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fisher_roe.py
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# author:linbirg
# 2019-09-04
# 本身存在的问题：
# 1 fisher财务因子
# 2 roe选股
# 3 行业过滤
# 优化了卖出条件的判断
import pandas as pd
import datetime as dt
from jqdata import get_trade_days
def log_time(f):
import time
def decorater(*args,**kw):
now = time.time() * 1000
ret = f(*args,**kw)
delta = time.time() * 1000 - now
log.info('函数[%s]用时[%d]'%(f.__name__,delta))
return ret
return decorater
class FileHelper:
@staticmethod
def dict_to_file(dict_data,path='history_rsrs.json'):
write_file(path, str(dict_data))
@staticmethod
def load_dict_from(path='history_rsrs.json'):
import json
try:
data = read_file(path)
str_data = str(data,'utf-8')
str_data = str_data.replace("'", '"')
dict_data= json.loads(str_data)
return dict_data
except Exception as e:
log.info('加载文件失败。'+str(e))
class DateHelper:
@classmethod
def to_date(cls,one):
import datetime
if isinstance(one,str):
one_date = datetime.datetime.strptime(one, "%Y-%m-%d")
return one_date.date()
if isinstance(one,datetime.datetime):
return one.date()
if isinstance(one,datetime.date):
return one
raise RuntimeError('不支持的日期格式')
@classmethod
def add_ndays(cls,one,ndays):
import datetime
one_date = cls.to_date(one)
one_date = one_date + datetime.timedelta(ndays)
return one_date
@classmethod
def date_is_after(cls, one, other):
one_date = cls.to_date(one)
other_date = cls.to_date(other)
is_after = one_date > other_date
# print('one[%s] after other[%s]? [%s]'%(str(one_date),str(other_date),str(is_after)))
return is_after
class BzUtil():
# 去极值
@staticmethod
def fun_winsorize(rs, type, num):
# rs为Series化的数据
rs = rs.dropna().copy()
low_line, up_line = 0, 0
if type == 1: # 标准差去极值
mean = rs.mean()
#取极值
mad = num*rs.std()
up_line = mean + mad
low_line = mean - mad
elif type == 2: #中位值去极值
rs = rs.replace([-np.inf, np.inf], np.nan)
median = rs.median()
md = abs(rs - median).median()
mad = md * num * 1.4826
up_line = median + mad
low_line = median - mad
elif type == 3: #Boxplot 去极值
if len(rs) < 2:
return rs
mc = sm.stats.stattools.medcouple(rs)
rs.sort()
q1 = rs[int(0.25*len(rs))]
q3 = rs[int(0.75*len(rs))]
iqr = q3-q1
if mc >= 0:
low_line = q1-1.5*np.exp(-3.5*mc)*iqr
up_line = q3+1.5*np.exp(4*mc)*iqr
else:
low_line = q1-1.5*np.exp(-4*mc)*iqr
up_line = q3+1.5*np.exp(3.5*mc)*iqr
rs[rs < low_line] = low_line
rs[rs > up_line] = up_line
return rs
#标准化
@staticmethod
def fun_standardize(s,type):
'''
s为Series数据
type为标准化类型:1 MinMax,2 Standard,3 maxabs
'''
data=s.dropna().copy()
if int(type)==1:
rs = (data - data.min())/(data.max() - data.min())
elif type==2:
rs = (data - data.mean())/data.std()
elif type==3:
rs = data/10**np.ceil(np.log10(data.abs().max()))
return rs
#中性化
@staticmethod
def fun_neutralize(s, df, module='pe_ratio', industry_type=None, level=2, statsDate=None):
'''
参数：
s为stock代码 如'000002.XSHE' 可为list,可为str
moduel:中性化的指标 默认为PE
industry_type:行业类型(可选), 如果行业不指定，全市场中性化
返回：
中性化后的Series index为股票代码 value为中性化后的值
'''
s = df[df.code.isin(list(s))]
s = s.reset_index(drop = True)
s = pd.Series(s[module].values, index=s['code'])
s = BzUtil.fun_winsorize(s,1,3)
if industry_type:
stocks = BzUtil.fun_get_industry_stocks(industry=industry_type, level=level, statsDate=statsDate)
else:
stocks = list(get_all_securities(['stock'], date=statsDate).index)
df = df[df.code.isin(stocks)]
df = df.reset_index(drop = True)
df = pd.Series(df[module].values, index=df['code'])
df = BzUtil.fun_winsorize(df,1, 3)
rs = (s - df.mean())/df.std()
return rs
@staticmethod
def unpaused(stock_list):
current_data = get_current_data()
tmpList = []
for stock in stock_list:
if not current_data[stock].paused:
# or stock in positions_list:
tmpList.append(stock)
return tmpList
@staticmethod
def remove_st(stock_list, statsDate):
current_data = get_current_data()
return [s for s in stock_list if not current_data[s].is_st]
@staticmethod
def remove_limit_up(stock_list):
h = history(1, '1m', 'close', stock_list, df=False, skip_paused=False, fq='pre')
h2 = history(1, '1m', 'high_limit', stock_list, df=False, skip_paused=False, fq='pre')
tmpList = []
for stock in stock_list:
if h[stock][0] < h2[stock][0]:
tmpList.append(stock)
return tmpList
# 剔除上市时间较短的产品
@staticmethod
def fun_delNewShare(current_dt, equity, deltaday):
deltaDate = DateHelper.to_date(current_dt) - dt.timedelta(deltaday)
tmpList = []
for stock in equity:
if get_security_info(stock).start_date < deltaDate:
tmpList.append(stock)
return tmpList
# 行业列表
@staticmethod
def fun_get_industry(cycle=None):
# cycle 的参数：None取所有行业，True取周期性行业，False取非周期性行业
industry_dict = {
'A01':False,# 农业 1993-09-17
'A02':False,# 林业 1996-12-06
'A03':False,# 畜牧业 1997-06-11
'A04':False,# 渔业 1993-05-07
'A05':False,# 农、林、牧、渔服务业 1997-05-30
'B06':True, # 煤炭开采和洗选业 1994-01-06
'B07':True, # 石油和天然气开采业 1996-06-28
'B08':True, # 黑色金属矿采选业 1997-07-08
'B09':True, # 有色金属矿采选业 1996-03-20
'B11':True, # 开采辅助活动 2002-02-05
'C13':False, # 农副食品加工业 1993-12-15
'C14':False,# 食品制造业 1994-08-18
'C15':False,# 酒、饮料和精制茶制造业 1992-10-12
'C17':True,# 纺织业 1992-06-16
'C18':True,# 纺织服装、服饰业 1993-12-31
'C19':True,# 皮革、毛皮、羽毛及其制品和制鞋业 1994-04-04
'C20':False,# 木材加工及木、竹、藤、棕、草制品业 2005-05-10
'C21':False,# 家具制造业 1996-04-25
'C22':False,# 造纸及纸制品业 1993-03-12
'C23':False,# 印刷和记录媒介复制业 1994-02-24
'C24':False,# 文教、工美、体育和娱乐用品制造业 2007-01-10
'C25':True, # 石油加工、炼焦及核燃料加工业 1993-10-25
'C26':True, # 化学原料及化学制品制造业 1990-12-19
'C27':False,# 医药制造业 1993-06-29
'C28':True, # 化学纤维制造业 1993-07-28
'C29':True, # 橡胶和塑料制品业 1992-08-28
'C30':True, # 非金属矿物制品业 1992-02-28
'C31':True, # 黑色金属冶炼及压延加工业 1994-01-06
'C32':True, # 有色金属冶炼和压延加工业 1996-02-15
'C33':True, # 金属制品业 1993-11-30
'C34':True, # 通用设备制造业 1992-03-27
'C35':True, # 专用设备制造业 1992-07-01
'C36':True, # 汽车制造业 1992-07-24
'C37':True, # 铁路、船舶、航空航天和其它运输设备制造业 1992-03-31
'C38':True, # 电气机械及器材制造业 1990-12-19
'C39':False,# 计算机、通信和其他电子设备制造业 1990-12-19
'C40':False,# 仪器仪表制造业 1993-09-17
'C41':True, # 其他制造业 1992-08-14
'C42':False,# 废弃资源综合利用业 2012-10-26
'D44':True, # 电力、热力生产和供应业 1993-04-16
'D45':False,# 燃气生产和供应业 2000-12-11
'D46':False,# 水的生产和供应业 1994-02-24
'E47':True, # 房屋建筑业 1993-04-29
'E48':True, # 土木工程建筑业 1994-01-28
'E50':True, # 建筑装饰和其他建筑业 1997-05-22
'F51':False,# 批发业 1992-05-06
'F52':False,# 零售业 1992-09-02
'G53':True, # 铁路运输业 1998-05-11
'G54':True, # 道路运输业 1991-01-14
'G55':True, # 水上运输业 1993-11-19
'G56':True, # 航空运输业 1997-11-05
'G58':True, # 装卸搬运和运输代理业 1993-05-05
'G59':False,# 仓储业 1996-06-14
'H61':False,# 住宿业 1993-11-18
'H62':False,# 餐饮业 1997-04-30
'I63':False,# 电信、广播电视和卫星传输服务 1992-12-02
'I64':False,# 互联网和相关服务 1992-05-07
'I65':False,# 软件和信息技术服务业 1992-08-20
'J66':True, # 货币金融服务 1991-04-03
'J67':True, # 资本市场服务 1994-01-10
'J68':True, # 保险业 2007-01-09
'J69':True, # 其他金融业 2012-10-26
'K70':True, # 房地产业 1992-01-13
'L71':False,# 租赁业 1997-01-30
'L72':False,# 商务服务业 1996-08-29
'M73':False,# 研究和试验发展 2012-10-26
'M74':True, # 专业技术服务业 2007-02-15
'N77':False,# 生态保护和环境治理业 2012-10-26
'N78':False,# 公共设施管理业 1992-08-07
'P82':False,# 教育 2012-10-26
'Q83':False,# 卫生 2007-02-05
'R85':False,# 新闻和出版业 1992-12-08
'R86':False,# 广播、电视、电影和影视录音制作业 1994-02-24
'R87':False,# 文化艺术业 2012-10-26
'S90':False,# 综合 1990-12-10
}
industry_list = []
if cycle == True:
for industry in list(industry_dict.keys()):
if industry_dict[industry] == True:
industry_list.append(industry)
elif cycle == False:
for industry in list(industry_dict.keys()):
if industry_dict[industry] == False:
industry_list.append(industry)
else:
industry_list = list(industry_dict.keys())
return industry_list
# 一级行业列表
@staticmethod
def fun_get_industry_levelI(industry=None):
industry_dict = {
'A':['A01', 'A02', 'A03', 'A04', 'A05'] #农、林、牧、渔业
,'B':['B06', 'B07', 'B08', 'B09', 'B11'] #采矿业
,'C':['C13', 'C14', 'C15', 'C17', 'C18', 'C19', 'C20', 'C21', 'C22', 'C23', 'C24', 'C25', 'C26', 'C27', 'C28', 'C29', 'C30', 'C31', 'C32',\
'C33', 'C34', 'C35', 'C36', 'C37', 'C38', 'C39', 'C40', 'C41', 'C42'] #制造业
,'D':['D44', 'D45', 'D46'] #电力、热力、燃气及水生产和供应业
,'E':['E47', 'E48', 'E50'] #建筑业
,'F':['F51', 'F52'] #批发和零售业
,'G':['G53', 'G54', 'G55', 'G56', 'G58', 'G59'] #交通运输、仓储和邮政业
,'H':['H61', 'H62'] #住宿和餐饮业
,'I':['I63', 'I64', 'I65'] #信息传输、软件和信息技术服务业
,'J':['J66', 'J67', 'J68', 'J69'] #金融业
,'K':['K70'] #房地产业
,'L':['L71', 'L72'] #租赁和商务服务业
,'M':['M73', 'M74'] #科学研究和技术服务业
,'N':['N78'] #水利、环境和公共设施管理业
#,'O':[] #居民服务、修理和其他服务业
,'P':['P82'] #教育
,'Q':['Q83'] #卫生和社会工作
,'R':['R85', 'R86', 'R87'] #文化、体育和娱乐业
,'S':['S90'] #综合
}
if industry:
return industry_dict[industry]
return industry_dict
# 根据行业取股票列表
@staticmethod
def fun_get_industry_stocks(industry, level=2, statsDate=None):
if level == 2:
stock_list = get_industry_stocks(industry, statsDate)
elif level == 1:
industry_list = BzUtil.fun_get_industry_levelI(industry)
stock_list = []
for industry_code in industry_list:
tmpList = get_industry_stocks(industry_code, statsDate)
stock_list = stock_list + tmpList
stock_list = list(set(stock_list))
else:
stock_list = []
return stock_list
@staticmethod
def filter_without(stocks, bad_stocks):
tmpList = []
for stock in stocks:
if stock not in bad_stocks:
tmpList.append(stock)
return tmpList
@staticmethod
def filter_intersection(stocks,others):
ret = list(set(stocks) & set(others))
return ret
@classmethod
def financial_data_filter_bigger(cls, stocks, factor=indicator.gross_profit_margin,val=40,startDate=None):
q = query(indicator.code, factor).filter(factor>val,indicator.code.in_(stocks))
df = get_fundamentals(q,date=startDate)
return list(df['code'])
@classmethod
def filter_financial_data_area(cls, security_list, factor=valuation.pe_ratio, area=(5,35),startDate=None):
q = query(indicator.code, factor).filter(factor>area[0],factor<area[1],indicator.code.in_(security_list))
df = get_fundamentals(q,date=startDate)
return list(df['code'])
@classmethod
def get_all_stocks(cls,startDate=None):
q = query(indicator.code)
df = get_fundamentals(q, date=startDate)
return list(df['code'])
@classmethod
def print_with_name(cls, stocks):
for s in stocks:
info = get_security_info(s)
log.info(info.code,info.display_name)
class QuantLib():
@classmethod
def get_fundamentals_sum(cls, table_name=indicator, search=indicator.adjusted_profit, statsDate=None):
# 取最近的五个季度财报的日期
def __get_quarter(table_name, statsDate):
'''
返回最近 n 个财报的日期
返回每个股票最近一个财报的日期
'''
# 取最新一季度的统计日期
if table_name == 'indicator':
q = query(indicator.code, indicator.statDate)
elif table_name == 'income':
q = query(income.code, income.statDate)
elif table_name == 'cash_flow':
q = query(cash_flow.code, cash_flow.statDate)
elif table_name == 'balance':
q = query(balance.code, balance.statDate)
df = get_fundamentals(q, date = statsDate)
stock_last_statDate = {}
tmpDict = df.to_dict()
for i in range(len(list(tmpDict['statDate'].keys()))):
# 取得每个股票的代码，以及最新的财报发布日
stock_last_statDate[tmpDict['code'][i]] = tmpDict['statDate'][i]
df = df.sort_values("statDate",ascending= False)
# 取得最新的财报日期
last_statDate = df.iloc[0,1]
this_year = int(str(last_statDate)[0:4])
this_month = str(last_statDate)[5:7]
if this_month == '12':
last_quarter = str(this_year) + 'q4'
last_two_quarter = str(this_year) + 'q3'
last_three_quarter = str(this_year) + 'q2'
last_four_quarter = str(this_year) + 'q1'
last_five_quarter = str(this_year - 1) + 'q4'
elif this_month == '09':
last_quarter = str(this_year) + 'q3'
last_two_quarter = str(this_year) + 'q2'
last_three_quarter = str(this_year) + 'q1'
last_four_quarter = str(this_year - 1) + 'q4'
last_five_quarter = str(this_year - 1) + 'q3'
elif this_month == '06':
last_quarter = str(this_year) + 'q2'
last_two_quarter = str(this_year) + 'q1'
last_three_quarter = str(this_year - 1) + 'q4'
last_four_quarter = str(this_year - 1) + 'q3'
last_five_quarter = str(this_year - 1) + 'q2'
else: #this_month == '03':
last_quarter = str(this_year) + 'q1'
last_two_quarter = str(this_year - 1) + 'q4'
last_three_quarter = str(this_year - 1) + 'q3'
last_four_quarter = str(this_year - 1) + 'q2'
last_five_quarter = str(this_year - 1) + 'q1'
return last_quarter, last_two_quarter, last_three_quarter, last_four_quarter, last_five_quarter, stock_last_statDate
# 查财报，返回指定值
def __get_fundamentals_value(table_name, search, myDate):
'''
输入查询日期
返回指定的财务数据，格式 dict
'''
if table_name == 'indicator':
q = query(indicator.code, search, indicator.statDate)
elif table_name == 'income':
q = query(income.code, search, income.statDate)
elif table_name == 'cash_flow':
q = query(cash_flow.code, search, cash_flow.statDate)
elif table_name == 'balance':
q = query(balance.code, search, balance.statDate)
df = get_fundamentals(q, statDate = myDate).fillna(value=0)
tmpDict = df.to_dict()
stock_dict = {}
name = str(search).split('.')[-1]
for i in range(len(list(tmpDict['statDate'].keys()))):
tmpList = []
tmpList.append(tmpDict['statDate'][i])
tmpList.append(tmpDict[name][i])
stock_dict[tmpDict['code'][i]] = tmpList
return stock_dict
# 得到最近 n 个季度的统计时间
last_quarter, last_two_quarter, last_three_quarter, last_four_quarter, last_five_quarter, stock_last_statDate = __get_quarter(table_name, statsDate)
last_quarter_dict = __get_fundamentals_value(table_name, search, last_quarter)
last_two_quarter_dict = __get_fundamentals_value(table_name, search, last_two_quarter)
last_three_quarter_dict = __get_fundamentals_value(table_name, search, last_three_quarter)
last_four_quarter_dict = __get_fundamentals_value(table_name, search, last_four_quarter)
last_five_quarter_dict = __get_fundamentals_value(table_name, search, last_five_quarter)
tmp_list = []
stock_list = list(stock_last_statDate.keys())
for stock in stock_list:
tmp_dict = {}
tmp_dict['code'] = stock
value_list = []
if stock in last_quarter_dict:
if stock_last_statDate[stock] == last_quarter_dict[stock][0]:
value_list.append(last_quarter_dict[stock][1])
if stock in last_two_quarter_dict:
value_list.append(last_two_quarter_dict[stock][1])
if stock in last_three_quarter_dict:
value_list.append(last_three_quarter_dict[stock][1])
if stock in last_four_quarter_dict:
value_list.append(last_four_quarter_dict[stock][1])
if stock in last_five_quarter_dict:
value_list.append(last_five_quarter_dict[stock][1])
for i in range(4 - len(value_list)):
value_list.append(0)
tmp_dict['0Q'] = value_list[0]
tmp_dict['1Q'] = value_list[1]
tmp_dict['2Q'] = value_list[2]
tmp_dict['3Q'] = value_list[3]
tmp_dict['sum_value'] = value_list[0] + value_list[1] + value_list[2] + value_list[3]
tmp_list.append(tmp_dict)
df = pd.DataFrame(tmp_list)
return df
@classmethod
def fun_get_factor(cls, df, factor_name, industry, level, statsDate):
stock_list = BzUtil.fun_get_industry_stocks(industry, level, statsDate)
rs = BzUtil.fun_neutralize(stock_list, df, module=factor_name, industry_type=industry, level=level, statsDate=statsDate)
rs = BzUtil.fun_standardize(rs, 2)
return rs
@classmethod
def fun_diversity_by_industry(cls, stock_list, max_num, statsDate):
if stock_list is None:
return None
industry_list = BzUtil.fun_get_industry(cycle=None)
tmpList = []
for industry in industry_list:
i = 0
stocks = BzUtil.fun_get_industry_stocks(industry, 2, statsDate)
for stock in stock_list:
if stock in stocks: #by 行业选入 top max_num 的标的（如有）
i += 1
if i <= max_num:
tmpList.append(stock) #可能一个股票横跨多个行业，会导致多次入选，但不影响后面计算
final_stocks = []
for stock in stock_list:
if stock in tmpList:
final_stocks.append(stock)
return final_stocks
@classmethod
def fun_get_bad_stock_list(cls, statsDate):
#0、剔除商誉占比 > 30% 的股票
df = get_fundamentals(
query(valuation.code, balance.good_will, balance.equities_parent_company_owners),
date = statsDate
)
df = df.fillna(value = 0)
df['good_will_ratio'] = 1.0*df['good_will'] / df['equities_parent_company_owners']
list_good_will = list(df[df.good_will_ratio > 0.3].code)
bad_stocks = list_good_will
bad_stocks = list(set(bad_stocks))
return bad_stocks
@staticmethod
@log_time
def get_inc_net_profile(statsDate):
# 取净利润增长率为正的
df = QuantLib.get_fundamentals_sum('income', income.net_profit, statsDate)
df = df.drop(['0Q', '1Q', '2Q', '3Q'], axis=1)
df.rename(columns={'sum_value':'ttm_1y'}, inplace=True)
df1 = QuantLib.get_fundamentals_sum('income', income.net_profit, (statsDate - dt.timedelta(365)))
df1 = df1.drop(['0Q', '1Q', '2Q', '3Q'], axis=1)
df1.rename(columns={'sum_value':'ttm_2y'}, inplace=True)
df = df.merge(df1, on='code')
df = df.fillna(value=0)
df['inc_net_profit'] = 1.0*(df['ttm_1y'] - df['ttm_2y'])
df = df[df.inc_net_profit > 0]
inc_net_profit_list = list(df.code)
return inc_net_profit_list
@staticmethod
def get_inc_operating_revenue_list(statsDate):
# 按行业取营业收入增长率前 1/3
df = QuantLib.get_fundamentals_sum('income', income.operating_revenue, statsDate)
df = df.drop(['0Q', '1Q', '2Q', '3Q'], axis=1)
df.rename(columns={'sum_value':'ttm_1y'}, inplace=True)
df1 = QuantLib.get_fundamentals_sum('income', income.operating_revenue, (statsDate - dt.timedelta(365)))
df1 = df1.drop(['0Q', '1Q', '2Q', '3Q'], axis=1)
df1.rename(columns={'sum_value':'ttm_2y'}, inplace=True)
df = df.merge(df1, on='code')
df = df.fillna(value=0)
df['inc_operating_revenue'] = 1.0*(df['ttm_1y'] - df['ttm_2y']) / abs(df['ttm_2y'])
df = df.fillna(value = 0)
industry_list = BzUtil.fun_get_industry(cycle=None)
inc_operating_revenue_list = []
for industry in industry_list:
stock_list = BzUtil.fun_get_industry_stocks(industry, 2, statsDate)
df_inc_operating_revenue = df[df.code.isin(stock_list)]
df_inc_operating_revenue = df_inc_operating_revenue.sort_values("inc_operating_revenue",ascending= False)
inc_operating_revenue_list = inc_operating_revenue_list + list(df_inc_operating_revenue[:int(len(df_inc_operating_revenue)*0.33)].code)
return inc_operating_revenue_list
@staticmethod
def get_low_liability_ratio(statsDate):
# 指标剔除资产负债率相对行业最高的1/3的股票
df = get_fundamentals(query(balance.code, balance.total_liability, balance.total_assets), date = statsDate)
df = df.fillna(value=0)
df['liability_ratio'] = 1.0*(df['total_liability'] / df['total_assets'])
industry_list = BzUtil.fun_get_industry(cycle=None)
liability_ratio_list = []
for industry in industry_list:
stock_list = BzUtil.fun_get_industry_stocks(industry, 2, statsDate)
df_liability_ratio = df[df.code.isin(stock_list)]
df_liability_ratio = df_liability_ratio.sort_values('liability_ratio', ascending=True)
liability_ratio_list = liability_ratio_list + list(df_liability_ratio[:int(len(df_liability_ratio)*0.66)].code)
return liability_ratio_list
@staticmethod
def get_high_profit_ratio(statsDate):
# 剔除净利润率相对行业最低的1/3的股票；
df = get_fundamentals(query(indicator.code, indicator.net_profit_to_total_revenue), date = statsDate)
df = df.fillna(value=0)
industry_list = BzUtil.fun_get_industry(cycle=None)
profit_ratio_list = []
for industry in industry_list:
stock_list = BzUtil.fun_get_industry_stocks(industry, 2, statsDate)
df_profit_ratio = df[df.code.isin(stock_list)]
df_profit_ratio = df_profit_ratio.sort_values('net_profit_to_total_revenue', ascending=False)
profit_ratio_list = profit_ratio_list + list(df_profit_ratio[:int(len(df_profit_ratio)*0.66)].code)
return profit_ratio_list
@classmethod
@log_time
def get_high_grow_stocks(cls,startDate=None):
# 根据增长率和roe（暂时去掉）选取的高增长股票
all_stocks = BzUtil.get_all_stocks(startDate)
all_stocks = BzUtil.financial_data_filter_bigger(all_stocks,indicator.gross_profit_margin,40,startDate)
all_stocks = BzUtil.filter_financial_data_area(all_stocks, factor=valuation.pe_ratio, area=(5,35),startDate=startDate)
all_stocks = BzUtil.financial_data_filter_bigger(all_stocks,indicator.roe,0)
return all_stocks
# 用于缓存查询的ps数据。
class CacheDataFramePs:
def __init__(self):
self.df = None
self.means = {}
def __fun_get_ps(self,startDate):
__df = get_fundamentals(query(valuation.code, valuation.ps_ratio), date = startDate)
__df.rename(columns={'ps_ratio':str(DateHelper.to_date(startDate))}, inplace=True)
return __df
def append_cache_ps(self, current_dt,df=None):
df1 = self.__fun_get_ps(current_dt)
if df is not None:
df = df.merge(df1, on='code')
if df is None:
df =df1
return df
def get_his_mon_ps(self, current_dt, df=None,num=48):
for i in range(num):
last_mon = DateHelper.to_date(current_dt) - dt.timedelta(30*(num-i))
df = self.append_cache_ps(last_mon,df)
return df
@log_time
def get_curr_mon_ps(self, current_dt, df=None):
df = self.append_cache_ps(current_dt,df)
return df
def init_last_48_ps(self, current_dt):
self.df = self.get_his_mon_ps(current_dt)
return self.df
def has_curr_mon(self,current_dt):
if self.df is None:
return False
last_mon_str = self.df.columns[-1]
next_mon = DateHelper.add_ndays(last_mon_str,30)
has_curr = DateHelper.date_is_after(next_mon, current_dt)
# log.info('current[%s] is already in curr mon[end:%s]? %s.'%(str(current_dt),str(next_mon),str(has_curr)))
return has_curr
def too_more(self, max_cols=49):
if self.df is None:
return False
return len(self.df.columns) > max_cols
def drop_column(self,index=1):
# 0 code列 1 最早一列，前面初始化时是按时间序列排序的
self.df.drop([self.df.columns[index]],axis=1,inplace=True)
def is_last_same_mon(self,current_dt):
if self.df is None:
return False
last_mon_str = self.df.columns[-1]
return DateHelper.to_date(last_mon_str).month == DateHelper.to_date(current_dt).month
def last_day_same(self,current_dt):
if self.df is None:
return False
last_mon_str = self.df.columns[-1]
return DateHelper.to_date(last_mon_str) == DateHelper.to_date(current_dt)
def replace_last(self, current_dt):
if self.df is None:
return
self.drop_column(-1)
self.df = self.get_curr_mon_ps(current_dt,self.df)
def refresh_tdy(self):
if self.df is None:
return
df = self.df.copy()
s = df.iloc[:,-1]
median = s.median()
df.iloc[:,-1] = s / median
df.index = list(df['code'])
for stock in list(df.index):
cur,mean,std,score = self.means[stock]
cur = df.loc[stock][-1]
if score <=0:
score = -1
elif cur <=0:
score = -1
else:
score = mean/score
self.means[stock] = cur,mean,std,score
def try_get_current_copy(self, current_dt):
if self.df is None:
self.df = self.init_last_48_ps(current_dt)
self.df = self.get_curr_mon_ps(current_dt,self.df)
self.re_calc_mean()
return self.df.copy()
if self.last_day_same(current_dt):
return self.df.copy()
if self.is_last_same_mon(current_dt):
self.replace_last(current_dt)
self.refresh_tdy()
return self.df.copy()
self.df = self.get_curr_mon_ps(current_dt,self.df)
if self.too_more():
self.drop_column()
self.re_calc_mean()
return self.df.copy()
@log_time
def re_calc_mean(self):
if self.df is None:
return
def pre_handle_df(df):
df.index = list(df['code'])
df = df.drop(['code'], axis=1)
df = df.fillna(value=0, axis=0)
# 1. 计算相对市收率，相对市收率等于个股市收率除以全市场的市收率，这样处理的目的是为了剔除市场估值变化的影响
for i in range(len(df.columns)):
s = df.iloc[:,i]
median = s.median()
df.iloc[:,i] = s / median
return df
@log_time
def get_relative_stocks_dict(df):
length, stock_list, stock_dict = len(df), list(df.index), {}
# 2. 计算相对市收率N个月的移动平均值的N个月的标准差，并据此计算布林带上下轨（N个月的移动平均值+/-N个月移动平均的标准差）。N = 24
col_num = len(df.columns)
for i in range(length):
s = df.iloc[i,:]
if s.min() <= 0:
stock_dict[stock_list[i]] = s[-1], 0, 0, -1
continue
# tmp_list 是24个月的相对市收率均值
tmp_list = []
for j in range(24):
tmp_list.append(s[col_num-j-24:col_num-j].mean())
# mean_value 是最近 24个月的相对市收率均值
mean_value = tmp_list[0]
# std_value 是相对市收率24个月的移动平均值的24个月的标准差
std_value = np.std(tmp_list)
# tmp_dict = {}
# (mean_value - std_value)，是布林线下轨（此处定义和一般布林线不一样，一般是 均线 - 2 倍标准差）
'''
研报原始的策略，选择 s[0] < mean_value - std_value 的标的，但因为 ps_ratio十分不稳定，跳跃很大，此区间里的测试结果非常不稳定
本策略退而求其次，选择均线-1倍标准差 和 均线 - 2 倍标准差之间的标的
大致反映策略的有效性
'''
# if s[-1] > (mean_value - 2.0*std_value) and s[-1] < mean_value:
# 记录 相对市收率均值 / 当期相对市收率
stock_dict[stock_list[i]] = s[-1], mean_value, std_value, (1.0*mean_value/s[-1])
return stock_dict
dfc = self.df.copy()
dfc = pre_handle_df(dfc)
self.means = get_relative_stocks_dict(dfc)
#
def sort_by_score(self,stocks):
score_dict = {}
stock_list = []
for s in stocks:
cur,mean,std,score = self.means[s]
score_dict[s] = score
dict_score = sorted(list(score_dict.items()), key=lambda d:d[1], reverse=True)
for idx in dict_score:
stock = idx[0]
stock_list.append(stock)
return stock_list
def get_mean_std(self,stock):
return self.means[stock]
def get_stock_state(self, stock, current_dt):
self.try_get_current_copy(current_dt)
cur_ps,mean,std,score = self.get_mean_std(stock)
if cur_ps > 0 and cur_ps > mean + 2*std and score > 0:
return 'H'
if score > 0 and cur_ps > 0 and cur_ps > mean - 2 * std and cur_ps < mean:
return 'M'
if cur_ps < 0:
return '-'
if cur_ps < mean - 2 * std:
return 'L'
return 'L'
def is_too_high(self, stock, current_dt):
return self.get_stock_state(stock,current_dt) == 'H'
def is_too_low(self, stock, current_dt):
s = self.get_stock_state(stock,current_dt)
return s == '-' or s == 'L'
def is_in_low_area(self,stock, current_dt):
s = self.get_stock_state(stock,current_dt)
return s == 'M'
def get_all_stocks(self):
if self.df is None:
return None
return list(self.df['code'])
def get_tdy_all_stocks(self,current_dt):
self.try_get_current_copy(current_dt)
return self.get_all_stocks()
class ValueFactorLib():
def __init__(self):
pass
@staticmethod
@log_time
def fun_get_stock_list(now_date, hold_number=10, statsDate=None):
# relative_ps = ValueFactorLib.fun_get_relative_ps(statsDate)
# low_ps = ValueFactorLib.fun_get_low_ps(statsDate)
# good_stock_list = BzUtil.filter_intersection(relative_ps,low_ps)
good_stock_list = QuantLib.get_high_grow_stocks(startDate=statsDate)
log.info('选取的高增长股数%d'%(len(good_stock_list)))
# 取净利润增长率为正的
inc_net_profit_list = QuantLib.get_inc_net_profile(statsDate)
good_stock_list = BzUtil.filter_intersection(good_stock_list,inc_net_profit_list)
print((len(good_stock_list)))
# 按行业取营业收入增长率前 1/3
inc_operating_revenue_list = QuantLib.get_inc_operating_revenue_list(statsDate)
good_stock_list = list(set(good_stock_list) & set(inc_operating_revenue_list))
print((len(good_stock_list)))
# 指标剔除资产负债率相对行业最高的1/3的股票
liability_ratio_list = QuantLib.get_low_liability_ratio(statsDate)
good_stock_list = BzUtil.filter_intersection(good_stock_list,liability_ratio_list)
# 剔除净利润率相对行业最低的1/3的股票；
profit_ratio_list = QuantLib.get_high_profit_ratio(statsDate)
good_stock_list = BzUtil.filter_intersection(good_stock_list,profit_ratio_list)
# stock_list = []
# for stock in relative_ps:
# if stock in good_stock_list:
# stock_list.append(stock)
low_ps = ValueFactorLib.fun_get_low_ps(statsDate)
good_stock_list = BzUtil.filter_intersection(good_stock_list,low_ps)
print((len(good_stock_list)))
stock_list = BzUtil.fun_delNewShare(now_date, good_stock_list, 30)
bad_stock_list = QuantLib.fun_get_bad_stock_list(statsDate)
stock_list = stock_list[:hold_number*10]
stock_list = BzUtil.filter_without(stock_list, bad_stock_list)
stock_list = BzUtil.remove_limit_up(stock_list)
stock_list = QuantLib.fun_diversity_by_industry(stock_list, int(hold_number*0.4), statsDate)
return stock_list[:hold_number]
@staticmethod
def fun_get_copy_ps_from_cache(startDate=None):
return g.cacher.try_get_current_copy(startDate)
@staticmethod
def fun_get_cacher_from_g():
return g.cacher
@staticmethod
@log_time
def fun_get_relative_ps(statsDate=None):
cacher = ValueFactorLib.fun_get_cacher_from_g()
stocks = cacher.get_tdy_all_stocks(statsDate)
ok_stocks = []
for s in stocks:
if cacher.is_in_low_area(s,statsDate):
ok_stocks.append(s)
sorted_stock_list = cacher.sort_by_score(ok_stocks)
return sorted_stock_list
@staticmethod
@log_time
def fun_get_not_relative_ps(stocks,statsDate=None):
cacher = ValueFactorLib.fun_get_cacher_from_g()
not_relative_stocks = []
# 2. 计算相对市收率N个月的移动平均值的N个月的标准差，并据此计算布林带上下轨（N个月的移动平均值+/-N个月移动平均的标准差）。N = 24
for stock in stocks:
if cacher.is_too_high(stock,statsDate) or cacher.is_too_low(stock,statsDate):
not_relative_stocks.append(stock)
return not_relative_stocks
@staticmethod
@log_time
def get_sorted_ps(startDate):
df = get_fundamentals(
query(valuation.code, valuation.ps_ratio),
date = startDate
)
# 根据 sp 去极值、中性化、标准化后，跨行业选最佳的标的
industry_list = BzUtil.fun_get_industry(cycle=None)
df = df.fillna(value = 0)