feat(Forecasting): pytorch-forecasting scaffolding is now working, added narrow data format, fixed missing time column index name (#5)

* feat: Started implementing pytorch-forecasting.

* feat(Forecasting): pytorch-forecasting scaffolding is now working, added narrow data format, fixed missing `time` column index name

Co-authored-by: Daniel Szemerey <szemy2@gmail.com>

lightning/forecasting.py

@@ -0,0 +1,114 @@
+#%%
+import pytorch_lightning as pl
+from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
+
+from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer
+from pytorch_forecasting.data import GroupNormalizer
+from pytorch_forecasting.metrics import QuantileLoss
+
+import sys
+sys.path.insert(0, '..')
+
+from load_data import load_files
+
+print("success")
+
+#%%
+# load data
+data = load_files('data/', add_features=True, log_returns=False, narrow_format=True)
+# need to treat time as an independent column
+data = data.reset_index().rename({'index':'time'}, axis = 'columns')
+# we need a `time_idx` column for pytorch-forecasting, so we convert the time column to a time index by encoding the dates as consecutive days from the first date.
+data['time_idx'] = (data['time']-data['time'].min()).astype('timedelta64[D]').astype(int)+1
+# volume needs some love before we can use it
+data.drop(columns=['volume'], inplace=True)
+
+data['month'] = data['month'].astype(str)
+data['day_month'] = data['day_month'].astype(str)
+data['day_week'] = data['day_week'].astype(str)
+
+
+
+#%%
+# define dataset
+max_encoder_length = 36 # this is the look-back window, see https://github.com/jdb78/pytorch-forecasting/issues/448
+max_prediction_length = 6
+# training_cutoff = "YYYY-MM-DD"  # day for cutoff
+
+# training_cutoff = data["time_idx"].max() - max_prediction_length
+
+training = TimeSeriesDataSet(
+    data, # data[lambda x: x.date < training_cutoff],
+    time_idx= 'time_idx',
+    target= 'returns',
+    # weight="weight",
+    group_ids=[ 'ticker' ],
+    min_encoder_length = max_encoder_length,##max_encoder_length//2,
+    max_encoder_length = max_encoder_length,
+    min_prediction_length = 1,
+    max_prediction_length = 1,#max_prediction_length,
+    static_categoricals=[  ],
+    static_reals=[  ],
+    time_varying_known_categoricals=[ 'day_month', 'day_week', 'month' ],
+    time_varying_known_reals=[ 'vol_10', 'vol_20', 'vol_30', 'vol_60', 'mom_10', 'mom_20', 'mom_30', 'mom_60', 'mom_90'],
+    time_varying_unknown_categoricals=[  ],
+    time_varying_unknown_reals=[ 'returns' ],
+    allow_missing_timesteps=True,
+    # target_normalizer=GroupNormalizer(
+    #     groups=['ADA_returns', 'BTC_returns'], transformation="softplus")
+)
+
+#%%
+print(training.index.time)
+print(training.index.time.max())
+
+#%%
+# create validation and training dataset
+
+validation = TimeSeriesDataSet.from_dataset(training, data, predict=True, stop_randomization=True)
+batch_size = 128
+train_dataloader = training.to_dataloader(train=True, batch_size=batch_size, num_workers=2)
+val_dataloader = validation.to_dataloader(train=False, batch_size=batch_size, num_workers=2)
+
+# define trainer with early stopping
+early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=1, verbose=False, mode="min")
+lr_logger = LearningRateMonitor()
+trainer = pl.Trainer(
+    max_epochs=100,
+    gpus=0,
+    gradient_clip_val=0.1,
+    limit_train_batches=30,
+    callbacks=[lr_logger, early_stop_callback],
+)
+
+# create the model
+tft = TemporalFusionTransformer.from_dataset(
+    training,
+    learning_rate=0.03,
+    hidden_size=32,
+    attention_head_size=1,
+    dropout=0.1,
+    hidden_continuous_size=16,
+    output_size=7,
+    loss=QuantileLoss(),
+    log_interval=2,
+    reduce_on_plateau_patience=4
+)
+print(f"Number of parameters in network: {tft.size()/1e3:.1f}k")
+
+# find optimal learning rate (set limit_train_batches to 1.0 and log_interval = -1)
+res = trainer.tuner.lr_find(
+    tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader, early_stop_threshold=1000.0, max_lr=0.3,
+)
+
+print(f"suggested learning rate: {res.suggestion()}")
+fig = res.plot(show=True, suggest=True)
+fig.show()
+
+# fit the model
+trainer.fit(
+    tft, train_dataloader=train_dataloader, val_dataloaders=val_dataloader,
+)
+
+
+# %%