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* init commit * change the version number * rich the docs&fix cache docs * update index readme * Modify cache class name * Modify sharpe to information_ratio * Modify Group- to Group * add the description of graphical results & fix the backtest docs * fix docs in details * update docs * Update introduction.rst * Update README.md * Update introduction.rst * Update introduction.rst * Update introduction.rst * Update installation.rst * Update installation.rst * Update initialization.rst * Update getdata.rst * Update integration.rst * Update initialization.rst * Update getdata.rst * Update estimator.rst Modify some typos. * Update README.md Modify the typos. * Update initialization.rst * Update data.rst * Update report.rst * Update estimator.rst * Update cumulative_return.py * Update model.rst * Update rank_label.py * Update cumulative_return.py * Update strategy.rst * Update getdata.rst * Update backtest.rst * Update integration.rst * Update getdata.rst * Update introduction.rst * Update introduction.rst * Update README.md * Update report.rst * Update integration.rst Fix typos * Update installation.rst Fix typos * Update getdata.rst * Update initialization.rst Fix typos. * add quick start docs&fix detials * fix estimator docs & fix strategy docs * fix the cahce in data.rst * update documents * Fix Corr && Rsquare * fix data retrival example to csi300 & fix a data bug * fix filter bug * Fix data collector * Modift model args * add the log & fix README.md\quick.rst * add enviroment depend & add intoduction of qlib-server online mode * fix image center fomat & set log_only of docs is True * fix README.md format * update data preparation & readme logo image * get_data support version * Modify analysis names * Modify analysis graph * update report.rst & data.rst * commmit estimator for merge * minimal requirements * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update READEME.md * Update READEME.md * update estimator * Fix doc urls * fix get_data.py docstring * update test_get_data.py * Upate docs * Upate docs * Upate docs Co-authored-by: bxdd <bxddream@gmail.com> Co-authored-by: zhupr <zhu.pengrong@foxmail.com> Co-authored-by: Wendi Li <wendili.academic@qq.com> Co-authored-by: Dingsu Wang <dingsu.wang@gmail.com> Co-authored-by: bxdd <45119470+bxdd@users.noreply.github.com> Co-authored-by: cslwqxx <cslwqxx@users.noreply.github.com>
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import logging
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from ...log import get_module_logger
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from ..evaluate import risk_analysis
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from ...data import D
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class User:
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def __init__(self, account, strategy, model, verbose=False):
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"""
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A user in online system, which contains account, strategy and model three module.
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Parameter
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account : Account()
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strategy :
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a strategy instance
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model :
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a model instance
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report_save_path : string
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the path to save report. Will not save report if None
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verbose : bool
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Whether to print the info during the process
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"""
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self.logger = get_module_logger("User", level=logging.INFO)
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self.account = account
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self.strategy = strategy
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self.model = model
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self.verbose = verbose
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def init_state(self, date):
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"""
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init state when each trading date begin
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Parameter
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date : pd.Timestamp
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"""
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self.account.init_state(today=date)
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self.strategy.init_state(trade_date=date, model=self.model, account=self.account)
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return
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def get_latest_trading_date(self):
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"""
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return the latest trading date for user {user_id}
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Parameter
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user_id : string
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:return
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date : string (e.g '2018-10-08')
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"""
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if not self.account.last_trade_date:
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return None
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return str(self.account.last_trade_date.date())
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def showReport(self, benchmark="SH000905"):
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"""
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show the newly report (mean, std, information_ratio, annualized_return)
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Parameter
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benchmark : string
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bench that to be compared, 'SH000905' for csi500
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"""
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bench = D.features([benchmark], ["$change"], disk_cache=True).loc[benchmark, "$change"]
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report = self.account.report.generate_report_dataframe()
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report["bench"] = bench
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analysis_result = {"pred": {}, "excess_return_without_cost": {}, "excess_return_with_cost": {}}
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r = (report["return"] - report["bench"]).dropna()
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analysis_result["excess_return_without_cost"][0] = risk_analysis(r)
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r = (report["return"] - report["bench"] - report["cost"]).dropna()
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analysis_result["excess_return_with_cost"][0] = risk_analysis(r)
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self.logger.info("Result of porfolio:")
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self.logger.info("excess_return_without_cost:")
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self.logger.info(analysis_result["excess_return_without_cost"][0])
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self.logger.info("excess_return_with_cost:")
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self.logger.info(analysis_result["excess_return_with_cost"][0])
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return report
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