# 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
models[asset_name] = model
except KeyboardInterrupt as e:
logging.exception('model training failed')
Hello quants! The submission phase for the Q18 Quantiacs contest started. You have time until end of September 2022 to submit your code. Once the live evaluation phase is over the best systems will receive allocations for a total of 2M USD.
This contest focuses on Nasdaq-100 stock data and it allows for shorting.
Screenshot from 2022-06-10 16-08-35.png
In your user area you will find a Q18 Quick Start template which will show you how to use a simple filter function for automatically selecting the stocks which belong or belonged to the Nasdaq-100 index avoiding survivorship bias, and more complex examples using machine learning methods.