def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股APM因子的stat统计量
--------
:param code: string
个股代码，如600000
:param calc_date: datetime-like, str
因子载荷计算日期，格式YYYY-MM-DD
:return: float
--------
stat统计量，计算APM因子载荷的中间变量
"""
# 1.取得过去40个交易日序列，交易日按降序排列
calc_date = Utils.to_date(calc_date)
trading_days = Utils.get_trading_days(end=calc_date,
ndays=40,
ascending=False)
# 2.取得个股及指数过去__days+1个交易日每个交易日的开盘价、中午收盘价和当天收盘价
# 开盘价为09:31分钟线的开盘价，中午收盘价为11:30分钟线的收盘价，当天收盘价为15:00分钟线的收盘价
# 返回的数据格式为DataFrame，columns=['date','open','mid_close','close']，按日期升序排列
# secu_mkt_data = DataFrame()
# index_mkt_data = DataFrame()
# mkt_data_header = ['date', 'open', 'mid_close', 'close']
# k = 0
# for trading_day in trading_days:
# df_1min_data = Utils.get_min_mkt(Utils.code_to_symbol(code), trading_day, fq=True)
# if df_1min_data is not None:
# str_date = Utils.datetimelike_to_str(trading_day)
# fopen = df_1min_data[df_1min_data.datetime == '%s 09:31:00' % str_date].iloc[0].open
# fmid_close = df_1min_data[df_1min_data.datetime == '%s 11:30:00' % str_date].iloc[0].close
# fclose = df_1min_data[df_1min_data.datetime == '%s 15:00:00' % str_date].iloc[0].close
# secu_mkt_data = secu_mkt_data.append(
# Series([str_date, fopen, fmid_close, fclose], index=mkt_data_header), ignore_index=True)
#
# df_1min_data = Utils.get_min_mkt(factor_ct.APM_CT.index_code, trading_day, index=True, fq=True)
# fopen = df_1min_data[df_1min_data.datetime == '%s 09:31:00' % str_date].iloc[0].open
# fmid_close = df_1min_data[df_1min_data.datetime == '%s 11:30:00' % str_date].iloc[0].close
# fclose = df_1min_data[df_1min_data.datetime == '%s 15:00:00' % str_date].iloc[0].close
# index_mkt_data = index_mkt_data.append(
# Series([str_date, fopen, fmid_close, fclose], index=mkt_data_header), ignore_index=True)
# k += 1
# if k > cls.__days:
# break
# if k <= cls.__days:
# return None
# secu_mkt_data = secu_mkt_data.sort_values(by='date')
# secu_mkt_data = secu_mkt_data.reset_index(drop=True)
# index_mkt_data = index_mkt_data.sort_values(by='date')
# index_mkt_data = index_mkt_data.reset_index(drop=True)
# # 3.计算个股及指数的上午收益率数组r_t^{am},R_t^{am}和下午收益率数组r_t^{pm},R_t^{pm}，并拼接为一个数组
# # 拼接后的收益率数组，上半部分为r_t^{am} or R_t^{am}，下半部分为r_t^{pm} or R_t^{pm}
# r_am_array = np.zeros((cls.__days, 1))
# r_pm_array = np.zeros((cls.__days, 1))
# for ind in secu_mkt_data.index[1:]:
# r_am_array[ind-1, 0] = secu_mkt_data.loc[ind, 'mid_close'] / secu_mkt_data.loc[ind-1, 'close'] - 1.0
# r_pm_array[ind-1, 0] = secu_mkt_data.loc[ind, 'close'] / secu_mkt_data.loc[ind, 'mid_close'] - 1.0
# r_apm_array = np.concatenate((r_am_array, r_pm_array), axis=0)
#
# R_am_array = np.zeros((cls.__days, 1))
# R_pm_array = np.zeros((cls.__days, 1))
# for ind in index_mkt_data.index[1:]:
# R_am_array[ind-1, 0] = index_mkt_data.loc[ind, 'mid_close'] / index_mkt_data.loc[ind-1, 'close'] - 1.0
# R_pm_array[ind-1, 0] = index_mkt_data.loc[ind, 'close'] / index_mkt_data.loc[ind, 'mid_close'] - 1.0
# R_apm_array = np.concatenate((R_am_array, R_pm_array), axis=0)
# 遍历交易日序列，计算个股及指数的上午收益率(r_am_array,R_am_array)和下午收益率序列(r_pm_array,R_pm_array)
r_am_array = np.zeros((cls.__days, 1))
r_pm_array = np.zeros((cls.__days, 1))
R_am_array = np.zeros((cls.__days, 1))
R_pm_array = np.zeros((cls.__days, 1))
k = 0
for trading_day in trading_days:
df_1min_data = Utils.get_min_mkt(Utils.code_to_symbol(code),
trading_day,
fq=True)
if df_1min_data is not None:
str_date = Utils.datetimelike_to_str(trading_day)
fopen = df_1min_data[df_1min_data.datetime == '%s 09:31:00' %
str_date].iloc[0].open
fmid_close = df_1min_data[df_1min_data.datetime ==
'%s 11:30:00' %
str_date].iloc[0].close
fclose = df_1min_data[df_1min_data.datetime == '%s 15:00:00' %
str_date].iloc[0].close
r_am_array[k, 0] = fmid_close / fopen - 1.0
r_pm_array[k, 0] = fclose / fmid_close - 1.0
df_1min_data = Utils.get_min_mkt(factor_ct.APM_CT.index_code,
trading_day,
index=True,
fq=True)
fopen = df_1min_data[df_1min_data.datetime == '%s 09:31:00' %
str_date].iloc[0].open
fmid_close = df_1min_data[df_1min_data.datetime ==
'%s 11:30:00' %
str_date].iloc[0].close
fclose = df_1min_data[df_1min_data.datetime == '%s 15:00:00' %
str_date].iloc[0].close
R_am_array[k, 0] = fmid_close / fopen - 1.0
R_pm_array[k, 0] = fclose / fmid_close - 1.0
k += 1
if k == cls.__days:
break
if k < cls.__days:
return None
r_apm_array = np.concatenate((r_am_array, r_pm_array), axis=0)
R_apm_array = np.concatenate((R_am_array, R_pm_array), axis=0)
# 4.个股收益率数组相对于指数收益率进行线性回归
# 将指数收益率数组添加常数项
R_apm_array = sm.add_constant(R_apm_array)
# 线性回归：r_i = \alpha + \beta * R_i + \epsilon_i
stat_model = sm.OLS(r_apm_array, R_apm_array)
stat_result = stat_model.fit()
resid_array = stat_result.resid.reshape((cls.__days * 2, 1)) # 回归残差数组
# 5.计算stat统计量
# 以上得到的__days*2个残差\epsilon_i中，属于上午的记为\epsilon_i^{am},属于下午的记为\epsilong_i^{pm}，计算每日上午与
# 下午残差的差值：$\sigma_t = \spsilon_i^{am} - \epsilon_i^{pm}$，为了衡量上午与下午残差的差异程度，设计统计量：
# $stat = \frac{\mu(\sigma_t)}{\delta(\sigma_t)\sqrt(N)}$，其中\mu为均值，\sigma为标准差,N=__days，总的来说
# 统计量stat反映了剔除市场影响后股价行为上午与下午的差异程度。stat数值大（小）于0越多，则股票在上午的表现越好（差）于下午。
delta_array = resid_array[:cls.__days] - resid_array[
cls.__days:] # 上午与 下午的残差差值
delta_avg = np.mean(delta_array) # 残差差值的均值
delta_std = np.std(delta_array) # 残差差值的标准差
# 如果残差差值的标准差接近于0，返回None
if np.fabs(delta_std) < 0.0001:
return None
stat = delta_avg / delta_std / np.sqrt(cls.__days)
# logging.info('%s, stat = %.6f' % (code, stat))
return stat
def _calc_factor_loading(cls, code, calc_date):
"""
计算指定日期、指定个股日内各时点动量值
Parameters
--------
:param code: str
个股代码，如600000或SH600000
:param calc_date: datetime-like, str
因子载荷计算日期，格式YYYY-MM-DD
:return: pd.Series
--------
日内个时点的动量值，各个index对应的含义如下：
0. m0: 隔夜时段动量
1. m1: 第一个小时动量
2. m2: 第二个小时动量
3. m3: 第三个小时动量
4. m4: 第四个小时动量
5. m_normal: 传统动量
若计算失败，返回None
"""
# 取得过去90天的交易日序列，按日期降序排列
trading_days = Utils.get_trading_days(end=calc_date,
ndays=90,
ascending=False)
# 取得个股过去90天中的最近21天的1分钟行情数据，根据每天的分钟行情读取日内5个时点的价格，并计算日内收益值
mkt_data = DataFrame()
mkt_data_header = ['date', 'p0930', 'p1030', 'p1130', 'p1400', 'p1500']
intra_day_ret = DataFrame()
ret_header = ['date', 'r0', 'r1', 'r2', 'r3', 'r4']
k = 0
for trading_day in trading_days:
df_1m_mkt = Utils.get_min_mkt(code, trading_day, fq=True)
if df_1m_mkt is None:
continue
# 计算日内5个时点的价格
time_label = '%s 09:31:00' % trading_day.strftime('%Y-%m-%d')
p0930 = df_1m_mkt[df_1m_mkt.datetime == time_label].iloc[0].open
time_label = '%s 10:30:00' % trading_day.strftime('%Y-%m-%d')
p1030 = df_1m_mkt[df_1m_mkt.datetime == time_label].iloc[0].close
time_label = '%s 11:30:00' % trading_day.strftime('%Y-%m-%d')
p1130 = df_1m_mkt[df_1m_mkt.datetime == time_label].iloc[0].close
time_label = '%s 14:00:00' % trading_day.strftime('%Y-%m-%d')
p1400 = df_1m_mkt[df_1m_mkt.datetime == time_label].iloc[0].close
time_label = '%s 15:00:00' % trading_day.strftime('%Y-%m-%d')
p1500 = df_1m_mkt[df_1m_mkt.datetime == time_label].iloc[0].close
s = Series([trading_day, p0930, p1030, p1130, p1400, p1500],
index=mkt_data_header)
mkt_data = mkt_data.append(s, ignore_index=True)
# 计算日内收益
if k > 0:
r0 = math.log(mkt_data.iloc[k - 1].p0930 /
mkt_data.iloc[k].p1500)
r1 = math.log(mkt_data.iloc[k - 1].p1030 /
mkt_data.iloc[k - 1].p0930)
r2 = math.log(mkt_data.iloc[k - 1].p1130 /
mkt_data.iloc[k - 1].p1030)
r3 = math.log(mkt_data.iloc[k - 1].p1400 /
mkt_data.iloc[k - 1].p1130)
r4 = math.log(mkt_data.iloc[k - 1].p1500 /
mkt_data.iloc[k - 1].p1400)
# r0 = mkt_data.iloc[k - 1].p0930 / mkt_data.iloc[k].p1500 -1.0
# r1 = mkt_data.iloc[k - 1].p1030 / mkt_data.iloc[k - 1].p0930 - 1.0
# r2 = mkt_data.iloc[k - 1].p1130 / mkt_data.iloc[k - 1].p1030 - 1.0
# r3 = mkt_data.iloc[k - 1].p1400 / mkt_data.iloc[k - 1].p1130 - 1.0
# r4 = mkt_data.iloc[k - 1].p1500 / mkt_data.iloc[k - 1].p1400 - 1.0
s = Series([mkt_data.iloc[k - 1].date, r0, r1, r2, r3, r4],
index=ret_header)
intra_day_ret = intra_day_ret.append(s, ignore_index=True)
k += 1
if k > cls.__days:
break
if k <= cls.__days:
return None
intra_day_ret = intra_day_ret.sort_values(by='date')
# mkt_data = mkt_data.sort_values(by='date')
# mkt_data = mkt_data.reset_index(drop=True)
# 计算传统动量因子值，=过去20日的涨跌幅
m_normal = math.log(mkt_data.iloc[0].p1500 / mkt_data.iloc[-1].p1500)
# m_normal = mkt_data.iloc[0].p1500 / mkt_data.iloc[-1].p1500 - 1.0
# 遍历上述取得的行情数据，计算每日的日内收益值
# intra_day_ret = DataFrame()
# ret_header = ['date', 'r0', 'r1', 'r2', 'r3', 'r4']
# for k in range(1, len(mkt_data)):
# r0 = math.log(mkt_data.iloc[k].p0930 / mkt_data.iloc[k-1].p1500)
# r1 = math.log(mkt_data.iloc[k].p1030 / mkt_data.iloc[k].p0930)
# r2 = math.log(mkt_data.iloc[k].p1130 / mkt_data.iloc[k].p1030)
# r3 = math.log(mkt_data.iloc[k].p1400 / mkt_data.iloc[k].p1130)
# r4 = math.log(mkt_data.iloc[k].p1500 / mkt_data.iloc[k].p1400)
# s = Series([mkt_data.iloc[k].date, r0, r1, r2, r3, r4], index=ret_header)
# intra_day_ret = intra_day_ret.append(s, ignore_index=True)
intra_day_ret = intra_day_ret.set_index('date')
# 个股的日内各时点的动量因子值等于过去20个交易日各个r_i累加
intra_day_momentum = intra_day_ret.sum()
intra_day_momentum.index = ['m0', 'm1', 'm2', 'm3', 'm4']
intra_day_momentum['m_normal'] = m_normal
return intra_day_momentum
