@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

@support thanks!!!

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

@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? )

@support ok, thank you!

Dear @quani42,

Your submissions are in the queue and will be processed. Also, all submissions that are sent to the contest before the deadline will be eligible to take part in it.

Regards

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

@support
Thanks.
You guys are the best!🏆

@magenta-grimer Hello, we updated the documentation.

Now there are 78 futures contracts. Yes, we allow allocating to only 1 asset. If you trade more assets, then you can go long on some of them and short others.

Using more assets helps in increasing the Sharpe ratio, as the mean return grows linearly with the number of assets, and the volatility in the denominator with the square root of the number of assets if there are no correlation terms.

Using uncorrelated assets would then lead to a scaling of the Sharpe ratio with the square root of the number of assets. In practice, however, correlation terms are decreasing this growth.

Stated more simply, it is a good idea to avoid putting all your eggs in the same basket...

@captain-nidoran hi, all your strategies which will take part to the contest should be under the "In Contest" tab in the "Competition" section.

The migration "Candidates" -> "In Contest" was not immediate as we released minor improvements to the front-end side once the submission phase was over.

@rezhak21 Rules are defined at: https://quantiacs.com/contest and more details for the current contests (submission time till end of May) can be found at: https://quantiacs.com/contest/15

For Futures the in sample period starts on January 1st 2006, for the BTC Futures on January 1st, 2014

@support I see, need to push them to submit then....

Hello. The processing speed has been stabilized and should now be working normally.

@theflyingdutchman Hello, before the end of the week the update will be ready, sorry for the delay

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'

@support Ok, I see, thanks

@dark-pidgeot Yes, of course. Please note that we do not implement leverage, and the sum of the absolute values of the weights has to be equal or smaller than 1. If it is larger, they will be rescaled down.

@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.

@magenta-grimer There are 2 things you might want to change:

1: the lookback_period is 365 but you want a 400-day SMA. This will only produce NaNs, so the boolean array sma20 < sma20_crypto will be False everywhere resulting in -1 weights. 2*365 as lookback does the trick for these settings.

2: Bitcoin is trading 24/7, futures aren't. Better use crypto.time.values instead of futures.time.values for the output of load_data.

There might be something else that I didn't catch but the resulting sharpe is at least close to what would be expected (1.109 with 5 and 385)

Thanks !