def smartq_backtest(start, end):
"""
SmartQ因子的历史回测
Parameters:
--------
:param start: datetime-like, str
回测开始日期，格式：YYYY-MM-DD，开始日期应该为月初
:param end: datetime-like, str
回测结束日期，格式：YYYY-MM-DD
:return:
"""
# 取得开始结束日期间的交易日序列
trading_days = Utils.get_trading_days(start, end)
# 读取截止开始日期前最新的组合回测数据
prev_trading_day = Utils.get_prev_n_day(trading_days.iloc[0], 1)
backtest_path = os.path.join(SETTINGS.FACTOR_DB_PATH,
alphafactor_ct.SMARTMONEY_CT.backtest_path)
factor_data, port_nav = Utils.get_backtest_data(backtest_path,
trading_days.iloc[0])
# factor_data = None # 记录每次调仓时最新入选个股的SmartQ因子信息，pd.DataFrame<date,factorvalue,id,buprice>
if port_nav is None:
port_nav = DataFrame({
'date': [prev_trading_day.strftime('%Y-%m-%d')],
'nav': [1.0]
})
# 遍历交易日，如果是月初，则读取SmartQ因子载荷值，进行调仓；如果不是月初，则进行组合估值
t = 0 # 记录调仓次数
for trading_day in trading_days:
if factor_data is None:
nav = port_nav[port_nav.date == prev_trading_day.strftime(
'%Y-%m-%d')].iloc[0].nav
else:
nav = port_nav[port_nav.date ==
factor_data.iloc[0].date].iloc[0].nav
interval_ret = 0.0
# 月初进行调仓
if Utils.is_month_start(trading_day):
logging.info('[%s] 月初调仓.' %
Utils.datetimelike_to_str(trading_day, True))
# 调仓前，先计算组合按均价卖出原先组合个股在当天的估值
if factor_data is not None:
for ind, factor_info in factor_data.iterrows():
daily_mkt = Utils.get_secu_daily_mkt(factor_info.id,
trading_day,
fq=True,
range_lookup=True)
if daily_mkt.date == trading_day.strftime('%Y-%m-%d'):
vwap_price = daily_mkt.amount / daily_mkt.vol * daily_mkt.factor
else:
vwap_price = daily_mkt.close
interval_ret += vwap_price / factor_info.buyprice - 1.0
interval_ret /= float(len(factor_data))
nav *= (1.0 + interval_ret)
# 读取factor_data
factor_data = Utils.read_factor_loading(
SmartMoney.get_db_file(),
Utils.datetimelike_to_str(prev_trading_day, False))
# 遍历factor_data, 计算每个个股过去20天的涨跌幅，并剔除在调仓日没有正常交易（如停牌）及涨停的个股
ind_to_be_deleted = []
factor_data['ret20'] = np.zeros(len(factor_data))
for ind, factor_info in factor_data.iterrows():
trading_status = Utils.trading_status(factor_info.id,
trading_day)
if trading_status == SecuTradingStatus.Suspend or trading_status == SecuTradingStatus.LimitUp:
ind_to_be_deleted.append(ind)
fret20 = Utils.calc_interval_ret(factor_info.id,
end=prev_trading_day,
ndays=20)
if fret20 is None:
if ind not in ind_to_be_deleted:
ind_to_be_deleted.append(ind)
else:
factor_data.loc[ind, 'ret20'] = fret20
factor_data = factor_data.drop(ind_to_be_deleted, axis=0)
# 对factor_data过去20天涨跌幅降序排列，剔除涨幅最大的20%个股
k = int(factor_data.shape[0] * 0.2)
factor_data = factor_data.sort_values(by='ret20',
ascending=False).iloc[k:]
del factor_data['ret20'] # 删除ret20列
# 对factor_data按因子值升序排列，取前10%个股
factor_data = factor_data.sort_values(by='factorvalue',
ascending=True)
k = int(factor_data.shape[0] * 0.1)
factor_data = factor_data.iloc[:k]
# 遍历factor_data，添加买入价格，并估值计算当天调仓后的组合收益
factor_data['buyprice'] = 0.0
interval_ret = 0.0
for ind, factor_info in factor_data.iterrows():
daily_mkt = Utils.get_secu_daily_mkt(factor_info.id,
trading_day,
fq=True,
range_lookup=False)
assert len(daily_mkt) > 0
factor_data.loc[
ind,
'buyprice'] = daily_mkt.amount / daily_mkt.vol * daily_mkt.factor
interval_ret += daily_mkt.close / factor_data.loc[
ind, 'buyprice'] - 1.0
interval_ret /= float(factor_data.shape[0])
nav *= (1.0 + interval_ret)
# 保存factor_data
port_data_path = os.path.join(
SETTINGS.FACTOR_DB_PATH,
alphafactor_ct.SMARTMONEY_CT.backtest_path,
'port_data_%s.csv' %
Utils.datetimelike_to_str(trading_day, False))
factor_data.to_csv(port_data_path, index=False)
t += 1
if t % 6 == 0:
logging.info('Suspended for 300s.')
time.sleep(300)
else:
# 非调仓日，对组合进行估值
logging.info('[%s] 月中估值.' %
Utils.datetimelike_to_str(trading_day, True))
if factor_data is not None:
for ind, factor_info in factor_data.iterrows():
daily_mkt = Utils.get_secu_daily_mkt(factor_info.id,
trading_day,
fq=True,
range_lookup=True)
interval_ret += daily_mkt.close / factor_info.buyprice - 1.0
interval_ret /= float(factor_data.shape[0])
nav *= (1.0 + interval_ret)
# 添加nav
port_nav = port_nav.append(Series({
'date':
Utils.datetimelike_to_str(trading_day, True),
'nav':
nav
}),
ignore_index=True)
# 设置prev_trading_day
prev_trading_day = trading_day
# 保存port_nav
port_nav_path = os.path.join(SETTINGS.FACTOR_DB_PATH,
alphafactor_ct.SMARTMONEY_CT.backtest_path,
'port_nav.csv')
port_nav.to_csv(port_nav_path, index=False)
def apm_backtest(start, end, pure_factor=False):
"""
APM因子的历史回测
Parameters:
--------
:param start: datetime-like, str
回测开始日期，格式：YYYY-MM-DD，开始日期应该为月初的前一个交易日，即月末交易日
:param end: datetime-like, str
回测结束日期，格式：YYYY-MM-DD
:param pure_factor: bool, 默认False
是否是对纯净因子做回测
:return:
"""
# 取得开始结束日期间的交易日数据
trading_days = Utils.get_trading_days(start, end)
# 读取截止开始日期前最新的组合回测数据
prev_trading_day = Utils.get_prev_n_day(trading_days.iloc[0], 1)
if pure_factor:
backtest_path = os.path.join(factor_ct.FACTOR_DB.db_path,
factor_ct.APM_CT.pure_backtest_path)
else:
backtest_path = os.path.join(factor_ct.FACTOR_DB.db_path,
factor_ct.APM_CT.backtest_path)
factor_data, port_nav = Utils.get_backtest_data(backtest_path,
trading_days.iloc[0])
# factor_data = None # 记录每次调仓时最新入选个股的APM因子信息,pd.DataFrame<date,factorvalue,id,buyprice>
if port_nav is None:
port_nav = DataFrame({
'date': [prev_trading_day.strftime('%Y-%m-%d')],
'nav': [1.0]
})
# 遍历交易日，如果是月初，则读取APM因子载荷值；如果不是月初，则进行组合估值
for trading_day in trading_days:
if factor_data is None:
nav = port_nav[port_nav.date == prev_trading_day.strftime(
'%Y-%m-%d')].iloc[0].nav
else:
nav = port_nav[port_nav.date ==
factor_data.iloc[0].date].iloc[0].nav
interval_ret = 0.0
# 月初进行调仓
if Utils.is_month_start(trading_day):
logging.info('[%s] 月初调仓.' %
Utils.datetimelike_to_str(trading_day, True))
# 调仓前，先估值计算按均价卖出原先组合个股在当天的估值
if factor_data is not None:
for ind, factor_info in factor_data.iterrows():
daily_mkt = Utils.get_secu_daily_mkt(factor_info.id,
trading_day,
fq=True,
range_lookup=True)
if daily_mkt.date == trading_day.strftime('%Y-%m-%d'):
vwap_price = daily_mkt.amount / daily_mkt.vol * daily_mkt.factor
else:
vwap_price = daily_mkt.close
interval_ret += vwap_price / factor_info.buyprice - 1.0
interval_ret /= float(len(factor_data))
nav *= (1.0 + interval_ret)
# 读取factor_data
if pure_factor:
factor_data_path = os.path.join(
factor_ct.FACTOR_DB.db_path,
factor_ct.APM_CT.pure_apm_db_file)
else:
factor_data_path = os.path.join(factor_ct.FACTOR_DB.db_path,
factor_ct.APM_CT.apm_db_file)
factor_data = Utils.read_factor_loading(
factor_data_path,
Utils.datetimelike_to_str(prev_trading_day, False))
# 遍历factor_data，剔除在调仓日没有正常交易（如停牌）、及涨停的个股
ind_to_be_delted = []
for ind, factor_info in factor_data.iterrows():
trading_status = Utils.trading_status(factor_info.id,
trading_day)
if trading_status == SecuTradingStatus.Suspend or trading_status == SecuTradingStatus.LimitUp:
ind_to_be_delted.append(ind)
factor_data = factor_data.drop(ind_to_be_delted, axis=0)
# 对factor_data按因子值降序排列，取前10%个股
factor_data = factor_data.sort_values(by='factorvalue',
ascending=False)
factor_data = factor_data.iloc[:int(len(factor_data) * 0.1)]
# 遍历factor_data，添加买入价格，并估值计算当天调仓后的组合收益
factor_data['buyprice'] = 0.0
interval_ret = 0.0
for ind, factor_info in factor_data.iterrows():
daily_mkt = Utils.get_secu_daily_mkt(factor_info.id,
trading_day,
fq=True,
range_lookup=False)
assert len(daily_mkt) > 0
factor_data.loc[
ind,
'buyprice'] = daily_mkt.amount / daily_mkt.vol * daily_mkt.factor
interval_ret += daily_mkt.close / factor_data.loc[
ind, 'buyprice'] - 1.0
interval_ret /= float(len(factor_data))
nav *= (1.0 + interval_ret)
# 保存factor_data
if pure_factor:
port_data_path = os.path.join(
factor_ct.FACTOR_DB.db_path,
factor_ct.APM_CT.pure_backtest_path, 'port_data_%s.csv' %
Utils.datetimelike_to_str(trading_day, False))
else:
port_data_path = os.path.join(
factor_ct.FACTOR_DB.db_path,
factor_ct.APM_CT.backtest_path, 'port_data_%s.csv' %
Utils.datetimelike_to_str(trading_day, False))
factor_data.to_csv(port_data_path, index=False)
else:
# 非调仓日，对组合进行估值
logging.info('[%s] 月中估值.' %
Utils.datetimelike_to_str(trading_day, True))
if factor_data is not None:
for ind, factor_info in factor_data.iterrows():
daily_mkt = Utils.get_secu_daily_mkt(factor_info.id,
trading_day,
fq=True,
range_lookup=True)
interval_ret += daily_mkt.close / factor_info.buyprice - 1.0
interval_ret /= float(len(factor_data))
nav *= (1.0 + interval_ret)
# 添加nav
port_nav = port_nav.append(Series({
'date':
trading_day.strftime('%Y-%m-%d'),
'nav':
nav
}),
ignore_index=True)
# 设置prev_trading_day
prev_trading_day = trading_day
# 保存port_nav
if pure_factor:
port_nav_path = os.path.join(factor_ct.FACTOR_DB.db_path,
factor_ct.APM_CT.pure_backtest_path,
'port_nav.csv')
else:
port_nav_path = os.path.join(factor_ct.FACTOR_DB.db_path,
factor_ct.APM_CT.backtest_path,
'port_nav.csv')
port_nav.to_csv(port_nav_path, index=False)
