Q17 Neural Networks Algo Template; is there an error in train_model()?

In the function "train_model()" the forloop "for asset_name in asset_name_all:" finishes before the modeloptimization. So basically the optimized LSTM neural network is always based on the last asset. This can't be on purpose, can it?
best
eduarddef train_model(data):
"""
train the LSTM network
"""asset_name_all = data.coords['asset'].values features_all = get_features(data) target_all = get_target_classes(data) model = get_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') 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

Yes, I noticed that too. And after fixing it the backtest takes forever...
Another thing to consider is that it redefines the model with each training but I belive you can retrain already trainded NNs with new Data so they learn based on what they previously learned. 
@eddiee We are sorry, you are right and we are fixing.


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='20140101', # backtest start date build_plots=True # do you need the chart? )

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