## Intro
- Neural Network models for trading: MLP, CNN, Transformer
## Usage
```bash
pip install trading-models
```
```py
import torch as tc
from torch.utils.data import DataLoader
from trading_models.simple_models import CNN, MLP, Transformer
from trading_models.utils import WindowDataset, model_size
"""
trading models
input: x.shape = (T, F)
output: a.shape = (T-W+1, A)
T: time
F: features
W: window length
A: actions
at each time step t in range(W-1, T),
the model looks at data in the window
x[t+1-W : t+1, :]
to make A actions
"""
T, F, W, A = 100, 2, 50, 1
device = tc.device("cuda" if tc.cuda.is_available() else "cpu")
x = tc.randn(T, F).to(device)
dataset = WindowDataset(x, W)
dataloader = DataLoader(dataset, batch_size=32, shuffle=False)
net1 = MLP([W * F, 64, 64, A]).to(device)
net2 = CNN([F, 64, 64, A]).to(device)
net3 = Transformer(W, F, A, d_model=64, d_ff=64, n_head=2, n_layer=2).to(device)
for net in [net1, net2, net3]:
outputs = []
for batch in dataloader:
outputs.append(net(batch.to(device)).detach())
output = tc.cat(outputs, dim=0)
print(net, output.shape, model_size(net), "\n")
```
- Output
```bash
MLP(
(mlp): Sequential(
(0): Linear(in_features=100, out_features=64, bias=True)
(1): SiLU()
(2): Linear(in_features=64, out_features=64, bias=True)
(3): SiLU()
(4): Linear(in_features=64, out_features=1, bias=True)
)
) torch.Size([51, 1]) trainable: 10689/10689
CNN(
(cnn): Sequential(
(0): Conv1d(2, 64, kernel_size=(3,), stride=(1,), padding=(1,))
(1): SiLU()
(2): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))
(3): SiLU()
(4): AdaptiveAvgPool1d(output_size=1)
)
(fc): Linear(in_features=64, out_features=1, bias=True)
) torch.Size([51, 1]) trainable: 12865/12865
Transformer(
(proj): Linear(in_features=2, out_features=64, bias=True)
(trans): TransformerEncoder(
(layers): ModuleList(
(0-1): 2 x TransformerEncoderLayer(
(self_attn): MultiheadAttention(
(out_proj): NonDynamicallyQuantizableLinear(in_features=64, out_features=64, bias=True)
)
(linear1): Linear(in_features=64, out_features=64, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
(linear2): Linear(in_features=64, out_features=64, bias=True)
(norm1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(norm2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)
(dropout1): Dropout(p=0.1, inplace=False)
(dropout2): Dropout(p=0.1, inplace=False)
)
)
)
(fc): Linear(in_features=64, out_features=1, bias=True)
) torch.Size([51, 1]) trainable: 50689/50689
```
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