def do_update(start_date, end_date, count):
# 读取本地交易日
_trade_date = TradeDate()
trade_date_sets = _trade_date.trade_date_sets_ago(start_date, end_date,
count)
for trade_date in trade_date_sets:
print('因子计算日期： %s' % trade_date)
prepare_calculate(trade_date)
print('----->')
def do_update(start_date, end_date, count):
# 读取本地交易日
_trade_date = TradeDate()
trade_date_sets = _trade_date.trade_date_sets_ago(start_date, end_date,
count)
for trade_date in trade_date_sets:
print('因子计算日期： %s' % trade_date)
cash_flow_calculate(trade_date)
constrain_calculate(trade_date)
growth_calculate(trade_date)
earning_calculate(trade_date)
historical_value_calculate(trade_date)
per_share_calculate(trade_date)
print('----->')
class FactorVolatilityValue(FactorBase):
def __init__(self, name):
super(FactorVolatilityValue, self).__init__(name)
self._trade_date = TradeDate()
# 构建因子表
def create_dest_tables(self):
"""
创建数据库表
:return:
"""
drop_sql = """drop table if exists `{0}`""".format(self._name)
create_sql = """create table `{0}`(
`id` varchar(32) NOT NULL,
`symbol` varchar(24) NOT NULL,
`trade_date` date NOT NULL,
`variance_20d` decimal(19,4) NOT NULL,
`variance_60d` decimal(19,4) NOT NULL,
`variance_120d` decimal(19,4) NOT NULL,
`kurtosis_20d` decimal(19,4) NOT NULL,
`kurtosis_60d` decimal(19,4) NOT NULL,
`kurtosis_120d` decimal(19,4) NOT NULL,
`alpha_20d` decimal(19,4) NOT NULL,
`alpha_60d` decimal(19,4) NOT NULL,
`alpha_120d` decimal(19,4) NOT NULL,
`beta_20d` decimal(19,4) NOT NULL,
`beta_60d` decimal(19,4) NOT NULL,
`beta_120d` decimal(19,4) NOT NULL,
`sharp_20d` decimal(19,4) NOT NULL,
`sharp_60d` decimal(19,4) NOT NULL,
`sharp_120d` decimal(19,4) NOT NULL,
`tr_20d` decimal(19,4) NOT NULL,
`tr_60d` decimal(19,4) NOT NULL,
`tr_120d` decimal(19,4) NOT NULL,
`ir_20d` decimal(19,4) NOT NULL,
`ir_60d` decimal(19,4) NOT NULL,
`ir_120d` decimal(19,4) NOT NULL,
`gain_variance_20d` decimal(19,4) NOT NULL,
`gain_variance_60d` decimal(19,4) NOT NULL,
`gain_variance_120d` decimal(19,4) NOT NULL,
`loss_variance_20d` decimal(19,4) NOT NULL,
`loss_variance_60d` decimal(19,4) NOT NULL,
`loss_variance_120d` decimal(19,4) NOT NULL,
`gain_loss_variance_ratio_20d` decimal(19,4) NOT NULL,
`gain_loss_variance_ratio_60d` decimal(19,4) NOT NULL,
`gain_loss_variance_ratio_120d` decimal(19,4) NOT NULL,
`dastd_252d` decimal(19,4) NOT NULL,
`ddnsr_12m` decimal(19,4) NOT NULL,
`ddncr_12m` decimal(19,4) NOT NULL,
`dvrat` decimal(19,4) NOT NULL,
PRIMARY KEY(`id`,`trade_date`,`symbol`)
)ENGINE=InnoDB DEFAULT CHARSET=utf8;""".format(self._name)
super(FactorVolatilityValue, self)._create_tables(create_sql, drop_sql)
def get_trade_date(self, trade_date, n):
"""
获取当前时间前n年的时间点，且为交易日，如果非交易日，则往前提取最近的一天。
:param trade_date: 当前交易日
:param n:
:return:
"""
# print("trade_date %s" % trade_date)
trade_date_sets = collections.OrderedDict(
sorted(self._trade_date._trade_date_sets.items(), key=lambda t: t[0], reverse=False))
time_array = datetime.strptime(str(trade_date), "%Y%m%d")
time_array = time_array - timedelta(days=365) * n
date_time = int(datetime.strftime(time_array, "%Y%m%d"))
if date_time < min(trade_date_sets.keys()):
# print('date_time %s is outof trade_date_sets' % date_time)
return date_time
else:
while not date_time in trade_date_sets:
date_time = date_time - 1
# print('trade_date pre %s year %s' % (n, date_time))
return date_time
def get_basic_data(self, trade_date):
"""
获取基础数据
按天获取当天交易日所有股票的基础数据
:param trade_date: 交易日
:return:
"""
# market_cap，circulating_market_cap，total_operating_revenue
count = 300
sk_daily_price_sets = get_sk_history_price([], trade_date, count, [SKDailyPrice.symbol,
SKDailyPrice.trade_date, SKDailyPrice.open,
SKDailyPrice.close, SKDailyPrice.high,
SKDailyPrice.low])
index_daily_price_sets = get_index_history_price(["000300.XSHG"], trade_date, count,
["symbol", "trade_date", "close"])
temp_price_sets = index_daily_price_sets[index_daily_price_sets.trade_date <= trade_date]
return sk_daily_price_sets, temp_price_sets[:count]
def prepare_calculate(self, trade_date):
self.trade_date = trade_date
tp_price_return, temp_price_sets = self.get_basic_data(trade_date)
# tp_price_return.set_index('symbol', inplace=True)
# tp_price_return['symbol'] = tp_price_return.index
# symbol_sets = list(set(tp_price_return['symbol']))
# tp_price_return_list = pd.DataFrame()
#
# for symbol in symbol_sets:
# if len(tp_price_return[tp_price_return['symbol'] == symbol]) < 3:
# continue
# tp_price_return_list = tp_price_return_list.append(
# tp_price_return.loc[symbol].sort_values(by='trade_date', ascending=True))
if len(tp_price_return) <= 0:
print("%s has no data" % trade_date)
return
else:
session = str(int(time.time() * 1000000 + datetime.now().microsecond))
data = {
'total_data': tp_price_return.to_json(orient='records'),
'index_daily_price_sets': temp_price_sets.to_json(orient='records')
}
cache_data.set_cache(session, 'volatility' + str(trade_date),
json.dumps(data))
# cache_data.set_cache(session, 'volatility' + str(trade_date) + '_a',
# tp_price_return_list.to_json(orient='records'))
# cache_data.set_cache(session, 'volatility' + str(trade_date) + '_b',
# temp_price_sets.to_json(orient='records'))
factor_volatility_value_task.calculate.delay(factor_name='volatility' + str(trade_date), trade_date=trade_date,
session=session)
def do_update(self, start_date, end_date, count):
# 读取本地交易日
trade_date_sets = self._trade_date.trade_date_sets_ago(start_date, end_date, count)
for trade_date in trade_date_sets:
print('因子计算日期： %s' % trade_date)
self.prepare_calculate(trade_date)
print('----->')
class FactorScaleValue(FactorBase):
def __init__(self, name):
super(FactorScaleValue, self).__init__(name)
self._trade_date = TradeDate()
# 构建因子表
def create_dest_tables(self):
"""
创建数据库表
:return:
"""
drop_sql = """drop table if exists `{0}`""".format(self._name)
create_sql = """create table `{0}`(
`id` varchar(32) NOT NULL,
`symbol` varchar(24) NOT NULL,
`trade_date` date NOT NULL,
`mkt_value` decimal(19,4) NOT NULL,
`cir_mkt_value` decimal(19,4),
`sales_ttm` decimal(19,4),
`total_assets` decimal(19,4),
`log_of_mkt_value` decimal(19, 4),
`log_of_neg_mkt_value` decimal(19,4),
`nl_size` decimal(19,4),
`log_sales_ttm` decimal(19,4),
`log_total_last_qua_assets` decimal(19,4),
PRIMARY KEY(`id`,`trade_date`,`symbol`)
)ENGINE=InnoDB DEFAULT CHARSET=utf8;""".format(self._name)
super(FactorScaleValue, self)._create_tables(create_sql, drop_sql)
def get_trade_date(self, trade_date, n):
"""
获取当前时间前n年的时间点，且为交易日，如果非交易日，则往前提取最近的一天。
:param trade_date: 当前交易日
:param n:
:return:
"""
# print("trade_date %s" % trade_date)
trade_date_sets = collections.OrderedDict(
sorted(self._trade_date._trade_date_sets.items(),
key=lambda t: t[0],
reverse=False))
time_array = datetime.strptime(str(trade_date), "%Y%m%d")
time_array = time_array - timedelta(days=365) * n
date_time = int(datetime.strftime(time_array, "%Y%m%d"))
if date_time < min(trade_date_sets.keys()):
# print('date_time %s is outof trade_date_sets' % date_time)
return date_time
else:
while not date_time in trade_date_sets:
date_time = date_time - 1
# print('trade_date pre %s year %s' % (n, date_time))
return date_time
def get_basic_data(self, trade_date):
"""
获取基础数据
按天获取当天交易日所有股票的基础数据
:param trade_date: 交易日
:return:
"""
# market_cap，circulating_market_cap，total_operating_revenue
valuation_sets = get_fundamentals(
add_filter_trade(
query(Valuation._name_, [
Valuation.symbol, Valuation.market_cap,
Valuation.circulating_market_cap
]), [trade_date]))
income_sets = get_fundamentals(
add_filter_trade(
query(Income._name_,
[Income.symbol, Income.total_operating_revenue]),
[trade_date]))
balance_set = get_fundamentals(
add_filter_trade(
query(Balance._name_, [Balance.symbol, Balance.total_assets]),
[trade_date]))
# TTM计算
ttm_factors = {
Income._name_: [Income.symbol, Income.total_operating_revenue]
}
ttm_factor_sets = get_ttm_fundamental([], ttm_factors,
trade_date).reset_index()
# ttm 周期内计算需要优化
# ttm_factor_sets_sum = get_ttm_fundamental([], ttm_factors_sum_list, trade_date, 5).reset_index()
ttm_factor_sets = ttm_factor_sets.drop(columns={"trade_date"})
return valuation_sets, ttm_factor_sets, income_sets, balance_set
def prepaer_calculate(self, trade_date):
valuation_sets, ttm_factor_sets, income_sets, balance_set = self.get_basic_data(
trade_date)
# valuation_sets = pd.merge(valuation_sets, income_sets, on='symbol')
valuation_sets = pd.merge(valuation_sets, ttm_factor_sets, on='symbol')
valuation_sets = pd.merge(valuation_sets, balance_set, on='symbol')
if len(valuation_sets) <= 0:
print("%s has no data" % trade_date)
return
else:
session = str(
int(time.time() * 1000000 + datetime.now().microsecond))
cache_data.set_cache(session, 'scale' + str(trade_date),
valuation_sets.to_json(orient='records'))
factor_scale_value_task.calculate.delay(factor_name='scale' +
str(trade_date),
trade_date=trade_date,
session=session)
def do_update(self, start_date, end_date, count):
# 读取本地交易日
trade_date_sets = self._trade_date.trade_date_sets_ago(
start_date, end_date, count)
for trade_date in trade_date_sets:
print('因子计算日期： %s' % trade_date)
self.prepaer_calculate(trade_date)
print('----->')