mirror of
https://github.com/microsoft/qlib.git
synced 2026-07-12 23:36:54 +08:00
Merge branch 'nested_decision_exe' of https://github.com/microsoft/qlib into rl-dummy
This commit is contained in:
v-mingzhehan
331
qlib/contrib/model/pytorch_localformer.py
Normal file
331
qlib/contrib/model/pytorch_localformer.py
Normal file
@@ -0,0 +1,331 @@
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from typing import Text, Union
|
||||
import copy
|
||||
import math
|
||||
from ...utils import get_or_create_path
|
||||
from ...log import get_module_logger
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from .pytorch_utils import count_parameters
|
||||
from ...model.base import Model
|
||||
from ...data.dataset import DatasetH, TSDatasetH
|
||||
from ...data.dataset.handler import DataHandlerLP
|
||||
from torch.nn.modules.container import ModuleList
|
||||
|
||||
# qrun examples/benchmarks/Localformer/workflow_config_localformer_Alpha360.yaml ”
|
||||
|
||||
|
||||
class LocalformerModel(Model):
|
||||
def __init__(
|
||||
self,
|
||||
d_feat: int = 20,
|
||||
d_model: int = 64,
|
||||
batch_size: int = 2048,
|
||||
nhead: int = 2,
|
||||
num_layers: int = 2,
|
||||
dropout: float = 0,
|
||||
n_epochs=100,
|
||||
lr=0.0001,
|
||||
metric="",
|
||||
early_stop=5,
|
||||
loss="mse",
|
||||
optimizer="adam",
|
||||
reg=1e-3,
|
||||
n_jobs=10,
|
||||
GPU=0,
|
||||
seed=None,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
# set hyper-parameters.
|
||||
self.d_model = d_model
|
||||
self.dropout = dropout
|
||||
self.n_epochs = n_epochs
|
||||
self.lr = lr
|
||||
self.reg = reg
|
||||
self.metric = metric
|
||||
self.batch_size = batch_size
|
||||
self.early_stop = early_stop
|
||||
self.optimizer = optimizer.lower()
|
||||
self.loss = loss
|
||||
self.n_jobs = n_jobs
|
||||
self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
|
||||
self.seed = seed
|
||||
self.logger = get_module_logger("TransformerModel")
|
||||
self.logger.info("Naive Transformer:" "\nbatch_size : {}" "\ndevice : {}".format(self.batch_size, self.device))
|
||||
|
||||
if self.seed is not None:
|
||||
np.random.seed(self.seed)
|
||||
torch.manual_seed(self.seed)
|
||||
|
||||
self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device)
|
||||
if optimizer.lower() == "adam":
|
||||
self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
|
||||
elif optimizer.lower() == "gd":
|
||||
self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
|
||||
else:
|
||||
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
|
||||
|
||||
self.fitted = False
|
||||
self.model.to(self.device)
|
||||
|
||||
@property
|
||||
def use_gpu(self):
|
||||
return self.device != torch.device("cpu")
|
||||
|
||||
def mse(self, pred, label):
|
||||
loss = (pred.float() - label.float()) ** 2
|
||||
return torch.mean(loss)
|
||||
|
||||
def loss_fn(self, pred, label):
|
||||
mask = ~torch.isnan(label)
|
||||
|
||||
if self.loss == "mse":
|
||||
return self.mse(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown loss `%s`" % self.loss)
|
||||
|
||||
def metric_fn(self, pred, label):
|
||||
|
||||
mask = torch.isfinite(label)
|
||||
|
||||
if self.metric == "" or self.metric == "loss":
|
||||
return -self.loss_fn(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown metric `%s`" % self.metric)
|
||||
|
||||
def train_epoch(self, x_train, y_train):
|
||||
|
||||
x_train_values = x_train.values
|
||||
y_train_values = np.squeeze(y_train.values)
|
||||
|
||||
self.model.train()
|
||||
|
||||
indices = np.arange(len(x_train_values))
|
||||
np.random.shuffle(indices)
|
||||
|
||||
for i in range(len(indices))[:: self.batch_size]:
|
||||
|
||||
if len(indices) - i < self.batch_size:
|
||||
break
|
||||
|
||||
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
|
||||
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
|
||||
|
||||
pred = self.model(feature)
|
||||
loss = self.loss_fn(pred, label)
|
||||
|
||||
self.train_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
|
||||
self.train_optimizer.step()
|
||||
|
||||
def test_epoch(self, data_x, data_y):
|
||||
|
||||
# prepare training data
|
||||
x_values = data_x.values
|
||||
y_values = np.squeeze(data_y.values)
|
||||
|
||||
self.model.eval()
|
||||
|
||||
scores = []
|
||||
losses = []
|
||||
|
||||
indices = np.arange(len(x_values))
|
||||
|
||||
for i in range(len(indices))[:: self.batch_size]:
|
||||
|
||||
if len(indices) - i < self.batch_size:
|
||||
break
|
||||
|
||||
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
|
||||
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred = self.model(feature)
|
||||
loss = self.loss_fn(pred, label)
|
||||
losses.append(loss.item())
|
||||
|
||||
score = self.metric_fn(pred, label)
|
||||
scores.append(score.item())
|
||||
|
||||
return np.mean(losses), np.mean(scores)
|
||||
|
||||
def fit(
|
||||
self,
|
||||
dataset: DatasetH,
|
||||
evals_result=dict(),
|
||||
save_path=None,
|
||||
):
|
||||
|
||||
df_train, df_valid, df_test = dataset.prepare(
|
||||
["train", "valid", "test"],
|
||||
col_set=["feature", "label"],
|
||||
data_key=DataHandlerLP.DK_L,
|
||||
)
|
||||
|
||||
x_train, y_train = df_train["feature"], df_train["label"]
|
||||
x_valid, y_valid = df_valid["feature"], df_valid["label"]
|
||||
|
||||
save_path = get_or_create_path(save_path)
|
||||
stop_steps = 0
|
||||
train_loss = 0
|
||||
best_score = -np.inf
|
||||
best_epoch = 0
|
||||
evals_result["train"] = []
|
||||
evals_result["valid"] = []
|
||||
|
||||
# train
|
||||
self.logger.info("training...")
|
||||
self.fitted = True
|
||||
|
||||
for step in range(self.n_epochs):
|
||||
self.logger.info("Epoch%d:", step)
|
||||
self.logger.info("training...")
|
||||
self.train_epoch(x_train, y_train)
|
||||
self.logger.info("evaluating...")
|
||||
train_loss, train_score = self.test_epoch(x_train, y_train)
|
||||
val_loss, val_score = self.test_epoch(x_valid, y_valid)
|
||||
self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
|
||||
evals_result["train"].append(train_score)
|
||||
evals_result["valid"].append(val_score)
|
||||
|
||||
if val_score > best_score:
|
||||
best_score = val_score
|
||||
stop_steps = 0
|
||||
best_epoch = step
|
||||
best_param = copy.deepcopy(self.model.state_dict())
|
||||
else:
|
||||
stop_steps += 1
|
||||
if stop_steps >= self.early_stop:
|
||||
self.logger.info("early stop")
|
||||
break
|
||||
|
||||
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
|
||||
self.model.load_state_dict(best_param)
|
||||
torch.save(best_param, save_path)
|
||||
|
||||
if self.use_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
|
||||
if not self.fitted:
|
||||
raise ValueError("model is not fitted yet!")
|
||||
|
||||
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
|
||||
index = x_test.index
|
||||
self.model.eval()
|
||||
x_values = x_test.values
|
||||
sample_num = x_values.shape[0]
|
||||
preds = []
|
||||
|
||||
for begin in range(sample_num)[:: self.batch_size]:
|
||||
|
||||
if sample_num - begin < self.batch_size:
|
||||
end = sample_num
|
||||
else:
|
||||
end = begin + self.batch_size
|
||||
|
||||
x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred = self.model(x_batch).detach().cpu().numpy()
|
||||
|
||||
preds.append(pred)
|
||||
|
||||
return pd.Series(np.concatenate(preds), index=index)
|
||||
|
||||
|
||||
class PositionalEncoding(nn.Module):
|
||||
def __init__(self, d_model, max_len=1000):
|
||||
super(PositionalEncoding, self).__init__()
|
||||
pe = torch.zeros(max_len, d_model)
|
||||
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
|
||||
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
|
||||
pe[:, 0::2] = torch.sin(position * div_term)
|
||||
pe[:, 1::2] = torch.cos(position * div_term)
|
||||
pe = pe.unsqueeze(0).transpose(0, 1)
|
||||
self.register_buffer("pe", pe)
|
||||
|
||||
def forward(self, x):
|
||||
# [T, N, F]
|
||||
return x + self.pe[: x.size(0), :]
|
||||
|
||||
|
||||
def _get_clones(module, N):
|
||||
return ModuleList([copy.deepcopy(module) for i in range(N)])
|
||||
|
||||
|
||||
class LocalformerEncoder(nn.Module):
|
||||
__constants__ = ["norm"]
|
||||
|
||||
def __init__(self, encoder_layer, num_layers, d_model):
|
||||
super(LocalformerEncoder, self).__init__()
|
||||
self.layers = _get_clones(encoder_layer, num_layers)
|
||||
self.conv = _get_clones(nn.Conv1d(d_model, d_model, 3, 1, 1), num_layers)
|
||||
self.num_layers = num_layers
|
||||
|
||||
def forward(self, src, mask):
|
||||
output = src
|
||||
out = src
|
||||
|
||||
for i, mod in enumerate(self.layers):
|
||||
# [T, N, F] --> [N, T, F] --> [N, F, T]
|
||||
out = output.transpose(1, 0).transpose(2, 1)
|
||||
out = self.conv[i](out).transpose(2, 1).transpose(1, 0)
|
||||
|
||||
output = mod(output + out, src_mask=mask)
|
||||
|
||||
return output + out
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None):
|
||||
super(Transformer, self).__init__()
|
||||
self.rnn = nn.GRU(
|
||||
input_size=d_model,
|
||||
hidden_size=d_model,
|
||||
num_layers=num_layers,
|
||||
batch_first=False,
|
||||
dropout=dropout,
|
||||
)
|
||||
self.feature_layer = nn.Linear(d_feat, d_model)
|
||||
self.pos_encoder = PositionalEncoding(d_model)
|
||||
self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
|
||||
self.transformer_encoder = LocalformerEncoder(self.encoder_layer, num_layers=num_layers, d_model=d_model)
|
||||
self.decoder_layer = nn.Linear(d_model, 1)
|
||||
self.device = device
|
||||
self.d_feat = d_feat
|
||||
|
||||
def forward(self, src):
|
||||
# src [N, F*T] --> [N, T, F]
|
||||
src = src.reshape(len(src), self.d_feat, -1).permute(0, 2, 1)
|
||||
src = self.feature_layer(src)
|
||||
|
||||
# src [N, T, F] --> [T, N, F], [60, 512, 8]
|
||||
src = src.transpose(1, 0) # not batch first
|
||||
|
||||
mask = None
|
||||
|
||||
src = self.pos_encoder(src)
|
||||
output = self.transformer_encoder(src, mask) # [60, 512, 8]
|
||||
|
||||
output, _ = self.rnn(output)
|
||||
|
||||
# [T, N, F] --> [N, T*F]
|
||||
output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1]
|
||||
|
||||
return output.squeeze()
|
||||
308
qlib/contrib/model/pytorch_localformer_ts.py
Normal file
308
qlib/contrib/model/pytorch_localformer_ts.py
Normal file
@@ -0,0 +1,308 @@
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import copy
|
||||
import math
|
||||
from ...utils import get_or_create_path
|
||||
from ...log import get_module_logger
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from .pytorch_utils import count_parameters
|
||||
from ...model.base import Model
|
||||
from ...data.dataset import DatasetH, TSDatasetH
|
||||
from ...data.dataset.handler import DataHandlerLP
|
||||
from torch.nn.modules.container import ModuleList
|
||||
|
||||
|
||||
class LocalformerModel(Model):
|
||||
def __init__(
|
||||
self,
|
||||
d_feat: int = 20,
|
||||
d_model: int = 64,
|
||||
batch_size: int = 8192,
|
||||
nhead: int = 2,
|
||||
num_layers: int = 2,
|
||||
dropout: float = 0,
|
||||
n_epochs=100,
|
||||
lr=0.0001,
|
||||
metric="",
|
||||
early_stop=5,
|
||||
loss="mse",
|
||||
optimizer="adam",
|
||||
reg=1e-3,
|
||||
n_jobs=10,
|
||||
GPU=0,
|
||||
seed=None,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
# set hyper-parameters.
|
||||
self.d_model = d_model
|
||||
self.dropout = dropout
|
||||
self.n_epochs = n_epochs
|
||||
self.lr = lr
|
||||
self.reg = reg
|
||||
self.metric = metric
|
||||
self.batch_size = batch_size
|
||||
self.early_stop = early_stop
|
||||
self.optimizer = optimizer.lower()
|
||||
self.loss = loss
|
||||
self.n_jobs = n_jobs
|
||||
self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
|
||||
self.seed = seed
|
||||
self.logger = get_module_logger("TransformerModel")
|
||||
self.logger.info(
|
||||
"Improved Transformer:" "\nbatch_size : {}" "\ndevice : {}".format(self.batch_size, self.device)
|
||||
)
|
||||
|
||||
if self.seed is not None:
|
||||
np.random.seed(self.seed)
|
||||
torch.manual_seed(self.seed)
|
||||
|
||||
self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device)
|
||||
if optimizer.lower() == "adam":
|
||||
self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
|
||||
elif optimizer.lower() == "gd":
|
||||
self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
|
||||
else:
|
||||
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
|
||||
|
||||
self.fitted = False
|
||||
self.model.to(self.device)
|
||||
|
||||
@property
|
||||
def use_gpu(self):
|
||||
return self.device != torch.device("cpu")
|
||||
|
||||
def mse(self, pred, label):
|
||||
loss = (pred.float() - label.float()) ** 2
|
||||
return torch.mean(loss)
|
||||
|
||||
def loss_fn(self, pred, label):
|
||||
mask = ~torch.isnan(label)
|
||||
|
||||
if self.loss == "mse":
|
||||
return self.mse(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown loss `%s`" % self.loss)
|
||||
|
||||
def metric_fn(self, pred, label):
|
||||
|
||||
mask = torch.isfinite(label)
|
||||
|
||||
if self.metric == "" or self.metric == "loss":
|
||||
return -self.loss_fn(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown metric `%s`" % self.metric)
|
||||
|
||||
def train_epoch(self, data_loader):
|
||||
|
||||
self.model.train()
|
||||
|
||||
for data in data_loader:
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
label = data[:, -1, -1].to(self.device)
|
||||
|
||||
pred = self.model(feature.float()) # .float()
|
||||
loss = self.loss_fn(pred, label)
|
||||
|
||||
self.train_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
|
||||
self.train_optimizer.step()
|
||||
|
||||
def test_epoch(self, data_loader):
|
||||
|
||||
self.model.eval()
|
||||
|
||||
scores = []
|
||||
losses = []
|
||||
|
||||
for data in data_loader:
|
||||
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
label = data[:, -1, -1].to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred = self.model(feature.float()) # .float()
|
||||
loss = self.loss_fn(pred, label)
|
||||
losses.append(loss.item())
|
||||
|
||||
score = self.metric_fn(pred, label)
|
||||
scores.append(score.item())
|
||||
|
||||
return np.mean(losses), np.mean(scores)
|
||||
|
||||
def fit(
|
||||
self,
|
||||
dataset: DatasetH,
|
||||
evals_result=dict(),
|
||||
save_path=None,
|
||||
):
|
||||
|
||||
dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
|
||||
dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
|
||||
|
||||
dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
|
||||
dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
|
||||
|
||||
train_loader = DataLoader(
|
||||
dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True
|
||||
)
|
||||
valid_loader = DataLoader(
|
||||
dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
|
||||
)
|
||||
|
||||
save_path = get_or_create_path(save_path)
|
||||
|
||||
stop_steps = 0
|
||||
train_loss = 0
|
||||
best_score = -np.inf
|
||||
best_epoch = 0
|
||||
evals_result["train"] = []
|
||||
evals_result["valid"] = []
|
||||
|
||||
# train
|
||||
self.logger.info("training...")
|
||||
self.fitted = True
|
||||
|
||||
for step in range(self.n_epochs):
|
||||
self.logger.info("Epoch%d:", step)
|
||||
self.logger.info("training...")
|
||||
self.train_epoch(train_loader)
|
||||
self.logger.info("evaluating...")
|
||||
train_loss, train_score = self.test_epoch(train_loader)
|
||||
val_loss, val_score = self.test_epoch(valid_loader)
|
||||
self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
|
||||
evals_result["train"].append(train_score)
|
||||
evals_result["valid"].append(val_score)
|
||||
|
||||
if val_score > best_score:
|
||||
best_score = val_score
|
||||
stop_steps = 0
|
||||
best_epoch = step
|
||||
best_param = copy.deepcopy(self.model.state_dict())
|
||||
else:
|
||||
stop_steps += 1
|
||||
if stop_steps >= self.early_stop:
|
||||
self.logger.info("early stop")
|
||||
break
|
||||
|
||||
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
|
||||
self.model.load_state_dict(best_param)
|
||||
torch.save(best_param, save_path)
|
||||
|
||||
if self.use_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def predict(self, dataset):
|
||||
if not self.fitted:
|
||||
raise ValueError("model is not fitted yet!")
|
||||
|
||||
dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
|
||||
dl_test.config(fillna_type="ffill+bfill")
|
||||
test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
|
||||
self.model.eval()
|
||||
preds = []
|
||||
|
||||
for data in test_loader:
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred = self.model(feature.float()).detach().cpu().numpy()
|
||||
|
||||
preds.append(pred)
|
||||
|
||||
return pd.Series(np.concatenate(preds), index=dl_test.get_index())
|
||||
|
||||
|
||||
class PositionalEncoding(nn.Module):
|
||||
def __init__(self, d_model, max_len=1000):
|
||||
super(PositionalEncoding, self).__init__()
|
||||
pe = torch.zeros(max_len, d_model)
|
||||
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
|
||||
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
|
||||
pe[:, 0::2] = torch.sin(position * div_term)
|
||||
pe[:, 1::2] = torch.cos(position * div_term)
|
||||
pe = pe.unsqueeze(0).transpose(0, 1)
|
||||
self.register_buffer("pe", pe)
|
||||
|
||||
def forward(self, x):
|
||||
# [T, N, F]
|
||||
return x + self.pe[: x.size(0), :]
|
||||
|
||||
|
||||
def _get_clones(module, N):
|
||||
return ModuleList([copy.deepcopy(module) for i in range(N)])
|
||||
|
||||
|
||||
class LocalformerEncoder(nn.Module):
|
||||
__constants__ = ["norm"]
|
||||
|
||||
def __init__(self, encoder_layer, num_layers, d_model):
|
||||
super(LocalformerEncoder, self).__init__()
|
||||
self.layers = _get_clones(encoder_layer, num_layers)
|
||||
self.conv = _get_clones(nn.Conv1d(d_model, d_model, 3, 1, 1), num_layers)
|
||||
self.num_layers = num_layers
|
||||
|
||||
def forward(self, src, mask):
|
||||
output = src
|
||||
out = src
|
||||
|
||||
for i, mod in enumerate(self.layers):
|
||||
# [T, N, F] --> [N, T, F] --> [N, F, T]
|
||||
out = output.transpose(1, 0).transpose(2, 1)
|
||||
out = self.conv[i](out).transpose(2, 1).transpose(1, 0)
|
||||
|
||||
output = mod(output + out, src_mask=mask)
|
||||
|
||||
return output + out
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None):
|
||||
super(Transformer, self).__init__()
|
||||
self.rnn = nn.GRU(
|
||||
input_size=d_model,
|
||||
hidden_size=d_model,
|
||||
num_layers=num_layers,
|
||||
batch_first=False,
|
||||
dropout=dropout,
|
||||
)
|
||||
self.feature_layer = nn.Linear(d_feat, d_model)
|
||||
self.pos_encoder = PositionalEncoding(d_model)
|
||||
self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
|
||||
self.transformer_encoder = LocalformerEncoder(self.encoder_layer, num_layers=num_layers, d_model=d_model)
|
||||
self.decoder_layer = nn.Linear(d_model, 1)
|
||||
self.device = device
|
||||
self.d_feat = d_feat
|
||||
|
||||
def forward(self, src):
|
||||
# src [N, T, F], [512, 60, 6]
|
||||
src = self.feature_layer(src) # [512, 60, 8]
|
||||
|
||||
# src [N, T, F] --> [T, N, F], [60, 512, 8]
|
||||
src = src.transpose(1, 0) # not batch first
|
||||
|
||||
mask = None
|
||||
|
||||
src = self.pos_encoder(src)
|
||||
output = self.transformer_encoder(src, mask) # [60, 512, 8]
|
||||
|
||||
output, _ = self.rnn(output)
|
||||
|
||||
# [T, N, F] --> [N, T*F]
|
||||
output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1]
|
||||
|
||||
return output.squeeze()
|
||||
294
qlib/contrib/model/pytorch_transformer.py
Normal file
294
qlib/contrib/model/pytorch_transformer.py
Normal file
@@ -0,0 +1,294 @@
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from typing import Text, Union
|
||||
import copy
|
||||
import math
|
||||
from ...utils import get_or_create_path
|
||||
from ...log import get_module_logger
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from .pytorch_utils import count_parameters
|
||||
from ...model.base import Model
|
||||
from ...data.dataset import DatasetH, TSDatasetH
|
||||
from ...data.dataset.handler import DataHandlerLP
|
||||
|
||||
# qrun examples/benchmarks/Transformer/workflow_config_transformer_Alpha360.yaml ”
|
||||
|
||||
|
||||
class TransformerModel(Model):
|
||||
def __init__(
|
||||
self,
|
||||
d_feat: int = 20,
|
||||
d_model: int = 64,
|
||||
batch_size: int = 2048,
|
||||
nhead: int = 2,
|
||||
num_layers: int = 2,
|
||||
dropout: float = 0,
|
||||
n_epochs=100,
|
||||
lr=0.0001,
|
||||
metric="",
|
||||
early_stop=5,
|
||||
loss="mse",
|
||||
optimizer="adam",
|
||||
reg=1e-3,
|
||||
n_jobs=10,
|
||||
GPU=0,
|
||||
seed=None,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
# set hyper-parameters.
|
||||
self.d_model = d_model
|
||||
self.dropout = dropout
|
||||
self.n_epochs = n_epochs
|
||||
self.lr = lr
|
||||
self.reg = reg
|
||||
self.metric = metric
|
||||
self.batch_size = batch_size
|
||||
self.early_stop = early_stop
|
||||
self.optimizer = optimizer.lower()
|
||||
self.loss = loss
|
||||
self.n_jobs = n_jobs
|
||||
self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
|
||||
self.seed = seed
|
||||
self.logger = get_module_logger("TransformerModel")
|
||||
self.logger.info("Naive Transformer:" "\nbatch_size : {}" "\ndevice : {}".format(self.batch_size, self.device))
|
||||
|
||||
if self.seed is not None:
|
||||
np.random.seed(self.seed)
|
||||
torch.manual_seed(self.seed)
|
||||
|
||||
self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device)
|
||||
if optimizer.lower() == "adam":
|
||||
self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
|
||||
elif optimizer.lower() == "gd":
|
||||
self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
|
||||
else:
|
||||
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
|
||||
|
||||
self.fitted = False
|
||||
self.model.to(self.device)
|
||||
|
||||
@property
|
||||
def use_gpu(self):
|
||||
return self.device != torch.device("cpu")
|
||||
|
||||
def mse(self, pred, label):
|
||||
loss = (pred.float() - label.float()) ** 2
|
||||
return torch.mean(loss)
|
||||
|
||||
def loss_fn(self, pred, label):
|
||||
mask = ~torch.isnan(label)
|
||||
|
||||
if self.loss == "mse":
|
||||
return self.mse(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown loss `%s`" % self.loss)
|
||||
|
||||
def metric_fn(self, pred, label):
|
||||
|
||||
mask = torch.isfinite(label)
|
||||
|
||||
if self.metric == "" or self.metric == "loss":
|
||||
return -self.loss_fn(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown metric `%s`" % self.metric)
|
||||
|
||||
def train_epoch(self, x_train, y_train):
|
||||
|
||||
x_train_values = x_train.values
|
||||
y_train_values = np.squeeze(y_train.values)
|
||||
|
||||
self.model.train()
|
||||
|
||||
indices = np.arange(len(x_train_values))
|
||||
np.random.shuffle(indices)
|
||||
|
||||
for i in range(len(indices))[:: self.batch_size]:
|
||||
|
||||
if len(indices) - i < self.batch_size:
|
||||
break
|
||||
|
||||
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
|
||||
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
|
||||
|
||||
pred = self.model(feature)
|
||||
loss = self.loss_fn(pred, label)
|
||||
|
||||
self.train_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
|
||||
self.train_optimizer.step()
|
||||
|
||||
def test_epoch(self, data_x, data_y):
|
||||
|
||||
# prepare training data
|
||||
x_values = data_x.values
|
||||
y_values = np.squeeze(data_y.values)
|
||||
|
||||
self.model.eval()
|
||||
|
||||
scores = []
|
||||
losses = []
|
||||
|
||||
indices = np.arange(len(x_values))
|
||||
|
||||
for i in range(len(indices))[:: self.batch_size]:
|
||||
|
||||
if len(indices) - i < self.batch_size:
|
||||
break
|
||||
|
||||
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
|
||||
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred = self.model(feature)
|
||||
loss = self.loss_fn(pred, label)
|
||||
losses.append(loss.item())
|
||||
|
||||
score = self.metric_fn(pred, label)
|
||||
scores.append(score.item())
|
||||
|
||||
return np.mean(losses), np.mean(scores)
|
||||
|
||||
def fit(
|
||||
self,
|
||||
dataset: DatasetH,
|
||||
evals_result=dict(),
|
||||
save_path=None,
|
||||
):
|
||||
|
||||
df_train, df_valid, df_test = dataset.prepare(
|
||||
["train", "valid", "test"],
|
||||
col_set=["feature", "label"],
|
||||
data_key=DataHandlerLP.DK_L,
|
||||
)
|
||||
|
||||
x_train, y_train = df_train["feature"], df_train["label"]
|
||||
x_valid, y_valid = df_valid["feature"], df_valid["label"]
|
||||
|
||||
save_path = get_or_create_path(save_path)
|
||||
stop_steps = 0
|
||||
train_loss = 0
|
||||
best_score = -np.inf
|
||||
best_epoch = 0
|
||||
evals_result["train"] = []
|
||||
evals_result["valid"] = []
|
||||
|
||||
# train
|
||||
self.logger.info("training...")
|
||||
self.fitted = True
|
||||
|
||||
for step in range(self.n_epochs):
|
||||
self.logger.info("Epoch%d:", step)
|
||||
self.logger.info("training...")
|
||||
self.train_epoch(x_train, y_train)
|
||||
self.logger.info("evaluating...")
|
||||
train_loss, train_score = self.test_epoch(x_train, y_train)
|
||||
val_loss, val_score = self.test_epoch(x_valid, y_valid)
|
||||
self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
|
||||
evals_result["train"].append(train_score)
|
||||
evals_result["valid"].append(val_score)
|
||||
|
||||
if val_score > best_score:
|
||||
best_score = val_score
|
||||
stop_steps = 0
|
||||
best_epoch = step
|
||||
best_param = copy.deepcopy(self.model.state_dict())
|
||||
else:
|
||||
stop_steps += 1
|
||||
if stop_steps >= self.early_stop:
|
||||
self.logger.info("early stop")
|
||||
break
|
||||
|
||||
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
|
||||
self.model.load_state_dict(best_param)
|
||||
torch.save(best_param, save_path)
|
||||
|
||||
if self.use_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
|
||||
if not self.fitted:
|
||||
raise ValueError("model is not fitted yet!")
|
||||
|
||||
x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
|
||||
index = x_test.index
|
||||
self.model.eval()
|
||||
x_values = x_test.values
|
||||
sample_num = x_values.shape[0]
|
||||
preds = []
|
||||
|
||||
for begin in range(sample_num)[:: self.batch_size]:
|
||||
|
||||
if sample_num - begin < self.batch_size:
|
||||
end = sample_num
|
||||
else:
|
||||
end = begin + self.batch_size
|
||||
|
||||
x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred = self.model(x_batch).detach().cpu().numpy()
|
||||
|
||||
preds.append(pred)
|
||||
|
||||
return pd.Series(np.concatenate(preds), index=index)
|
||||
|
||||
|
||||
class PositionalEncoding(nn.Module):
|
||||
def __init__(self, d_model, max_len=1000):
|
||||
super(PositionalEncoding, self).__init__()
|
||||
pe = torch.zeros(max_len, d_model)
|
||||
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
|
||||
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
|
||||
pe[:, 0::2] = torch.sin(position * div_term)
|
||||
pe[:, 1::2] = torch.cos(position * div_term)
|
||||
pe = pe.unsqueeze(0).transpose(0, 1)
|
||||
self.register_buffer("pe", pe)
|
||||
|
||||
def forward(self, x):
|
||||
# [T, N, F]
|
||||
return x + self.pe[: x.size(0), :]
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None):
|
||||
super(Transformer, self).__init__()
|
||||
self.feature_layer = nn.Linear(d_feat, d_model)
|
||||
self.pos_encoder = PositionalEncoding(d_model)
|
||||
self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
|
||||
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)
|
||||
self.decoder_layer = nn.Linear(d_model, 1)
|
||||
self.device = device
|
||||
self.d_feat = d_feat
|
||||
|
||||
def forward(self, src):
|
||||
# src [N, F*T] --> [N, T, F]
|
||||
src = src.reshape(len(src), self.d_feat, -1).permute(0, 2, 1)
|
||||
src = self.feature_layer(src)
|
||||
|
||||
# src [N, T, F] --> [T, N, F], [60, 512, 8]
|
||||
src = src.transpose(1, 0) # not batch first
|
||||
|
||||
mask = None
|
||||
|
||||
src = self.pos_encoder(src)
|
||||
output = self.transformer_encoder(src, mask) # [60, 512, 8]
|
||||
|
||||
# [T, N, F] --> [N, T*F]
|
||||
output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1]
|
||||
|
||||
return output.squeeze()
|
||||
269
qlib/contrib/model/pytorch_transformer_ts.py
Normal file
269
qlib/contrib/model/pytorch_transformer_ts.py
Normal file
@@ -0,0 +1,269 @@
|
||||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT License.
|
||||
|
||||
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import copy
|
||||
import math
|
||||
from ...utils import get_or_create_path
|
||||
from ...log import get_module_logger
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.optim as optim
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from .pytorch_utils import count_parameters
|
||||
from ...model.base import Model
|
||||
from ...data.dataset import DatasetH, TSDatasetH
|
||||
from ...data.dataset.handler import DataHandlerLP
|
||||
|
||||
|
||||
class TransformerModel(Model):
|
||||
def __init__(
|
||||
self,
|
||||
d_feat: int = 20,
|
||||
d_model: int = 64,
|
||||
batch_size: int = 8192,
|
||||
nhead: int = 2,
|
||||
num_layers: int = 2,
|
||||
dropout: float = 0,
|
||||
n_epochs=100,
|
||||
lr=0.0001,
|
||||
metric="",
|
||||
early_stop=5,
|
||||
loss="mse",
|
||||
optimizer="adam",
|
||||
reg=1e-3,
|
||||
n_jobs=10,
|
||||
GPU=0,
|
||||
seed=None,
|
||||
**kwargs
|
||||
):
|
||||
|
||||
# set hyper-parameters.
|
||||
self.d_model = d_model
|
||||
self.dropout = dropout
|
||||
self.n_epochs = n_epochs
|
||||
self.lr = lr
|
||||
self.reg = reg
|
||||
self.metric = metric
|
||||
self.batch_size = batch_size
|
||||
self.early_stop = early_stop
|
||||
self.optimizer = optimizer.lower()
|
||||
self.loss = loss
|
||||
self.n_jobs = n_jobs
|
||||
self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
|
||||
self.seed = seed
|
||||
self.logger = get_module_logger("TransformerModel")
|
||||
self.logger.info("Naive Transformer:" "\nbatch_size : {}" "\ndevice : {}".format(self.batch_size, self.device))
|
||||
|
||||
if self.seed is not None:
|
||||
np.random.seed(self.seed)
|
||||
torch.manual_seed(self.seed)
|
||||
|
||||
self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device)
|
||||
if optimizer.lower() == "adam":
|
||||
self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
|
||||
elif optimizer.lower() == "gd":
|
||||
self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
|
||||
else:
|
||||
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
|
||||
|
||||
self.fitted = False
|
||||
self.model.to(self.device)
|
||||
|
||||
@property
|
||||
def use_gpu(self):
|
||||
return self.device != torch.device("cpu")
|
||||
|
||||
def mse(self, pred, label):
|
||||
loss = (pred.float() - label.float()) ** 2
|
||||
return torch.mean(loss)
|
||||
|
||||
def loss_fn(self, pred, label):
|
||||
mask = ~torch.isnan(label)
|
||||
|
||||
if self.loss == "mse":
|
||||
return self.mse(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown loss `%s`" % self.loss)
|
||||
|
||||
def metric_fn(self, pred, label):
|
||||
|
||||
mask = torch.isfinite(label)
|
||||
|
||||
if self.metric == "" or self.metric == "loss":
|
||||
return -self.loss_fn(pred[mask], label[mask])
|
||||
|
||||
raise ValueError("unknown metric `%s`" % self.metric)
|
||||
|
||||
def train_epoch(self, data_loader):
|
||||
|
||||
self.model.train()
|
||||
|
||||
for data in data_loader:
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
label = data[:, -1, -1].to(self.device)
|
||||
|
||||
pred = self.model(feature.float()) # .float()
|
||||
loss = self.loss_fn(pred, label)
|
||||
|
||||
self.train_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
|
||||
self.train_optimizer.step()
|
||||
|
||||
def test_epoch(self, data_loader):
|
||||
|
||||
self.model.eval()
|
||||
|
||||
scores = []
|
||||
losses = []
|
||||
|
||||
for data in data_loader:
|
||||
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
label = data[:, -1, -1].to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred = self.model(feature.float()) # .float()
|
||||
loss = self.loss_fn(pred, label)
|
||||
losses.append(loss.item())
|
||||
|
||||
score = self.metric_fn(pred, label)
|
||||
scores.append(score.item())
|
||||
|
||||
return np.mean(losses), np.mean(scores)
|
||||
|
||||
def fit(
|
||||
self,
|
||||
dataset: DatasetH,
|
||||
evals_result=dict(),
|
||||
save_path=None,
|
||||
):
|
||||
|
||||
dl_train = dataset.prepare("train", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
|
||||
dl_valid = dataset.prepare("valid", col_set=["feature", "label"], data_key=DataHandlerLP.DK_L)
|
||||
|
||||
dl_train.config(fillna_type="ffill+bfill") # process nan brought by dataloader
|
||||
dl_valid.config(fillna_type="ffill+bfill") # process nan brought by dataloader
|
||||
|
||||
train_loader = DataLoader(
|
||||
dl_train, batch_size=self.batch_size, shuffle=True, num_workers=self.n_jobs, drop_last=True
|
||||
)
|
||||
valid_loader = DataLoader(
|
||||
dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
|
||||
)
|
||||
|
||||
save_path = get_or_create_path(save_path)
|
||||
|
||||
stop_steps = 0
|
||||
train_loss = 0
|
||||
best_score = -np.inf
|
||||
best_epoch = 0
|
||||
evals_result["train"] = []
|
||||
evals_result["valid"] = []
|
||||
|
||||
# train
|
||||
self.logger.info("training...")
|
||||
self.fitted = True
|
||||
|
||||
for step in range(self.n_epochs):
|
||||
self.logger.info("Epoch%d:", step)
|
||||
self.logger.info("training...")
|
||||
self.train_epoch(train_loader)
|
||||
self.logger.info("evaluating...")
|
||||
train_loss, train_score = self.test_epoch(train_loader)
|
||||
val_loss, val_score = self.test_epoch(valid_loader)
|
||||
self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
|
||||
evals_result["train"].append(train_score)
|
||||
evals_result["valid"].append(val_score)
|
||||
|
||||
if val_score > best_score:
|
||||
best_score = val_score
|
||||
stop_steps = 0
|
||||
best_epoch = step
|
||||
best_param = copy.deepcopy(self.model.state_dict())
|
||||
else:
|
||||
stop_steps += 1
|
||||
if stop_steps >= self.early_stop:
|
||||
self.logger.info("early stop")
|
||||
break
|
||||
|
||||
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
|
||||
self.model.load_state_dict(best_param)
|
||||
torch.save(best_param, save_path)
|
||||
|
||||
if self.use_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def predict(self, dataset):
|
||||
if not self.fitted:
|
||||
raise ValueError("model is not fitted yet!")
|
||||
|
||||
dl_test = dataset.prepare("test", col_set=["feature", "label"], data_key=DataHandlerLP.DK_I)
|
||||
dl_test.config(fillna_type="ffill+bfill")
|
||||
test_loader = DataLoader(dl_test, batch_size=self.batch_size, num_workers=self.n_jobs)
|
||||
self.model.eval()
|
||||
preds = []
|
||||
|
||||
for data in test_loader:
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
pred = self.model(feature.float()).detach().cpu().numpy()
|
||||
|
||||
preds.append(pred)
|
||||
|
||||
return pd.Series(np.concatenate(preds), index=dl_test.get_index())
|
||||
|
||||
|
||||
class PositionalEncoding(nn.Module):
|
||||
def __init__(self, d_model, max_len=1000):
|
||||
super(PositionalEncoding, self).__init__()
|
||||
pe = torch.zeros(max_len, d_model)
|
||||
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
|
||||
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
|
||||
pe[:, 0::2] = torch.sin(position * div_term)
|
||||
pe[:, 1::2] = torch.cos(position * div_term)
|
||||
pe = pe.unsqueeze(0).transpose(0, 1)
|
||||
self.register_buffer("pe", pe)
|
||||
|
||||
def forward(self, x):
|
||||
# [T, N, F]
|
||||
return x + self.pe[: x.size(0), :]
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None):
|
||||
super(Transformer, self).__init__()
|
||||
self.feature_layer = nn.Linear(d_feat, d_model)
|
||||
self.pos_encoder = PositionalEncoding(d_model)
|
||||
self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
|
||||
self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=num_layers)
|
||||
self.decoder_layer = nn.Linear(d_model, 1)
|
||||
self.device = device
|
||||
self.d_feat = d_feat
|
||||
|
||||
def forward(self, src):
|
||||
# src [N, T, F], [512, 60, 6]
|
||||
src = self.feature_layer(src) # [512, 60, 8]
|
||||
|
||||
# src [N, T, F] --> [T, N, F], [60, 512, 8]
|
||||
src = src.transpose(1, 0) # not batch first
|
||||
|
||||
mask = None
|
||||
|
||||
src = self.pos_encoder(src)
|
||||
output = self.transformer_encoder(src, mask) # [60, 512, 8]
|
||||
|
||||
# [T, N, F] --> [N, T*F]
|
||||
output = self.decoder_layer(output.transpose(1, 0)[:, -1, :]) # [512, 1]
|
||||
|
||||
return output.squeeze()
|
||||
@@ -18,7 +18,12 @@ from qlib.backtest.utils import get_start_end_idx
|
||||
|
||||
|
||||
class TWAPStrategy(BaseStrategy):
|
||||
"""TWAP Strategy for trading"""
|
||||
"""TWAP Strategy for trading
|
||||
|
||||
NOTE:
|
||||
- This TWAP strategy will celling round when trading. This will make the TWAP trading strategy produce the order
|
||||
ealier when the total trade unit of amount is less than the trading step
|
||||
"""
|
||||
|
||||
def reset(self, outer_trade_decision: BaseTradeDecision = None, **kwargs):
|
||||
"""
|
||||
@@ -58,11 +63,11 @@ class TWAPStrategy(BaseStrategy):
|
||||
trade_start_time, trade_end_time = self.trade_calendar.get_step_time(trade_step)
|
||||
order_list = []
|
||||
for order in self.outer_trade_decision.get_decision():
|
||||
# if not tradable, continue
|
||||
if not self.trade_exchange.is_stock_tradable(
|
||||
stock_id=order.stock_id, start_time=trade_start_time, end_time=trade_end_time
|
||||
):
|
||||
continue
|
||||
# Don't peek the future information
|
||||
# if not self.trade_exchange.is_stock_tradable(
|
||||
# stock_id=order.stock_id, start_time=trade_start_time, end_time=trade_end_time
|
||||
# ):
|
||||
# continue
|
||||
_amount_trade_unit = self.trade_exchange.get_amount_of_trade_unit(
|
||||
stock_id=order.stock_id, start_time=order.start_time, end_time=order.end_time
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user