福州网站建设好的公司,商业网站建设开发,推广哪个平台好,海尔网站建设投入Qlib https://github.com/microsoft/qlib
将csv文件转化为Qlib的数据格式#xff1a;https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format 注意每支股票都要保存成单独一个文档#xff0c;且文档名字与股票代号一致。 其中f…Qlib https://github.com/microsoft/qlib
将csv文件转化为Qlib的数据格式https://qlib.readthedocs.io/en/latest/component/data.html#converting-csv-format-into-qlib-format 注意每支股票都要保存成单独一个文档且文档名字与股票代号一致。 其中factor 也就是 https://crm.htsc.com.cn/doc/2020/10750101/d287ebf2-7f3f-4382-bf3f-cfabd4b90161.pdf中提到的复权。
youbube 教程 https://www.youtube.com/watch?vz6a4mQTkMwg
from qlib.data.dataset.loader import QlibDataLoaderMACD_EXP (EMA($close, 12) - EMA($close, 26))/$close - EMA((EMA($close, 12) - EMA($close, 26))/$close, 9)/$closefields [MACD_EXP,$close] # MACDnames [MACD,收盘价]labels [Ref($close, -2)/Ref($close, -1) - 1] # labellabel_names [LABEL]data_loader_config {feature: (fields, names),label: (labels, label_names)}data_loader QlibDataLoader(configdata_loader_config)df data_loader.load(instrumentsall, start_time2010-01-01, end_time2017-12-31)print(df)使用Qlib时候直接使用 Data Handler 是上面 QlibDataLoader 的封装所以Data Handler 也自然可以使用label的设置。
一个完整的例子
from qlib.data.dataset import DatasetH
# 实例化Data Loader
market sh000300 # 沪深300股票池代码在instruments文件夹下有对应的sh000300.txt
close_ma [EMA($close, 10), EMA($close, 30)] # EMA($close, 10)表示计算close的10日指数加权均线
ma_names [EMA10, EMA30]
ret [Ref($close, -1)/$close-1] # 下一日收益率, Ref($close, -1)表示下一日收盘价
ret_name [next_ret]
qdl_ma_gp QlibDataLoader(config{feature:(close_ma, ma_names), label: (ret, ret_name)}) # 实例化Data Handler
shared_processors [DropnaProcessor()]
learn_processors [CSZScoreNorm()]
infer_processors [ZScoreNorm(fit_start_time20190101, fit_end_time20211231)]dh_pr_test DataHandlerLP(instrumentssh000300, start_time20190101, end_time20211231,process_typeDataHandlerLP.PTYPE_I, learn_processorslearn_processors,shared_processorsshared_processors,infer_processorsinfer_processors,data_loaderqdl_ma_gp)ds DatasetH(dh_pr_test, segments{train: (20190101, 20201231), test: (20210101, 20211231)})from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLP自定义 https://blog.csdn.net/qq_37373209/article/details/125224210
所以 最后其实是可以直接用 DatasetH 来设置的
Alpha360:
其data_loader 是指定了feature的不可改变 但是label 是可以从kwargs里边导入的。所以想要用自己的alpha 因子得从data_loader 开始写起而不能直接使用这个类
class Alpha360(DataHandlerLP):def __init__(self,instrumentscsi500,start_timeNone,end_timeNone,freqday,infer_processors_DEFAULT_INFER_PROCESSORS,learn_processors_DEFAULT_LEARN_PROCESSORS,fit_start_timeNone,fit_end_timeNone,filter_pipeNone,inst_processorNone,**kwargs):infer_processors check_transform_proc(infer_processors, fit_start_time, fit_end_time)learn_processors check_transform_proc(learn_processors, fit_start_time, fit_end_time)data_loader {class: QlibDataLoader,kwargs: {config: {feature: self.get_feature_config(), ## 这里是特征label: kwargs.pop(label, self.get_label_config()), # 这里为标签},filter_pipe: filter_pipe,freq: freq,inst_processor: inst_processor,},}super().__init__(instrumentsinstruments,start_timestart_time,end_timeend_time,data_loaderdata_loader,learn_processorslearn_processors,infer_processorsinfer_processors,**kwargs)def get_feature_config(): # 可以拿来直接使用# NOTE:# Alpha360 tries to provide a dataset with original price data# the original price data includes the prices and volume in the last 60 days.# To make it easier to learn models from this dataset, all the prices and volume# are normalized by the latest price and volume data ( dividing by $close, $volume)# So the latest normalized $close will be 1 (with name CLOSE0), the latest normalized $volume will be 1 (with name VOLUME0)# If further normalization are executed (e.g. centralization), CLOSE0 and VOLUME0 will be 0.fields []names []for i in range(59, 0, -1):fields [Ref($close, %d)/$close % i]names [CLOSE%d % i]fields [$close/$close]names [CLOSE0]for i in range(59, 0, -1):fields [Ref($open, %d)/$close % i]names [OPEN%d % i]fields [$open/$close]names [OPEN0]for i in range(59, 0, -1):fields [Ref($high, %d)/$close % i]names [HIGH%d % i]fields [$high/$close]names [HIGH0]for i in range(59, 0, -1):fields [Ref($low, %d)/$close % i]names [LOW%d % i]fields [$low/$close]names [LOW0]for i in range(59, 0, -1):fields [Ref($vwap, %d)/$close % i]names [VWAP%d % i]fields [$vwap/$close]names [VWAP0]for i in range(59, 0, -1):fields [Ref($volume, %d)/($volume1e-12) % i]names [VOLUME%d % i]fields [$volume/($volume1e-12)]names [VOLUME0]return fields, names使用Alpha360的代码为
from qlib.data.dataset import DatasetH
from qlib.data.dataset.handler import DataHandlerLPstart_time datetime.datetime.strptime(args.train_start_date, %Y-%m-%d)
end_time datetime.datetime.strptime(args.test_end_date, %Y-%m-%d)
train_end_time datetime.datetime.strptime(args.train_end_date, %Y-%m-%d)hanlder {class: Alpha360, module_path: qlib.contrib.data.handler, kwargs: {start_time: start_time, end_time: end_time, fit_start_time: start_time, fit_end_time: train_end_time, instruments: args.data_set,infer_processors: [{class: RobustZScoreNorm, kwargs: {fields_group: feature, clip_outlier: True}}, {class: Fillna, kwargs: {fields_group: feature}}],learn_processors: [{class: DropnaLabel}, {class: CSRankNorm, kwargs: {fields_group: label}}],label: [Ref($close, -1) / $close - 1]}}
segments { train: (args.train_start_date, args.train_end_date), valid: (args.valid_start_date, args.valid_end_date), test: (args.test_start_date, args.test_end_date)}
dataset DatasetH(hanlder,segments)df_train, df_valid, df_test dataset.prepare( [train, valid, test], col_set[feature, label], data_keyDataHandlerLP.DK_L,)x np.arange(10) # x例子
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) np.roll(x, 2) # axis为None则会先进行扁平化然后再向水平滚动2个位置
array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])
https://blog.csdn.net/qq_37373209/article/details/125224210
