mirror of
https://github.com/microsoft/qlib.git
synced 2026-06-06 05:51:17 +08:00
Delete the setting of SFM on the Alpha158.
This commit is contained in:
lwwang1995
@@ -1,93 +0,0 @@
|
||||
qlib_init:
|
||||
provider_uri: "~/.qlib/qlib_data/cn_data"
|
||||
region: cn
|
||||
market: &market csi300
|
||||
benchmark: &benchmark SH000300
|
||||
data_handler_config: &data_handler_config
|
||||
start_time: 2008-01-01
|
||||
end_time: 2020-08-01
|
||||
fit_start_time: 2008-01-01
|
||||
fit_end_time: 2014-12-31
|
||||
instruments: *market
|
||||
infer_processors:
|
||||
- class: FilterCol
|
||||
kwargs:
|
||||
fields_group: feature
|
||||
col_list: ["RESI5", "WVMA5", "RSQR5", "KLEN", "RSQR10", "CORR5", "CORD5", "CORR10",
|
||||
"ROC60", "RESI10", "VSTD5", "RSQR60", "CORR60", "WVMA60", "STD5",
|
||||
"RSQR20", "CORD60", "CORD10", "CORR20", "KLOW"
|
||||
]
|
||||
- class: RobustZScoreNorm
|
||||
kwargs:
|
||||
fields_group: feature
|
||||
clip_outlier: true
|
||||
- class: Fillna
|
||||
kwargs:
|
||||
fields_group: feature
|
||||
learn_processors:
|
||||
- class: DropnaLabel
|
||||
- class: CSRankNorm
|
||||
kwargs:
|
||||
fields_group: label
|
||||
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
|
||||
|
||||
port_analysis_config: &port_analysis_config
|
||||
strategy:
|
||||
class: TopkDropoutStrategy
|
||||
module_path: qlib.contrib.strategy.strategy
|
||||
kwargs:
|
||||
topk: 50
|
||||
n_drop: 5
|
||||
backtest:
|
||||
verbose: False
|
||||
limit_threshold: 0.095
|
||||
account: 100000000
|
||||
benchmark: *benchmark
|
||||
deal_price: close
|
||||
open_cost: 0.0005
|
||||
close_cost: 0.0015
|
||||
min_cost: 5
|
||||
task:
|
||||
model:
|
||||
class: SFM
|
||||
module_path: qlib.contrib.model.pytorch_sfm_ts
|
||||
kwargs:
|
||||
d_feat: 20
|
||||
hidden_size: 64
|
||||
num_layers: 2
|
||||
dropout: 0.0
|
||||
n_epochs: 200
|
||||
lr: 5e-2
|
||||
early_stop: 10
|
||||
batch_size: 800
|
||||
metric: loss
|
||||
loss: mse
|
||||
n_jobs: 20
|
||||
GPU: 0
|
||||
rnn_type: GRU
|
||||
dataset:
|
||||
class: TSDatasetH
|
||||
module_path: qlib.data.dataset
|
||||
kwargs:
|
||||
handler:
|
||||
class: Alpha158
|
||||
module_path: qlib.contrib.data.handler
|
||||
kwargs: *data_handler_config
|
||||
segments:
|
||||
train: [2008-01-01, 2014-12-31]
|
||||
valid: [2015-01-01, 2016-12-31]
|
||||
test: [2017-01-01, 2020-08-01]
|
||||
step_len: 20
|
||||
record:
|
||||
- class: SignalRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs: {}
|
||||
- class: SigAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
ana_long_short: False
|
||||
ann_scaler: 252
|
||||
- class: PortAnaRecord
|
||||
module_path: qlib.workflow.record_temp
|
||||
kwargs:
|
||||
config: *port_analysis_config
|
||||
@@ -1,471 +0,0 @@
|
||||
# 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
|
||||
from sklearn.metrics import roc_auc_score, mean_squared_error
|
||||
import logging
|
||||
from ...utils import (
|
||||
unpack_archive_with_buffer,
|
||||
save_multiple_parts_file,
|
||||
create_save_path,
|
||||
drop_nan_by_y_index,
|
||||
)
|
||||
from ...log import get_module_logger, TimeInspector
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.init as init
|
||||
import torch.optim as optim
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from ...model.base import Model
|
||||
from ...data.dataset import DatasetH
|
||||
from ...data.dataset.handler import DataHandlerLP
|
||||
|
||||
|
||||
class SFM_Model(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
d_feat=6,
|
||||
output_dim=1,
|
||||
freq_dim=10,
|
||||
hidden_size=64,
|
||||
dropout_W=0.0,
|
||||
dropout_U=0.0,
|
||||
device="cpu",
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.input_dim = d_feat
|
||||
self.output_dim = output_dim
|
||||
self.freq_dim = freq_dim
|
||||
self.hidden_dim = hidden_size
|
||||
self.device = device
|
||||
|
||||
self.W_i = nn.Parameter(init.xavier_uniform_(torch.empty((self.input_dim, self.hidden_dim))))
|
||||
self.U_i = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
|
||||
self.b_i = nn.Parameter(torch.zeros(self.hidden_dim))
|
||||
|
||||
self.W_ste = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
|
||||
self.U_ste = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
|
||||
self.b_ste = nn.Parameter(torch.ones(self.hidden_dim))
|
||||
|
||||
self.W_fre = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.freq_dim)))
|
||||
self.U_fre = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.freq_dim)))
|
||||
self.b_fre = nn.Parameter(torch.ones(self.freq_dim))
|
||||
|
||||
self.W_c = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
|
||||
self.U_c = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
|
||||
self.b_c = nn.Parameter(torch.zeros(self.hidden_dim))
|
||||
|
||||
self.W_o = nn.Parameter(init.xavier_uniform_(torch.empty(self.input_dim, self.hidden_dim)))
|
||||
self.U_o = nn.Parameter(init.orthogonal_(torch.empty(self.hidden_dim, self.hidden_dim)))
|
||||
self.b_o = nn.Parameter(torch.zeros(self.hidden_dim))
|
||||
|
||||
self.U_a = nn.Parameter(init.orthogonal_(torch.empty(self.freq_dim, 1)))
|
||||
self.b_a = nn.Parameter(torch.zeros(self.hidden_dim))
|
||||
|
||||
self.W_p = nn.Parameter(init.xavier_uniform_(torch.empty(self.hidden_dim, self.output_dim)))
|
||||
self.b_p = nn.Parameter(torch.zeros(self.output_dim))
|
||||
|
||||
self.activation = nn.Tanh()
|
||||
self.inner_activation = nn.Hardsigmoid()
|
||||
self.dropout_W, self.dropout_U = (dropout_W, dropout_U)
|
||||
self.fc_out = nn.Linear(self.output_dim, 1)
|
||||
|
||||
self.states = []
|
||||
|
||||
def forward(self, input):
|
||||
input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
|
||||
input = input.permute(0, 2, 1) # [N, T, F]
|
||||
time_step = input.shape[1]
|
||||
|
||||
for ts in range(time_step):
|
||||
x = input[:, ts, :]
|
||||
if len(self.states) == 0: # hasn't initialized yet
|
||||
self.init_states(x)
|
||||
self.get_constants(x)
|
||||
p_tm1 = self.states[0]
|
||||
h_tm1 = self.states[1]
|
||||
S_re_tm1 = self.states[2]
|
||||
S_im_tm1 = self.states[3]
|
||||
time_tm1 = self.states[4]
|
||||
B_U = self.states[5]
|
||||
B_W = self.states[6]
|
||||
frequency = self.states[7]
|
||||
|
||||
x_i = torch.matmul(x * B_W[0], self.W_i) + self.b_i
|
||||
x_ste = torch.matmul(x * B_W[0], self.W_ste) + self.b_ste
|
||||
x_fre = torch.matmul(x * B_W[0], self.W_fre) + self.b_fre
|
||||
x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c
|
||||
x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
|
||||
|
||||
i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i))
|
||||
ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste))
|
||||
fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre))
|
||||
|
||||
ste = torch.reshape(ste, (-1, self.hidden_dim, 1))
|
||||
fre = torch.reshape(fre, (-1, 1, self.freq_dim))
|
||||
|
||||
f = ste * fre
|
||||
|
||||
c = i * self.activation(x_c + torch.matmul(h_tm1 * B_U[0], self.U_c))
|
||||
|
||||
time = time_tm1 + 1
|
||||
|
||||
omega = torch.tensor(2 * np.pi) * time * frequency
|
||||
|
||||
re = torch.cos(omega)
|
||||
im = torch.sin(omega)
|
||||
|
||||
c = torch.reshape(c, (-1, self.hidden_dim, 1))
|
||||
|
||||
S_re = f * S_re_tm1 + c * re
|
||||
S_im = f * S_im_tm1 + c * im
|
||||
|
||||
A = torch.square(S_re) + torch.square(S_im)
|
||||
|
||||
A = torch.reshape(A, (-1, self.freq_dim)).float()
|
||||
A_a = torch.matmul(A * B_U[0], self.U_a)
|
||||
A_a = torch.reshape(A_a, (-1, self.hidden_dim))
|
||||
a = self.activation(A_a + self.b_a)
|
||||
|
||||
o = self.inner_activation(x_o + torch.matmul(h_tm1 * B_U[0], self.U_o))
|
||||
|
||||
h = o * a
|
||||
p = torch.matmul(h, self.W_p) + self.b_p
|
||||
|
||||
self.states = [p, h, S_re, S_im, time, None, None, None]
|
||||
self.states = []
|
||||
return self.fc_out(p).squeeze()
|
||||
|
||||
def init_states(self, x):
|
||||
reducer_f = torch.zeros((self.hidden_dim, self.freq_dim)).to(self.device)
|
||||
reducer_p = torch.zeros((self.hidden_dim, self.output_dim)).to(self.device)
|
||||
|
||||
init_state_h = torch.zeros(self.hidden_dim).to(self.device)
|
||||
init_state_p = torch.matmul(init_state_h, reducer_p)
|
||||
|
||||
init_state = torch.zeros_like(init_state_h).to(self.device)
|
||||
init_freq = torch.matmul(init_state_h, reducer_f)
|
||||
|
||||
init_state = torch.reshape(init_state, (-1, self.hidden_dim, 1))
|
||||
init_freq = torch.reshape(init_freq, (-1, 1, self.freq_dim))
|
||||
|
||||
init_state_S_re = init_state * init_freq
|
||||
init_state_S_im = init_state * init_freq
|
||||
|
||||
init_state_time = torch.tensor(0).to(self.device)
|
||||
|
||||
self.states = [
|
||||
init_state_p,
|
||||
init_state_h,
|
||||
init_state_S_re,
|
||||
init_state_S_im,
|
||||
init_state_time,
|
||||
None,
|
||||
None,
|
||||
None,
|
||||
]
|
||||
|
||||
def get_constants(self, x):
|
||||
constants = []
|
||||
constants.append([torch.tensor(1.0).to(self.device) for _ in range(6)])
|
||||
constants.append([torch.tensor(1.0).to(self.device) for _ in range(7)])
|
||||
array = np.array([float(ii) / self.freq_dim for ii in range(self.freq_dim)])
|
||||
constants.append(torch.tensor(array).to(self.device))
|
||||
|
||||
self.states[5:] = constants
|
||||
|
||||
|
||||
class SFM(Model):
|
||||
"""SFM Model
|
||||
|
||||
Parameters
|
||||
----------
|
||||
input_dim : int
|
||||
input dimension
|
||||
output_dim : int
|
||||
output dimension
|
||||
lr : float
|
||||
learning rate
|
||||
optimizer : str
|
||||
optimizer name
|
||||
GPU : str
|
||||
the GPU ID(s) used for training
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
d_feat=6,
|
||||
hidden_size=64,
|
||||
output_dim=1,
|
||||
freq_dim=10,
|
||||
dropout_W=0.0,
|
||||
dropout_U=0.0,
|
||||
n_epochs=200,
|
||||
lr=0.001,
|
||||
metric="",
|
||||
batch_size=2000,
|
||||
early_stop=20,
|
||||
eval_steps=5,
|
||||
loss="mse",
|
||||
optimizer="gd",
|
||||
n_jobs=10,
|
||||
GPU="0",
|
||||
seed=None,
|
||||
**kwargs
|
||||
):
|
||||
# Set logger.
|
||||
self.logger = get_module_logger("SFM")
|
||||
self.logger.info("SFM pytorch version...")
|
||||
|
||||
# set hyper-parameters.
|
||||
self.d_feat = d_feat
|
||||
self.hidden_size = hidden_size
|
||||
self.output_dim = output_dim
|
||||
self.freq_dim = freq_dim
|
||||
self.dropout_W = dropout_W
|
||||
self.dropout_U = dropout_U
|
||||
self.n_epochs = n_epochs
|
||||
self.lr = lr
|
||||
self.metric = metric
|
||||
self.batch_size = batch_size
|
||||
self.early_stop = early_stop
|
||||
self.eval_steps = eval_steps
|
||||
self.optimizer = optimizer.lower()
|
||||
self.loss = loss
|
||||
self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
|
||||
self.n_jobs = n_jobs
|
||||
self.use_gpu = torch.cuda.is_available()
|
||||
self.seed = seed
|
||||
|
||||
self.logger.info(
|
||||
"SFM parameters setting:"
|
||||
"\nd_feat : {}"
|
||||
"\nhidden_size : {}"
|
||||
"\noutput_size : {}"
|
||||
"\nfrequency_dimension : {}"
|
||||
"\ndropout_W: {}"
|
||||
"\ndropout_U: {}"
|
||||
"\nn_epochs : {}"
|
||||
"\nlr : {}"
|
||||
"\nmetric : {}"
|
||||
"\nbatch_size : {}"
|
||||
"\nearly_stop : {}"
|
||||
"\neval_steps : {}"
|
||||
"\noptimizer : {}"
|
||||
"\nloss_type : {}"
|
||||
"\nvisible_GPU : {}"
|
||||
"\nuse_GPU : {}"
|
||||
"\nseed : {}".format(
|
||||
d_feat,
|
||||
hidden_size,
|
||||
output_dim,
|
||||
freq_dim,
|
||||
dropout_W,
|
||||
dropout_U,
|
||||
n_epochs,
|
||||
lr,
|
||||
metric,
|
||||
batch_size,
|
||||
early_stop,
|
||||
eval_steps,
|
||||
optimizer.lower(),
|
||||
loss,
|
||||
GPU,
|
||||
self.use_gpu,
|
||||
seed,
|
||||
)
|
||||
)
|
||||
|
||||
if self.seed is not None:
|
||||
np.random.seed(self.seed)
|
||||
torch.manual_seed(self.seed)
|
||||
|
||||
self.sfm_model = SFM_Model(
|
||||
d_feat=self.d_feat,
|
||||
output_dim=self.output_dim,
|
||||
hidden_size=self.hidden_size,
|
||||
freq_dim=self.freq_dim,
|
||||
dropout_W=self.dropout_W,
|
||||
dropout_U=self.dropout_U,
|
||||
device=self.device,
|
||||
)
|
||||
if optimizer.lower() == "adam":
|
||||
self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
|
||||
elif optimizer.lower() == "gd":
|
||||
self.train_optimizer = optim.SGD(self.sfm_model.parameters(), lr=self.lr)
|
||||
else:
|
||||
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
|
||||
|
||||
self._fitted = False
|
||||
self.sfm_model.to(self.device)
|
||||
|
||||
def train_epoch(self, data_loader):
|
||||
|
||||
self.sfm_model.train()
|
||||
|
||||
for data in data_loader:
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
label = data[:, -1, -1].to(self.device)
|
||||
|
||||
pred = self.sfm_model(feature.float())
|
||||
loss = self.loss_fn(pred, label)
|
||||
|
||||
self.train_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
torch.nn.utils.clip_grad_value_(self.sfm_model.parameters(), 3.0)
|
||||
self.train_optimizer.step()
|
||||
|
||||
def test_epoch(self, data_loader):
|
||||
|
||||
self.sfm_model.eval()
|
||||
|
||||
scores = []
|
||||
losses = []
|
||||
|
||||
for data in data_loader:
|
||||
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
# feature[torch.isnan(feature)] = 0
|
||||
label = data[:, -1, -1].to(self.device)
|
||||
|
||||
pred = self.sfm_model(feature.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(),
|
||||
verbose=True,
|
||||
save_path=None,
|
||||
):
|
||||
|
||||
dl_train = dataset.prepare("train", data_key=DataHandlerLP.DK_L)
|
||||
dl_valid = dataset.prepare("valid", 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)
|
||||
valid_loader = DataLoader(dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs)
|
||||
|
||||
if save_path == None:
|
||||
save_path = create_save_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.sfm_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.sfm_model.load_state_dict(best_param)
|
||||
torch.save(best_param, save_path)
|
||||
|
||||
if self.use_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def mse(self, pred, label):
|
||||
loss = (pred - label) ** 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 predict(self, dataset):
|
||||
if not self._fitted:
|
||||
raise ValueError("model is not fitted yet!")
|
||||
|
||||
dl_test = dataset.prepare("test", 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.sfm_model.eval()
|
||||
preds = []
|
||||
|
||||
for data in test_loader:
|
||||
|
||||
feature = data[:, :, 0:-1].to(self.device)
|
||||
|
||||
with torch.no_grad():
|
||||
if self.use_gpu:
|
||||
pred = self.sfm_model(feature.float()).detach().cpu().numpy()
|
||||
else:
|
||||
pred = self.sfm_model(feature.float()).detach().numpy()
|
||||
|
||||
preds.append(pred)
|
||||
|
||||
return pd.Series(np.concatenate(preds), index=dl_test.get_index())
|
||||
|
||||
|
||||
class AverageMeter(object):
|
||||
"""Computes and stores the average and current value"""
|
||||
|
||||
def __init__(self):
|
||||
self.reset()
|
||||
|
||||
def reset(self):
|
||||
self.val = 0
|
||||
self.avg = 0
|
||||
self.sum = 0
|
||||
self.count = 0
|
||||
|
||||
def update(self, val, n=1):
|
||||
self.val = val
|
||||
self.sum += val * n
|
||||
self.count += n
|
||||
self.avg = self.sum / self.count
|
||||
Reference in New Issue
Block a user