model() try: model.fit(feature_for_learn_df.values, target_for_learn_df) models[asset_name] = model except: logging.exception('model training failed') return models

If there are less than 10 features for training the model, then the model is not created (if len(features_cur.time) < 10).

This condition makes sense. I would not remove it.

The second thing that can affect is the retraining interval of the model ("retrain_interval").

weights = qnbt.backtest_ml( train=train_model, predict=predict_weights, train_period=2 *365, # the data length for training in calendar days retrain_interval=10 *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 analyze = True, build_plots=True # do you need the chart? )