add tcts baseline

examples/benchmarks/TCTS/workflow_config_tcts_Alpha360.yaml

@@ -0,0 +1,93 @@
+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: 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",
+            "Ref($close, -3) / Ref($close, -1) - 1",
+            "Ref($close, -4) / Ref($close, -1) - 1",
+            "Ref($close, -5) / Ref($close, -1) - 1",
+            "Ref($close, -6) / Ref($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: TCTS
+        module_path: qlib.contrib.model.pytorch_tcts
+        kwargs:
+            d_feat: 6
+            hidden_size: 64
+            num_layers: 2
+            dropout: 0.0
+            n_epochs: 200
+            lr: 1e-3
+            early_stop: 20
+            batch_size: 800
+            metric: loss
+            loss: mse
+            GPU: 0
+            fore_optimizer: adam
+            weight_optimizer: adam
+            output_dim: 5
+            fore_lr: 5e-7
+            weight_lr: 5e-7
+            steps: 3
+            target_label: 0
+    dataset:
+        class: DatasetH
+        module_path: qlib.data.dataset
+        kwargs:
+            handler:
+                class: Alpha360
+                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]
+    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

qlib/contrib/model/pytorch_tcts.py

@@ -0,0 +1,393 @@
+# 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.optim as optim
+
+from ...model.base import Model
+from ...data.dataset import DatasetH
+from ...data.dataset.handler import DataHandlerLP
+
+
+class TCTS(Model):
+    """TCTS Model
+
+    Parameters
+    ----------
+    d_feat : int
+        input dimension for each time step
+    metric: str
+        the evaluate metric used in early stop
+    optimizer : str
+        optimizer name
+    GPU : str
+        the GPU ID(s) used for training
+    """
+
+    def __init__(
+        self,
+        d_feat=6,
+        hidden_size=64,
+        num_layers=2,
+        dropout=0.0,
+        n_epochs=200,
+        batch_size=2000,
+        early_stop=20,
+        loss="mse",
+        fore_optimizer="adam",
+        weight_optimizer="adam",
+        output_dim=5,
+        fore_lr=5e-7,
+        weight_lr=5e-7,
+        steps=3,
+        GPU=0,
+        seed=None,
+        target_label=0,
+        **kwargs
+    ):
+        # Set logger.
+        self.logger = get_module_logger("TCTS")
+        self.logger.info("TCTS pytorch version...")
+
+        # set hyper-parameters.
+        self.d_feat = d_feat
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.dropout = dropout
+        self.n_epochs = n_epochs
+        self.batch_size = batch_size
+        self.early_stop = early_stop
+        self.loss = loss
+        self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu")
+        self.use_gpu = torch.cuda.is_available()
+        self.seed = seed
+        self.output_dim = output_dim
+        self.fore_lr = fore_lr
+        self.weight_lr = weight_lr
+        self.steps = steps
+        self.target_label = target_label
+
+        self.logger.info(
+            "TCTS parameters setting:"
+            "\nd_feat : {}"
+            "\nhidden_size : {}"
+            "\nnum_layers : {}"
+            "\ndropout : {}"
+            "\nn_epochs : {}"
+            "\nbatch_size : {}"
+            "\nearly_stop : {}"
+            "\nloss_type : {}"
+            "\nvisible_GPU : {}"
+            "\nuse_GPU : {}"
+            "\nseed : {}".format(
+                d_feat,
+                hidden_size,
+                num_layers,
+                dropout,
+                n_epochs,
+                batch_size,
+                early_stop,
+                loss,
+                GPU,
+                self.use_gpu,
+                seed,
+            )
+        )
+
+        if self.seed is not None:
+            np.random.seed(self.seed)
+            torch.manual_seed(self.seed)
+
+        self.fore_model = GRUModel(
+            d_feat=self.d_feat,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers,
+            dropout=self.dropout,
+        )
+        self.weight_model = MLPModel(
+            d_feat=360 + 2 * self.output_dim + 1,
+            hidden_size=self.hidden_size,
+            num_layers=self.num_layers,
+            dropout=self.dropout,
+            output_dim=self.output_dim,
+        )
+        if fore_optimizer.lower() == "adam":
+            self.fore_optimizer = optim.Adam(self.fore_model.parameters(), lr=self.fore_lr)
+        elif fore_optimizer.lower() == "gd":
+            self.fore_optimizer = optim.SGD(self.fore_model.parameters(), lr=self.fore_lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(fore_optimizer))
+        if weight_optimizer.lower() == "adam":
+            self.weight_optimizer = optim.Adam(self.weight_model.parameters(), lr=self.weight_lr)
+        elif weight_optimizer.lower() == "gd":
+            self.weight_optimizer = optim.SGD(self.weight_model.parameters(), lr=self.weight_lr)
+        else:
+            raise NotImplementedError("optimizer {} is not supported!".format(weight_optimizer))
+
+        self.fitted = False
+        self.fore_model.to(self.device)
+        self.weight_model.to(self.device)
+
+    def loss_fn(self, pred, label, weight):
+
+        loc = torch.argmax(weight, 1)
+        loss = (pred - label[np.arange(weight.shape[0]), loc]) ** 2
+        return torch.mean(loss)
+
+    def train_epoch(self, x_train, y_train, x_valid, y_valid):
+
+        x_train_values = x_train.values
+        y_train_values = np.squeeze(y_train.values)
+
+        indices = np.arange(len(x_train_values))
+        np.random.shuffle(indices)
+
+        init_fore_model = copy.deepcopy(self.fore_model)
+        for p in init_fore_model.parameters():
+            p.init_fore_model = False
+
+        self.fore_model.train()
+        self.weight_model.train()
+
+        for p in self.weight_model.parameters():
+            p.requires_grad = False
+        for p in self.fore_model.parameters():
+            p.requires_grad = True
+
+        for i in range(self.steps):
+            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)
+
+                init_pred = init_fore_model(feature)
+                pred = self.fore_model(feature)
+
+                dis = init_pred - label.transpose(0, 1)
+                weight_feature = torch.cat((feature, dis.transpose(0, 1), label, init_pred.view(-1, 1)), 1)
+                weight = self.weight_model(weight_feature)
+
+                loss = self.loss_fn(pred, label, weight)  # hard
+
+                self.fore_optimizer.zero_grad()
+                loss.backward()
+                torch.nn.utils.clip_grad_value_(self.fore_model.parameters(), 3.0)
+                self.fore_optimizer.step()
+
+        x_valid_values = x_valid.values
+        y_valid_values = np.squeeze(y_valid.values)
+
+        indices = np.arange(len(x_valid_values))
+        np.random.shuffle(indices)
+        for p in self.weight_model.parameters():
+            p.requires_grad = True
+        for p in self.fore_model.parameters():
+            p.requires_grad = False
+
+        # fix forecasting model and valid weight model
+        for i in range(len(indices))[:: self.batch_size]:
+
+            if len(indices) - i < self.batch_size:
+                break
+
+            feature = torch.from_numpy(x_valid_values[indices[i : i + self.batch_size]]).float().to(self.device)
+            label = torch.from_numpy(y_valid_values[indices[i : i + self.batch_size]]).float().to(self.device)
+
+            pred = self.fore_model(feature)
+            dis = pred - label.transpose(0, 1)
+            weight_feature = torch.cat((feature, dis.transpose(0, 1), label, pred.view(-1, 1)), 1)
+            weight = self.weight_model(weight_feature)
+            loc = torch.argmax(weight, 1)
+            valid_loss = torch.mean((pred - label[:, 0]) ** 2)
+            loss = torch.mean(-valid_loss * torch.log(weight[np.arange(weight.shape[0]), loc]))
+
+            self.weight_optimizer.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_value_(self.weight_model.parameters(), 3.0)
+            self.weight_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.fore_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)
+
+            pred = self.fore_model(feature)
+            loss = torch.mean((pred - label[:, abs(self.target_label)]) ** 2)
+            losses.append(loss.item())
+
+        return np.mean(losses)
+
+    def fit(
+        self,
+        dataset: DatasetH,
+        evals_result=dict(),
+        verbose=True,
+        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"]
+        x_test, y_test = df_test["feature"], df_test["label"]
+
+        if save_path == None:
+            save_path = create_save_path(save_path)
+
+        best_loss = np.inf
+        best_epoch = 0
+        stop_round = 0
+        fore_best_param = copy.deepcopy(self.fore_optimizer.state_dict())
+        weight_best_param = copy.deepcopy(self.weight_optimizer.state_dict())
+
+        for epoch in range(self.n_epochs):
+            print("Epoch:", epoch)
+
+            print("training...")
+            self.train_epoch(x_train, y_train, x_valid, y_valid)
+            print("evaluating...")
+            val_loss = self.test_epoch(x_valid, y_valid)
+            test_loss = self.test_epoch(x_test, y_test)
+
+            print("valid %.6f, test %.6f" % (val_loss, test_loss))
+
+            if val_loss < best_loss:
+                best_loss = val_loss
+                stop_round = 0
+                best_epoch = epoch
+                torch.save(copy.deepcopy(self.fore_model.state_dict()), save_path + "_fore_model.bin")
+                torch.save(copy.deepcopy(self.weight_model.state_dict()), save_path + "_weight_model.bin")
+
+            else:
+                stop_round += 1
+                if stop_round >= self.early_stop:
+                    print("early stop")
+                    break
+
+        print("best loss:", best_loss, "@", best_epoch)
+        best_param = torch.load(save_path + "_fore_model.bin")
+        self.fore_model.load_state_dict(best_param)
+        best_param = torch.load(save_path + "_weight_model.bin")
+        self.weight_model.load_state_dict(best_param)
+        self.fitted = True
+
+        if self.use_gpu:
+            torch.cuda.empty_cache()
+
+    def predict(self, dataset):
+        if not self.fitted:
+            raise ValueError("model is not fitted yet!")
+
+        x_test = dataset.prepare("test", col_set="feature")
+        index = x_test.index
+        self.fore_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():
+                if self.use_gpu:
+                    pred = self.fore_model(x_batch).detach().cpu().numpy()
+                else:
+                    pred = self.fore_model(x_batch).detach().numpy()
+
+            preds.append(pred)
+
+        return pd.Series(np.concatenate(preds), index=index)
+
+
+class MLPModel(nn.Module):
+    def __init__(self, d_feat, hidden_size=256, num_layers=3, dropout=0.0, output_dim=1):
+        super().__init__()
+
+        self.mlp = nn.Sequential()
+        self.softmax = nn.Softmax(dim=1)
+
+        for i in range(num_layers):
+            if i > 0:
+                self.mlp.add_module("drop_%d" % i, nn.Dropout(dropout))
+            self.mlp.add_module("fc_%d" % i, nn.Linear(d_feat if i == 0 else hidden_size, hidden_size))
+            self.mlp.add_module("relu_%d" % i, nn.ReLU())
+
+        self.mlp.add_module("fc_out", nn.Linear(hidden_size, output_dim))
+
+    def forward(self, x):
+        # feature
+        # [N, F]
+        out = self.mlp(x).squeeze()
+        out = self.softmax(out)
+        return out
+
+
+class GRUModel(nn.Module):
+    def __init__(self, d_feat=6, hidden_size=64, num_layers=2, dropout=0.0):
+        super().__init__()
+
+        self.rnn = nn.GRU(
+            input_size=d_feat,
+            hidden_size=hidden_size,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=dropout,
+        )
+        self.fc_out = nn.Linear(hidden_size, 1)
+
+        self.d_feat = d_feat
+
+    def forward(self, x):
+        # x: [N, F*T]
+        x = x.reshape(len(x), self.d_feat, -1)  # [N, F, T]
+        x = x.permute(0, 2, 1)  # [N, T, F]
+        out, _ = self.rnn(x)
+        return self.fc_out(out[:, -1, :]).squeeze()