def __QA_backtest_prepare(self):
"""
这是模型内部的 初始化,主要是初始化一些账户和市场资产
写成了私有函数
@yutiansut
2017/7/20
"""
if len(str(self.strategy_start_date))==10:
self.strategy_start_time=str(self.strategy_start_date)+' 15:00:00'
elif len(str(self.strategy_start_date))==19:
self.strategy_start_time=str(self.strategy_start_date)
self.strategy_start_date=str(self.strategy_start_date)[0:10]
else:
QA_util_log_info('Wrong start date format')
if len(str(self.strategy_end_date))==10:
self.strategy_end_time=str(self.strategy_end_date)+' 15:00:00'
elif len(str(self.strategy_end_date))==19:
self.strategy_end_time=str(self.strategy_end_date)
self.strategy_end_date=str(self.strategy_end_date)[0:10]
else:
QA_util_log_info('Wrong end date format')
# 重新初始账户资产
self.market = QA_Market(self.commission_fee_coeff)
self.setting.QA_setting_init()
self.account.init()
self.start_real_date = QA_util_get_real_date(
self.strategy_start_date, self.trade_list, 1)
self.start_real_time=str(self.start_real_date)+' '+self.strategy_start_time.split(' ')[1]
self.start_real_id = self.trade_list.index(self.start_real_date)
self.end_real_date = QA_util_get_real_date(
self.strategy_end_date, self.trade_list, -1)
self.end_real_id = self.trade_list.index(self.end_real_date)
self.end_real_time=str(self.end_real_date)+' '+self.strategy_end_time.split(' ')[1]
# 重新初始化账户的cookie
self.account.account_cookie = str(random.random())
# 初始化股票池的市场数据
if self.benchmark_type in ['I','index']:
self.benchmark_data = QA_fetch_index_day_adv(
self.benchmark_code, self.start_real_date, self.end_real_date)
elif self.benchmark_type in ['S','stock']:
self.benchmark_data = QA_fetch_stock_day_adv(
self.benchmark_code, self.start_real_date, self.end_real_date)
if self.backtest_type in ['day', 'd', '0x00']:
self.market_data = QA_fetch_stocklist_day_adv(
self.strategy_stock_list, self.trade_list[self.start_real_id - int(
self.strategy_gap+1)], self.trade_list[self.end_real_id]).to_qfq()
elif self.backtest_type in ['1min', '5min', '15min', '30min', '60min']:
self.market_data = QA_fetch_stocklist_min_adv(
self.strategy_stock_list, QA_util_time_gap(self.start_real_time,self.strategy_gap+1,'<',self.backtest_type),
QA_util_time_gap(self.end_real_time,1,'>',self.backtest_type), self.backtest_type).to_qfq()
elif self.backtest_type in ['index_day']:
self.market_data = QA_fetch_index_day_adv(self.strategy_stock_list, self.trade_list[self.start_real_id - int(
self.strategy_gap+1)], self.end_real_date)
elif self.backtest_type in ['index_1min', 'index_5min', 'index_15min', 'index_30min', 'index_60min']:
self.market_data = QA_fetch_index_min_adv(
self.strategy_stock_list, QA_util_time_gap(self.start_real_time,self.strategy_gap+1,'<',self.backtest_type.split('_')[1]), QA_util_time_gap(self.end_real_time,1,'>',self.backtest_type.split('_')[1]), self.backtest_type.split('_')[1])
def __QA_backtest_prepare(self):
"""
这是模型内部的 初始化,主要是初始化一些账户和市场资产
写成了私有函数
@yutiansut
2017/7/20
"""
# 重新初始账户资产
self.market = QA_Market(self.commission_fee_coeff)
self.setting.QA_setting_init()
self.account.init()
self.start_real_date = QA_util_get_real_date(self.strategy_start_date,
self.trade_list, 1)
self.start_real_id = self.trade_list.index(self.start_real_date)
self.end_real_date = QA_util_get_real_date(self.strategy_end_date,
self.trade_list, -1)
self.end_real_id = self.trade_list.index(self.end_real_date)
# 重新初始化账户的cookie
self.account.account_cookie = str(random.random())
# 初始化股票池的市场数据
self.benchmark_data = QA_fetch_index_day_adv(self.benchmark_code,
self.start_real_date,
self.end_real_date)
if self.backtest_type in ['day', 'd', '0x00']:
self.market_data = QA_fetch_stocklist_day_adv(
self.strategy_stock_list,
self.trade_list[self.start_real_id - int(self.strategy_gap)],
self.trade_list[self.end_real_id]).to_qfq()
elif self.backtest_type in ['1min', '5min', '15min', '30min', '60min']:
self.market_data = QA_fetch_stocklist_min_adv(
self.strategy_stock_list,
self.trade_list[self.start_real_id - int(self.strategy_gap)],
self.trade_list[self.end_real_id + 1],
self.backtest_type).to_qfq()
elif self.backtest_type in ['index_day']:
self.market_data = QA_fetch_index_day_adv(
self.strategy_stock_list,
self.trade_list[self.start_real_id - int(self.strategy_gap)],
self.trade_list[self.end_real_id])
elif self.backtest_type in [
'index_1min', 'index_5min', 'index_15min', 'index_30min',
'index_60min'
]:
self.market_data = QA_fetch_index_min_adv(
self.strategy_stock_list, self.start_real_date,
self.end_real_date,
self.backtest_type.split('_')[1])
def subscribe_callback(self, code):
"""
订阅回调
:param code:
:return:
"""
if not isinstance(code, str):
logger.error('not string , %s' % code)
return
today = datetime.datetime(self.cur_year, self.cur_month,
self.cur_day).isoformat()[:10]
end_date = QA_util_get_pre_trade_date(cursor_date=today, n=1)[:10]
if code not in self.code_list:
self.code_list.append(code)
# ETF or Stock, 获取前天的收盘价格
logger.info("try fetch %s ,%s" % (code, end_date))
if code.startswith('5') or code.startswith('1'):
_data = QA_fetch_index_day_adv(code, end_date, end_date)
else:
_data = QA_fetch_stock_day_adv(code, end_date, end_date)
if _data is not None:
self.pre_market_data = concat(
[self.pre_market_data,
_data.data.reset_index()])
logger.info("fetch %s" % _data.data.to_csv(header=False))
def test_getAdv_diffQA(self):
"""和QA返回的数据对比一致性
"""
code = '000001'
days = 365 * 1.2
start = datetime.datetime.now() - datetime.timedelta(days)
end = datetime.datetime.now() - datetime.timedelta(0)
df = qm.getAdv(code, start, end)
self.assertTrue(len(df) > 0, "返回数据数量应该大于0。")
df2 = QA_fetch_index_day_adv(code, start, end)
self.assertTrue(
len(df.data) == len(df2.data),
"和QA返回的数据,长度不一致{} {}".format(len(df.data), len(df2.data)))
# 两种方式检测numpy数据一致性
obo = self.differOneByOne(df.data, df2.data)
self.assertTrue(df.data.equals(df2.data), "和QA返回的数据不一致{}".format(obo))
def get_prices(
self,
code_list: Union[str, Tuple[str], List[str]] = None,
start_time: Union[str, datetime.datetime] = None,
end_time: Union[str, datetime.datetime] = None,
fq: str = None,
frequence: str = None,
price_type: str = None,
):
"""
价格数据获取接口，单因子输入后，可以通过单因子获取股票代码，时间等参数信息
参数
---
:param code_list: 股票代码
:param start_time: 起始时间
:param end_time: 截止时间
:param fq: 复权方式
:param frequence: 时间频率
"""
# 1. 股票池
if isinstance(code_list, tuple):
code_list = list(code_list)
# 2. 时间频率
if not frequence:
frequence = self.frequence
frequence = utils.get_frequence(frequence)
if not start_time:
start_time = self.start_time
if not end_time:
end_time = self.end_time
if (not start_time) or (not end_time):
raise ValueError("价格获取接口需要指定起始时间与结束时间")
start_time = str(pd.Timestamp(start_time))[:19]
end_time = str(pd.Timestamp(end_time))[:19]
data = QA_fetch_stock_day_adv(code=code_list,
start=start_time,
end=end_time)
index_data = QA_fetch_index_day_adv(code="000001",
start=start_time,
end=end_time)
# 3. 复权
if not fq:
fq = self.fq
if not fq:
data = data
elif fq.lower() in ["pre", "qfq", "前复权"]:
data = data.to_qfq()
elif fq.lower() in ["post", "hfq", "后复权"]:
data = data.to_hfq()
elif fq.lower() in ["none", "bfq", "不复权"]:
data = data
# 4. 重采样
# 考虑到停牌退市等原因，重采样会有异常值，即日期与我们需要的日期不一致
# 这里采用指数作为基准，对重采样数据进行再处理
# 对于停牌数据缺失，采用前值作为填充
if frequence == "1d":
data = data.data.unstack().ffill().stack()
else:
index_data = index_data.resample(frequence).unstack()
data = data.resample(frequence).unstack().ffill()
data = data.reindex(index_data.index).stack()
if frequence == '1q':
data.index = data.index.map(lambda x:
(utils.QA_fmt_quarter(x[0]), x[1]))
# 5. 价格类型
if not price_type:
price_type = self.price_type
if price_type.lower() is "avg":
avg = data["amount"] / data["volume"] / 100.0
return avg.unstack()
return data[price_type.lower()].unstack()
def __QA_backtest_prepare(self):
"""
这是模型内部的 初始化,主要是初始化一些账户和市场资产
写成了私有函数
@yutiansut
2017/7/20
"""
self.strategy_stock_list = np.unique(
self.strategy_stock_list).tolist() # 保证不会重复
if len(str(self.strategy_start_date)) == 10:
self.strategy_start_time = str(
self.strategy_start_date) + ' 15:00:00'
elif len(str(self.strategy_start_date)) == 19:
self.strategy_start_time = str(self.strategy_start_date)
self.strategy_start_date = str(self.strategy_start_date)[0:10]
else:
self.__QA_backtest_log_info(self, 'Wrong start date format')
if len(str(self.strategy_end_date)) == 10:
self.strategy_end_time = str(self.strategy_end_date) + ' 15:00:00'
elif len(str(self.strategy_end_date)) == 19:
self.strategy_end_time = str(self.strategy_end_date)
self.strategy_end_date = str(self.strategy_end_date)[0:10]
else:
self.__QA_backtest_log_info(self, 'Wrong end date format')
# 重新初始账户资产
self.market = QA_Market(self.commission_fee_coeff)
self.setting.QA_setting_init()
self.account.init()
self.account_d_value.append(self.account.init_assest)
self.start_real_date = QA_util_get_real_date(self.strategy_start_date,
self.trade_list, 1)
self.start_real_time = str(
self.start_real_date) + ' ' + self.strategy_start_time.split(
' ')[1]
self.start_real_id = self.trade_list.index(self.start_real_date)
self.end_real_date = QA_util_get_real_date(self.strategy_end_date,
self.trade_list, -1)
self.end_real_id = self.trade_list.index(self.end_real_date)
self.end_real_time = str(self.end_real_date) + \
' ' + self.strategy_end_time.split(' ')[1]
# 重新初始化账户的cookie
self.account.account_cookie = str(random.random())
# 初始化股票池的市场数据
if self.benchmark_type in ['I', 'index']:
self.benchmark_data = QA_fetch_index_day_adv(
self.benchmark_code, self.trade_list[self.start_real_id - 1],
self.end_real_date)
elif self.benchmark_type in ['S', 'stock']:
self.benchmark_data = QA_fetch_stock_day_adv(
self.benchmark_code, self.trade_list[self.start_real_id - 1],
self.end_real_date)
if self.backtest_type in ['day', 'd', '0x00']:
self.market_data = QA_fetch_stocklist_day_adv(
self.strategy_stock_list,
self.trade_list[self.start_real_id -
int(self.strategy_gap + 1)],
self.trade_list[self.end_real_id]).to_qfq()
elif self.backtest_type in ['1min', '5min', '15min', '30min', '60min']:
self.market_data = QA_fetch_stocklist_min_adv(
self.strategy_stock_list,
QA_util_time_gap(self.start_real_time, self.strategy_gap + 1,
'<', self.backtest_type),
QA_util_time_gap(self.end_real_time, 1, '>',
self.backtest_type),
self.backtest_type).to_qfq()
elif self.backtest_type in ['index_day']:
self.market_data = QA_fetch_index_day_adv(
self.strategy_stock_list,
self.trade_list[self.start_real_id -
int(self.strategy_gap + 1)],
self.end_real_date)
elif self.backtest_type in [
'index_1min', 'index_5min', 'index_15min', 'index_30min',
'index_60min'
]:
self.market_data = QA_fetch_index_min_adv(
self.strategy_stock_list,
QA_util_time_gap(self.start_real_time, self.strategy_gap + 1,
'<',
self.backtest_type.split('_')[1]),
QA_util_time_gap(self.end_real_time, 1, '>',
self.backtest_type.split('_')[1]),
self.backtest_type.split('_')[1])
self.market_data_dict = dict(
zip(list(self.market_data.code), self.market_data.splits()))
self.market_data_hashable = self.market_data.dicts
self.dirs = '.{}QUANTAXIS_RESULT{}{}{}{}{}'.format(
os.sep, os.sep, self.topic_name, os.sep, self.stratey_version,
os.sep)
os.makedirs(self.dirs, exist_ok=True)