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add time series model GRU
This commit is contained in:
lwwang1995
146
examples/workflow_by_code_gru.py
Executable file
146
examples/workflow_by_code_gru.py
Executable file
@@ -0,0 +1,146 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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from pathlib import Path
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import qlib
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import pandas as pd
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from qlib.config import REG_CN
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from qlib.contrib.model.pytorch_gru import GRU
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from qlib.contrib.data.handler import ALPHA360
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from qlib.contrib.strategy.strategy import TopkDropoutStrategy
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from qlib.contrib.evaluate import (
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backtest as normal_backtest,
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risk_analysis,
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)
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from qlib.utils import exists_qlib_data
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# from qlib.model.learner import train_model
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from qlib.utils import init_instance_by_config
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if __name__ == "__main__":
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# use default data
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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if not exists_qlib_data(provider_uri):
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print(f"Qlib data is not found in {provider_uri}")
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sys.path.append(str(Path(__file__).resolve().parent.parent.joinpath("scripts")))
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from get_data import GetData
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GetData().qlib_data_cn(target_dir=provider_uri)
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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MARKET = "csi300"
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BENCHMARK = "SH000300"
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###################################
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# train model
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###################################
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DATA_HANDLER_CONFIG = {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time":"2008-01-01",
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"fit_end_time":"2014-12-31",
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"instruments": MARKET,
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}
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TRAINER_CONFIG = {
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"train_start_time": "2008-01-01",
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"train_end_time": "2014-12-31",
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"validate_start_time": "2015-01-01",
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"validate_end_time": "2016-12-31",
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"test_start_time": "2017-01-01",
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"test_end_time": "2020-08-01",
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}
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task = {
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"model": {
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"class": "GRU",
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"module_path": "qlib.contrib.model.pytorch_gru",
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"kwargs": {
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"d_feat": 6,
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"hidden_size": 64,
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"num_layers": 3,
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"dropout": 0.0,
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"n_epochs": 2000,
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"lr": 1e-1,
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"early_stop": 200,
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"batch_size":800,
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"smooth_steps": 5,
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"metric": "mse",
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"loss": "mse",
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"seed": 0,
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"GPU": 0,
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}
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},
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"dataset": {
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"class": "DatasetH",
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"module_path": "qlib.data.dataset",
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"kwargs": {
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'handler': {
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"class": "ALPHA360",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": DATA_HANDLER_CONFIG
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},
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'segments': {
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'train': ("2008-01-01", "2014-12-31"),
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'valid': ("2015-01-01", "2016-12-31",),
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'test': ("2017-01-01", "2020-08-01",),
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}
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}
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}
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# You shoud record the data in specific sequence
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# "record": ['SignalRecord', 'SigAnaRecord', 'PortAnaRecord'],
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}
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# model = train_model(task)
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model = init_instance_by_config(task['model'])
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dataset = init_instance_by_config(task['dataset'])
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model.fit(dataset)
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pred_score = model.predict(dataset)
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# save pred_score to file
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pred_score_path = Path("~/tmp/qlib/pred_score.pkl").expanduser()
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pred_score_path.parent.mkdir(exist_ok=True, parents=True)
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pred_score.to_pickle(pred_score_path)
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###################################
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# backtest
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###################################
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STRATEGY_CONFIG = {
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"topk": 50,
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"n_drop": 5,
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}
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BACKTEST_CONFIG = {
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"verbose": False,
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"limit_threshold": 0.095,
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"account": 100000000,
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"benchmark": BENCHMARK,
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"deal_price": "close",
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"open_cost": 0.0005,
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"close_cost": 0.0015,
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"min_cost": 5,
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}
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# use default strategy
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# custom Strategy, refer to: TODO: Strategy API url
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strategy = TopkDropoutStrategy(**STRATEGY_CONFIG)
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report_normal, positions_normal = normal_backtest(pred_score, strategy=strategy, **BACKTEST_CONFIG)
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###################################
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# analyze
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# If need a more detailed analysis, refer to: examples/train_and_bakctest.ipynb
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###################################
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analysis = dict()
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analysis["excess_return_without_cost"] = risk_analysis(report_normal["return"] - report_normal["bench"])
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analysis["excess_return_with_cost"] = risk_analysis(
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report_normal["return"] - report_normal["bench"] - report_normal["cost"]
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)
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analysis_df = pd.concat(analysis) # type: pd.DataFrame
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print(analysis_df)
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@@ -8,29 +8,81 @@ from ...data.dataset import processor as processor_module
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from ...log import TimeInspector
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import copy
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class ALPHA360(DataHandlerLP):
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def __init__(self, instruments="csi500", start_time=None, end_time=None):
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def __init__(
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self,
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instruments="csi500",
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start_time=None,
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end_time=None,
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fit_start_time=None,
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fit_end_time=None
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):
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data_loader = {
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"class": "QlibDataLoader",
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"kwargs": {
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"config": {
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"feature": {
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"price": {"windows": range(60)},
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"volume": {"windows": range(60)},
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},
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"feature": self.get_feature_config(),
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"label": self.get_label_config(),
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},
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},
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}
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learn_processors = [
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{"class": "DropnaLabel", "kwargs": {'group': 'label'}},
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{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
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]
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infer_processors = [
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{"class": "ConfigSectionProcessor", "module_path": "qlib.contrib.data.processor"}
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] # ConfigSectionProcessor will normalize LABEL0
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super().__init__(instruments, start_time, end_time, data_loader=data_loader, infer_processors=infer_processors)
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{"class": "ProcessInf", "kwargs": {}},
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{"class": "ZscoreNorm", "kwargs": {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time}},
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{"class": "Fillna", "kwargs": {}},
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]
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super().__init__(
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instruments,
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start_time,
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end_time,
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data_loader=data_loader,
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learn_processors=learn_processors,
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infer_processors=infer_processors
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)
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def get_label_config(self):
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return (["Ref($close, -2)/Ref($close, -1) - 1"], ["LABEL0"])
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def get_feature_config(self):
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fields = []
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names = []
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for i in range(59,0,-1):
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fields += ["Ref($close, %d)/$close"%(i)]
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names += ["CLOSE%d"%(i)]
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fields += ["Ref($open, %d)/$close"%(i)]
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names += ["OPEN%d"%(i)]
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fields += ["Ref($high, %d)/$close"%(i)]
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names += ["HIGH%d"%(i)]
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fields += ["Ref($low, %d)/$close"%(i)]
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names += ["LOW%d"%(i)]
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fields += ["Ref($vwap, %d)/$close"%(i)]
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names += ["VWAP%d"%(i)]
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fields += ["Ref($volume, %d)/$volume"%(i)]
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names += ["VOLUME%d"%(i)]
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fields += ["$close/$close"]
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fields += ["$open/$close"]
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fields += ["$high/$close"]
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fields += ["$low/$close"]
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fields += ["$vwap/$close"]
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fields += ["$volume/$volume"]
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names += ["CLOSE0"]
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names += ["OPEN0"]
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names += ["HIGH0"]
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names += ["LOW0"]
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names += ["VWAP0"]
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names += ["VOLUME0"]
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return fields, names
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class ALPHA360vwap(ALPHA360):
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def get_label_config(self):
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@@ -90,7 +142,7 @@ class Alpha158(DataHandlerLP):
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"kbar": {},
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"price": {
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"windows": [0],
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"feature": ["OPEN", "HIGH", "LOW"],
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"feature": ["OPEN", "HIGH", "LOW", "VWAP"],
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},
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"rolling": {},
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}
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@@ -281,16 +333,5 @@ class Alpha158(DataHandlerLP):
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class Alpha158vwap(Alpha158):
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def get_feature_config(self):
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conf = {
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"kbar": {},
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"price": {
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"windows": [0],
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"feature": ["OPEN", "HIGH", "LOW", "VWAP"],
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},
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"rolling": {},
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}
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return self.parse_config_to_fields(conf)
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def get_label_config(self):
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return (["Ref($vwap, -2)/Ref($vwap, -1) - 1"], ["LABEL0"])
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362
qlib/contrib/model/pytorch_gru.py
Executable file
362
qlib/contrib/model/pytorch_gru.py
Executable file
@@ -0,0 +1,362 @@
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# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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from __future__ import division
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from __future__ import print_function
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import os
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import numpy as np
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import pandas as pd
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import copy
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from sklearn.metrics import roc_auc_score, mean_squared_error
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import logging
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from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, create_save_path, drop_nan_by_y_index
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from ...log import get_module_logger, TimeInspector
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from ...model.base import Model
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from ...data.dataset import DatasetH
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from ...data.dataset.handler import DataHandlerLP
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class GRU(Model):
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"""GRU Model
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Parameters
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----------
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input_dim : int
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input dimension
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output_dim : int
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output dimension
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layers : tuple
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layer sizes
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lr : float
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learning rate
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lr_decay : float
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learning rate decay
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lr_decay_steps : int
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learning rate decay steps
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optimizer : str
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optimizer name
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GPU : str
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the GPU ID(s) used for training
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"""
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def __init__(
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self,
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d_feat=6,
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hidden_size=64,
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num_layers=2,
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dropout=0.0,
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n_epochs=200,
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lr=0.001,
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batch_size=2000,
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early_stop=20,
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eval_steps=5,
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loss="mse",
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lr_decay=0.96,
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lr_decay_steps=100,
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optimizer="gd",
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GPU="0",
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seed=0,
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**kwargs
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):
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# Set logger.
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self.logger = get_module_logger("GRU")
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self.logger.info("GRU pytorch version...")
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# set hyper-parameters.
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self.d_feat = d_feat
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self.hidden_size = hidden_size
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self.num_layers = num_layers
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self.dropout = dropout
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self.n_epochs = n_epochs
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self.lr = lr
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self.batch_size = batch_size
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self.early_stop = early_stop
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self.eval_steps = eval_steps
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self.lr_decay = lr_decay
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self.lr_decay_steps = lr_decay_steps
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self.optimizer = optimizer.lower()
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self.loss_type = loss
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self.visible_GPU = GPU
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self.use_gpu = torch.cuda.is_available()
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self.seed = seed
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self.logger.info(
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"GRU parameters setting:"
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"\nd_feat : {}"
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"\nhidden_size : {}"
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"\nnum_layers : {}"
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"\ndropout : {}"
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"\nn_epochs : {}"
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"\nlr : {}"
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"\nbatch_size : {}"
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"\nearly_stop : {}"
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"\neval_steps : {}"
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"\nlr_decay : {}"
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"\nlr_decay_steps : {}"
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"\noptimizer : {}"
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"\nloss_type : {}"
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"\nvisible_GPU : {}"
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"\nuse_GPU : {}"
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"\nseed : {}".format(
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d_feat,
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hidden_size,
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num_layers,
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dropout,
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n_epochs,
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lr,
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batch_size,
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early_stop,
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eval_steps,
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lr_decay,
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lr_decay_steps,
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optimizer.lower(),
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loss,
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GPU,
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self.use_gpu,
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seed,
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)
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)
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if loss not in {"mse", "binary"}:
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raise NotImplementedError("loss {} is not supported!".format(loss))
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self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
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self.gru_model = GRUModel(d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout)
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if optimizer.lower() == "adam":
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self.train_optimizer = optim.Adam(self.gru_model.parameters(), lr=self.lr)
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elif optimizer.lower() == "gd":
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self.train_optimizer = optim.SGD(self.gru_model.parameters(), lr=self.lr)
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else:
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raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
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# Reduce learning rate when loss has stopped decrease
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self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
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self.train_optimizer,
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mode="min",
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factor=0.5,
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patience=10,
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verbose=True,
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threshold=0.0001,
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threshold_mode="rel",
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cooldown=0,
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min_lr=0.00001,
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eps=1e-08,
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)
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self._fitted = False
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if self.use_gpu:
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self.gru_model.cuda()
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# set the visible GPU
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if self.visible_GPU:
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os.environ["CUDA_VISIBLE_DEVICES"] = self.visible_GPU
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def fit(
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self,
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dataset: DatasetH,
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evals_result=dict(),
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verbose=True,
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save_path=None,
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):
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df_train, df_valid = dataset.prepare(
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["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
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)
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x_train, y_train = df_train["feature"], df_train["label"]
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x_valid, y_valid = df_valid["feature"], df_valid["label"]
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x_train.to_pickle('~/x_train_init.pkl')
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y_train.to_pickle('~/y_train_init.pkl')
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x_train = x_train.fillna(0)
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y_train = y_train.fillna(0)
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x_valid = x_valid.fillna(0)
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y_valid = y_valid.fillna(0)
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x_train.to_pickle('~/x_train.pkl')
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y_train.to_pickle('~/y_train.pkl')
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# Lightgbm need 1D array as its label
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save_path = create_save_path(save_path)
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stop_steps = 0
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train_loss = 0
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best_loss = np.inf
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evals_result["train"] = []
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evals_result["valid"] = []
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# train
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self.logger.info("training...")
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self._fitted = True
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# return
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# prepare training data
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x_train_values = torch.from_numpy(x_train.values).float()
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y_train_values = torch.from_numpy(np.squeeze(y_train.values)).float()
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train_num = y_train_values.shape[0]
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# prepare validation data
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x_val_auto = torch.from_numpy(x_valid.values).float()
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y_val_auto = torch.from_numpy(np.squeeze(y_valid.values)).float()
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if self.use_gpu:
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x_val_auto = x_val_auto.cuda()
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y_val_auto = y_val_auto.cuda()
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for step in range(self.n_epochs):
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if stop_steps >= self.early_stop:
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if verbose:
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self.logger.info("\tearly stop")
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break
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loss = AverageMeter()
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self.gru_model.train()
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self.train_optimizer.zero_grad()
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choice = np.random.choice(train_num, self.batch_size)
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x_batch_auto = x_train_values[choice]
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y_batch_auto = y_train_values[choice]
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if self.use_gpu:
|
||||
x_batch_auto = x_batch_auto.float().cuda()
|
||||
y_batch_auto = y_batch_auto.float().cuda()
|
||||
|
||||
# forward
|
||||
preds = self.gru_model(x_batch_auto)
|
||||
cur_loss = self.get_loss(preds, y_batch_auto, self.loss_type)
|
||||
cur_loss.backward()
|
||||
self.train_optimizer.step()
|
||||
loss.update(cur_loss.item())
|
||||
|
||||
# validation
|
||||
train_loss += loss.val
|
||||
# print(loss.val)
|
||||
if step and step % self.eval_steps == 0:
|
||||
stop_steps += 1
|
||||
train_loss /= self.eval_steps
|
||||
|
||||
with torch.no_grad():
|
||||
self.gru_model.eval()
|
||||
loss_val = AverageMeter()
|
||||
|
||||
# forward
|
||||
preds = self.gru_model(x_val_auto)
|
||||
cur_loss_val = self.get_loss(preds, y_val_auto, self.loss_type)
|
||||
loss_val.update(cur_loss_val.item())
|
||||
|
||||
if verbose:
|
||||
self.logger.info(
|
||||
"[Epoch {}]: train_loss {:.6f}, valid_loss {:.6f}".format(step, train_loss, loss_val.val)
|
||||
)
|
||||
evals_result["train"].append(train_loss)
|
||||
evals_result["valid"].append(loss_val.val)
|
||||
if loss_val.val < best_loss:
|
||||
if verbose:
|
||||
self.logger.info(
|
||||
"\tvalid loss update from {:.6f} to {:.6f}, save checkpoint.".format(
|
||||
best_loss, loss_val.val
|
||||
)
|
||||
)
|
||||
best_loss = loss_val.val
|
||||
stop_steps = 0
|
||||
torch.save(self.gru_model.state_dict(), save_path)
|
||||
train_loss = 0
|
||||
# update learning rate
|
||||
self.scheduler.step(cur_loss_val)
|
||||
|
||||
# restore the optimal parameters after training ??
|
||||
# self.gru_model.load_state_dict(torch.load(save_path))
|
||||
if self.use_gpu:
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def get_loss(self, pred, target, loss_type):
|
||||
if loss_type == "mse":
|
||||
sqr_loss = (pred - target)**2
|
||||
loss = sqr_loss.mean()
|
||||
return loss
|
||||
elif loss_type == "binary":
|
||||
loss = nn.BCELoss()
|
||||
return loss(pred, target)
|
||||
else:
|
||||
raise NotImplementedError("loss {} is not supported!".format(loss_type))
|
||||
|
||||
def predict(self, dataset):
|
||||
if not self._fitted:
|
||||
raise ValueError("model is not fitted yet!")
|
||||
|
||||
x_test = dataset.prepare("test", col_set="feature")
|
||||
x_test = x_test.fillna(0)
|
||||
index = x_test.index
|
||||
x_test = torch.from_numpy(x_test.values).float()
|
||||
|
||||
if self.use_gpu:
|
||||
x_test = x_test.cuda()
|
||||
self.gru_model.eval()
|
||||
|
||||
with torch.no_grad():
|
||||
if self.use_gpu:
|
||||
preds = self.gru_model(x_test).detach().cpu().numpy()
|
||||
else:
|
||||
preds = self.gru_model(x_test).detach().numpy()
|
||||
return pd.Series(preds, index=index)
|
||||
|
||||
def save(self, filename, **kwargs):
|
||||
with save_multiple_parts_file(filename) as model_dir:
|
||||
model_path = os.path.join(model_dir, os.path.split(model_dir)[-1])
|
||||
# Save model
|
||||
torch.save(self.gru_model.state_dict(), model_path)
|
||||
|
||||
def load(self, buffer, **kwargs):
|
||||
with unpack_archive_with_buffer(buffer) as model_dir:
|
||||
# Get model name
|
||||
_model_name = os.path.splitext(list(filter(lambda x: x.startswith("model.bin"), os.listdir(model_dir)))[0])[
|
||||
0
|
||||
]
|
||||
_model_path = os.path.join(model_dir, _model_name)
|
||||
# Load model
|
||||
self.gru_model.load_state_dict(torch.load(_model_path))
|
||||
self._fitted = True
|
||||
|
||||
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
|
||||
|
||||
|
||||
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()
|
||||
|
||||
@@ -106,6 +106,22 @@ class ProcessInf(Processor):
|
||||
|
||||
return replace_inf(df)
|
||||
|
||||
class Fillna(Processor):
|
||||
"""Process infinity """
|
||||
|
||||
def __call__(self, df):
|
||||
def fill_na(data):
|
||||
def process_na(df):
|
||||
for col in df.columns:
|
||||
# FIXME: Such behavior is very weird
|
||||
df[col] = df[col].fillna(0)
|
||||
return df
|
||||
|
||||
data = datetime_groupby_apply(data, process_na)
|
||||
data.sort_index(inplace=True)
|
||||
return data
|
||||
|
||||
return fill_na(df)
|
||||
|
||||
class MinMaxNorm(Processor):
|
||||
def __init__(self, fit_start_time, fit_end_time, fields_group=None):
|
||||
|
||||
Reference in New Issue
Block a user