networks package¶

networks.register_network(network_name: str) → Callable[[T], T]¶

the decorator to register a network

to add a new class as a registered network, add the following code to the top of the file:
@register_network("network_name")
YourEnvClass(BaseEnv):
    ...

Parameters:: network_name (str) – the name of the registered network
Returns:: the decorator to register the network
Return type:: Callable[[_T], _T]

Submodules¶

networks.MultiValueLSTM module¶

class networks.MultiValueLSTM.MultiValueLSTM(args: Namespace)¶

Bases: Module

The MultiValueLSTM model

References

https://arxiv.org/abs/1907.03665

https://github.com/Jogima-cyber/portfolio-manager

static add_args(parser: ArgumentParser) → None¶

add arguments to the parser

to add arguments to the parser, modify the method as follows:
@staticmethod
def add_args(parser: argparse.ArgumentParser) -> None:
    parser.add_argument(
        ...
    )
then add arguments to the parser

Parameters:: parser (argparse.ArgumentParser) – the parser to add arguments to

__init__(args: Namespace) → None¶

initialize the MultiValueLSTM model

Parameters:: args (argparse.Namespace) – the arguments

forward(state: Dict[str, Tensor], pretrain: bool, only_LSTM: bool = False, no_LSTM: bool = False) → Tensor¶

the overridden forward method

Parameters:

state (Dict[str, torch.Tensor]) – the state dictionary, including “Xt_Matrix” and “Portfolio_Weight”
pretrain (bool) – is this a pretrain step or not
only_LSTM (bool) – only use the LSTM encoder
no_LSTM (bool) – do not use the LSTM encoder

Returns:

the output tensor

Return type:

torch.Tensor

class networks.MultiValueLSTM.LSTMEncoder(args: Namespace)¶

Bases: Module

the LSTM encoder for the MultiValueLSTM model

__init__(args: Namespace) → None¶

initialize the LSTM encoder

Parameters:: args (argparse.Namespace) – the arguments

forward(Xt: Tensor) → Tensor¶

the overridden forward method

Parameters:: Xt (torch.Tensor) – the state tensor Xt
Returns:: the output tensor
Return type:: torch.Tensor

class networks.MultiValueLSTM.Decoder(args: Namespace)¶

Bases: Module

the decoder for the MultiValueLSTM model on pretraining

__init__(args: Namespace) → None¶

initialize the decoder

Parameters:: args (argparse.Namespace) – the arguments

forward(hn: Tensor) → Tensor¶

the overridden forward method

Parameters:: hn (torch.Tensor) – the hidden state tensor hn from the LSTM encoder
Returns:: the output tensor
Return type:: torch.Tensor

class networks.MultiValueLSTM.DNN(args: Namespace)¶

Bases: Module

the DNN for the MultiValueLSTM model on training

__init__(args: Namespace) → None¶

initialize the DNN

Parameters:: args (argparse.Namespace) – the arguments

forward(hn: Tensor, wt: Tensor) → Tensor¶

the overridden forward method

Parameters:

hn (torch.Tensor) – the hidden state tensor hn from the LSTM encoder
wt (torch.Tensor) – the weight tensor wt

Returns:

the output tensor

Return type:

torch.Tensor

networks.PolicyCNN module¶

class networks.PolicyCNN.PolicyCNN(args: Namespace)¶

Bases: Module

The PolicyCNN model

static add_args(parser: ArgumentParser) → None¶

add arguments to the parser

to add arguments to the parser, modify the method as follows:

@staticmethod
def add_args(parser: argparse.ArgumentParser) -> None:
    parser.add_argument(
        ...
    )


then add arguments to the parser

Parameters:: parser (argparse.ArgumentParser) – the parser to add arguments to

__init__(args: Namespace) → None¶

initialize the PolicyCNN model

Parameters:: args (argparse.Namespace) – the arguments

forward(state: Dict[str, Tensor]) → Tensor¶

forward pass of the PolicyCNN model

Parameters:: x (Dict[str, torch.Tensor]) – the input tensors
Returns:: the out**put tensor
Return type:: torch.Tensor

networks.PolicyWeightCNN module¶

class networks.PolicyWeightCNN.PolicyWeightCNN(args: Namespace)¶

Bases: Module

The PolicyWeightCNN model

Reference:: original paper: https://arxiv.org/abs/1706.10059

static add_args(parser: ArgumentParser) → None¶

add arguments to the parser

to add arguments to the parser, modify the method as follows:

@staticmethod
def add_args(parser: argparse.ArgumentParser) -> None:
    parser.add_argument(
        ...
    )


then add arguments to the parser

Parameters:: parser (argparse.ArgumentParser) – the parser to add arguments to

__init__(args: Namespace) → None¶

initialize the PolicyWeightCNN model

Parameters:: args (argparse.Namespace) – the arguments

forward(state: Dict[str, Tensor]) → Tensor¶

forward pass of the PolicyWeightCNN model

Parameters:: x (Dict[str, torch.Tensor]) – the input tensors
Returns:: the output tensor
Return type:: torch.Tensor

networks.PolicyWeightLSTM module¶

class networks.PolicyWeightLSTM.PolicyWeightLSTM(args: Namespace)¶

Bases: Module

The PolicyWeightLSTM model

Reference:: original paper: https://arxiv.org/abs/1706.10059

static add_args(parser: ArgumentParser) → None¶

add arguments to the parser

to add arguments to the parser, modify the method as follows:

@staticmethod
def add_args(parser: argparse.ArgumentParser) -> None:
    parser.add_argument(
        ...
    )


then add arguments to the parser

Parameters:: parser (argparse.ArgumentParser) – the parser to add arguments to

__init__(args: Namespace) → None¶

initialize the PolicyWeightLSTM model

Parameters:: args (argparse.Namespace) – the arguments

forward(state: Dict[str, Tensor]) → Tensor¶

forward pass of the PolicyWeightLSTM model

Parameters:: x (Dict[str, torch.Tensor]) – the input tensors
Returns:: the output tensor
Return type:: torch.Tensor

networks.PolicyWeightRNN module¶

class networks.PolicyWeightRNN.PolicyWeightRNN(args: Namespace)¶

Bases: Module

The PolicyWeightRNN model

Reference:: original paper: https://arxiv.org/abs/1706.10059

static add_args(parser: ArgumentParser) → None¶

add arguments to the parser

to add arguments to the parser, modify the method as follows:

@staticmethod
def add_args(parser: argparse.ArgumentParser) -> None:
    parser.add_argument(
        ...
    )


then add arguments to the parser

Parameters:: parser (argparse.ArgumentParser) – the parser to add arguments to

__init__(args: Namespace) → None¶

initialize the PolicyWeightRNN model

Parameters:: args (argparse.Namespace) – the arguments

forward(state: Dict[str, Tensor]) → Tensor¶

forward pass of the PolicyWeightRNN model

Parameters:: x (Dict[str, torch.Tensor]) – the input tensors
Returns:: the output tensor
Return type:: torch.Tensor

networks.ValueDNN module¶

class networks.ValueDNN.ValueDNN(args: Namespace)¶

Bases: Module

The ValueDNN model

Reference:: original paper: https://arxiv.org/abs/1907.03665

static add_args(parser: ArgumentParser) → None¶

add arguments to the parser

to add arguments to the parser, modify the method as follows:
@staticmethod
def add_args(parser: argparse.ArgumentParser) -> None:
    parser.add_argument(
        ...
    )
then add arguments to the parser

Parameters:: parser (argparse.ArgumentParser) – the parser to add arguments to

__init__(args: Namespace) → None¶

initialize the ValueDNN model

Parameters:: args (argparse.Namespace) – the arguments

forward(state: Dict[str, Tensor], action: Tensor)¶

forward pass

Parameters:

state (Dict[str, torch.Tensor]) – the state
action (torch.Tensor) – the action

Returns:

the output

Return type:

torch.Tensor

networks package¶

Submodules¶

networks.MultiValueLSTM module¶

networks.PolicyCNN module¶

networks.PolicyWeightCNN module¶

networks.PolicyWeightLSTM module¶

networks.PolicyWeightRNN module¶

networks.ValueDNN module¶

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