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coding: UTF-8
import configparser
import os
import psutil
import scipy.io as scio
import numpy as np
import pandas as pd
import sys
from backtest_framework.print_type import PrintType as p
import copy
import warnings
from sklearn.linear_model import LinearRegression
import scipy.stats as st
from wby_utils.path.Path import get_all_dirs
warnings.filterwarnings("ignore")
class DataApi:
def getConfig(self):
"""
赋值路径用函数
:param:
:return:
"""
try:
self.config = configparser.ConfigParser()
self.config.read(self.configPath, encoding="utf-8")
except Exception as e:
p.error_print(sys._getframe().f_code.co_filename, sys._getframe().f_code.co_name, "配置文件读取失败!")
else:
self.local_db_path = self.config['data_path']['local_db']
self.dataPathList.append(self.config['data_path']['base_data_path'])
self.factorNpzPath = self.config['data_path']['derivative_factor_npz_path'] # 因子路径
self.dataPathList.append(self.factorNpzPath)
self.result_path = self.config['data_path']['result_path'] # 结果路径
self.dataPathList.append(self.result_path)
def getLpp(self):
"""
获取回测下标用函数
:return: int，回测下标
"""
return self.lpp
def setLpp(self, lpp):
"""
设置回测下标用函数
:return:
"""
self.lpp = lpp
def version(process_name):
"""
展示版本用函数
:param process_name: 运行程序类名，char类型
:return:
"""
crossbar = "==============================================="
version = "==" + process_name + " for Python3, Version: 0.0.1=="
p.version_print(sys._getframe().f_code.co_filename, sys._getframe().f_code.co_name, crossbar)
p.version_print(sys._getframe().f_code.co_filename, sys._getframe().f_code.co_name, version)
p.version_print(sys._getframe().f_code.co_filename, sys._getframe().f_code.co_name, crossbar)
def loadDerivativeFactorToGlobal(self, factorName, shapeType, typeLen):
"""
从指定目录读取因子数据用函数
:param factorName:因子名
:param shapeType:char类型，‘tensor’表示三维矩阵型因子，’matrix‘表示矩阵型因子，’vector‘表示列表型因子
:param typeLen:typeLen在当type为’vector‘的时候的列数，‘tensor’二维和三维的长度
:return:
"""
if factorName in self.globalSave.keys():
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "请求的因子已被导入！")
return
tradeDate = self.getAllData("trade_date_int")
filePath_npz = self.factorNpzPath + factorName + ".npz"
filePath_mat = self.factorNpzPath + factorName + ".mat"
try:
try:
data = np.load(filePath_npz)
single_key = data.files[0]
factorData = data[single_key]
except:
factorData = scio.loadmat(filePath_mat)[factorName]
site = np.max([1, np.shape(factorData)[0] - self.factorGeneratorTradeDateCount - 1])
self.startDate = str(tradeDate[site][0])
self.endDate = str(tradeDate[-1][0])
if shapeType == "matrix":
factorData = self.myFactorReshape(factorData)
elif shapeType == 'vector':
factor = np.nan * np.zeros((len(tradeDate), factorData.shape[1]))
factor[:factorData.shape[0], :factorData.shape[1]] = factorData
factorData = factor
elif shapeType == 'tensor':
factor = np.nan * np.zeros((len(tradeDate), factorData.shape[1], factorData.shape[2]))
factor[:factorData.shape[0], :factorData.shape[1], :factorData.shape[2]] = factorData
factorData = factor
except:
self.startDate = str(tradeDate[0][0])
self.endDate = str(tradeDate[-1][0])
if shapeType == 'matrix':
factorData = self.myFactorReshape()
elif shapeType == 'vector':
factorData = np.nan * np.zeros((len(tradeDate), typeLen))
elif shapeType == 'tensor':
if len(typeLen) == 1:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "typeLen参数不能为标量！")
factorData = np.nan * np.zeros((len(tradeDate), typeLen[0], typeLen[1]))
self.globalSave[factorName] = factorData
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "导入" + factorName + "文件！")
def setGlobal(self, globalName, globalValue):
"""
存储全局变量用函数
:param globalName: char类型，全局变量名
:param globalValue: 任意类型，全局变量值
:return:
"""
self.globalSave[globalName] = globalValue
def getGlobal(self, globalName):
"""
获取全局变量用函数
:param globalName: char类型，全局变量名
:return: 任意类型， 全局变量值
"""
try:
return self.globalSave[globalName]
except:
p.warning_print(sys._getframe().f_code.co_filename, sys._getframe().f_code.co_name,
f"请求的全局变量不存在！{globalName}")
return None
def getAllData(self, key):
"""
获取数据文件用函数
:param key: char类型，数据名称
:return: 数据文件，np.array格式
"""
if key not in self.allData:
npzFile = key + '.npz'
matFile = key + '.mat'
for dataPath in self.dataPathList:
if npzFile in os.listdir(dataPath):
data = np.load(dataPath + "\\\\" + npzFile, allow_pickle=True)
single_key = data.files[0]
value = data[single_key]
self.allData[key] = value
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"因子文件" + key + "成功导入！")
return self.allData[key]
elif matFile in os.listdir(dataPath):
value = scio.loadmat(dataPath + matFile)[key]
self.allData[key] = value
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"因子文件" + key + "成功导入！")
return self.allData[key]
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"因子文件导入失败！找不到因子文件" + key + "，请更新因子！" + dataPath)
return self.allData[key]
def getDataPackage(self, key):
"""
获取数据文件用函数
:param key: char类型，数据名称
:return: 数据文件，np.array格式
"""
if key not in self.allData:
npzFile = key + '.npz'
matFile = key + '.mat'
for dataPath in self.dataPathList:
if npzFile in os.listdir(dataPath):
data = np.load(dataPath + "\\\\" + npzFile, allow_pickle=True)
self.allData[key] = data
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"数据包" + key + "成功导入！")
return self.allData[key]
elif matFile in os.listdir(dataPath):
value = scio.loadmat(dataPath + matFile)[key]
self.allData[key] = value
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"数据包" + key + "成功导入！")
return self.allData[key]
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"数据包导入失败！找不到数据包" + key + "，请更新数据包！")
return self.allData[key]
def getFieldTickData(self, code, tradeDate):
"""
获取tick交易数据用函数
:param code: char类型，股票代码
:param tradeDate:int类型，交易日期
:return: np.array类型，tick交易数据
"""
trade_date = self.getAllData('trade_date_int')
tradeDateSite = np.where(trade_date == int(tradeDate))[0][0]
if not tradeDateSite:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "请求的日期为非交易日！")
elif tradeDateSite > self.getLpp():
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "禁止提取未来数据！")
else:
quotePath = self.local_db_path + 'quote_data\\stock\\xt_tick_data\\' + str(tradeDate) + '\\'
dataFile = f"{quotePath}{code}.npz"
ret = np.load(dataFile, allow_pickle=True)
return ret
def getFieldMinuteData(self, fieldName, tradeDate):
"""
获取日内交易数据用函数
:param fieldName: char类型，日内交易数据名称
:param tradeDate: int类型，交易日期
:return: np.array类型，日内交易数据
"""
trade_date = self.getAllData('trade_date_int')
tradeDateSite = np.where(trade_date == int(tradeDate))[0][0]
if not tradeDateSite:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "请求的日期为非交易日！")
elif tradeDateSite > self.getLpp():
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "禁止提取未来数据！")
else:
if fieldName[-2:] == '1m':
quotePath = self.local_db_path + 'quote_data\\stock\\minute_data\\1m\\'
elif fieldName[-2:] == '5m' and not fieldName[-3:] == '15m':
quotePath = self.local_db_path + 'quote_data\\stock\\minute_data\\5m\\'
elif fieldName[-3:] == '10m':
quotePath = self.local_db_path + 'quote_data\\stock\\minute_data\\10m\\'
elif fieldName[-3:] == '15m':
quotePath = self.local_db_path + 'quote_data\\stock\\minute_data\\15m\\'
elif fieldName[-3:] == '30m':
quotePath = self.local_db_path + 'quote_data\\stock\\minute_data\\30m\\'
elif fieldName[-3:] == '60m':
quotePath = self.local_db_path + 'quote_data\\stock\\minute_data\\60m\\'
else:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"请求的因子文件为非日内行情数据！")
dataFile = f"{quotePath}{fieldName}_{tradeDate}.npz"
data = np.load(dataFile, allow_pickle=True)
single_key = data.files[0]
value = data[single_key]
ret = self.quoteCorrection(value)
return ret
def getStartTradeDate(self, startTradeDate, change):
"""
设置开始交易日期标签用函数
:param startTradeDate: int类型，开始交易日期
:return: startDateFlag数值，int类型
"""
tradeDate = self.getAllData('trade_date_int')
if startTradeDate and startTradeDate != np.inf:
date = int(startTradeDate)
ret = 0
minDistant = np.inf
for i in list(range(0, len(tradeDate))):
if tradeDate[i] - date < minDistant and tradeDate[i] - date >= 0:
minDistant = tradeDate[i] - date
ret = i
else:
ret = 0
if change == True:
self.startDateFlag = ret
return ret
def getEndTradeDate(self, endTradeDate=""):
"""
设置结束交易日期标签用函数
:param endTradeDate: int类型，结束交易日期
:return: endDateFlag数值，int类型
"""
tradeDate = self.getAllData('trade_date_int')
if endTradeDate and endTradeDate != np.inf:
date = int(endTradeDate)
ret = 0
minDistant = np.inf
for i in list(range(0, len(tradeDate))):
if date - tradeDate[i] < minDistant and date - tradeDate[
i] >= 0:
minDistant = date - tradeDate[i]
ret = i
else:
ret = len(tradeDate)
self.endDateFlag = ret
return ret
def myFactorReshape(self, inputData):
"""
规范数据形状用函数
:param inputData: np.array类型，输入的数据
:return: np.array类型，重塑形状之后的数据
"""
tradeDate = self.getAllData("trade_date_int")
stock_list = self.getAllData("stock_code")
if not inputData is None:
if inputData.shape[1] < len(stock_list):
ret = np.hstack((inputData, np.nan * np.zeros(
(inputData.shape[0],
len(stock_list) - inputData.shape[1]))))
else:
ret = inputData
if inputData.shape[0] < len(tradeDate):
ret = np.vstack((ret, np.nan * np.zeros(
(len(tradeDate) - ret.shape[0], ret.shape[1]))))
else:
ret = np.nan * np.zeros([len(tradeDate), len(stock_list)])
return ret
def myWinsorizeCalc(self, inputValue, sigma, calcType):
"""
去极值用函数
:param inputValue: np.array类型，输入的数据
:param sigma: int类型，控制宽度参数
:param calcType: char类型，“std”、“median”、“Briner”，代表去极值方法
:return: np.array类型，去极值后的数据
"""
inputValue_ = copy.deepcopy(inputValue)
if calcType == 'std' or calcType == 'median':
if calcType == 'std':
meanValue = np.nanmean(inputValue_)
stdValue = np.nanstd(inputValue_)
elif calcType == 'median':
meanValue = np.nanmedian(inputValue_)
stdValue = np.nanmedian(np.abs(inputValue_ - meanValue))
upperThreshold = meanValue + stdValue * sigma
lowerThreshold = meanValue - stdValue * sigma
while len(np.where(inputValue_ > upperThreshold)[1]) > 0 or len(
np.where(inputValue_ < lowerThreshold)[1]) > 0:
inputValue_[0, np.where(
inputValue_ > upperThreshold)[1]] = upperThreshold
inputValue_[0, np.where(
inputValue_ < lowerThreshold)[1]] = lowerThreshold
if calcType == 'std':
meanValue = np.nanmean(inputValue_)
stdValue = np.nanstd(inputValue_)
elif calcType == 'median':
meanValue = np.nanmedian(inputValue_)
stdValue = np.nanmedian(np.abs(inputValue_ - meanValue))
upperThreshold = meanValue + stdValue * sigma
lowerThreshold = meanValue - stdValue * sigma
elif calcType == 'Briner':
inputValue_ = self.myStandardizeCalc(inputValue_)
s_plus = max(0, min(1, 0.5 / np.nanmax(inputValue_ - sigma)))
s_minus = max(0, min(1, -0.5 / np.nanmin(inputValue_ + sigma)))
inputValue_[0, np.where(
inputValue_ > sigma)[1]] = sigma * (1 - s_plus) + inputValue_[
0, np.where(inputValue_ > sigma)[1]] * s_plus
inputValue_[0, np.where(
inputValue_ < -sigma)[1]] = -sigma * (1 - s_minus) + inputValue_[
0, np.where(inputValue_ < -sigma)[1]] * s_minus
else:
meanValue = np.nan
stdValue = np.nan
p.warning_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "标准化类型的入参有误！")
return inputValue_
def myStandardizeCalc(self, inputValue, calcType, filter):
"""
标准化用函数
:param inputValue: np.array类型，输入的数据
:param calcType: char类型，“zscore”或“0-1”，代表标准化方法
:return: np.array类型，标准化后的数据
"""
if calcType == 'zscore':
return (inputValue -
np.nanmean(inputValue)) / np.nanstd(inputValue)
elif calcType == '0-1':
return (inputValue - np.nanmin(inputValue)) / (
np.nanmax(inputValue) - np.nanmin(inputValue))
elif calcType == 'ppf':
ppf = st.norm.ppf(1 - np.sum(~np.isnan(inputValue)) / np.sum(~np.isnan(filter)))
return ppf + (np.nanmax(inputValue) - ppf) / (np.nanmax(inputValue) - np.nanmin(inputValue)) * (
inputValue - np.nanmin(inputValue))
def myNanFillAsData(self, inputValue, data):
"""
缺失值填充用函数
:param inputValue: np.array类型，输入的数据
:param data: float类型，填充缺失值用参数
:return: np.array类型，填充缺失值后的数据
"""
stockTradeDayCount = self.getFieldData('stockTradeDayCount', 1)
inTheMarket = np.nan * np.zeros([1, np.shape(stockTradeDayCount)[1]])
inTheMarket[0, np.where(stockTradeDayCount > 0)[1]] = 1
for i in range(np.shape(inTheMarket)[1]):
if np.isnan(inTheMarket[0, i]) == False and np.isnan(
inputValue[0, i]):
inputValue[0, i] = data
return inputValue * inTheMarket
def myIndustryFlag(zx):
"""
生成各股行业one-hot矩阵用函数
:param zx: np.array类型，中信行业数据
:return: np.array类型，各股行业one-hot矩阵
"""
ret = np.zeros([int(np.nanmax(zx)), int(np.shape(zx)[1])])
for i in list(range(int(np.nanmax(zx)))):
ret[i, np.where(zx == i + 1)[1]] = 1
return ret
def removeAllData(self, key, usedMemoProcess, usedMemoPercent):
"""
从内存移除数据用函数
:param key: list类型，包含数据名称的列表
:param usedMemoProcess: float类型，python进程占用内存GB上限
:param usedMemoPercent: float类型，计算机内存占用百分比上限
:return:
"""
if not usedMemoProcess and not usedMemoPercent:
for i in range(len(key)):
self.allData.pop(key[i])
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"请求的因子/数据包" + key[i] + "删除完毕！")
else:
mem_Process = round(
psutil.Process(os.getpid()).memory_info().rss / 1024 / 1024 /
1024, 2)
memPercent = round(psutil.virtual_memory().percent / 100, 2)
if (usedMemoProcess and mem_Process > usedMemoProcess) or (
usedMemoPercent and memPercent > usedMemoPercent):
for i in range(len(key)):
self.allData.pop(key[i])
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"请求的因子/数据包" + key[i] + "删除完毕！")
def getStockName(self):
"""
获取股票名称用函数
:param
:return: np.array类型，股票名称列表
"""
return self.getAllData("stockName")
def getStockCode(self):
"""
获取股票数字代码用函数
:param
:return: np.array类型，股票数字代码列表
"""
stock_list = self.getStockList()
return np.array(list(map(lambda x: int(x[:6]), stock_list)))
def getStockList(self):
"""
获取股票代码用函数
:param
:return: np.array类型，股票代码列表
"""
return self.getAllData("stock_code")
def getTradeDateList(self, varargin):
"""
返回交易日历，不传入参数返回历史所有的交易日历，传入参数返回固定交易日数的交易日历
:param days: 返回固定交易日期数
:return: List
"""
ret = self.getAllData("trade_date_int")
if varargin:
return ret[self.getLpp() - varargin + 1:self.getLpp() + 1, :]
else:
return ret[:self.getLpp() + 1, :]
def isLastDate(self):
"""
判断是否为最后一个回测截面，如果是，返回True，否则返回False
:return: bool
"""
return self.lpp == self.endDateFlag
def getStockIndexQuotes(self, indexCode, varargin): # TODO
"""
获取股票指数行情数据
:param indexCode:指数代码
:param varargin:可选参数，截取的日期行数
:return:
"""
lpp = self.lpp
nowDate = self.getNowDate()
indexQuotes = self.getAllData("stockIndexQuotes")[()][indexCode]
site = np.where(indexQuotes[:, 0] == nowDate)[0][0]
if varargin:
return indexQuotes[lpp - varargin + 1:lpp + 1]
else:
return indexQuotes[:lpp + 1]
def getStockIndexDailyQuotes(self, indexCode, tradeDate):
"""
获取股票指数日内行情数据
:param indexCode:指数代码
:param tradeDate:行情日期
:return:
"""
return self.getAllData("stockIndexDailyQuotes")[()][indexCode][tradeDate]
def getFieldData(self, fieldName, varargin):
"""
获取因子矩阵用函数
:param fieldName:因子名
:param varargin: 返回的因子行数
:return:
"""
lpp = self.lpp
if varargin:
return self.getAllData(fieldName)[lpp - varargin + 1:lpp + 1]
else:
return self.getAllData(fieldName)[:lpp + 1]
def getFieldDataByIndex(self, fieldName, index):
"""
获取因子矩阵用函数
:param fieldName:因子名
:param index: 回测下标
:return:
"""
return self.getAllData(fieldName)[index].reshape(1, -1)
def saveGlobal(self, globalName):
"""
存储因子到指定文件夹目录
:param globalName:请求存储的因子名
:return:
"""
factorData = self.getGlobal(globalName)
if sum(factorData[-1] == 0) + sum(np.isnan(
factorData[-1])) == factorData.shape[1]:
p.warning_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"衍生因子 " + globalName + " 异常，结果未保存！")
else:
np.savez_compressed(self.factorNpzPath + globalName + '.npz', factorData)
if self.saveFactorResultAsMat == True:
scio.savemat(self.factorNpzPath + globalName + '.mat', {globalName: factorData})
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"衍生因子 " + globalName + " 保存完成！")
def saveGlobalUnconditionally(self, globalName):
"""
无条件存储因子到指定文件夹目录
:param globalName:请求存储的因子名
:return:
"""
factorData = self.getGlobal(globalName)
np.savez_compressed(self.factorNpzPath + globalName + '.npz', factorData)
if self.saveFactorResultAsMat == True:
scio.savemat(self.factorNpzPath + globalName + '.mat', {globalName: factorData})
p.disp_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"衍生因子 " + globalName + " 保存完成！")
def getBarCount(self):
"""
返回bar计数
:return:
"""
return self.barCount
def getNowDate(self, type):
"""
返回回测当日下标的日期
:return:
"""
if type == 'int':
return self.getAllData("trade_date_int")[self.lpp][0]
elif type == 'datetime':
return self.getAllData("trade_date_datetime")[self.lpp][0]
else:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"type类型有误！")
def getYesterdayDate(self, type):
"""
返回回测前一日下标的日期
:return:
"""
if type == 'int':
return self.getAllData("trade_date_int")[self.lpp - 1][0]
elif type == 'datetime':
return self.getAllData("trade_date_datetime")[self.lpp - 1][0]
else:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"type类型有误！")
def getNextDate(self, type):
"""
返回回测下一日下标的日期
:return:
"""
if type == 'int':
tradeDate = self.getAllData("trade_date_int")
tradeDateHistory = self.getAllData("calendar_date_int")
elif type == 'datetime':
tradeDate = self.getAllData("trade_date_datetime")
tradeDateHistory = self.getAllData("calendar_date_datetime")
else:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"type类型有误！")
index = np.where(tradeDateHistory == tradeDate[self.lpp])[0][0]
return tradeDateHistory[index + 1][0]
def getHalfTimeWeight(halflife, window):
'''
得到半衰期权重,WLS回归用
:param halflife: 半衰期时长，int类型
:param window: 观察期窗口，int类型
:return: 半衰期标准化后的权重，np.array类型，一行
'''
sigma = 0.5 ** (1 / halflife)
weight = np.empty([1, window])
for i in range(window):
weight[0, i] = sigma ** (window - i)
weight = weight / np.sum(weight)
return weight.reshape(1, -1) # 转化为1Darray 方便
def myFiltering(self, inputValue):
"""
将非交易日的因子值设置为np.nan用函数,同时将inf转化为nan
:param inputValue: np.array类型，输入的数据
:param data: float类型，填充缺失值用参数
:return: np.array类型，填充缺失值后的数据
"""
stockTradeDayCount = self.getFieldData('stock_on_list_duration', 1)
stockTradeDayCount = stockTradeDayCount.astype(np.float32)
stockTradeDayCount[stockTradeDayCount > 0] = 1
stockTradeDayCount[stockTradeDayCount == 0] = np.nan
ret = inputValue * stockTradeDayCount
ret[np.isinf(ret)] = np.nan
return ret
def myRank(self, data, type):
"""
将因子截面数据做排名返回
:param data: np.array类型，因子截面数据的输入值
:param type: char类型，降序（descend）、升序（ascend）
:return: np.array类型，data的排名返回值
"""
if type == '降序' or type == 'descend':
rev = -1
elif type == '升序' or type == 'ascend':
rev = 1
data_ = copy.deepcopy(data)
data_ = np.array(data_)
Site = np.where(~np.isnan(data_))
data_[Site] = np.argsort(data_[Site] * rev)
data_[Site] = np.argsort(data_[Site])
return data_.reshape(1, -1)
def mySort(self, data, type):
"""
将因子排序下标返回
:param data: np.array类型，因子截面数据的输入值
:param type: char类型，降序（descend）、升序（ascend）
:return: np.array类型，data的排名下标返回值
"""
if type == '降序' or type == 'descend':
rev = -1
elif type == '升序' or type == 'ascend':
rev = 1
data_ = copy.deepcopy(data)
Site = np.where(~np.isnan(data_))
data_ = np.array(data_)
data_[Site] = np.argsort(data_[Site] * rev)
return data_.reshape(1, -1)
def quoteCorrection(self, inputvalue):
'''
纠正getFieldDailyData获得数据列数错误用函数
:param inputValue: np.array类型，输入的数据
:return: np.array类型，列数正确的函数
'''
column_num = len(self.getAllData('stock_code'))
value_num = inputvalue.shape[1]
if column_num > value_num:
ret = np.hstack(
(inputvalue, np.nan *
np.zeros([inputvalue.shape[0], column_num - value_num])))
elif column_num <= value_num:
ret = inputvalue[:, :column_num]
return ret
def regForNeutralizeFactor(self, factor, list1):
'''
中性化因子用函数
:param factor: 被中性化的因子,二维数组(1,)
:param list: 包含中性化因子数组的列表，列表内部数据格式：二维数组(1,)
:return: ndarray类型，回归后的残差，二维数组(1,)
'''
factor = factor.T
neutralizeList = np.zeros((factor.shape[0], len(list1))) * np.nan
result = np.zeros((1, factor.shape[0])) * np.nan
for i in range(len(list1)):
neutralizeList[:, i] = list1[i][0, :]
y = factor
x = neutralizeList
for i in range(x.shape[1]):
if i == 0:
y_not_empty = np.where(~np.isnan(y))[0].tolist()
x_not_empty = np.where(~np.isnan(x[:, i]))[0].tolist()
not_empty = list(set(x_not_empty).intersection(set(y_not_empty)))
else:
y_not_empty = not_empty
x_not_empty = np.where(~np.isnan(x[:, i]))[0].tolist()
not_empty = list(set(x_not_empty).intersection(set(y_not_empty)))
y = y[not_empty]
x = x[not_empty]
m = LinearRegression()
m.fit(x, y)
residual = y - m.predict(x)
for i in range(len(not_empty)):
result[0, not_empty[i]] = residual[i, 0]
return result
def myNeutralizeCalc(self, y, x):
'''
:param x:
:param y:
:return:
'''
fit_data = np.vstack((x, y))
site = np.where(~np.isnan(np.sum(fit_data, axis=0)))[0]
x_ = x[:, site]
y_ = y[:, site]
resid = np.nan * np.zeros(y.shape).reshape(1, -1)
if x_.size and y_.size:
m = LinearRegression()
m.fit(x_.T, y_.T)
resid[0, site] = (y_.T - m.predict(x_.T)).reshape(1, -1)
return resid
def getReportPeriodsList(self, modified):
'''
获取最新报告期列表用函数
:param modified: bool类型，确定是否需要扩展
:return: np.array类型，报告期数组
'''
report_periods_list = self.getFieldData('reportPeriodsList')
report_periods_list = report_periods_list[~np.isnan(report_periods_list
)]
report_periods_list = np.array(list(set(report_periods_list.tolist())))
report_periods_list = np.flipud(np.sort(report_periods_list))
if modified == False:
return report_periods_list
else:
if report_periods_list[0] % 10000 == 1231:
report_periods_list = np.insert(report_periods_list, 0,
report_periods_list[0] + 9100)
elif report_periods_list[0] % 10000 == 930:
report_periods_list = np.insert(report_periods_list, 0,
report_periods_list[0] + 301)
elif report_periods_list[0] % 10000 == 630:
report_periods_list = np.insert(report_periods_list, 0,
report_periods_list[0] + 300)
elif report_periods_list[0] % 10000 == 331:
report_periods_list = np.insert(report_periods_list, 0,
report_periods_list[0] + 299)
return report_periods_list
def getFinancialAllData(self, key):
"""
导入数据包用函数
:param key: char类型，数据包名
:return: tuple类型，包含数据包数据值和数据名称的元组
"""
if key in self.allData:
return self.allData[key]
npzFile = key + '.npz'
for dataPath in self.dataPathList:
if npzFile in os.listdir(dataPath):
dataFile = dataPath + "\\\\" + npzFile
break
try:
data = np.load(dataFile, allow_pickle=True)
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"数据包导入失败！找不到数据包" + key + "，请更新数据包！")
else:
value = data['value']
fieldName = data['fieldname']
v = (value, fieldName)
self.allData[key] = v
return self.allData[key]
def getFinancialFieldData(self, tableName, fieldName, reportDate):
"""
获取金融数据
:param tableName: char类型，数据包名称
:param fieldName: char/list/tuple类型，数据包中具体数据名称
:param reportDate: int类型，报告期
:return: np.array类型，具体的金融数据
"""
nextDate = self.getNextDate()
tableData = self.getFinancialAllData(tableName)
fieldData = tableData[0]
fieldNameList = tableData[1]
if isinstance(fieldName, str):
try:
site = np.where(fieldNameList == fieldName)[0][0]
except:
p.error_print(
sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"在" + tableName + "数据包里找不到对应的" + fieldName + "字段！")
else:
fieldData = fieldData[:, [0, 1, 2, site]]
stockCode = self.getStockCode()
IA_ret = np.ones([1, len(stockCode)])
fieldData = fieldData[np.where(
fieldData[:, 1] <= nextDate)[0], :]
ret = np.nan * np.zeros([1, len(stockCode)])
fieldData = fieldData[np.where(
fieldData[:, 2] == reportDate)[0], :]
if fieldData.size == 0:
return ret
else:
for i in range(len(stockCode)):
dataTemp = fieldData[np.where(
fieldData[:, 0] == stockCode[i])[0]]
if dataTemp.size == 0:
IA_ret[0, i] = np.nan
continue
if np.shape(dataTemp)[0] > 1:
dataTemp = dataTemp[np.argsort(dataTemp[:, 1])]
ret[0, i] = dataTemp[-1, -1]
if tableName != 'ASHARE_PROFITNOTICE':
ret[np.where(np.isnan(ret))] = 0
ret = ret * IA_ret
elif isinstance(fieldName, (np.ndarray, list, tuple)):
try:
site = list(
map(lambda x: np.where(fieldNameList == x)[0][0],
fieldName))
except:
var_str = fieldName[0]
for i in list(range(1, len(fieldName))):
var_str += ',' + fieldName[i]
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"在" + tableName + "数据包里找不到对应的" + var_str + "字段！")
else:
tmp = [0, 1, 2]
tmp.extend(site)
fieldData = fieldData[:, tmp]
stockCode = self.getStockCode()
IA_ret = np.ones([1, len(stockCode)])
fieldData = fieldData[np.where(
fieldData[:, 1] <= nextDate)[0], :]
ret = np.nan * np.zeros([len(site), len(stockCode)])
fieldData = fieldData[np.where(
fieldData[:, 2] == reportDate)[0], :]
if fieldData.size == 0:
return ret
else:
for i in list(range(len(stockCode))):
dataTemp = fieldData[np.where(
fieldData[:, 0] == stockCode[i])[0]]
if dataTemp.size == 0:
IA_ret[0, i] = np.nan
continue
if np.shape(dataTemp)[0] > 1:
dataTemp = dataTemp[np.argsort(dataTemp[:, 1])]
for j in list(range(3, np.shape(dataTemp)[1])):
ret[j - 3, i] = dataTemp[-1, j]
if tableName != 'ASHARE_PROFITNOTICE':
ret[np.where(np.isnan(ret))] = 0
ret = ret * IA_ret
return ret
def getFinancialFieldDataMatrix(self, tablename, fieldname,
report_periods_list, datalen):
'''
获取财务数据矩阵用函数
:param tablename: char类型，表名
:param fieldname: char类型，字段名
:param report_periods_list: 1darray数组，报告期列表
:param datalen: int类型，数据行数
:return: np.array类型，财务数据矩阵
'''
ret = np.nan * np.zeros((int(datalen), len(self.getStockList())))
for i in range(datalen):
try:
ret[i] = self.getFinancialFieldData(tablename, fieldname,
report_periods_list[i])
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"数据包导入失败！找不到数据包" + tablename + "，请更新数据包！")
return ret
def getFinancialFieldDataMatrixModified(self, value, tablename, fieldname,
report_periods_list):
'''
注意：此函数对value进行操作，返回的是value的修正值，内存占用不变
获取财务数据修正矩阵用函数
:param tablename: char类型，修正表名
:param fieldname: char类型，具体修正字段名
:param report_periods_list: 1darray数组，报告期列表
:return: np.array类型，财务数据矩阵
'''
datalen = min(3, value.shape[0])
if fieldname != 'NET_PROFIT_EXCL_MIN_INT_INC':
ret = np.nan * np.zeros((datalen, len(self.getStockList())))
for i in range(datalen):
try:
ret[i] = self.getFinancialFieldData(tablename, fieldname,
report_periods_list[i])
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"逐行报告期数据提取失败！")
value[:datalen][np.where(np.isnan(
value[:datalen]))] = ret[np.where(np.isnan(value[:datalen]))]
return value
else:
ret_1 = np.nan * np.zeros((datalen, len(self.getStockList())))
for i in range(datalen):
try:
ret_1[i] = self.getFinancialFieldData(tablename, fieldname,
report_periods_list[i])
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"逐行报告期数据提取失败！")
ret_2 = np.nan * np.zeros((datalen, len(self.getStockList())))
for i in range(datalen):
try:
upDown = self.getFinancialFieldData('ASHARE_PROFITNOTICE', [
'S_PROFITNOTICE_NETPROFITMIN',
'S_PROFITNOTICE_NETPROFITMAX'
], report_periods_list[i]) * 10000
ret_2[i] = np.nanmean(upDown, axis=0)
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"字段为净利润，业绩预告逐行报告期数据提取失败！")
value[:datalen][np.where(np.isnan(
value[:datalen]))] = ret_1[np.where(np.isnan(value[:datalen]))]
value[:datalen][np.where(np.isnan(
value[:datalen]))] = ret_2[np.where(np.isnan(value[:datalen]))]
return value
def getFinancialFieldDataMatrixModified_gta(self, value, tablename, fieldname,
report_periods_list):
'''
函数
注意：此函数对value进行操作，返回的是value的修正值，内存占用不变
获取财务数据修正矩阵用函数
:param tablename: char类型，修正表名
:param fieldname: char类型，具体修正字段名
:param report_periods_list: 1darray数组，报告期列表
:return: np.array类型，财务数据矩阵
'''
dic_fieldname = {'B0011': 'GROSSREVENUE', 'B0013': 'OPERATEPROFIT', 'B001': 'TOTALPROFIT', 'A001': 'ASSET'
, 'A0031': 'EQUITYPARENT', 'A003': 'EQUITY', 'B0024': 'PROFITPARENT', 'B001101': 'GROSSREVENUE',
'B002': 'PROFIT'}
fieldname = dic_fieldname[fieldname]
datalen = min(3, value.shape[0])
if fieldname != 'PROFITPARENT' and 'PROFIT':
ret = np.nan * np.zeros((datalen, len(self.getStockList())))
for i in range(datalen):
try:
ret[i] = self.getFinancialFieldData(tablename, fieldname,
report_periods_list[i])
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"逐行报告期数据提取失败！")
value[:datalen][np.where(np.isnan(
value[:datalen]))] = ret[np.where(np.isnan(value[:datalen]))]
return value
elif fieldname == 'PROFITPARENT': # 归母净利润
ret_1 = np.nan * np.zeros((datalen, len(self.getStockList())))
for i in range(datalen):
try:
ret_1[i] = self.getFinancialFieldData(tablename, fieldname,
report_periods_list[i])
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"逐行报告期数据提取失败！")
ret_2 = np.nan * np.zeros((datalen, len(self.getStockList())))
for i in range(datalen):
try:
upDown = self.getFinancialFieldData('STK_FIN_FORECFIN', [
'PROFITPARENTFLOOR',
'PROFITPARENTCEILING'
], report_periods_list[i])
ret_2[i] = np.nanmean(upDown, axis=0)
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"字段为归母净利润，业绩预告逐行报告期数据提取失败！")
value[:datalen][np.where(np.isnan(
value[:datalen]))] = ret_1[np.where(np.isnan(value[:datalen]))]
value[:datalen][np.where(np.isnan(
value[:datalen]))] = ret_2[np.where(np.isnan(value[:datalen]))]
return value
elif fieldname == 'PROFIT': # 净利润
ret_1 = np.nan * np.zeros((datalen, len(self.getStockList())))
for i in range(datalen):
try:
ret_1[i] = self.getFinancialFieldData(tablename, fieldname,
report_periods_list[i])
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"逐行报告期数据提取失败！")
ret_2 = np.nan * np.zeros((datalen, len(self.getStockList())))
for i in range(datalen):
try:
upDown = self.getFinancialFieldData('STK_FIN_FORECFIN', [
'PROFITFLOOR',
'PROFITCEILING'
], report_periods_list[i])
ret_2[i] = np.nanmean(upDown, axis=0)
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"字段为净利润，业绩预告逐行报告期数据提取失败！")
value[:datalen][np.where(np.isnan(
value[:datalen]))] = ret_1[np.where(np.isnan(value[:datalen]))]
value[:datalen][np.where(np.isnan(
value[:datalen]))] = ret_2[np.where(np.isnan(value[:datalen]))]
return value
def getFinancialFieldDataMatrix_Q(self, value, report_periods_list):
'''
获取财务单季度数据矩阵用函数
:param value: ndarray类型，getFieldDataMatrix获得的财务数据矩阵
:param report_periods_list: 1darray数组，报告期列表
:return: np.array类型，财务单季度数据矩阵
'''
datalen = value.shape[0]
ret = np.nan * np.zeros((datalen - 1, len(self.getStockList())))
for i in range(datalen - 1):
try:
if report_periods_list[i] % 10000 == 331:
ret[i] = value[i]
else:
ret[i] = value[i] - value[i + 1]
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"财务数据单季度计算失败！")
return ret
def getFinancialFieldDataMatrix_TTM(self, value, n):
'''
获取财务TTM数据矩阵用函数
:param value: ndarray类型，getFieldDataMatrix_Q获得的财务单季度数据矩阵
:param n: int类型，期数，默认为4
:return: np.array类型，财务TTM数据矩阵
'''
datalen = value.shape[0]
ret = np.nan * np.zeros((datalen - (n - 1), len(self.getStockList())))
for i in range(datalen - (n - 1)):
try:
ret[i] = np.sum(value[i:(i + n), :], axis=0)
except:
p.error_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name,
"财务数据TTM计算失败！")
return ret
def getFirstNonnan(arr, axis, invalid_val):
'''
获得矩阵每行或者每列第一个非空值所在位置用函数
:param arr: ndarray类型
:param axis: 0/1 0为每列提取，1为每行提取
:param invalid_val: int/float，若行/列全空返回的数字
:return: 1darray数组
'''
mask = ~np.isnan(arr)
return np.where(mask.any(axis=axis), mask.argmax(axis=axis), invalid_val)
def getNewestData(arr):
'''
获取矩阵每一列第一个非空值用函数
:param arr: ndarray类型
:return: ndarray类型，一行矩阵
'''
ret = np.nan * np.zeros((1, arr.shape[1]))
for i in range(arr.shape[0]):
ret[0, np.where(np.isnan(ret[0]))[0]] = arr[i][np.where(np.isnan(ret[0]))[0]]
return ret
def getGrowthData(self, factor_list, dataLen, type):
'''
计算growth因子用函数
:param factor_list: ndarray类型，growth因子基础值矩阵
:param dataLen: int类型，默认为1，前后两期的间隔
:param type: char类型，默认为rate，计算的growth因子种类
:return: ndarray类型，一行矩阵
'''
factor_list = np.flipud(factor_list)
if type == 'rate':
growth = (factor_list[dataLen:] - factor_list[:-dataLen]) / np.abs(factor_list[:-dataLen])
elif type == 'value':
growth = factor_list[dataLen:] - factor_list[:-dataLen]
growth = growth.astype('float')
else:
p.warning_print(sys._getframe().f_code.co_filename,
sys._getframe().f_code.co_name, "传入的type类型有误")
return None
growth[np.where(growth == np.inf)] = np.nan
growth[np.where(growth == -np.inf)] = np.nan
growth = np.flipud(growth)
growth = self.getNewestData(growth)
return growth
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