feat(Pytorch): added custom model to pytorch-forecasting (#8)

* feat: Refractored and created new model. Pipeline not ready yet.

* feat: Implemented and refactored a data pipeline.

* ref: Refractored to make more sense.

* feat: Training works now with models that you can change.

* feat: Added predict function but without working instructions.

* feat: gitignore.

lightning/options.py

@@ -0,0 +1,63 @@
+from pytorch_forecasting.data import GroupNormalizer
+from pytorch_forecasting.models.temporal_fusion_transformer import TemporalFusionTransformer
+
+from lightning.models.custom_model import FullyConnectedModel
+
+training_options = dict(
+    max_epochs=100,
+    gpus=0,
+    gradient_clip_val=0.1,
+    limit_train_batches=30
+)
+
+model_options_fcn = dict(
+    hidden_size=64,
+    n_hidden_layers=2,
+)
+
+model_options_tft = dict(
+    hidden_size=64,
+    n_hidden_layers=2,
+)
+
+dataset_options_tft = dict(
+    time_idx= 'time_idx',
+    target= 'returns',
+    # weight="weight",
+    group_ids=[ 'ticker' ],
+    min_encoder_length = 36, # this is the look-back window, see https://github.com/jdb78/pytorch-forecasting/issues/448
+    max_encoder_length = 36,
+    min_prediction_length = 1,
+    max_prediction_length = 1,
+    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=['ticker'], transformation="softplus"),
+    batch_size=128
+)
+
+dataset_options_fcn = dict(
+    time_idx= 'time_idx',
+    target= 'returns',
+    # weight="weight",
+    group_ids=[ 'ticker' ],
+    min_encoder_length = 36, # this is the look-back window, see https://github.com/jdb78/pytorch-forecasting/issues/448
+    max_encoder_length = 36,
+    min_prediction_length = 1,
+    max_prediction_length = 1,
+    static_categoricals=[  ],
+    static_reals=[  ],
+    time_varying_known_categoricals=[ ],
+    time_varying_known_reals=[ ],
+    time_varying_unknown_categoricals=[  ],
+    time_varying_unknown_reals=[ 'returns' ],
+    allow_missing_timesteps=True,
+    target_normalizer=GroupNormalizer(
+        groups=['ticker'], transformation="softplus"),
+    batch_size=128
+)