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【转】多因子策略探索（1）
为什么要用聚宽
以市场上知名的TB、文化等期货研究平台为代表，均采用了数据驱动的回测方式，不能按照复利回测（浮赢加仓），也不能方便的在单次回测中操作多个标的的仓位，更不用说一揽子标的的组合策略了。而聚宽平台的架构可以在定义的任意时点，操作市场上任意标的的仓位，非常适合研究/实盘一揽子标的的策略，笔者在这里就用聚宽框架展示几个横截面因子的回测。
期开始研究多因子策略，看了一些研报，初步代码实现了一些经常用到的模块。
本文重在代码实现，底层选股逻辑可以在代码框架基础上灵活变动，本文最后的选股策略借鉴广发证券多年前的研报，逻辑较为简单，且策略已经失效。但最后的结果对于理解多因子分层及策略探索有较大帮助，所以代码和大家分享。
本文前面大部分代码之前已经发过，介绍不再重复，后面新增了两个模块，分别是：选股准备和选股方法尝试。
选股准备模块代码主要实现因子分层、因子权重计算、个股打分。
分层：基于因子值排序分层，分层数可调整；
权重计算：每个因子最高收益组减去最低收益组的差值表征因子有效性，单个因子差值除以各个因子差值的和为权重；
个股打分：对因子分层，收益最高的一层打分为1，最低的为-1，其余的为0，打分值是基于过去一个时间段内计算的均值；
选股方法：
基于因子分层，依次找到分层收益最好的因子，取该因子最佳收益分层的股票，多个因子取多组股票，取交集，股票数不小于10支；
2.基于因子权重和个股打分对股票排序，选择打分最高的股票组合。
用到的数据有：原始数据，中性化后的数据。
因子选择：因子选择的评判标准通常有信息系数IC，包含稳定性的IR（IC除以IC的标准差）、回归系数。本文添加了机器学习的特征选择方法，用于进行因子有效性评判，在FeatureSelection类中identify_collinear方法可以先去除相关性高的特征，identify_importance_lgbm方法使用lightgbm算法进行特征选择，此方法本身能有效避免因子间的影响，embedded_select方法可以使用岭回归或者lasso回归，去除因子间共线性。以上方法都可以作为新的因子选择的标准。
第一模块 数据准备
import pandas as pd
import numpy as np
import time
import datetime
import statsmodels.api as sm
import pickle
import warnings
from jqdata import *
warnings.filterwarnings(‘ignore’)
start_date = ‘2013-01-01’
end_date = ‘2014-01-01’
all_trade_days = (get_trade_days(start_date=start_date,end_date=end_date)).tolist() #所有交易日
trade_days = all_trade_days[::20] #每隔20天取一次数据，基本面数据更新频率较慢，数据获取频率尽量与之对应
securities = get_all_securities()
start_data_dt = datetime.datetime.strptime(start_date,’%Y-%m-%d’).date()
securities_after_start_date = securities[(securities[‘start_date’]<start_data_dt)] #选择起始时间之前上市的股票
all_stocks = list(securities_after_start_date.index)
INDUSTRY_NAME = ‘sw_l1’
ttm_factors = []
‘’’
基本面因子映射
‘’’
fac_dict = {
‘MC’:valuation.market_cap, # 总市值
‘GP’:indicator.gross_profit_margin * income.operating_revenue, # 毛利润
‘OP’:income.operating_profit,
‘OR’:income.operating_revenue, # 营业收入
‘NP’:income_profit, # 净利润
‘EV’:valuation.market_cap + balance.shortterm_loan+balance.non_current_liability_in_one_year+balance.longterm_loan+balance.bonds_payable+balance.longterm_account_payable - cash_flow.cash_and_equivalents_at_end,
'TOE':balance.total_owner_equities, # 股东权益合计(元)
'TOR':income.total_operating_revenue, # 营业总收入
'EBIT':income_profit+income.financial_expense+income.income_tax_expense,
'TOC':income.total_operating_cost,#营业总成本
'NOCF/MC':cash_flow_operate_cash_flow / valuation.market_cap, #经营活动产生的现金流量净额/总市值
'OTR':indicator.ocf_to_revenue, #经营活动产生的现金流量净额/营业收入(%)
'GPOA':indicator.gross_profit_margin * income.operating_revenue / balance.total_assets, #毛利润 / 总资产 = 毛利率*营业收入 / 总资产
'GPM':indicator.gross_profit_margin, # 毛利率
'OPM':income.operating_profit / income.operating_revenue, #营业利润率
'NPM':indicator_profit_margin, # 净利率
'ROA':indicator.roa, # ROA
'ROE':indicator.roe, # ROE
'INC':indicator.inc_return, # 净资产收益率(扣除非经常损益)(%)
'EPS':indicator.eps, # 净资产收益率(扣除非经常损益)(%)
'AP':indicator.adjusted_profit, # 扣除非经常损益后的净利润(元)
'OP':indicator.operating_profit, # 经营活动净收益(元)
'VCP':indicator.value_change_profit, # 价值变动净收益(元) = 公允价值变动净收益+投资净收益+汇兑净收益
'ETTR':indicator.expense_to_total_revenue, # 营业总成本/营业总收入(%)
'OPTTR':indicator.operation_profit_to_total_revenue, # 营业利润/营业总收入(%)
'NPTTR':indicator_profit_to_total_revenue, # 净利润/营业总收入(%)
'OETTR':indicator.operating_expense_to_total_revenue, # 营业费用/营业总收入
'GETTR':indicator.ga_expense_to_total_revenue, # 管理费用/营业总收入(%)
'FETTR':indicator.financing_expense_to_total_revenue, # 财务费用/营业总收入(%)
'OPTP':indicator.operating_profit_to_profit, # 经营活动净收益/利润总额(%)
'IPTP':indicator.invesment_profit_to_profit, # 价值变动净收益/利润总额(%)
'GSASTR':indicator.goods_sale_and_service_to_revenue, # 销售商品提供劳务收到的现金/营业收入(%)
'OTR':indicator.ocf_to_revenue, # 经营活动产生的现金流量净额/营业收入(%)
'OTOP':indicator.ocf_to_operating_profit, # 经营活动产生的现金流量净额/经营活动净收益(%)
'ITRYOY':indicator.inc_total_revenue_year_on_year, # 营业总收入同比增长率(%)
'ITRA':indicator.inc_total_revenue_annual, # 营业总收入环比增长率(%)
'IRYOY':indicator.inc_revenue_year_on_year, # 营业收入同比增长率(%)
'IRA':indicator.inc_revenue_annual, # 营业收入环比增长率(%)
'IOPYOY':indicator.inc_operation_profit_year_on_year, # 营业利润同比增长率(%)
'IOPA':indicator.inc_operation_profit_annual, # 营业利润环比增长率(%)
'INPYOY':indicator.inc_net_profit_year_on_year, # 净利润同比增长率(%)
'INPA':indicator.inc_net_profit_annual, # 净利润环比增长率(%)
'INPTSYOY':indicator.inc_net_profit_to_shareholders_year_on_year, # 归属母公司股东的净利润同比增长率(%)
'INPTSA':indicator.inc_net_profit_to_shareholders_annual, # 归属母公司股东的净利润环比增长率(%)
'INPTSA':indicator.inc_net_profit_to_shareholders_annual, # 归属母公司股东的净利润环比增长率(%)
'ROIC':(income_profit+income.financial_expense+income.income_tax_expense)/(balance.total_owner_equities+balance.shortterm_loan+balance.non_current_liability_in_one_year+balance.longterm_loan+balance.bonds_payable+balance.longterm_account_payable),
'OPTT':income.operating_profit / income.total_profit, # 营业利润占比
'TP/TOR':income.total_profit / income.total_operating_revenue, #利润总额/营业总收入
'OP/TOR':income.operating_profit / income.total_operating_revenue,
'NP/TOR':income_profit / income.total_operating_revenue,
'NP':income_profit, # 净利润
'TA':balance.total_assets, # 总资产
'DER':balance.total_liability / balance.equities_parent_company_owners, # 产权比率 = 负债合计/归属母公司所有者权益合计
'FCFF/TNCL':(cash_flow_operate_cash_flow - cash_flow_invest_cash_flow) / balance.total_non_current_liability, #自由现金流比非流动负债
'NOCF/TL': cash_flow_operate_cash_flow / balance.total_liability, # 经营活动产生的现金流量净额/负债合计
'TCA/TCL':balance.total_current_assets / balance.total_current_liability, # 流动比率
'PE':valuation.pe_ratio, # PE 市盈率
'PB':valuation.pb_ratio, # PB 市净率
'PR':valuation.pcf_ratio, # PR 市现率
'PS':valuation.ps_ratio, # PS 市销率
'TOR/TA':income.total_operating_revenue / balance.total_assets, #总资产周转率
'TOR/FA':income.total_operating_revenue / balance.fixed_assets, #固定资产周转率
'TOR/TCA':income.total_operating_revenue / balance.total_current_assets, #流动资产周转率
'LTL/OC':balance.longterm_loan / income.operating_cost, #长期借款/营业成本
'TL/TA':balance.total_liability / balance.total_assets, #总资产/总负债
'TL/TOE':balance.total_liability / balance.total_owner_equities,#负债权益比
}
adjust_factors = {
‘TOR/TA’:income.total_operating_revenue / balance.total_assets, #总资产周转率
‘TOR/FA’:income.total_operating_revenue / balance.fixed_assets, #固定资产周转率
‘TOR/TCA’:income.total_operating_revenue / balance.total_current_assets, #流动资产周转率
‘LTL/OC’:balance.longterm_loan / income.operating_cost, #长期借款/营业成本
'TL/TA':balance.total_liability / balance.total_assets, #总资产/总负债
'TL/TOE':balance.total_liability / balance.total_owner_equities,#负债权益比
'DER':balance.total_liability / balance.equities_parent_company_owners, # 产权比率 = 负债合计/归属母公司所有者权益合计
'FCFF/TNCL':(cash_flow_operate_cash_flow - cash_flow_invest_cash_flow) / balance.total_non_current_liability, #自由现金流比非流动负债
'NOCF/TL': cash_flow_operate_cash_flow / balance.total_liability, # 经营活动产生的现金流量净额/负债合计
'TCA/TCL':balance.total_current_assets / balance.total_current_liability, # 流动比率
'ROIC':(income_profit+income.financial_expense+income.income_tax_expense)/(balance.total_owner_equities+balance.shortterm_loan+balance.non_current_liability_in_one_year+balance.longterm_loan+balance.bonds_payable+balance.longterm_account_payable),
'OPTT':income.operating_profit / income.total_profit, # 营业利润占比
'TP/TOR':income.total_profit / income.total_operating_revenue, #利润总额/营业总收入
'OP/TOR':income.operating_profit / income.total_operating_revenue,
'NP/TOR':income_profit / income.total_operating_revenue,
'NOCF/MC':cash_flow_operate_cash_flow / valuation.market_cap, #经营活动产生的现金流量净额/总市值
'GPOA':indicator.gross_profit_margin * income.operating_revenue / balance.total_assets, #毛利润 / 总资产 = 毛利率*营业收入 / 总资产
'OPM':income.operating_profit / income.operating_revenue, #营业利润率
'EBIT':income_profit+income.financial_expense+income.income_tax_expense,
}
#获取所有因子列表
factor_list = list(fac_dict.keys())
def get_fundamental_data(securities,factor_list,ttm_factors, date):
‘’’
获取基本面数据,横截面数据，时间、股票、因子三个参数确定
获取的数据中含有Nan值，一般用行业均值填充
输入：
factor_list:list, 普通因子
ttm_factors:list, ttm因子，获取过去四个季度财报数据的和
date:str 或者 datetime.data, 获取数据的时间
securities：list,查询的股票
输出：
DataFrame,普通因子和ttm因子的合并，index为股票代码，values为因子值
‘’’
if len(factor_list) == 0:
return ‘factors list is empty, please input data’
#获取查询的factor list
q = query(valuation.code)
for fac in factor_list:
q = q.add_column(fac_dict[fac])
q = q.filter(valuation.code.in_(securities))
fundamental_df = get_fundamentals(q,date)
fundamental_df.index = fundamental_df[‘code’]
fundamental_df.columns = [‘code’] + factor_list
if type(date) == str:
year = int(date[:4])
month_day = date[5:]
elif type(date) == datetime.date:
date = date.strftime('%Y-%m-%d')
year = int(date[:4])
month_day = date[5:]
else:
return 'input date error'
if month_day < '05-01':
statdate_list = [str(year-2)+'q4', str(year-1)+'q1', str(year-1)+'q2', str(year-1)+'q3']
elif month_day >= '05-01' and month_day < '09-01':
statdate_list = [str(year-1)+'q1', str(year-1)+'q2', str(year-1)+'q3',str(year)+'q1']
elif month_day >= '09-01' and month_day < '11-01':
statdate_list = [str(year-1)+'q2', str(year-1)+'q3', str(year)+'q1', str(year)+'q2']
elif month_day >= '11-01':
statdate_list = [str(year-1)+'q4', str(year)+'q1', str(year)+'q2', str(year)+'q3']
ttm_fundamental_data = ''
ttm_q = query(valuation.code)
for fac in ttm_factors:
ttm_q = ttm_q.add_column(fac_dict[fac])
ttm_q = ttm_q.filter(valuation.code.in_(securities))
for statdate in statdate_list:
if type(ttm_fundamental_data) == str:
fundamental_data = get_fundamentals(ttm_q, statDate=statdate)
fundamental_data.index = fundamental_data['code']
ttm_fundamental_data = fundamental_data
else:
fundamental_data = get_fundamentals(ttm_q, statDate=statdate)
fundamental_data.index = fundamental_data['code']
ttm_fundamental_data.iloc[:,1:] += fundamental_data.iloc[:,1:]
ttm_fundamental_data.columns = ['code'] + ttm_factors
results = pd.merge(fundamental_df,ttm_fundamental_data,on=['code'],how='inner')
results = results.sort_values(by='code')
results.index = results['code']
results = results.drop(['code'],axis=1)
#删除非数值列
columns = list(results.columns)
for column in columns:
if not(isinstance(results[column][0],int) or isinstance(results[column][0],float)):
results = results.drop([column],axis=1)
return results
def get_all_fundamentals(securities, date):
'''
获取所有基本面因子
输入：
securies:list,查询的股票代码
date:str or datetime，查询的时间
输出：
fundamentals:dataframe,index为股票代码，values为因子值
'''
q = query(valuation,balance,cash_flow,income,indicator).filter(valuation.code.in_(securities))
fundamentals = get_fundamentals(q,date)
fundamentals.index = fundamentals['code']
#删除非数值列
columns = list(fundamentals.columns)
for column in columns:
if not(isinstance(fundamentals[column][0],int) or isinstance(fundamentals[column][0],float)):
fundamentals = fundamentals.drop([column],axis=1)
fundamentals = fundamentals.sort_index()
return fundamentals
all_fundamentals = get_all_fundamentals(all_stocks,start_date)
def get_stock_industry(industry_name,date,output_csv = False):
‘’’
获取股票对应的行业
input：
industry_name: str,
“sw_l1”: 申万一级行业
“sw_l2”: 申万二级行业
“sw_l3”: 申万三级行业
“jq_l1”: 聚宽一级行业
“jq_l2”: 聚宽二级行业
“zjw”: 证监会行业
date:时间
output: DataFrame,index 为股票代码，columns 为所属行业代码
‘’’
industries = list(get_industries(industry_name).index)
all_securities = get_all_securities(date=date) #获取当天所有股票代码
all_securities[‘industry_code’] = 1
for ind in industries:
industry_stocks = get_industry_stocks(ind,date)
#有的行业股票不在all_stocks列表之中
industry_stocks = set(all_securities) & set(industry_stocks)
all_securities[‘industry_code’][industry_stocks] = ind
stock_industry = all_securities[‘industry_code’].to_frame()
if output_csv == True:
stock_industry.to_csv(‘stock_industry.csv’) #输出csv文件，股票对应行业
return stock_industry
def fillna_with_industry(data,date,industry_name=‘sw_l1’):
‘’’
使用行业均值填充nan值
input:
data：DataFrame,输入数据，index为股票代码
date:string,时间必须和data数值对应时间一致
output：
DataFrame,缺失值用行业中值填充，无行业数据的用列均值填充
‘’’
stocks = list(data.index)
stocks_industry = get_stock_industry(industry_name,date)
stocks_industry_merge = data.merge(stocks_industry, left_index=True,right_index=True,how=‘left’)
stocks_dropna = stocks_industry_merge.dropna()
columns = list(data.columns)
select_data = []
group_data = stocks_industry_merge.groupby(‘industry_code’)
group_data_mean = group_data.mean()
group_data = stocks_industry_merge.merge(group_data_mean,left_on=‘industry_code’,right_index=True,how=‘left’)
for column in columns:
if type(data[column][0]) != str:
group_data[column+'_x'][pd.isnull(group_data[column+'_x'])] = group_data[column+'_y'][pd.isnull(group_data[column+'_x'])]
group_data[column] = group_data[column+'_x']
#print(group_data.head())
select_data.append(group_data[column])
result = pd.concat(select_data,axis=1)
#行业均值为Nan,用总体均值填充
mean = result.mean()
for i in result.columns:
result[i].fillna(mean[i],inplace=True)
return result
#获取日期列表
def get_tradeday_list(start,end,frequency=None,count=None):
‘’’
input:
start:str or datetime,起始时间，与count二选一
end:str or datetime，终止时间
frequency:
str: day,month,quarter,halfyear,默认为day
int:间隔天数
count:int,与start二选一，默认使用start
‘’’
if isinstance(frequency,int):
all_trade_days = get_trade_days(start,end)
trade_days = all_trade_days[::frequency]
days = [datetime.datetime.strftime(i,’%Y-%m-%d’) for i in trade_days]
return days
if count != None:
df = get_price('000001.XSHG',end_date=end,count=count)
else:
df = get_price('000001.XSHG',start_date=start,end_date=end)
if frequency == None or frequency =='day':
days = df.index
else:
df['year-month'] = [str(i)[0:7] for i in df.index]
if frequency == 'month':
days = df.drop_duplicates('year-month').index
elif frequency == 'quarter':
df['month'] = [str(i)[5:7] for i in df.index]
df = df[(df['month']=='01') | (df['month']=='04') | (df['month']=='07') | (df['month']=='10') ]
days = df.drop_duplicates('year-month').index
elif frequency =='halfyear':
df['month'] = [str(i)[5:7] for i in df.index]
df = df[(df['month']=='01') | (df['month']=='06')]
days = df.drop_duplicates('year-month').index
trade_days = [datetime.datetime.strftime(i,'%Y-%m-%d') for i in days]
return trade_days
tl = get_tradeday_list(start_date,end_date,frequency=‘month’)
def get_date_list(begin_date, end_date):
‘’’
得到datetime类型时间序列
‘’’
dates = []
dt = datetime.datetime.strptime(begin_date,"%Y-%m-%d")
date = begin_date[:]
while date <= end_date:
dates.append(date)
dt += datetime.timedelta(days=1)
date = dt.strftime("%Y-%m-%d")
return dates
#去极值函数
#mad中位数去极值法
def filter_extreme_MAD(series,n): #MAD: 中位数去极值
median = series.quantile(0.5)
new_median = ((series - median).abs()).quantile(0.50)
max_range = median + nnew_median
min_range = median - nnew_median
return np.clip(series,min_range,max_range)
#进行标准化处理
def winsorize(factor, std=3, have_negative = True):
‘’’
去极值函数
factor:以股票code为index，因子值为value的Series
std为几倍的标准差，have_negative 为布尔值，是否包括负值
输出Series
‘’’
r=factor.dropna().copy()
if have_negative == False:
r = r[r>=0]
else:
pass
#取极值
edge_up = r.mean()+stdr.std()
edge_low = r.mean()-stdr.std()
r[r>edge_up] = edge_up
r[r<edge_low] = edge_low
return r
#标准化函数：
def standardize(s,ty=2):
‘’’
s为Series数据
ty为标准化类型:1 MinMax,2 Standard,3 maxabs
‘’’
data=s.dropna().copy()
if int(ty)1:
re = (data - data.min())/(data.max() - data.min())
elif ty2:
re = (data - data.mean())/data.std()
elif ty==3:
re = data/10**np.ceil(np.log10(data.abs().max()))
return re
#数据去极值及标准化
def winsorize_and_standarlize(data,qrange=[0.05,0.95],axis=0):
‘’’
input:
data:Dataframe or series,输入数据
qrange:list,list[0]下分位数，list[1]，上分位数，极值用分位数代替
‘’’
if isinstance(data,pd.DataFrame):
if axis == 0:
q_down = data.quantile(qrange[0])
q_up = data.quantile(qrange[1])
index = data.index
col = data.columns
for n in col:
data[n][data[n] > q_up[n]] = q_up[n]
data[n][data[n] < q_down[n]] = q_down[n]
data = (data - data.mean())/data.std()
data = data.fillna(0)
else:
data = data.stack()
data = data.unstack(0)
q = data.quantile(qrange)
index = data.index
col = data.columns
for n in col:
data[n][data[n] > q[n]] = q[n]
data = (data - data.mean())/data.std()
data = data.stack().unstack(0)
data = data.fillna(0)
elif isinstance(data,pd.Series):
name = data.name
q = data.quantile(qrange)
data = np.clip(data,q.values[0],q.values[1])
data = (data - data.mean())/data.std()
return data
def neutralize(data,date,market_cap,industry_name=‘sw_l1’):
‘’’
中性化，使用行业和市值因子中性化
input：
data：DataFrame,index为股票代码，columns为因子，values为因子值
name:str,行业代码
“sw_l1”: 申万一级行业
“sw_l2”: 申万二级行业
“sw_l3”: 申万三级行业
“jq_l1”: 聚宽一级行业
“jq_l2”: 聚宽二级行业
“zjw”: 证监会行业
date:获取行业数据的时间
maket_cap:市值因子
‘’’
industry_se = get_stock_industry(industry_name,date)
columns = list(data.columns)
if isinstance(industry_se,pd.Series):
industry_se = industry_se.to_frame()
if isinstance(market_cap,pd.Series):
market_cap = market_cap.to_frame()
index = list(data.index)
industry_se = np.array(industry_se.ix[index,0].tolist())
industry_dummy = sm.categorical(industry_se,drop=True)
industry_dummy = pd.DataFrame(industry_dummy,index=index)
market_cap = np.log(market_cap.loc[index])
x = pd.concat([industry_dummy,market_cap],axis=1)
model = sm.OLS(data,x)
result = model.fit()
y_fitted = result.fittedvalues
neu_result = data - y_fitted
return neu_result
def get_month_profit(stocks,start_date,end_date,month_num=1,cal_num=3):
'''
获取月收益率数据，数据为本月相对于上月的增长率
input:
stocks:list 股票代码
start_date:str, 初始日期
end_date:str,终止日期
month_num:计算几个月的收益率，默认为1，即一个月的收益率
cal_num:int，计算每月最后n天的收盘价均值，默认为3
'''
start_year = int(start_date[:4])
end_year = int(end_date[:4])
start_month = int(start_date[5:7])
end_month = int(end_date[5:7])
len_month = (end_year - start_year)*12 + (end_month - start_month)
price_list = []
#获取初始时间之前一个月的价格数据
if start_month == 1:
last_date = str(start_year-1)+'-'+'12'+'-'+'01'
else:
last_date = str(start_year-1)+'-'+str(start_month-1)+'-'+'01'
last_price = get_price(stocks,fields=['close'],count=cal_num,end_date=last_date)['close']
last_price = last_price.mean().to_frame()
last_price.columns = [last_date]
price_list.append(last_price)
#计算给定时间段内的月度价格数据
for i in range(len_month):
date = str(start_year+i//12)+'-'+str(start_month+i%12)+'-'+'01'
price = get_price(stocks,fields=['close'],count=cal_num,end_date=date)['close']
price_mean = price.mean().to_frame()
price_mean.columns = [date]
price_list.append(price_mean)
month_profit = pd.concat(price_list,axis=1)
#计算月度收益率
month_profit_pct = month_profit.pct_change(month_num,axis=1).dropna(axis=1,how='all')
return month_profit_pct
def get_profit_depend_timelist(stocks,timelist,month_num=1,cal_num=3):
‘’’
input:
stocks:list 股票代码
timelist: 时间序列
month_num:计算几个月的收益率，默认为1，即一个月的收益率
cal_num:int，计算每月最后n天的收盘价均值，默认为3
‘’’
price_list = []
for date in timelist:
price = get_price(stocks,fields=[‘close’],count=cal_num,end_date=date)[‘close’]
price_mean = price.mean().to_frame()
price_mean.columns = [date]
price_list.append(price_mean)
profit = pd.concat(price_list,axis=1)
profit_pct = profit.pct_change(month_num,axis=1).dropna(axis=1,how=‘all’)
return profit_pct
def get_day_profit_forward(stocks,end_date,start_date=None,count=-1,pre_num=20):
‘’’
获取收益率,pre_num为计算时间差，在时间轴上的当期值是未来计算周期内的收益率，
例如：pre_num=3,2013-01-01对应的收益率是2013-01-04的收益率与01-01日收益率之差
input：
stocks:list or Series,股票代码
start_date:开始时间
end_date：结束时间
count:与start_date二选一，向前取值个数
pre_num:int,向后计算的天数
output:
profit:dataframe,index为日期，columns为股票代码，values为收益率
‘’’
if count == -1:
price = get_price(stocks,start_date,end_date,fields=[‘close’])[‘close’]
date_list = get_trade_days(start_date=start_date,end_date=end_date)
price.index = date_list
else:
price = get_price(stocks,end_date=end_date,count=count,fields=['close'])['close']
date_list = get_trade_days(end_date=end_date,count=count)
price.index = date_list
profit = price.pct_change(periods=pre_num).shift(-pre_num).dropna()
return profit
def get_one_day_data(stocks,factor_list,ttm_factors,date,neu=False):
'''
获取一天的基本面数据
input:
stocks:list,股票列表
factor_list:list,普通因子列表
ttm_factors:list,ttm因子列表
date:str or datetime， 获取数据时间
neu:bool,是否进行中性化处理，使用市值和行业进行中性化，默认不进行中性化
'''
fund_data = get_fundamental_data(stocks,factor_list,ttm_factors,date)
fillna_data = fillna_with_industry(fund_data,date)
if neu == False:
results = winsorize_and_standarlize(fillna_data)
elif 'MC' in fillna_data.columns:
neu_data = neutralize(fillna_data,date,fillna_data['MC'])
results = winsorize_and_standarlize(neu_data)
elif 'market_cap' in fillna_data.columns:
neu_data = neutralize(fillna_data,date,fillna_data['market_cap'])
results = winsorize_and_standarlize(neu_data)
else:
print("error: please input 'market_cap' for neutralize")
return None
return results
def get_timelist_data(stocks,factor_list,ttm_factors,timelist,neu=False):
dic = {}
for date in timelist:
fund_date = get_one_day_data(stocks,factor_list,ttm_factors,date,neu=neu)
dic[date] = fund_date
return dic
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