标签：01 Lib 微软 笔记 time print import qlib data
微软新出了一个Ｑ_Lib量化平台，导师让我们研究一下，我看一遍使用手册，很多ＡＰＩ都不会用哎。
print('----------0install--------------')
# 安装成功
import qlib
print(qlib.__version__)
print('----------1download.init--------------')
# 下载数据初始化
from qlib.config import REG_CN,REG_US
qlib.init(provider_uri="~/.qlib/qlib_data/cn_data",region=REG_CN)
# qlib.init(provider_uri="~/.qlib/qlib_data/us_data",region=REG_US)
# provider_url参数有两个："~/.qlib/qlib_data/cn_data"，"~/.qlib/qlib_data/us_data"
# region参数有两个：REG_CN REG_US
print('----------2download.ex--------------')
# 下载数据
from qlib.data import D
a=D.calendar(start_time='2020-01-01',end_time='2020-12-31',freq='day')[:10]
b=D.instruments(market='all')
instruments = D.instruments(market='csi300')
c=D.list_instruments(instruments=instruments, start_time='2010-01-01', end_time='2020-12-31', as_list=True)[:6]
print(a,b,c,sep='\n')
from qlib.data.filter import NameDFilter
nameDFilter = NameDFilter(name_rule_re='SH[0-9]{4}55')
instruments = D.instruments(market='csi300', filter_pipe=[nameDFilter])
d=D.list_instruments(instruments=instruments, start_time='2015-01-01', end_time='2016-02-15', as_list=True)
print(d)
from qlib.data.filter import ExpressionDFilter
expressionDFilter = ExpressionDFilter(rule_expression='$close>2000')
instruments = D.instruments(market='csi300', filter_pipe=[expressionDFilter])
e=D.list_instruments(instruments=instruments, start_time='2015-01-01', end_time='2016-02-15', as_list=True)
print(e)
instruments = ['SH600000']
fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
f=D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day').head()
print(f,type(f))
instruments = ['SH600000','SZ000001']
fields = ['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']
g=D.features(instruments, fields, start_time='2010-01-01', end_time='2017-12-31', freq='day')
print(g,type(g),g.index)
# type(g) = <class 'pandas.core.frame.DataFrame'>
# g.index = MultiIndex([……],names=['instrument', 'datetime'], length=3794)
print('----------3customerclass.ex--------------')
# 下面讲的是自设类
from qlib.model.base import Model
import pandas
class CJ_model(Model):
def __init__(self, loss='mse', **kwargs):
if loss not in {'mse', 'binary'}: raise NotImplementedError
self._scorer = mean_squared_error if loss == 'mse' else roc_auc_score
self._params.update(objective=loss, **kwargs)
self._model = None
def fit(self, num_boost_round=1000, **kwargs):
pass
def predict(self, **kwargs) -> pandas.Series:
pass
def finetune(self, num_boost_round=10, verbose_eval=20):
pass
# 下面是下载Ａlpha１５８数据
import qlib
from qlib.contrib.data.handler import Alpha158
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": "csi300",
}
if __name__ == "__main__":
qlib.init()
h = Alpha158(**data_handler_config)
print(type(h))
print(h.get_cols())
print(h.fetch(col_set="label"))
print(h.fetch(col_set="feature"))
print('----------4workflow.ex--------------')
# 在.py文件内使用工作流，求得分
from qlib.contrib.model.gbdt import LGBModel
from qlib.contrib.data.handler import Alpha158
from qlib.utils import init_instance_by_config, flatten_dict
from qlib.workflow import R
from qlib.workflow.record_temp import SignalRecord, PortAnaRecord
market = "csi300"
benchmark = "SH000300"
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,
}
task = {
"model": {
"class": "LGBModel",
"module_path": "qlib.contrib.model.gbdt",
"kwargs": {
"loss": "mse",
"colsample_bytree": 0.8879,
"learning_rate": 0.0421,
"subsample": 0.8789,
"lambda_l1": 205.6999,
"lambda_l2": 580.9768,
"max_depth": 8,
"num_leaves": 210,
"num_threads": 20,
},
},
"dataset": {
"class": "DatasetH",
"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"),
},
},
},
}
# model initiaiton
model = init_instance_by_config(task["model"])
dataset = init_instance_by_config(task["dataset"])
# start exp
with R.start(experiment_name="workflow"):
# train
R.log_params(**flatten_dict(task))
model.fit(dataset)
# prediction
recorder = R.get_recorder()
sr = SignalRecord(model, dataset, recorder)
sr.generate()
print('----------5dataAPI--------------')
from qlib.data.data import CalendarProvider
class subcla(CalendarProvider):
def calendar(self, start_time, end_time, freq, future):
pass
d= subcla()
print(1,d.calendar(start_time='2010-01-01', end_time='2020-12-31', freq='day', future=False))
print(2,d.locate_index(start_time='2010-01-01', end_time='2020-12-31', freq='day', future=False))
from qlib.data.data import InstrumentProvider
print(3,InstrumentProvider.instruments(market='all', filter_pipe=None))
from qlib.data.data import FeatureProvider
from qlib.data.data import ExpressionProvider
from qlib.data.data import DatasetProvider
print(4,DatasetProvider. get_instruments_d(instruments=['SH600000','SH600001'],freq='day'))
import pandas as pd
print(5,DatasetProvider. get_column_names(pd.read_csv('1.csv')))
print(6,DatasetProvider.parse_fields(fields=['$close', '$volume', 'Ref($close, 1)', 'Mean($close, 3)', '$high-$low']))
print(7,DatasetProvider. dataset_processor(instruments_d='SH600000',column_names=['$close'], start_time='2010-01-01', end_time='2020-12-31', freq='day'))
from qlib.data.data import LocalCalendarProvider
e=LocalCalendarProvider()
print(8,e.calendar(start_time='2010-01-01', end_time='2020-12-31', freq='day', future=False))
from qlib.data.data import LocalInstrumentProvider
f=LocalInstrumentProvider()
print(9,f.list_instruments(instruments=D.instruments(market='csi300'), start_time='2010-01-01', end_time='2020-12-31', freq='day', as_list=False))
from qlib.data.data import LocalFeatureProvider
g=LocalFeatureProvider()
print(10,g.feature(instrument='SH600000', field=['$close'],start_index='2010-01-01', end_index='2020-12-31', freq='day', ))
from qlib.data.data import LocalExpressionProvider
h=LocalExpressionProvider()
print(11,h.expression(instrument='SH600000', field='$close',start_time='2010-01-01', end_time='2020-12-31', freq='day'))
#!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
from qlib.data.data import LocalDatasetProvider
i=LocalDatasetProvider()
print(12,i.dataset(instruments=['SH600000'], fields=['$close'],start_time='2010-01-01', end_time='2020-12-31', freq='day'))
print(13,LocalDatasetProvider. multi_cache_walker(instruments=['SH600000'], fields=['$close'],start_time='2010-01-01', end_time='2020-12-31', freq='day'))
print('----------6filterAPI--------------')
from qlib.data.filter import BaseDFilter
from qlib.data.filter import SeriesDFilter
from qlib.data.filter import NameDFilter
from qlib.data.data import InstrumentProvider
cj_instruments=InstrumentProvider.instruments(market='csi300', filter_pipe=[NameDFilter(name_rule_re='SH[0-9]{4}55')])
print(14,f.list_instruments(instruments=cj_instruments, start_time='2010-01-01', end_time='2020-12-31', freq='day', as_list=False))
from qlib.data.filter import ExpressionDFilter
print(15,ExpressionDFilter(rule_expression = '$close /$open > 5',fstart_time=None, fend_time=None, keep=False))
print(16,ExpressionDFilter(rule_expression = '$rank($close) < 10', fstart_time=None, fend_time=None, keep=False))
print(17,ExpressionDFilter(rule_expression = '$Ref($close, 3) > 100', fstart_time=None, fend_time=None, keep=False))
print('----------7baseAPI--------------')
print('----------8operatorAPI--------------')
from qlib.data.ops import Abs,Sign,Log,Power,Lt
from qlib.data.data import LocalExpressionProvider
h=LocalExpressionProvider()
x1=h.expression(instrument='SH600000', field='$close',start_time='2010-01-01', end_time='2020-12-31', freq='day')
x2=h.expression(instrument='SH600000', field='$close',start_time='2010-01-01', end_time='2020-12-31', freq='day')
print(Abs(x1),Sign(x1),Log(x1),Lt(x1,x2))
print('---------9CacheAPI-----------------')
print('---------------10Datasetapi-----------')
from qlib.data.dataset import processor
import pandas as pd
x=pd.read_csv('1.csv',index_col="datetime")
y=processor.get_group_columns(x,'return')
print(y)#我常见得这个封装很奇怪
from qlib.data.dataset.processor import Processor
xx=Processor()
print(xx.fit(x))
from qlib.contrib import evaluate
print(evaluate.risk_analysis(x['return'], N=5))
现在开始二轮学习，看着官方给的例程逐行研究！
标签：01,Lib,微软,笔记,time,print,import,qlib,data 来源： https://blog.csdn.net/cj1064789374/article/details/111187107
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。