def calc_factor_loading_(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷, 并保存至因子数据库 Parameters: -------- :param start_date: datetime-like, str 开始日期, 格式: YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认为True 如果为True, 则只计算月末时点的因子载荷 :param save: bool, 默认为True 是否保存至因子数据库 :param kwargs: 'multi_proc': bool, True=采用多进程, False=采用单进程, 默认为False :return: dict 因子载荷数据 """ # 取得交易日序列 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) # 遍历交易日序列, 计算ResVolatility因子下各个成分因子的因子载荷 if 'multi_proc' not in kwargs: kwargs['multi_proc'] = False for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue # 计算各成分因子的因子载荷 for com_factor in risk_ct.RESVOLATILITY_CT.component: factor = eval(com_factor + '()') factor.calc_factor_loading(start_date=calc_date, end_date=None, month_end=month_end, save=save, multi_proc=kwargs['multi_proc']) # 合成ResVolatility因子载荷 resvol_factor = pd.DataFrame() for com_factor in risk_ct.RESVOLATILITY_CT.component: factor_path = os.path.join(factor_ct.FACTOR_DB.db_path, eval('risk_ct.' + com_factor + '_CT')['db_file']) factor_loading = Utils.read_factor_loading(factor_path, Utils.datetimelike_to_str(calc_date, dash=False)) factor_loading.drop(columns='date', inplace=True) factor_loading[com_factor] = Utils.normalize_data(Utils.clean_extreme_value(np.array(factor_loading['factorvalue']).reshape((len(factor_loading), 1)))) factor_loading.drop(columns='factorvalue', inplace=True) if resvol_factor.empty: resvol_factor = factor_loading else: resvol_factor = pd.merge(left=resvol_factor, right=factor_loading, how='inner', on='id') resvol_factor.set_index('id', inplace=True) weight = pd.Series(risk_ct.RESVOLATILITY_CT.weight) resvol_factor = (resvol_factor * weight).sum(axis=1) resvol_factor.name = 'factorvalue' resvol_factor.index.name = 'id' resvol_factor = pd.DataFrame(resvol_factor) resvol_factor.reset_index(inplace=True) resvol_factor['date'] = Utils.get_trading_days(start=calc_date, ndays=2)[1] # 保存ResVolatility因子载荷 if save: Utils.factor_loading_persistent(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), resvol_factor.to_dict('list'),['date', 'id', 'factorvalue'])
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷, 并保存至因子数据库 Parameters: -------- :param start_date: datetime-like, str 开始日期, 格式:YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认为True 如果为True, 则只计算月末时点的因子载荷 :param save: bool, 默认为True 是否保存至因子数据库 :param kwargs: :return: dict 因子载荷数据 """ # 取得交易日序列 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) # 遍历交易日序列, 计算NLSIZE因子载荷 dict_nlsize = None for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue logging.info('[%s] Calc NLSIZE factor loading.' % Utils.datetimelike_to_str(calc_date)) # 读取Size因子载荷数据 lncap_data_path = os.path.join(factor_ct.FACTOR_DB.db_path, '{}_{}.csv'.format(risk_ct.SIZE_CT.db_file, Utils.datetimelike_to_str(calc_date, dash=False))) if not os.path.exists(lncap_data_path): logging.info('[%s] 的Size因子载荷数据不存在.' % Utils.datetimelike_to_str(calc_date)) continue df_lncap = pd.read_csv(lncap_data_path, header=0) # Size因子数组 arr_size = np.array(df_lncap['factorvalue']) # Size因子三次方数组 arr_size_cube = arr_size ** 3 # 相对Size因子正交化 model = sm.OLS(arr_size_cube, arr_size) result = model.fit() # 对残差值进行缩尾处理和标准化 n = len(result.resid) arr_resid = result.resid # arr_resid = result.resid.reshape(n, 1) # arr_resid_winsorized = Utils.clean_extreme_value(arr_resid) # arr_resid_standardized = Utils.normalize_data(arr_resid_winsorized) # 保存NLSIZE因子载荷数据 dict_nlsize = dict({'date': df_lncap['date'].values, 'id': df_lncap['id'].values, 'factorvalue': arr_resid}) if save: Utils.factor_loading_persistent(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), dict_nlsize, ['date', 'id', 'factorvalue']) return dict_nlsize
def _calc_alphafactor_loading(start_date, end_date=None, factor_name=None, multi_proc=False, test=False): """ 计算alpha因子因子载荷值(原始载荷值及去极值标准化后载荷值) Parameters: -------- :param start_date: datetime-like, str 开始日期, e.g: YYYY-MM-DD, YYYYMMDD :param end_date: datetime-like, str, 默认为None 结束日期, e.g: YYYY-MM-DD, YYYYMMDD :param factor_name: str, 默认为None alpha因子名称, e.g: SmartMoney factor_namea为None时, 计算所有alpha因子载荷值; 不为None时, 计算指定alpha因子的载荷值 :param multi_proc: bool, 默认为None 是否进行并行计算 :param test: bool, 默认为False 是否是进行因子检验 :return: 保存因子载荷值(原始载荷值及去极值标准化后的载荷值) """ # param_cons = eval('alphafactor_ct.'+factor_name.upper() + '_CT') start_date = Utils.to_date(start_date) if end_date is None: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) else: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) for calc_date in trading_days_series: if factor_name is None: for alphafactor_name in alphafactor_ct.ALPHA_FACTORS: CAlphaFactor = eval(alphafactor_name + '()') CAlphaFactor.calc_factor_loading(calc_date, month_end=True, save=True, multi_proc=multi_proc) else: if (not test) and (factor_name not in alphafactor_ct.ALPHA_FACTORS): raise ValueError("alpha因子类: %s, 不存在." % factor_name) CAlphaFactor = eval(factor_name + '()') CAlphaFactor.calc_factor_loading(calc_date, month_end=True, save=True, multi_proc=multi_proc)
def _optimize_periodmomentum_weight(cls, calc_date): """ 优化计算日内各时段动量因子载荷的权重 Parameters: -------- :param calc_date: datetime-like, str 计算日期 :return: pd.Series -------- 日内各时段动量因子载荷的优化权重向量 0. date, 日期, datetimelike 1. w0, 隔夜时段动量因子权重 2. w1, 第1小时动量因子权重 3. w2, 第2小时动量因子权重 4. w3, 第3小时动量因子权重 5. w4, 第4小时动量因子权重 """ calc_date = Utils.to_date(calc_date) # 读取过去60个月日内各时段动量因子IC时间序列值 ic_filepath = os.path.join(SETTINGS.FACTOR_DB_PATH, alphafactor_ct.INTRADAYMOMENTUM_CT['factor_ic_file']) df_ic = pd.read_csv(ic_filepath, header=0, parse_dates=[0]) df_ic = df_ic[df_ic['date'] <= calc_date].iloc[-60:] # 计算IC的均值和协方差矩阵 df_ic.drop(columns='date', inplace=True) ic_mean = np.mat(df_ic.mean(axis=0)).reshape((df_ic.shape[1], 1)) ic_cov = np.mat(df_ic.cov()) # 计算日内时段因子的最优权重 optimal_weights = ic_cov.I * ic_mean optimal_weights /= optimal_weights.sum() optimal_weights = np.array(optimal_weights).flatten().tolist() optimal_weights.insert(0, calc_date) optimal_weights = pd.Series(optimal_weights, index=['date', 'w0', 'w1', 'w2', 'w3', 'w4']) # 保存最优权重 weight_filepath = os.path.join(SETTINGS.FACTOR_DB_PATH, alphafactor_ct.INTRADAYMOMENTUM_CT['optimal_weight_file']) Utils.save_timeseries_data(optimal_weights, weight_filepath, save_type='a', columns=['date', 'w0', 'w1', 'w2', 'w3', 'w4'])
def _calc_factor_loading(cls, code, calc_date): """ 计算指定日期、指定个股EGRLF因子载荷 Parameters: -------- :param code: str 个股代码, 如SH600000, 600000 :param calc_date: datetime-like, str 计算日期, 格式: YYYY-MM-DD :return: pd.Series -------- 个股的EGRLF因子载荷 0. code 1. egrlf 如果计算失败, 返回None """ code = Utils.code_to_symbol(code) calc_date = Utils.to_date(calc_date) # 读取个股的预期盈利增长率数据 earningsgrowth_data = Utils.get_consensus_data( calc_date, code, ConsensusType.PredictedEarningsGrowth) if earningsgrowth_data is None: # 如果个股的预期盈利增长率数据不存在, 那么用过去3年净利润增长率代替 hist_growth_data = Utils.get_hist_growth_data(code, calc_date, 3) if hist_growth_data is None: return None if np.isnan(hist_growth_data['netprofit']): return None egrlf = hist_growth_data['netprofit'] else: egrlf = earningsgrowth_data['growth_2y'] return pd.Series([code, egrlf], index=['code', 'egrlf'])
def get_factor_weight(cls, date): """ 取得日内各时点动量因子的权重 -------- :param date: datetime-like or str 日期 :return: pd.Series 各时点权重信息 -------- 0. date: 日期 1. w0: 第一个时点动量因子的权重 2. w1: 第二个时点动量因子的权重 3. w2: 第三个时点动量因子的权重 4. w3: 第四个时点动量因子的权重 5. w4: 第五个时点动量因子的权重 读取不到数据,返回None """ date = Utils.to_date(date) weight_file_path = os.path.join( factor_ct.FACTOR_DB.db_path, factor_ct.INTRADAYMOMENTUM_CT.optimal_weight_file) df_optimal_weight = pd.read_csv(weight_file_path, parse_dates=[0], header=0) df_optimal_weight.sort_values(by='date', inplace=True) df_optimal_weight = df_optimal_weight[df_optimal_weight.date <= date] if df_optimal_weight.shape[0] > 0: return df_optimal_weight.iloc[-1] else: return None
def _calc_factor_loading(cls, code, calc_date): """ 计算指定日期、指定个股RSTR因子载荷 Parameters: -------- :param code: str 个股代码, 如SH600000, 600000 :param calc_date: datetime-like, str 计算日期, 格式: YYYY-MM-DD :return: pd.Series -------- 个股的RSTR因子载荷 0. code 1. rstr 如果计算失败, 返回None """ # 取得个股复权行情数据 df_secu_quote = Utils.get_secu_daily_mkt(code, end=calc_date, ndays=risk_ct.RSTR_CT.trailing_start+1, fq=True) if df_secu_quote is None: return None if len(df_secu_quote) < risk_ct.RSTR_CT.half_life: return None # 如果行情数据的起始日期距离计算日期的长度大于trailing_start的2倍, 返回None s = Utils.to_date(calc_date) - datetime.timedelta(days=risk_ct.RSTR_CT.trailing_start*2) if Utils.to_date(df_secu_quote.iloc[0]['date']) < s: return None df_secu_quote = df_secu_quote.head(len(df_secu_quote) - risk_ct.RSTR_CT.trailing_end) df_secu_quote.reset_index(drop=True, inplace=True) # 计算个股的日对数收益率 arr_secu_close = np.array(df_secu_quote.iloc[1:]['close']) arr_secu_preclose = np.array(df_secu_quote.shift(1).iloc[1:]['close']) arr_secu_daily_ret = np.log(arr_secu_close / arr_secu_preclose) # 计算权重(指数移动加权平均) T = len(arr_secu_daily_ret) # time_spans = sorted(range(T), reverse=True) # alpha = 1 - np.exp(np.log(0.5)/risk_ct.RSTR_CT.half_life) # x = [1-alpha] * T # y = [alpha] * (T-1) # y.insert(0, 1) # weights = np.float_power(x, time_spans) * y weights = Algo.ewma_weight(T, risk_ct.RSTR_CT.half_life) # 计算RSTR rstr = np.sum(arr_secu_daily_ret * weights) return pd.Series([Utils.code_to_symbol(code), rstr], index=['code', 'rstr'])
def _calc_factor_loading(cls, code, calc_date): """ 计算指定日期、指定个股的价值因子,包含ep_ttm, bp_lr, ocf_ttm Parameters: -------- :param code: str 个股代码:如600000或SH600000 :param calc_date: datetime-like or str 计算日期,格式YYYY-MM-DD, YYYYMMDD :return: pd.Series -------- 价值类因子值 0. ep_ttm: TTM净利润/总市值 1. bp_lr: 净资产(最新财报)/总市值 2. ocf_ttm: TTM经营性现金流/总市值 若计算失败,返回None """ code = Utils.code_to_symbol(code) calc_date = Utils.to_date(calc_date) # 读取TTM财务数据 ttm_fin_data = Utils.get_ttm_fin_basic_data(code, calc_date) if ttm_fin_data is None: return None # 读取最新财报数据 report_date = Utils.get_fin_report_date(calc_date) fin_basic_data = Utils.get_fin_basic_data(code, report_date) if fin_basic_data is None: return None # 计算总市值 mkt_daily = Utils.get_secu_daily_mkt(code, calc_date, fq=False, range_lookup=True) if mkt_daily.shape[0] == 0: return None cap_struct = Utils.get_cap_struct(code, calc_date) if cap_struct is None: return None total_cap = cap_struct.total - cap_struct.liquid_b - cap_struct.liquid_h total_mkt_cap = total_cap * mkt_daily.close # 计算价值类因子 ep_ttm = ttm_fin_data[ 'NetProfit'] * util_ct.FIN_DATA_AMOUNT_UNIT / total_mkt_cap ocf_ttm = ttm_fin_data[ 'NetOperateCashFlow'] * util_ct.FIN_DATA_AMOUNT_UNIT / total_mkt_cap bp_lr = fin_basic_data[ 'ShareHolderEquity'] * util_ct.FIN_DATA_AMOUNT_UNIT / total_mkt_cap return Series([round(ep_ttm, 6), round(bp_lr, 6), round(ocf_ttm, 6)], index=['ep_ttm', 'bp_lr', 'ocf_ttm'])
def _calc_factor_loading(cls, code, calc_date): """ 计算指定日期、指定个股SGRO因子载荷 Parameters: -------- :param code: str 个股代码, 如SH600000, 600000 :param calc_date: datetime-like, str 计算日期, 格式: YYYY-MM-DD :return: pd.Series -------- 个股的SGRO因子载荷 0. code 1. sgro 如果计算失败, 返回None """ code = Utils.code_to_symbol(code) calc_date = Utils.to_date(calc_date) # 读取过去5年的主要财务指标数据 years = 5 prevN_years_finbasicdata = _get_prevN_years_finbasicdata( calc_date, code, years) if prevN_years_finbasicdata is None: return None # 复权因子调整后的主营业务收入对年度t进行线性回归(OLS), 计算斜率beta arr_revenue = np.asarray([ fin_basicdata['MainOperateRevenue'] for fin_basicdata in prevN_years_finbasicdata ]) if any(np.isnan(arr_revenue)): return None arr_t = np.arange(1, years + 1) arr_t = sm.add_constant(arr_t) model = sm.OLS(arr_revenue, arr_t) results = model.fit() beta = results.params[1] # 计算平均revenue avg_revenue = np.mean(arr_revenue) if abs(avg_revenue) < utils_con.TINY_ABS_VALUE: return None # sgro = beta / avg_revenue sgro = beta / avg_revenue return pd.Series([code, sgro], index=['code', 'sgro'])
def _get_factor_weight(cls, date=None): """ 取得日内各时点动量因子的权重 -------- :param date: datetime-like or str 日期, 默认为None 如果date=None, 返回全部权重数据 :return: pd.Series, pd.DataFrame 各时点权重信息 -------- 0. date: 日期 1. w0: 第一个时点动量因子的权重 2. w1: 第二个时点动量因子的权重 3. w2: 第三个时点动量因子的权重 4. w3: 第四个时点动量因子的权重 5. w4: 第五个时点动量因子的权重 读取不到数据,返回None """ weight_file_path = os.path.join(SETTINGS.FACTOR_DB_PATH, alphafactor_ct.INTRADAYMOMENTUM_CT.optimal_weight_file) if not os.path.isfile(weight_file_path): return None df_optimal_weight = pd.read_csv(weight_file_path, parse_dates=[0], header=0) df_optimal_weight.sort_values(by='date', inplace=True) if date is None: if df_optimal_weight.empty: return None else: return df_optimal_weight else: date = Utils.to_date(date) df_weight = df_optimal_weight[df_optimal_weight.date <= date] if df_weight.shape[0] > 0: return df_weight.iloc[-1] else: df_weight = df_optimal_weight[df_optimal_weight.date >= date] if df_weight.shape[0] > 0: return df_weight.iloc[0] else: return None
def calc_suspension_info(date): """ 计算个股停牌信息 Parameters: -------- :param date: datetime-like, str 计算日期, e.g: YYYY-MM-DD, YYYYMMDD :return: """ # TODO 可以更改为从tushare.pro接口取得个股停牌信息 date = Utils.to_date(date) df_stock_basics = Utils.get_stock_basics(date) df_stock_basics['trading_status'] = df_stock_basics.apply(lambda x: Utils.trading_status(x['symbol'], date), axis=1) df_stock_basics = df_stock_basics[df_stock_basics['trading_status'] == SecuTradingStatus.Suspend] df_stock_basics.drop(columns='trading_status', inplace=True) cfg = ConfigParser() cfg.read('config.ini') suspension_info_path = os.path.join(SETTINGS.FACTOR_DB_PATH, cfg.get('suspension_info', 'info_path'), '{}.csv'.format(Utils.datetimelike_to_str(date, dash=False))) df_stock_basics.to_csv(suspension_info_path, index=False, encoding='utf-8')
def _calc_factor_loading(cls, code, calc_date): """ 计算指定日期、指定个股的成长因子,包含npg_ttm, opg_ttm Parameters: -------- :param code: str 个股代码,如600000或SH600000 :param calc_date: datetime-like or str 计算日期,格式YYYY-MM-DD, YYYYMMDD :return: pd.Series -------- 成长类因子值 0. id: 证券代码 1. npg_ttm: 净利润增长率_TTM 2. opg_ttm: 营业收入增长率_TTM 若计算失败, 返回None """ code = Utils.code_to_symbol(code) calc_date = Utils.to_date(calc_date) # 读取最新的TTM财务数据 ttm_fin_data_latest = Utils.get_ttm_fin_basic_data(code, calc_date) if ttm_fin_data_latest is None: return None # 读取去年同期TTM财务数据 try: pre_date = datetime.datetime(calc_date.year-1, calc_date.month, calc_date.day) except ValueError: pre_date = calc_date - datetime.timedelta(days=366) ttm_fin_data_pre = Utils.get_ttm_fin_basic_data(code, pre_date) if ttm_fin_data_pre is None: return None # 计算成长类因子值 if abs(ttm_fin_data_pre['NetProfit']) < 0.1: return None npg_ttm = (ttm_fin_data_latest['NetProfit'] - ttm_fin_data_pre['NetProfit']) / abs(ttm_fin_data_pre['NetProfit']) if abs(ttm_fin_data_pre['MainOperateRevenue']) < 0.1: return None opg_ttm = (ttm_fin_data_latest['MainOperateRevenue'] - ttm_fin_data_pre['MainOperateRevenue']) / abs(ttm_fin_data_pre['MainOperateRevenue']) return Series([code, round(npg_ttm, 4), round(opg_ttm, 4)], index=['id', 'npg_ttm', 'opg_ttm'])
def _calc_factor_loading(cls, code, calc_date): """ 计算指定日期、指定个股的规模因子值 Parameters: -------- :param code: str 个股代码,如600000、SH600000 :param calc_date: datetime-like, str 规模因子计算日期,格式YYYY-MM-DD或YYYYMMDD :return: pd.Series -------- 个股规模因子值,各个index对应的含义如下: 0. LnTotalMktCap: 总市值对数 1. LnLiquidMktCap: 流通市值对数 若计算失败,返回None """ # 取得证券截止指定日期最新的非复权行情数据 code = Utils.code_to_symbol(code) calc_date = Utils.to_date(calc_date) mkt_daily = Utils.get_secu_daily_mkt(code, calc_date, fq=False, range_lookup=True) if mkt_daily.shape[0] == 0: return None # 取得证券截止指定日期前最新的股本结构数据 cap_struct = Utils.get_cap_struct(code, calc_date) if cap_struct is None: return None # 计算证券的规模因子 scale_factor = Series() total_cap = cap_struct.total - cap_struct.liquid_b - cap_struct.liquid_h scale_factor['LnTotalMktCap'] = math.log(total_cap * mkt_daily.close) scale_factor['LnLiquidMktCap'] = math.log(cap_struct.liquid_a * mkt_daily.close) return scale_factor
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本股的因子载荷,并保存至因子数据库 Parameters: -------- :param start_date: datetime-like or str 开始日期,格式:YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like or str 结束日期,格式:YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认True 如果为True,则只计算月末时点的因子载荷;否则每个交易日都计算 :param save: bool, 默认False 是否保存至因子数据库 :return: 因子载荷,DataFrame -------- 因子载荷,DataFrame 0. date: 日期 1. id: 证券symbol 2. LnTotalMktCap: 总市值对数值 3. LnLiquidMktCap: 流通市值对数值 """ # 取得交易日序列股票基本信息表 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列,计算规模因子值 dict_scale = None for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue dict_scale = { 'date': [], 'id': [], 'LnTotalMktCap': [], 'LnLiquidMktCap': [] } # 遍历个股,计算个股规模因子值 s = (calc_date - datetime.timedelta(days=90)).strftime('%Y%m%d') stock_basics = all_stock_basics[all_stock_basics.list_date < s] # 采用单进程进行计算规模因子 # for _, stock_info in stock_basics.iterrows(): # scale_data = cls._calc_factor_loading(stock_info.symbol, calc_date) # if scale_data is not None: # logging.info("[%s] %s's total mkt cap = %.0f, liquid mkt cap = %.0f" % (calc_date.strftime('%Y-%m-%d'), stock_info.symbol, scale_data.LnTotalMktCap, scale_data.LnLiquidMktCap)) # dict_scale['id'].append(Utils.code_to_symbol(stock_info.symbol)) # dict_scale['LnTotalMktCap'].append(round(scale_data.LnTotalMktCap, 4)) # dict_scale['LnLiquidMktCap'].append(round(scale_data.LnLiquidMktCap, 4)) # 采用多进程并行计算规模因子 q = Manager().Queue() # 队列,用于进程间通信,存储每个进程计算的规模因子值 p = Pool(4) # 进程池,最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=( stock_info.symbol, calc_date, q, )) p.close() p.join() while not q.empty(): scale_data = q.get(True) dict_scale['id'].append(scale_data[0]) dict_scale['LnTotalMktCap'].append(round(scale_data[1], 4)) dict_scale['LnLiquidMktCap'].append(round(scale_data[2], 4)) date_label = Utils.get_trading_days(start=calc_date, ndays=2)[1] dict_scale['date'] = [date_label] * len(dict_scale['id']) # 保存规模因子载荷至因子数据库 if save: Utils.factor_loading_persistent(cls._db_file, calc_date.strftime('%Y%m%d'), dict_scale) # 休息60秒 logging.info('Suspending for 60s.') time.sleep(60) return dict_scale
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷,并保存至因子数据库 Parameters -------- :param start_date: datetime-like, str 开始日期 :param end_date: datetime-like, str,默认None 结束日期,如果为None,则只计算start_date日期的因子载荷 :param month_end: bool,默认True 只计算月末时点的因子载荷 :param save: 是否保存至因子数据库,默认为False :return: 因子载荷,DataFrame -------- 因子载荷,DataFrame 0: ID, 证券ID,为索引 1: factorvalue, 因子载荷 如果end_date=None,返回start_date对应的因子载荷数据 如果end_date!=None,返回最后一天的对应的因子载荷数据 如果没有计算数据,返回None """ # 0.取得交易日序列 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) # 取得样本个股信息 all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列,计算SMartQ因子载荷 dict_factor = None for calc_date in trading_days_series: dict_factor = {'id': [], 'factorvalue': []} if month_end and (not Utils.is_month_end(calc_date)): continue # 1.获取用于读取分钟行情的交易日列表(过去30天的交易日列表,降序排列) # trading_days = _get_trading_days(calc_date, 30) # trading_days = Utils.get_trading_days(end=calc_date, ndays=30, ascending=False) # 2.取得样本个股信息 # stock_basics = ts.get_stock_basics() s = (calc_date - datetime.timedelta(days=90)).strftime('%Y%m%d') stock_basics = all_stock_basics[all_stock_basics.list_date < s] # 3.遍历样本个股代码,计算Smart_Q因子载荷值 dict_factor = {'id': [], 'factorvalue': []} # 采用单进程进行计算 # for _, stock_info in stock_basics.iterrows(): # # code = '%s%s' % ('SH' if code[:2] == '60' else 'SZ', code) # factor_loading = cls._calc_factor_loading(stock_info.symbol, calc_date) # print("[%s]Calculating %s's SmartMoney factor loading = %.4f." % (calc_date.strftime('%Y-%m-%d'), stock_info.symbol, -1.0 if factor_loading is None else factor_loading)) # if factor_loading is not None: # # df_factor.ix[code, 'factorvalue'] = factor_loading # dict_factor['id'].append(Utils.code_to_symbol(stock_info.symbol)) # dict_factor['factorvalue'].append(factor_loading) # 采用多进程并行计算SmartQ因子载荷 q = Manager().Queue() # 队列,用于进程间通信,存储每个进程计算的因子载荷值 p = Pool(4) # 进程池,最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=( stock_info.symbol, calc_date, q, )) p.close() p.join() while not q.empty(): smart_q = q.get(True) dict_factor['id'].append(smart_q[0]) dict_factor['factorvalue'].append(smart_q[1]) date_label = Utils.get_trading_days(calc_date, ndays=2)[1] dict_factor['date'] = [date_label] * len(dict_factor['id']) # 4.保存因子载荷至因子数据库 if save: # db = shelve.open(cls._db_file, flag='c', protocol=None, writeback=False) # try: # db[calc_date.strftime('%Y%m%d')] = df_factor # finally: # db.close() Utils.factor_loading_persistent(cls._db_file, calc_date.strftime('%Y%m%d'), dict_factor) # 休息300秒 logging.info('Suspending for 360s.') time.sleep(360) return dict_factor
def calc_factor_loading_(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷, 并保存至因子数据库 Parameters: -------- :param start_date: datetime-like, str 开始日期, 格式: YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认为True 如果为True, 则只计算月末时点的因子载荷 :param save: bool, 默认为True 是否保存至因子数据库 :param kwargs: 'multi_proc': bool, True=采用多进程, False=采用单进程, 默认为False :return: dict 因子载荷数据 """ # 取得交易日序列 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) # 遍历交易日序列, 计算growth因子下各个成分因子的因子载荷 if 'multi_proc' not in kwargs: kwargs['multi_proc'] = False for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue # 计算各成分因子的因子载荷 for com_factor in risk_ct.GROWTH_CT.component: factor = eval(com_factor + '()') factor.calc_factor_loading(start_date=calc_date, end_date=None, month_end=month_end, save=save, multi_proc=kwargs['multi_proc']) # 合成Growth因子载荷 growth_factor = pd.DataFrame() df_industry_classify = Utils.get_industry_classify() # 个股行业分类数据 for com_factor in risk_ct.GROWTH_CT.component: factor_path = os.path.join( factor_ct.FACTOR_DB.db_path, eval('risk_ct.' + com_factor + '_CT')['db_file']) factor_loading = Utils.read_factor_loading( factor_path, Utils.datetimelike_to_str(calc_date, dash=False)) factor_loading.drop(columns='date', inplace=True) # factor_loading[com_factor] = Utils.normalize_data(Utils.clean_extreme_value(np.array(factor_loading['factorvalue']).reshape((len(factor_loading), 1)))) # factor_loading.drop(columns='factorvalue', inplace=True) factor_loading.rename(columns={'factorvalue': com_factor}, inplace=True) # 添加行业分类数据 factor_loading = pd.merge( left=factor_loading, right=df_industry_classify[['id', 'ind_code']], how='inner', on='id') # 取得含缺失值的因子载荷数据 missingdata_factor = factor_loading[ factor_loading[com_factor].isna()] # 删除factor_loading中的缺失值 factor_loading.dropna(axis='index', how='any', inplace=True) # 对factor_loading去极值、标准化 factor_loading = Utils.normalize_data(factor_loading, id='id', columns=com_factor, treat_outlier=True, weight='cap', calc_date=calc_date) # 把missingdata_factor中的缺失值替换为行业均值 ind_codes = set(missingdata_factor['ind_code']) ind_mean_factor = {} for ind_code in ind_codes: ind_mean_factor[ind_code] = factor_loading[ factor_loading['ind_code'] == ind_code][com_factor].mean() for idx, missingdata in missingdata_factor.iterrows(): missingdata_factor.loc[idx, com_factor] = ind_mean_factor[ missingdata['ind_code']] # 把missingdata_factor和factor_loading合并 factor_loading = pd.concat( [factor_loading, missingdata_factor]) # 删除ind_code列 factor_loading.drop(columns='ind_code', inplace=True) if growth_factor.empty: growth_factor = factor_loading else: growth_factor = pd.merge(left=growth_factor, right=factor_loading, how='inner', on='id') # # 读取个股行业分类数据, 添加至growth_factor中 # df_industry_classify = Utils.get_industry_classify() # growth_factor = pd.merge(left=growth_factor, right=df_industry_classify[['id', 'ind_code']]) # # 取得含缺失值的因子载荷数据 # missingdata_factor = growth_factor.loc[[ind for ind, data in growth_factor.iterrows() if data.hasnans]] # # 删除growth_factot中的缺失值 # growth_factor.dropna(axis='index', how='any', inplace=True) # # 对growth_factor去极值、标准化 # growth_factor = Utils.normalize_data(growth_factor, id='id', columns=risk_ct.GROWTH_CT.component, treat_outlier=True, weight='cap', calc_date=calc_date) # # 把missingdata_factor中的缺失值替换为行业均值 # ind_codes = set(missingdata_factor['ind_code']) # ind_mean_factor = {} # for ind_code in ind_codes: # ind_mean_factor[ind_code] = growth_factor[growth_factor['ind_code'] == ind_code].mean() # missingdata_label = {ind: missingdata_factor.columns[missingdata.isna()].tolist() for ind, missingdata in missingdata_factor.iterrows()} # for ind, cols in missingdata_label.items(): # missingdata_factor.loc[ind, cols] = ind_mean_factor[missingdata_factor.loc[ind, 'ind_code']][cols] # # 把missingdata_factor和growth_factor合并 # growth_factor = pd.concat([growth_factor, missingdata_factor]) # # 删除ind_code列 # growth_factor.drop(columns='ind_code', inplace=True) # 合成Growth因子 growth_factor.set_index('id', inplace=True) weight = pd.Series(risk_ct.GROWTH_CT.weight) growth_factor = (growth_factor * weight).sum(axis=1) growth_factor.name = 'factorvalue' growth_factor.index.name = 'id' growth_factor = pd.DataFrame(growth_factor) growth_factor.reset_index(inplace=True) growth_factor['date'] = Utils.get_trading_days(start=calc_date, ndays=2)[1] # 保存growth因子载荷 if save: Utils.factor_loading_persistent( cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), growth_factor.to_dict('list'), ['date', 'id', 'factorvalue'])
def _calc_mvpfp_summary(factor_name, calc_date): """ 计算最小波动纯因子组合的汇总绩效数据 Parameters: -------- :param factor_name: str alpha因子名称, e.g: SmartMoney :param calc_date: datetime-like, str 计算日期, e.g: YYYY-MM-DD, YYYYMMDD :return: 计算汇总绩效数据, 并保存 """ calc_date = Utils.to_date(calc_date) dailyperformance_filepath = os.path.join( SETTINGS.FACTOR_DB_PATH, eval('alphafactor_ct.' + factor_name.uppper() + '.CT')['db_file'], 'performance/performance_daily.csv') df_daily_performance = pd.read_csv(dailyperformance_filepath, parse_dates=[0], header=0) df_daily_performance = df_daily_performance[ df_daily_performance['date'] <= calc_date] monthlyperformance_filepath = os.path.join( SETTINGS.FACTOR_DB_PATH, eval('alphafactor_ct.' + factor_name.upper() + '.CT')['db_file'], 'performance/performance_monthly.csv') df_monthly_performance = pd.read_csv(monthlyperformance_filepath, parse_dates=[0], header=0) df_monthly_performance = df_monthly_performance[ df_monthly_performance['date'] <= calc_date] if len(df_monthly_performance) < 12: logging.info("alpha因子'%s'的历史月度绩效数据长度小于12个月, 不计算汇总绩效数据." % factor_name) return summary_performance = pd.Series( index=alphamodel_ct.FACTOR_PERFORMANCE_HEADER['summary_performance']) for k in alphamodel_ct.SUMMARY_PERFORMANCE_MONTH_LENGTH: if k == 'total': daily_performance = df_daily_performance monthly_performance = df_monthly_performance summary_performance['type'] = k else: if not isinstance(k, int): raise TypeError("计算因子汇总绩效的时间区间类型除了'total'外, 应该为整型.") if len(df_monthly_performance) >= k: monthly_performance = df_monthly_performance.iloc[-k:] else: logging.info( "alpha因子'%s'的历史月度绩效数据长度小于%d个月, 不予计算该历史时间长度的汇总绩效." % (factor_name, k)) continue daily_performance = df_daily_performance[ df_daily_performance['date'] >= monthly_performance.iloc[0] ['date']] summary_performance['type'] = str(k) + 'm' summary_performance['date'] = daily_performance.iloc[-1]['date'] summary_performance['total_ret'] = daily_performance.iloc[-1][ 'nav'] / daily_performance.iloc[0]['nav'] - 1.0 summary_performance['annual_ret'] = math.pow( summary_performance['total_ret'] + 1, 12 / k) summary_performance['volatility'] = np.std( daily_performance['daily_ret']) * math.sqrt(250) summary_performance['monthly_winrate'] = len( monthly_performance[monthly_performance['monthly_ret'] > 0]) / k summary_performance['IR'] = summary_performance[ 'annual_ret'] / summary_performance['volatility'] fmax_drawdown = 0.0 for m in range(1, len(daily_performance)): fdrawdown = daily_performance.iloc[m]['nav'] / max( daily_performance.iloc[:m]['nav']) - 1.0 if fdrawdown < fmax_drawdown: fmax_drawdown = fdrawdown summary_performance['max_drawdown'] = fmax_drawdown _save_mvpfp_performance(summary_performance, factor_name, 'summary', 'a')
def _calc_synthetic_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的合成因子的载荷,并保存至因子数据库 Parameters -------- :param start_date: datetime-like, str 开始日期 :param end_date: datetime-like, str,默认None 结束日期,如果为None,则只计算start_date日期的因子载荷 :param month_end: bool,默认True 只计算月末时点的因子载荷,该参数只在end_date不为None时有效,并且不论end_date是否为None,都会计算第一天的因子载荷 :param save: 是否保存至因子数据库,默认为False :param kwargs: 'multi_proc': bool, True=采用多进程, False=采用单进程, 默认为False 'com_factors': list, 成分因子的类实例list :return: 因子载荷,DataFrame -------- 因子载荷,DataFrame 0: ID, 证券ID,为索引 1: factorvalue, 因子载荷 """ # 取得交易日序列 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) # 遍历交易日序列, 计算合成因子下各个成分因子的因子载荷 if 'multi_proc' not in kwargs: kwargs['multi_proc'] = False for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue # 计算各成分因子的因子载荷 # for com_factor in eval('risk_ct.' + cls.__name__.upper() + '_CT')['component']: # factor = eval(com_factor + '()') # factor.calc_factor_loading(start_date=calc_date, end_date=None, month_end=month_end, save=save, multi_proc=kwargs['multi_proc']) for com_factor in kwargs['com_factors']: com_factor.calc_factor_loading(start_date=calc_date, end_date=None, month_end=month_end, save=save, multi_proc=kwargs['multi_proc']) # 计算合成因子 synthetic_factor = pd.DataFrame() df_industry_classify = Utils.get_industry_classify() # 个股行业分类数据 for com_factor in eval('risk_ct.' + cls.__name__.upper() + '_CT')['component']: factor_path = os.path.join( factor_ct.FACTOR_DB.db_path, eval('risk_ct.' + com_factor + '_CT')['db_file']) factor_loading = Utils.read_factor_loading( factor_path, Utils.datetimelike_to_str(calc_date, dash=False)) factor_loading.drop(columns='date', inplace=True) factor_loading.rename(columns={'factorvalue': com_factor}, inplace=True) # 添加行业分类数据 factor_loading = pd.merge( left=factor_loading, right=df_industry_classify[['id', 'ind_code']], how='inner', on='id') # 取得含缺失值的因子载荷数据 missingdata_factor = factor_loading[ factor_loading[com_factor].isna()] # 删除factor_loading中的缺失值 factor_loading.dropna(axis='index', how='any', inplace=True) # 对factor_loading去极值、标准化 factor_loading = Utils.normalize_data(factor_loading, id='id', columns=com_factor, treat_outlier=True, weight='cap', calc_date=calc_date) # 把missingdata_factor中的缺失值替换为行业均值 ind_codes = set(missingdata_factor['ind_code']) ind_mean_factor = {} for ind_code in ind_codes: ind_mean_factor[ind_code] = factor_loading[ factor_loading['ind_code'] == ind_code][com_factor].mean() for idx, missingdata in missingdata_factor.iterrows(): missingdata_factor.loc[idx, com_factor] = ind_mean_factor[ missingdata['ind_code']] # 把missingdata_factor和factor_loading合并 factor_loading = pd.concat( [factor_loading, missingdata_factor]) # 删除ind_code列 factor_loading.drop(columns='ind_code', inplace=True) # merge成分因子 if synthetic_factor.empty: synthetic_factor = factor_loading else: synthetic_factor = pd.merge(left=synthetic_factor, right=factor_loading, how='inner', on='id') # 合成因子 synthetic_factor.set_index('id', inplace=True) weight = pd.Series( eval('risk_ct.' + cls.__name__.upper() + '_CT')['weight']) synthetic_factor = (synthetic_factor * weight).sum(axis=1) synthetic_factor.name = 'factorvalue' synthetic_factor.index.name = 'id' synthetic_factor = pd.DataFrame(synthetic_factor) synthetic_factor.reset_index(inplace=True) synthetic_factor['date'] = Utils.get_trading_days(start=calc_date, ndays=2)[1] # 保存synthetic_factor因子载荷 if save: Utils.factor_loading_persistent( cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), synthetic_factor.to_dict('list'), ['date', 'id', 'factorvalue'])
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷, 并保存至因子数据库 Parameters: -------- :param start_date: datetime-like or str 开始日期, 格式: YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式:YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认True 如果为True, 则只计算月末时点的因子载荷 :param save: bool, 默认True 是否保存至因子数据库 :param kwargs: :return: dict 因子载荷 -------- """ # 取得交易日序列及股票基本信息表 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列, 计算筹码分布因子载荷 dict_cyq = {} for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue logging.info('[%s] Calc CYQ factor loading.' % Utils.datetimelike_to_str(calc_date)) # 遍历个股, 计算个股筹码分布因子值 s = (calc_date - datetime.timedelta(days=180)).strftime('%Y%m%d') stock_basics = all_stock_basics[all_stock_basics.list_date < s] secu_cyq_path = Path( factor_ct.FACTOR_DB.db_path, factor_ct.CYQ_CT.db_file, 'secu_cyq/%s' % calc_date.strftime('%Y-%m-%d')) if not secu_cyq_path.exists(): secu_cyq_path.mkdir() ids = [] rps = [] # 采用单进程计算筹码分布数据, 及当前价格的相对位置(=当前价格-平均成本)/平均成本 # for _, stock_info in stock_basics.iterrows(): # logging.info("[%s] Calc %s's cyq data." % (calc_date.strftime('%Y-%m-%d'), stock_info.symbol)) # secu_cyq = cls._calc_factor_loading(stock_info.symbol, calc_date) # if secu_cyq is not None: # secu_code, secu_close, cyq_data = secu_cyq # # 保存个股的筹码分布数据 # cyq_data.to_csv(Path(secu_cyq_path, '%s.csv' % secu_code), header=True) # # 计算当前价格的相对位置 # avg_cyq = np.sum(np.array(cyq_data.index) * np.array(cyq_data.values)) # relative_position = round((secu_close - avg_cyq) / avg_cyq, 4) # ids.append(secu_code) # rps.append(relative_position) # 采用多进程进行并行计算筹码分布数据, 及当前价格的相对位置(=当前价格-平均成本)/平均成本 q = Manager().Queue() # 队列, 用于进程间通信, 存储每个进程计算的因子载荷 p = Pool(4) # 进程池, 最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=( stock_info.symbol, calc_date, q, )) p.close() p.join() while not q.empty(): secu_cyq = q.get(True) secu_code, secu_close, cyq_data = secu_cyq # 保存个股的筹码分布数据 cyq_data.to_csv(Path(secu_cyq_path, '%s.csv' % secu_code), header=True) # 计算当前价格的相对位置 avg_cyq = np.sum( np.array(cyq_data.index) * np.array(cyq_data.values)) relative_position = round((secu_close - avg_cyq) / avg_cyq, 4) ids.append(secu_code) rps.append(relative_position) date_label = Utils.get_trading_days(calc_date, ndays=2)[1] dict_cyq = { 'date': [date_label] * len(ids), 'id': ids, 'factorvalue': rps } if save: cyq_data_path = os.path.join(factor_ct.FACTOR_DB.db_path, factor_ct.CYQ_CT.db_file, factor_ct.CYQ_CT.CYQ_rp_file) Utils.factor_loading_persistent( cyq_data_path, Utils.datetimelike_to_str(calc_date, dash=False), dict_cyq, ['date', 'id', 'factorvalue']) # 休息90秒 logging.info('Suspending for 100s.') time.sleep(100) return dict_cyq
def calc_factor_loading1(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷, 并保存至因子数据库 Parameters: -------- :param start_date: datetime-like or str 开始日期, 格式: YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式:YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认True 如果为True, 则只计算月末时点的因子载荷 :param save: bool, 默认True 是否保存至因子数据库 :param kwargs: :return: dict 因子载荷 -------- """ # 取得交易日序列及股票基本信息表 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列, 计算筹码分布因子载荷 dict_cyq = None for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue logging.info('[%s] Calc CYQ factor loading.' % Utils.datetimelike_to_str(calc_date)) # 遍历个股, 计算个股筹码分布因子值 df_proxies = DataFrame() s = (calc_date - datetime.timedelta(days=365)).strftime('%Y%m%d') stock_basics = all_stock_basics[all_stock_basics.list_date < s] trading_day = Utils.get_trading_days(calc_date, ndays=2)[1] # 采用单进程计算筹码因子分布的代理变量 # for _, stock_info in stock_basics.iterrows(): # cyq_proxies = cls._calc_factor_loading(stock_info.symbol, calc_date) # if cyq_proxies is not None: # logging.info("[%s] %s's cyq proxies = (%0.4f,%0.4f,%0.4f,%0.4f,%0.4f)" % (calc_date.strftime('%Y-%m-%d'), stock_info.symbol, cyq_proxies['arc'], cyq_proxies['vrc'], cyq_proxies['src'], cyq_proxies['krc'], cyq_proxies['next_ret'])) # # cyq_proxies['date'] = trading_day # cyq_proxies['id'] = Utils.code_to_symbol(stock_info.symbol) # df_proxies = df_proxies.append(cyq_proxies, ignore_index=True) # 采用多进程进行并行计算筹码分布因子的代理变量 q = Manager().Queue() # 队列, 用于进程间通信, 存储每个进程计算的因子载荷 p = Pool(4) # 进程池, 最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=( stock_info.symbol, calc_date, q, )) p.close() p.join() while not q.empty(): cyq_proxies = q.get(True) # cyq_proxies['date'] = trading_day df_proxies = df_proxies.append(cyq_proxies, ignore_index=True) # 保存筹码分布代理变量数据 df_proxies['date'] = trading_day proxies_file_path = cls._db_proxies_path + '_%s.csv' % Utils.datetimelike_to_str( calc_date, dash=False) df_proxies.to_csv( proxies_file_path, index=False, columns=['date', 'id', 'arc', 'vrc', 'src', 'krc', 'next_ret']) # 导入筹码分布因子的代理变量数据 # cyq_proxies_path = cls._db_proxies_path + '_%s.csv' % Utils.datetimelike_to_str(calc_date, dash=False) # df_proxies = pd.read_csv(cyq_proxies_path, header=0) # 计算marc, 代理变量权重及筹码分布因子载荷 marc = df_proxies['arc'].median() proxies_weight_file = Path(factor_ct.FACTOR_DB.db_path, factor_ct.CYQ_CT.proxies_weight_file) if proxies_weight_file.exists(): df_proxies_weight = pd.read_csv(proxies_weight_file, header=0, parse_dates=[0]) df_proxies_weight = df_proxies_weight[ df_proxies_weight.date < calc_date].tail(24) if len(df_proxies_weight) < 24: with open(proxies_weight_file, 'a', newline='') as f: csv_writer = csv.writer(f) csv_writer.writerow([ calc_date.strftime('%Y-%m-%d'), marc, 0, 0, 0, 0, 0 ]) else: df_proxies_data = DataFrame() if marc > 0: df_proxies_weight = df_proxies_weight[ df_proxies_weight.marc > 0] elif marc < 0: df_proxies_weight = df_proxies_weight[ df_proxies_weight.marc < 0] for _, weight_info in df_proxies_weight.iterrows(): proxies_file_path = cls._db_proxies_path + '_%s.csv' % Utils.datetimelike_to_str( weight_info['date'], False) df_proxies_data = df_proxies_data.append( pd.read_csv(proxies_file_path, header=0), ignore_index=True) next_ret = np.array(df_proxies_data['next_ret']) cyq_data = np.array( df_proxies_data[['arc', 'vrc', 'src', 'krc']]) cyq_data = sm.add_constant(cyq_data) cyq_model = sm.OLS(next_ret, cyq_data) cyq_result = cyq_model.fit() cyq_weights = np.around(cyq_result.params, 6) with open(proxies_weight_file, 'a', newline='') as f: csv_writer = csv.writer(f) csv_writer.writerow([ calc_date.strftime('%Y-%m-%d'), marc, cyq_weights[0], cyq_weights[1], cyq_weights[2], cyq_weights[3], cyq_weights[4] ]) # 计算筹码分布因子载荷 arr_proxies = np.array( df_proxies[['arc', 'vrc', 'src', 'krc']]) arr_weight = np.array([ cyq_weights[1], cyq_weights[2], cyq_weights[3], cyq_weights[4] ]).reshape((4, 1)) intercept = cyq_weights[0] arr_cyq = np.around( np.dot(arr_proxies, arr_weight) + intercept, 6) dict_cyq = { 'date': list(df_proxies['date']), 'id': list(df_proxies['id']), 'factorvalue': list(arr_cyq.reshape((len(arr_cyq), ))) } # 保存因子载荷至因子数据库 if save: Utils.factor_loading_persistent( cls._db_file, calc_date.strftime('%Y%m%d'), dict_cyq, columns=['date', 'id', 'factorvalue']) else: with open(proxies_weight_file, 'w', newline='') as f: csv_writer = csv.writer(f) csv_writer.writerow([ 'date', 'marc', 'intcpt', 'arc_w', 'vrc_w', 'src_w', 'krc_w' ]) csv_writer.writerow( [calc_date.strftime('%Y-%m-%d'), marc, 0, 0, 0, 0, 0]) # 休息300秒 logging.info('Suspending for 200s.') time.sleep(200)
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, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷, 并保存至因子数据库 Parameters: -------- :param start_date: datetime-like, str 开始日期, 格式: YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认为True 如果为True, 则只计算月末时点的因子载荷 :param save: bool, 默认True 是否保存至因子数据库 :param kwargs: :return: dict 因子载荷 """ # 取得交易日序列及股票基本信息表 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) # all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列, 计算筹码分布因子载荷 dict_beta = {} dict_hsigma = {} for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue logging.info('[%s] Calc BETA factor loading.' % Utils.datetimelike_to_str(calc_date)) # 遍历个股, 计算个股BETA因子值 # s = (calc_date - datetime.timedelta(days=risk_ct.DBETA_CT.listed_days)).strftime('%Y%m%d') # stock_basics = all_stock_basics[all_stock_basics.list_date < s] s = calc_date - datetime.timedelta(days=risk_ct.DBETA_CT.listed_days) stock_basics = Utils.get_stock_basics(s, False) ids = [] # 个股代码list betas = [] # BETA因子值 hsigmas = [] # HSIGMA因子值 if 'multi_proc' not in kwargs: kwargs['multi_proc'] = False if not kwargs['multi_proc']: # 采用单进程计算BETA因子和HSIGMA因子值, for _, stock_info in stock_basics.iterrows(): logging.debug("[%s] Calc %s's BETA and HSIGMA factor data." % (calc_date.strftime('%Y-%m-%d'), stock_info.symbol)) beta_data = cls._calc_factor_loading(stock_info.symbol, calc_date) if beta_data is None: ids.append(Utils.code_to_symbol(stock_info.symbol)) betas.append(np.nan) hsigmas.append(np.nan) else: ids.append(beta_data['code']) betas.append(beta_data['beta']) hsigmas.append(beta_data['hsigma']) else: # 采用多进程并行计算BETA因子和HSIGMA因子值 q = Manager().Queue() # 队列, 用于进程间通信, 存储每个进程计算的因子载荷 p = Pool(SETTINGS.CONCURRENCY_KERNEL_NUM) # 进程池, 最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=(stock_info.symbol, calc_date, q,)) p.close() p.join() while not q.empty(): beta_data = q.get(True) ids.append(beta_data['code']) betas.append(beta_data['beta']) hsigmas.append(beta_data['hsigma']) date_label = Utils.get_trading_days(calc_date, ndays=2)[1] dict_beta = {'date': [date_label]*len(ids), 'id': ids, 'factorvalue': betas} dict_hsigma = {'date': [date_label]*len(ids), 'id': ids, 'factorvalue': hsigmas} if save: Utils.factor_loading_persistent(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), dict_beta, ['date', 'id', 'factorvalue']) hsigma_path = os.path.join(factor_ct.FACTOR_DB.db_path, risk_ct.HSIGMA_CT.db_file) Utils.factor_loading_persistent(hsigma_path, Utils.datetimelike_to_str(calc_date, dash=False), dict_hsigma, ['date', 'id', 'factorvalue']) # 休息180秒 # logging.info('Suspending for 180s.') # time.sleep(180) return dict_beta
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷, 并保存至因子数据库 Parameters: -------- :param start_date: datetime-like, str 开始日期, 格式: YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认为True 如果为True, 则只计算月末时点的因子载荷 :param save: bool, 默认为True 是否保存至因子数据库 :param kwargs: 'multi_proc': bool, True=采用多进程, False=采用单进程, 默认为False :return: dict 因子载荷 """ # 取得交易日序列及股票基本信息表 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列, 计算DASTD因子载荷 dict_dastd = None for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue logging.info('[%s] Calc DASTD factor loading.' % Utils.datetimelike_to_str(calc_date)) # 遍历个股, 计算个股的DASTD因子值 s = (calc_date - datetime.timedelta(days=risk_ct.DASTD_CT.listed_days)).strftime('%Y%m%d') stock_basics = all_stock_basics[all_stock_basics.list_date < s] ids = [] # 个股代码list dastds = [] # DASTD因子值list if 'multi_proc' not in kwargs: kwargs['multi_proc'] = False if not kwargs['multi_proc']: # 采用单进程计算DASTD因子值 for _, stock_info in stock_basics.iterrows(): logging.info("[%s] Calc %s's DASTD factor loading." % (calc_date.strftime('%Y-%m-%d'), stock_info.symbol)) dastd_data = cls._calc_factor_loading(stock_info.symbol, calc_date) if dastd_data is None: ids.append(Utils.code_to_symbol(stock_info.symbol)) dastds.append(np.nan) else: ids.append(dastd_data['code']) dastds.append(dastd_data['dastd']) else: # 采用多进程并行计算DASTD因子值 q = Manager().Queue() # 队列, 用于进程间通信, 存储每个进程计算的因子载荷 p = Pool(4) # 进程池, 最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=(stock_info.symbol, calc_date, q,)) p.close() p.join() while not q.empty(): dastd_data = q.get(True) ids.append(dastd_data['code']) dastds.append(dastd_data['dastd']) date_label = Utils.get_trading_days(start=calc_date, ndays=2)[1] dict_dastd = {'date': [date_label]*len(ids), 'id': ids, 'factorvalue': dastds} if save: Utils.factor_loading_persistent(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), dict_dastd, ['date', 'id', 'factorvalue']) # 暂停180秒 logging.info('Suspending for 180s.') # time.sleep(180) return dict_dastd
def _calc_factor_loading(cls, code, calc_date): """ 计算指定日期、指定个股BETA因子载荷 Parameters: -------- :param code: str 个股代码, 如600000或SH600000 :param calc_date: datetime-like, str 计算日期, 格式YYYY-MM-DD :return: pd.Series -------- 个股的BETA因子和HSIGMA因子载荷 0. code: 个股代码 1. beta: BETA因子载荷 2. hsigma: HSIGMA因子载荷 若计算失败, 返回None """ # 取得个股复权行情数据 df_secu_quote = Utils.get_secu_daily_mkt(code, end=calc_date, ndays=risk_ct.DBETA_CT.trailing+1, fq=True) if df_secu_quote is None: return None # 如果行情数据长度小于半年(126个交易日), 那么返回None if len(df_secu_quote) < 126: return None # 如果读取的行情数据起始日距离计算日期大于trailing的3倍, 返回None s = Utils.to_date(calc_date) - datetime.timedelta(days=risk_ct.DBETA_CT.trailing*3) if Utils.to_date(df_secu_quote.iloc[0]['date']) < s: return None df_secu_quote.reset_index(drop=True, inplace=True) # 取得基准复权行情数据 benchmark_code = risk_ct.DBETA_CT.benchmark df_benchmark_quote = Utils.get_secu_daily_mkt(benchmark_code, end=calc_date, fq=True) if df_benchmark_quote is None: return None df_benchmark_quote = df_benchmark_quote[df_benchmark_quote['date'].isin(list(df_secu_quote['date']))] if len(df_benchmark_quote) != len(df_secu_quote): raise ValueError("[beta计算]基准和个股的历史行情长度不一致.") df_benchmark_quote.reset_index(drop=True, inplace=True) # 计算个股和基准的日收益率序列 arr_secu_close = np.array(df_secu_quote.iloc[1:]['close']) arr_secu_preclose = np.array(df_secu_quote.shift(1).iloc[1:]['close']) arr_secu_daily_ret = arr_secu_close / arr_secu_preclose - 1. arr_benchmark_close = np.array(df_benchmark_quote.iloc[1:]['close']) arr_benchmark_preclose = np.array(df_benchmark_quote.shift(1).iloc[1:]['close']) arr_benchmark_daily_ret = arr_benchmark_close / arr_benchmark_preclose - 1. # 计算权重(指数移动加权平均) T = len(arr_benchmark_daily_ret) # time_spans = sorted(range(T), reverse=True) # alpha = 1 - np.exp(np.log(0.5)/risk_ct.DBETA_CT.half_life) # x = [1-alpha] * T # y = [alpha] * (T-1) # y.insert(0, 1) # weights = np.float_power(x, time_spans) * y weights = Algo.ewma_weight(T, risk_ct.DBETA_CT.half_life) # 采用加权最小二乘法计算Beta因子载荷及hsigma arr_benchmark_daily_ret = sm.add_constant(arr_benchmark_daily_ret) cap_model = sm.WLS(arr_secu_daily_ret, arr_benchmark_daily_ret, weights=weights) result = cap_model.fit() beta = result.params[1] hsigma = np.sqrt(result.mse_resid) return pd.Series([Utils.code_to_symbol(code), beta, hsigma], index=['code', 'beta', 'hsigma'])
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷,并保存至因子数据库 Parameters -------- :param start_date: datetime-like, str 开始日期,格式:YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期,如果为None,则只计算start_date日期的因子载荷,格式:YYYY-MM-DD or YYYYMMDD :param month_end: bool,默认True 如果为True,则只计算月末时点的因子载荷 :param save: bool,默认False 是否保存至因子数据库 :param kwargs['synthetic_factor']: bool, 默认为False 是否计算合成因子 :return: 因子载荷,DataFrame -------- 因子载荷,DataFrame 0. date: 日期 1. id: 证券symbol 2. m0: 隔夜时段动量 3. m1: 第一个小时动量 4. m2: 第二个小时动量 5. m3: 第三个小时动量 6. m4: 第四个小时动量 7. m_normal: 传统动量 """ # 取得交易日序列及股票基本信息表 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列,计算日内动量因子值 dict_intraday_momentum = None for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue logging.info( '[%s] calc synthetic intraday momentum factor loading.' % Utils.datetimelike_to_str(calc_date)) if 'synthetic_factor' in kwargs and kwargs[ 'synthetic_factor']: # 计算日内合成动量因子 dict_intraday_momentum = { 'date': [], 'id': [], 'factorvalue': [] } # 读取日内个时段动量因子值 df_factor_loading = Utils.read_factor_loading( cls._db_file, Utils.datetimelike_to_str(calc_date, False)) if df_factor_loading.shape[0] <= 0: logging.info( "[%s] It doesn't exist intraday momentum factor loading." % Utils.datetimelike_to_str(calc_date)) return df_factor_loading.fillna(0, inplace=True) # 读取因子最优权重 factor_weight = cls.get_factor_weight(calc_date) if factor_weight is None: logging.info("[%s] It doesn't exist factor weight.") return # 计算合成动量因子 arr_factor_loading = np.array( df_factor_loading[['m0', 'm1', 'm2', 'm3', 'm4']]) arr_factor_weight = np.array( factor_weight.drop('date')).reshape((5, 1)) arr_synthetic_factor = np.dot(arr_factor_loading, arr_factor_weight) # arr_synthetic_factor = np.around(arr_synthetic_factor, 6) dict_intraday_momentum['date'] = list( df_factor_loading['date']) dict_intraday_momentum['id'] = list(df_factor_loading['id']) dict_intraday_momentum['factorvalue'] = list( arr_synthetic_factor.astype(float).round(6).reshape( (arr_synthetic_factor.shape[0], ))) # 保存合成因子 if save: synthetic_db_file = os.path.join( factor_ct.FACTOR_DB.db_path, factor_ct.INTRADAYMOMENTUM_CT.synthetic_db_file) Utils.factor_loading_persistent( synthetic_db_file, Utils.datetimelike_to_str(calc_date, False), dict_intraday_momentum) else: # 计算日内各时段动量因子 dict_intraday_momentum = { 'date': [], 'id': [], 'm0': [], 'm1': [], 'm2': [], 'm3': [], 'm4': [], 'm_normal': [] } # 遍历个股,计算个股日内动量值 s = (calc_date - datetime.timedelta(days=90)).strftime('%Y%m%d') stock_basics = all_stock_basics[all_stock_basics.list_date < s] # 采用单进程进行计算 # for _, stock_info in stock_basics.iterrows(): # momentum_data = cls._calc_factor_loading(stock_info.symbol, calc_date) # if momentum_data is not None: # logging.info("[%s] %s's intraday momentum = (%0.4f,%0.4f,%0.4f,%0.4f,%0.4f,%0.4f)" % (calc_date.strftime('%Y-%m-%d'),stock_info.symbol, momentum_data.m0, momentum_data.m1, momentum_data.m2, momentum_data.m3, momentum_data.m4, momentum_data.m_normal)) # dict_intraday_momentum['id'].append(Utils.code_to_symbol(stock_info.symbol)) # dict_intraday_momentum['m0'].append(round(momentum_data.m0, 6)) # dict_intraday_momentum['m1'].append(round(momentum_data.m1, 6)) # dict_intraday_momentum['m2'].append(round(momentum_data.m2, 6)) # dict_intraday_momentum['m3'].append(round(momentum_data.m3, 6)) # dict_intraday_momentum['m4'].append(round(momentum_data.m4, 6)) # dict_intraday_momentum['m_normal'].append(round(momentum_data.m_normal, 6)) # 采用多进程并行计算日内动量因子载荷 q = Manager().Queue() # 队列,用于进程间通信,存储每个进程计算的因子载荷 p = Pool(4) # 进程池,最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=( stock_info.symbol, calc_date, q, )) p.close() p.join() while not q.empty(): momentum_data = q.get(True) dict_intraday_momentum['id'].append(momentum_data[0]) dict_intraday_momentum['m0'].append( round(momentum_data[1], 6)) dict_intraday_momentum['m1'].append( round(momentum_data[2], 6)) dict_intraday_momentum['m2'].append( round(momentum_data[3], 6)) dict_intraday_momentum['m3'].append( round(momentum_data[4], 6)) dict_intraday_momentum['m4'].append( round(momentum_data[5], 6)) dict_intraday_momentum['m_normal'].append( round(momentum_data[6], 6)) date_label = Utils.get_trading_days(calc_date, ndays=2)[1] dict_intraday_momentum['date'] = [date_label] * len( dict_intraday_momentum['id']) # 保存因子载荷至因子数据库 if save: Utils.factor_loading_persistent( cls._db_file, calc_date.strftime('%Y%m%d'), dict_intraday_momentum) # 休息360秒 logging.info('Suspending for 360s.') time.sleep(360) return dict_intraday_momentum
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷, 并保存至因子数据库 Parameters: -------- :param start_date: datetime-like, str 开始日期, 格式: YYYY-MM-DD or YYYYMMDD :param end_date: datetime-like, str 结束日期, 如果为None, 则只计算start_date日期的因子载荷, 格式: YYYY-MM-DD or YYYYMMDD :param month_end: bool, 默认为True 如果为True, 则只计算月末时点的因子载荷 :param save: bool, 默认为True 是否保存至因子数据库 :param kwargs: 'multi_proc': bool, True=采用多进程, False=采用单进程, 默认为False :return: dict 因子载荷数据 """ # 取得交易日序列及股票基本信息表 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列, 计算LIQUIDITY因子载荷 dict_raw_liquidity = None for calc_date in trading_days_series: if month_end and (not Utils.is_month_end(calc_date)): continue dict_stom = None dict_stoq = None dict_stoa = None dict_raw_liquidity = None logging.info('[%s] Calc LIQUIDITY factor loading.' % Utils.datetimelike_to_str(calc_date)) # 遍历个股,计算个股LIQUIDITY因子值 s = (calc_date - datetime.timedelta( days=risk_ct.LIQUID_CT.listed_days)).strftime('%Y%m%d') stock_basics = all_stock_basics[all_stock_basics.list_date < s] ids = [] stoms = [] stoqs = [] stoas = [] raw_liquidities = [] if 'multi_proc' not in kwargs: kwargs['multi_proc'] = False if not kwargs['multi_proc']: # 采用单进程计算LIQUIDITY因子值 for _, stock_info in stock_basics.iterrows(): logging.info("[%s] Calc %s's LIQUIDITY factor loading." % (Utils.datetimelike_to_str( calc_date, dash=True), stock_info.symbol)) liquidity_data = cls._calc_factor_loading( stock_info.symbol, calc_date) if liquidity_data is not None: ids.append(liquidity_data['code']) stoms.append(liquidity_data['stom']) stoqs.append(liquidity_data['stoq']) stoas.append(liquidity_data['stoa']) raw_liquidities.append(liquidity_data['liquidity']) else: # 采用多进程计算LIQUIDITY因子值 q = Manager().Queue() p = Pool(4) for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=( stock_info.symbol, calc_date, q, )) p.close() p.join() while not q.empty(): liquidity_data = q.get(True) ids.append(liquidity_data['code']) stoms.append(liquidity_data['stom']) stoqs.append(liquidity_data['stoq']) stoas.append(liquidity_data['stoa']) raw_liquidities.append(liquidity_data['liquidity']) date_label = Utils.get_trading_days(start=calc_date, ndays=2)[1] dict_stom = dict({ 'date': [date_label] * len(ids), 'id': ids, 'factorvalue': stoms }) dict_stoq = dict({ 'date': [date_label] * len(ids), 'id': ids, 'factorvalue': stoqs }) dict_stoa = dict({ 'date': [date_label] * len(ids), 'id': ids, 'factorvalue': stoas }) dict_raw_liquidity = dict({ 'date': [date_label] * len(ids), 'id': ids, 'factorvalue': raw_liquidities }) # 读取Size因子值, 将流动性因子与Size因子正交化 size_factor_path = os.path.join(factor_ct.FACTOR_DB.db_path, risk_ct.SIZE_CT.db_file) df_size = Utils.read_factor_loading( size_factor_path, Utils.datetimelike_to_str(calc_date, dash=False)) df_size.drop(columns='date', inplace=True) df_size.rename(columns={'factorvalue': 'size'}, inplace=True) df_liquidity = pd.DataFrame( dict({ 'id': ids, 'liquidity': raw_liquidities })) df_liquidity = pd.merge(left=df_liquidity, right=df_size, how='inner', on='id') arr_liquidity = Utils.normalize_data( Utils.clean_extreme_value( np.array(df_liquidity['liquidity']).reshape( (len(df_liquidity), 1)))) arr_size = Utils.normalize_data( Utils.clean_extreme_value( np.array(df_liquidity['size']).reshape( (len(df_liquidity), 1)))) model = sm.OLS(arr_liquidity, arr_size) results = model.fit() df_liquidity['liquidity'] = results.resid df_liquidity.drop(columns='size', inplace=True) df_liquidity.rename(columns={'liquidity': 'factorvalue'}, inplace=True) df_liquidity['date'] = date_label # 保存因子载荷 if save: str_date = Utils.datetimelike_to_str(calc_date, dash=False) factor_header = ['date', 'id', 'factorvalue'] Utils.factor_loading_persistent(cls._db_file, 'stom_{}'.format(str_date), dict_stom, factor_header) Utils.factor_loading_persistent(cls._db_file, 'stoq_{}'.format(str_date), dict_stoq, factor_header) Utils.factor_loading_persistent(cls._db_file, 'stoa_{}'.format(str_date), dict_stoa, factor_header) Utils.factor_loading_persistent( cls._db_file, 'rawliquidity_{}'.format(str_date), dict_raw_liquidity, factor_header) Utils.factor_loading_persistent(cls._db_file, str_date, df_liquidity.to_dict('list'), factor_header) # 暂停180秒 logging.info('Suspending for 180s.') time.sleep(180) return dict_raw_liquidity
def calc_factorloading(self, start_date, end_date=None): """ 计算风险因子的因子载荷 Parameters: -------- :param start_date: datetime-like, str 计算开始日期, 格式: YYYY-MM-DD :param end_date: datetime-like, str 计算结束日期, 格式: YYYY-MM-DD :return: None """ # 读取交易日序列 start_date = Utils.to_date(start_date) if not end_date is None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(start=start_date, ndays=1) # 遍历交易日序列, 计算风险因子的因子载荷 for calc_date in trading_days_series: Size.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) Beta.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) Momentum.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) ResVolatility.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) NonlinearSize.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) Value.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) Liquidity.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) EarningsYield.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) Growth.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True) Leverage.calc_factor_loading(start_date=start_date, end_date=None, month_end=False, save=True, multi_proc=True)
def get_dependent_factors(cls, date): """ 计算用于因子提纯的相关性因子值,包换行业、规模、价值、成长、短期动量、长期动量 Parameters: -------- :param date: datetime-like or str 日期 :return: pd.DataFrame index为个股代码, columns=[28个申万一级行业,规模(scale),价值(value),成长(growth),短期动量(short_momentum),长期动量(long_momentum)] """ str_date = Utils.to_date(date).strftime('%Y%m%d') # 1. 行业因子 # 1.1. 读取行业分类信息 df_industry_calssify = Utils.get_industry_classify() df_industry_calssify = df_industry_calssify.set_index('id') # 1.2. 构建行业分裂哑变量 df_industry_dummies = pd.get_dummies(df_industry_calssify['ind_code']) # 2. 规模因子 # 2.1. 读取规模因子 scale_factor_path = os.path.join(factor_ct.FACTOR_DB.db_path, factor_ct.SCALE_CT.db_file) df_scale_raw = Utils.read_factor_loading(scale_factor_path, str_date, nan_value=0) # 2.2. 规模因子去极值、标准化 scale_cleaned_arr = Utils.clean_extreme_value( np.array(df_scale_raw[['LnLiquidMktCap', 'LnTotalMktCap']])) scale_normalized_arr = Utils.normalize_data(scale_cleaned_arr) # 2.3. 规模因子降维 scale_factor_arr = np.mean(scale_normalized_arr, axis=1) scale_factor = Series(scale_factor_arr, index=df_scale_raw['id']) # 3. 价值因子 # 3.1. 读取价值因子 value_factor_path = os.path.join(factor_ct.FACTOR_DB.db_path, factor_ct.VALUE_CT.db_file) df_value_raw = Utils.read_factor_loading(value_factor_path, str_date, nan_value=0) # 3.2. 价值因子去极值、标准化 value_cleaned_arr = Utils.clean_extreme_value( np.array(df_value_raw[['ep_ttm', 'bp_lr', 'ocf_ttm']])) value_normalized_arr = Utils.normalize_data(value_cleaned_arr) # 3.3. 价值因子降维 value_factor_arr = np.mean(value_normalized_arr, axis=1) value_factor = Series(value_factor_arr, index=df_value_raw['id']) # 4. 成长因子 # 4.1. 读取成长因子 growth_factor_path = os.path.join(factor_ct.FACTOR_DB.db_path, factor_ct.GROWTH_CT.db_file) df_growth_raw = Utils.read_factor_loading(growth_factor_path, str_date, nan_value=0) # 4.2. 成长因子去极值、标准化 growth_cleaned_arr = Utils.clean_extreme_value( np.array(df_growth_raw[['npg_ttm', 'opg_ttm']])) growth_normalized_arr = Utils.normalize_data(growth_cleaned_arr) # 4.3. 成长因子降维 growth_factor_arr = np.mean(growth_normalized_arr, axis=1) growth_factor = Series(growth_factor_arr, index=df_growth_raw['id']) # 5. 动量因子 # 5.1. 读取动量因子 mom_factor_path = os.path.join(factor_ct.FACTOR_DB.db_path, factor_ct.MOMENTUM_CT.db_file) df_mom_raw = Utils.read_factor_loading(mom_factor_path, str_date, nan_value=0) # 5.2. 动量因子去极值、标准化 short_term_mom_header = [ 'short_term_' + d for d in factor_ct.MOMENTUM_CT.short_term_days.split('|') ] short_mom_cleaned_arr = Utils.clean_extreme_value( np.array(df_mom_raw[short_term_mom_header])) short_mom_normalized_arr = Utils.normalize_data(short_mom_cleaned_arr) long_term_mom_header = [ 'long_term_' + d for d in factor_ct.MOMENTUM_CT.long_term_days.split('|') ] long_mom_cleaned_arr = Utils.clean_extreme_value( np.array(df_mom_raw[long_term_mom_header])) long_mom_normalized_arr = Utils.normalize_data(long_mom_cleaned_arr) # 5.3. 动量因子降维 short_mom_arr = np.mean(short_mom_normalized_arr, axis=1) short_mom = Series(short_mom_arr, index=df_mom_raw['id']) long_mom_arr = np.mean(long_mom_normalized_arr, axis=1) long_mom = Series(long_mom_arr, index=df_mom_raw['id']) # 拼接除行业因子外的因子 df_style_factor = pd.concat( [scale_factor, value_factor, growth_factor, short_mom, long_mom], axis=1, keys=['scale', 'value', 'growth', 'short_mom', 'long_mom'], join='inner') # 再拼接行业因子 df_dependent_factor = pd.concat([df_industry_dummies, df_style_factor], axis=1, join='inner') return df_dependent_factor
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷,并保存至因子数据库 Parameters -------- :param start_date: datetime-like, str 开始日期 :param end_date: datetime-like, str,默认None 结束日期,如果为None,则只计算start_date日期的因子载荷 :param month_end: bool,默认True 只计算月末时点的因子载荷 :param save: 是否保存至因子数据库,默认为False :param kwargs: 'multi_proc': bool, True=采用多进程并行计算, False=采用单进程计算, 默认为False :return: 因子载荷,DataFrame -------- 因子载荷,DataFrame 0. date, 日期, 为计算日期的下一个交易日 1: id, 证券代码 2: factorvalue, 因子载荷 如果end_date=None,返回start_date对应的因子载荷数据 如果end_date!=None,返回最后一天的对应的因子载荷数据 如果没有计算数据,返回None """ # 0.取得交易日序列 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) # 取得样本个股信息 # all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 遍历交易日序列,计算SMartQ因子载荷 dict_factor = None for calc_date in trading_days_series: dict_factor = {'id': [], 'factorvalue': []} if month_end and (not Utils.is_month_end(calc_date)): continue # 1.获取用于读取分钟行情的交易日列表(过去30天的交易日列表,降序排列) # trading_days = _get_trading_days(calc_date, 30) # trading_days = Utils.get_trading_days(end=calc_date, ndays=30, ascending=False) # 2.取得样本个股信息 # stock_basics = ts.get_stock_basics() s = (calc_date - datetime.timedelta(days=90)).strftime('%Y%m%d') stock_basics = Utils.get_stock_basics(s) # 3.遍历样本个股代码,计算Smart_Q因子载荷值 dict_factor = {'date': None, 'id': [], 'factorvalue': []} if 'multi_proc' not in kwargs: kwargs['multi_proc'] = False if not kwargs['multi_proc']: # 采用单进程进行计算 for _, stock_info in stock_basics.iterrows(): # code = '%s%s' % ('SH' if code[:2] == '60' else 'SZ', code) factor_loading = cls._calc_factor_loading( stock_info.symbol, calc_date) print( "[%s]Calculating %s's SmartMoney factor loading = %.4f." % (calc_date.strftime('%Y-%m-%d'), stock_info.symbol, -1.0 if factor_loading is None else factor_loading)) if factor_loading is not None: # df_factor.ix[code, 'factorvalue'] = factor_loading dict_factor['id'].append( Utils.code_to_symbol(stock_info.symbol)) dict_factor['factorvalue'].append(factor_loading) else: # 采用多进程并行计算SmartQ因子载荷 q = Manager().Queue() # 队列,用于进程间通信,存储每个进程计算的因子载荷值 p = Pool(4) # 进程池,最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=( stock_info.symbol, calc_date, q, )) p.close() p.join() while not q.empty(): smart_q = q.get(True) dict_factor['id'].append(smart_q[0]) dict_factor['factorvalue'].append(smart_q[1]) date_label = Utils.get_trading_days(calc_date, ndays=2)[1] dict_factor['date'] = [date_label] * len(dict_factor['id']) # 4.计算去极值标准化后的因子载荷 df_std_factor = Utils.normalize_data(pd.DataFrame(dict_factor), columns='factorvalue', treat_outlier=True, weight='eq') # 5.保存因子载荷至因子数据库 if save: # Utils.factor_loading_persistent(cls._db_file, calc_date.strftime('%Y%m%d'), dict_factor) cls._save_factor_loading(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), dict_factor, 'SmartMoney', factor_type='raw', columns=['date', 'id', 'factorvalue']) cls._save_factor_loading(cls._db_file, Utils.datetimelike_to_str(calc_date, dash=False), df_std_factor, 'SmartMoney', factor_type='standardized', columns=['date', 'id', 'factorvalue']) # 休息300秒 logging.info('Suspending for 360s.') time.sleep(360) return dict_factor
def calc_factor_loading(cls, start_date, end_date=None, month_end=True, save=False, **kwargs): """ 计算指定日期的样本个股的因子载荷,并保存至因子数据库 Parameters -------- :param start_date: datetime-like, str 开始日期 :param end_date: datetime-like, str,默认None 结束日期,如果为None,则只计算start_date日期的因子载荷 :param month_end: bool,默认True 只计算月末时点的因子载荷,该参数只在end_date不为None时有效,并且不论end_date是否为None,都会计算第一天的因子载荷 :param save: 是否保存至因子数据库,默认为False :return: 因子载荷,DataFrame -------- 因子载荷,DataFrame 0: id, 证券ID 1: factorvalue, 因子载荷 如果end_date=None,返回start_date对应的因子载荷数据 如果end_date!=None,返回最后一天的对应的因子载荷数据 如果没有计算数据,返回None """ # 1.取得交易日序列及股票基本信息表 start_date = Utils.to_date(start_date) if end_date is not None: end_date = Utils.to_date(end_date) trading_days_series = Utils.get_trading_days(start=start_date, end=end_date) else: trading_days_series = Utils.get_trading_days(end=start_date, ndays=1) all_stock_basics = CDataHandler.DataApi.get_secu_basics() # 2.遍历交易日序列,计算APM因子载荷 dict_apm = None for calc_date in trading_days_series: dict_apm = {'date': [], 'id': [], 'factorvalue': []} if month_end and (not Utils.is_month_end(calc_date)): continue # 2.1.遍历个股,计算个股APM.stat统计量,过去20日收益率,分别放进stat_lst,ret20_lst列表中 s = (calc_date - datetime.timedelta(days=90)).strftime('%Y%m%d') stock_basics = all_stock_basics[all_stock_basics.list_date < s] stat_lst = [] ret20_lst = [] symbol_lst = [] # 采用单进程计算 # for _, stock_info in stock_basics.iterrows(): # stat_i = cls._calc_factor_loading(stock_info.symbol, calc_date) # ret20_i = Utils.calc_interval_ret(stock_info.symbol, end=calc_date, ndays=20) # if stat_i is not None and ret20_i is not None: # stat_lst.append(stat_i) # ret20_lst.append(ret20_i) # symbol_lst.append(Utils.code_to_symbol(stock_info.symbol)) # logging.info('APM of %s = %f' % (stock_info.symbol, stat_i)) # 采用多进程并行计算 q = Manager().Queue() p = Pool(4) # 最多同时开启4个进程 for _, stock_info in stock_basics.iterrows(): p.apply_async(cls._calc_factor_loading_proc, args=( stock_info.symbol, calc_date, q, )) p.close() p.join() while not q.empty(): apm_value = q.get(True) symbol_lst.append(apm_value[0]) stat_lst.append(apm_value[1]) ret20_lst.append(apm_value[2]) assert len(stat_lst) == len(ret20_lst) assert len(stat_lst) == len(symbol_lst) # 2.2.构建APM因子 # 2.2.1.将统计量stat对动量因子ret20j进行截面回归:stat_j = \beta * Ret20_j + \epsilon_j # 残差向量即为对应个股的APM因子 # 截面回归之前,先对stat统计量和动量因子进行去极值和标准化处理 stat_arr = np.array(stat_lst).reshape((len(stat_lst), 1)) ret20_arr = np.array(ret20_lst).reshape((len(ret20_lst), 1)) stat_arr = Utils.clean_extreme_value(stat_arr) stat_arr = Utils.normalize_data(stat_arr) ret20_arr = Utils.clean_extreme_value(ret20_arr) ret20_arr = Utils.normalize_data(ret20_arr) # 回归分析 # ret20_arr = sm.add_constant(ret20_arr) apm_model = sm.OLS(stat_arr, ret20_arr) apm_result = apm_model.fit() apm_lst = list(np.around(apm_result.resid, 6)) # amp因子载荷精确到6位小数 assert len(apm_lst) == len(symbol_lst) # 2.2.2.构造APM因子字典,并持久化 date_label = Utils.get_trading_days(calc_date, ndays=2)[1] dict_apm = { 'date': [date_label] * len(symbol_lst), 'id': symbol_lst, 'factorvalue': apm_lst } if save: Utils.factor_loading_persistent(cls._db_file, calc_date.strftime('%Y%m%d'), dict_apm) # 2.3.构建PureAPM因子 # 将stat_arr转换为DataFrame, 此时的stat_arr已经经过了去极值和标准化处理 df_stat = DataFrame(stat_arr, index=symbol_lst, columns=['stat']) # 取得提纯的因变量因子 df_dependent_factor = cls.get_dependent_factors(calc_date) # 将df_stat和因变量因子拼接 df_data = pd.concat([df_stat, df_dependent_factor], axis=1, join='inner') # OLS回归,提纯APM因子 arr_data = np.array(df_data) pure_apm_model = sm.OLS(arr_data[:, 0], arr_data[:, 1:]) pure_apm_result = pure_apm_model.fit() pure_apm_lst = list(np.around(pure_apm_result.resid, 6)) pure_symbol_lst = list(df_data.index) assert len(pure_apm_lst) == len(pure_symbol_lst) # 构造pure_apm因子字典,并持久化 dict_pure_apm = { 'date': [date_label] * len(pure_symbol_lst), 'id': pure_symbol_lst, 'factorvalue': pure_apm_lst } pure_apm_db_file = os.path.join(factor_ct.FACTOR_DB.db_path, factor_ct.APM_CT.pure_apm_db_file) if save: Utils.factor_loading_persistent(pure_apm_db_file, calc_date.strftime('%Y%m%d'), dict_pure_apm) # 休息360秒 logging.info('Suspended for 360s.') time.sleep(360) return dict_apm