import torch
import torch.nn as nn
from einops import rearrange
from finol.data_layer.scaler_selector import ScalerSelector
from finol.utils import load_config
# User-defined model class
[docs]class CustomModel(nn.Module):
"""
Class to serve as a base neural network model for portfolio selection. This class provides users with a framework
to extend and implement their own model architectures and functionality,
allowing for customization to meet specific requirements and objectives in financial modeling.
:param model_args: Dictionary containing model arguments, such as the number of features.
:param model_params: Dictionary containing model hyper-parameters, such as the parameter1, parameter2, etc.
Example:
.. code:: python
>>> from finol.data_layer.dataset_loader import DatasetLoader
>>> from finol.model_layer.model_instantiator import ModelInstantiator
>>> from finol.utils import load_config, update_config, portfolio_selection
>>>
>>> # Configuration
>>> config = load_config()
>>> config["MODEL_NAME"] = "CustomModel"
>>> config["MODEL_PARAMS"]["CustomModel"]["PARAMETER1"] = 2
>>> config["MODEL_PARAMS"]["CustomModel"]["PARAMETER1"] = 128
>>> update_config(config)
>>>
>>> # Data Layer
>>> load_dataset_output = DatasetLoader().load_dataset()
>>>
>>> # Model Layer & Optimization Layer
>>> ...
>>> model = ModelInstantiator(load_dataset_output).instantiate_model()
>>> print(f"model: {model}")
>>> ...
>>> train_loader = load_dataset_output["train_loader"]
>>> for i, data in enumerate(train_loader, 1):
... x_data, label = data
... final_scores = model(x_data.float())
... portfolio = portfolio_selection(final_scores)
... print(f"batch {i} input shape: {x_data.shape}")
... print(f"batch {i} label shape: {label.shape}")
... print(f"batch {i} output shape: {portfolio.shape}")
... print("-"*50)
.. warning::
When users define their own model, besides modifying this class, they must add different parameter keys and values
in the ``config.json`` at the location ``config["MODEL_PARAMS"]["CustomModel"]``. Similarly, if users want to implement
automatic hyper-parameters tuning for their custom model, they also need to specify the range and type of different
parameters at ``config["MODEL_PARAMS_SPACE"]["CustomModel"]``
\\
"""
def __init__(self, model_args, model_params):
super().__init__()
self.config = load_config()
self.model_args = model_args
self.model_parms = model_params
# Define your model architecture here
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Forward pass of the model.
:param x: Input tensor of shape ``(batch_size, num_assets, num_features_augmented)``.
:return: Output tensor of shape ``(batch_size, num_assets)`` containing the predicted scores for each asset.
"""
batch_size, num_assets, num_features_augmented = x.shape
"""Input Transformation"""
x = x.view(batch_size, num_assets, self.model_args["window_size"], self.model_args["num_features_original"])
x = rearrange(x, "b m n d -> (b m) n d")
if self.config["SCALER"].startswith("Window"):
x = ScalerSelector().window_normalize(x)
...
final_scores = x
return final_scores
