def get_stock_change_name(self):
""" 得到更改股票名称信息 """
file = os.path.join(self.data_path_static, "stockchangename.csv")
data = pd.read_csv(file, encoding='gbk')
data['name_change_date'] = data['name_change_date'].map(Date().change_to_str)
return data
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# param
term = 60
effective_term = int(term * 0.6)
# read data
ff3_r2 = FamaFrench().get_data("model_ff3", "FF3_R2")
# calculate data daily
date_series = Date().get_trade_date_series(beg_date, end_date)
date_series = list(set(ff3_r2.columns) & set(date_series))
date_series.sort()
res = pd.DataFrame()
# FamaFrench().cal_all_factor_pct()
# FamaFrench().ff3_model(beg_date, end_date)
for i in range(0, len(date_series)):
current_date = date_series[i]
data_beg_date = Date().get_trade_date_offset(
current_date, -(term - 1))
data_period = ff3_r2.loc[:, data_beg_date:current_date]
data_period = data_period.T.dropna(how='all')
if len(data_period) > effective_term:
print('Calculating factor %s at date %s' %
(self.raw_factor_name, current_date))
data_date = -data_period.std() * np.sqrt(250) / 100.0
effective_number = data_period.count()
data_date[effective_number <= effective_term] = np.nan
data_date = pd.DataFrame(data_date)
data_date.columns = [current_date]
else:
print('Calculating factor %s at date %s is null' %
(self.raw_factor_name, current_date))
data_date = pd.DataFrame([],
columns=[current_date],
index=ff3_r2.index)
res = pd.concat([res, data_date], axis=1)
res = res.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def upload_cash(self, port_name, date, cash="1000000000"):
""" 上传wind组合期初现金 """
change_date = Date().get_trade_date_offset(date, -1)
res = w.wupf(port_name, change_date, "CNY", cash, "1",
"Owner=%s;Direction=Long;CreditTrading=No;HedgeType=Spec;" % self.owner)
self.detect_error(res, "Upload Cash", port_name, date)
def get_stock_illegality(self):
""" 得到 公司违规处罚情况 """
file = os.path.join(self.data_path_static, "illegality.csv")
data = pd.read_csv(file, encoding='gbk')
data['announce_date'] = data['announce_date'].map(Date().change_to_str)
return data
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# params
long_term = 120
short_term = int(long_term * 0.5)
# read data
trade_amount = Stock().read_factor_h5("TradeAmount").T / 100000000
trade_amount = trade_amount.dropna(how='all')
# calculate data daily
date_series = Date().get_trade_date_series(beg_date, end_date)
date_series = list(set(trade_amount.index) & set(date_series))
date_series.sort()
res = pd.DataFrame([])
for i in range(0, len(date_series)):
current_date = date_series[i]
data_beg_date = Date().get_trade_date_offset(current_date, -(long_term - 1))
amount_before = trade_amount.loc[data_beg_date:current_date, :]
amount_before = amount_before.fillna(0.0)
if len(amount_before) == long_term:
print('Calculating factor %s at date %s' % (self.raw_factor_name, current_date))
zero_number = amount_before.applymap(lambda x: 1.0 if x == 0.0 else 0.0).sum()
code_filter_list = (zero_number[zero_number < short_term]).index
amount_before = trade_amount.loc[data_beg_date:current_date, code_filter_list]
amount_log = amount_before.applymap(lambda x: np.nan if x == 0 else -np.log(x))
weight = np.array(list(range(1, long_term + 1)))
weight_amount = np.dot(amount_log.T.values, weight)
weight_amount = pd.DataFrame(weight_amount, index=amount_log.columns, columns=[current_date])
else:
print('Calculating factor %s at date %s is null' % (self.raw_factor_name, current_date))
weight_amount = pd.DataFrame([], columns=[current_date], index=trade_amount.columns)
res = pd.concat([res, weight_amount], axis=1)
res = res.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def get_trade_statement(self, date):
""" 成交回报 """
date_int = Date().change_to_str(date)
file = os.path.join(self.data_path, 'mfc_holding\\成交回报',
'成交回报_' + str(date_int) + '.csv')
data = pd.read_csv(file, encoding='gbk', thousands=',')
return data
def get_group_security(self, date):
""" 组合证券 """
date_int = Date().change_to_str(date)
file = os.path.join(self.data_path, 'mfc_holding\\组合证券',
'组合证券_' + str(date_int) + '.csv')
data = pd.read_csv(file, encoding='gbk', thousands=',')
return data
def get_fund_asset(self, date):
""" 基金资产 """
date_int = Date().change_to_str(date)
file = os.path.join(self.data_path, 'mfc_holding\\基金资产',
'基金资产_' + str(date_int) + '.csv')
data = pd.read_csv(file, encoding='gbk', thousands=',')
return data
def update_data(self):
""" 更新计算基金业绩所需要的数据 """
# 下载公募复权净值和计算专户复权净值
##########################################################################################
MfcData().load_mfc_public_fund_nav()
MfcData().load_mfc_fund_div()
MfcData().cal_mfc_private_fund_nav_all()
self.get_zz500_adjust()
today = datetime.today()
# 下载指数价格数据
##########################################################################################
beg_date = Date().get_trade_date_offset(today, -30)
Index().load_index_factor_all(beg_date=beg_date, end_date=today)
# 合成指数价格数据
##########################################################################################
index_code = "H00905.CSI"
index_ratio = 0.8
fix_return = 0.01
make_index_name = "中证500全收益指数80%+固定收益1%"
Index().make_index_with_fixed(fix_return, index_ratio, index_code,
make_index_name)
fix_return = 0.08
index_ratio = 0.0
index_code = 'H00905.CSI'
make_index_name = '固定收益年化8%'
Index().make_index_with_fixed(fix_return, index_ratio, index_code,
make_index_name)
fix_return = 0.00
index_ratio = 0.6
index_code = 'H00905.CSI'
make_index_name = "中证500全收益指数60%"
Index().make_index_with_fixed(fix_return, index_ratio, index_code,
make_index_name)
fix_return = 0.0625
index_ratio = 0.0
index_code = 'H00905.CSI'
make_index_name = "固定收益年化6.52%"
Index().make_index_with_fixed(fix_return, index_ratio, index_code,
make_index_name)
fix_return = 0.00
index_ratio = 0.3
index_code = 'H00905.CSI'
make_index_name = "中证500全收益指数30%"
Index().make_index_with_fixed(fix_return, index_ratio, index_code,
make_index_name)
# 基金净值数据
##########################################################################################
Fund().load_fund_factor("Repair_Nav", "20180101", today)
Fund().load_fund_factor("Repair_Nav_Pct", "20180101", today)
Fund().load_fund_factor("Stock_Ratio", "20180101", today)
def weight_allstock_holding_date(report_date):
report_date = Date().change_to_str(report_date)
data = Fund().get_fund_holding_report_date(report_date)
data = data[['FundCode', 'Weight', 'StockCode']]
pool = Fund().get_fund_pool_code(report_date, "基金持仓基准基金池")
fund_code = list(set(pool))
fund_code.sort()
weight = Fund().get_wind_fund_asset(report_date)
for i_fund in range(len(fund_code)):
fund = fund_code[i_fund]
data_fund = data[data['FundCode'] == fund]
data_fund = data_fund.dropna(subset=['Weight'])
data_fund = data_fund.sort_values(by=['Weight'], ascending=False)
try:
asset = weight.ix[fund, report_date]
asset /= 100000000
except:
asset = 1.0
if i_fund == 0:
data_fund_all = data_fund.copy()
data_fund_all["Asset_Weight"] = data_fund_all['Weight'] * asset
all_weight = data_fund_all['Weight'].sum()
if all_weight < 60:
data_fund_all = pd.DataFrame([], columns=data_fund.columns)
else:
data_fund_all_add = data_fund.copy()
data_fund_all_add["Asset_Weight"] = data_fund_all_add['Weight'] * asset
all_weight = data_fund_all_add['Weight'].sum()
if all_weight < 60:
data_fund_all_add = pd.DataFrame([], columns=data_fund.columns)
data_fund_all = pd.concat([data_fund_all, data_fund_all_add], axis=0)
stock_code = list(set(data_fund_all['StockCode'].values))
stock_code.sort()
weight_sum = data_fund_all['Asset_Weight'].sum()
weight_code = pd.DataFrame([], index=stock_code, columns=['Asset_Weight'])
for i_stock in range(len(stock_code)):
stock = stock_code[i_stock]
data_stock = data_fund_all[data_fund_all['StockCode'] == stock]
stock_weight_sum = data_stock['Asset_Weight'].sum()
weight_code.ix[stock, 'Asset_Weight'] = stock_weight_sum / weight_sum
weight_code.index = weight_code.index.map(lambda x: x[0:6] + '-CN')
out_path = Parameter().get_read_file("Fund_Stock_Holding_BenchMark")
out_path = os.path.join(out_path, "weight_halfyear_all")
if not os.path.exists(out_path):
os.makedirs(out_path)
out_file = os.path.join(out_path, "weight_halfyear_all_" + report_date + '.csv')
print(out_file)
weight_code.to_csv(out_file, header=None)
def upload_weight_date(self, port_name, date):
"""
上传wind组合权重
将现金项去掉
价格都为空 而不是0
"""
# 参数 第二天上传
#########################################################################################################
file = os.path.join(self.path, port_name, port_name + '_' + date + '.csv')
print(file)
data_pd = pd.read_csv(file, encoding='gbk')
data_pd = data_pd[data_pd.Code != 'Cash']
data_pd = data_pd[~data_pd.Code.duplicated()]
change_date = Date().get_trade_date_offset(date, 1)
data_pd.Weight = data_pd.Weight.round(4)
# data_pd = data_pd[data_pd.Weight >= 0.003]
data_pd = data_pd.astype(str)
data_pd["Price"] = ""
# from quant.multi_factor.multi_factor import Stock
# price = Stock().read_factor_h5("PriceCloseUnadjust")
# try:
# price_date = pd.DataFrame(price[date])
# price_date.columns = ["PriceClose"]
# data = data_pd
# data.index = data_pd.Code
# data = pd.concat([data, price_date], axis=1)
# data = data.dropna()
# data.Price = data.PriceClose
# data_pd = data
# data_pd = data_pd.astype(str)
#
# except Exception as e:
# print(" Date %s Price is None " % date)
# 整理字符串
##########################################################
code_str = ','.join(list(data_pd.Code.values))
weight_str = ','.join(list(data_pd.Weight.values))
price_str = ','.join(list(data_pd.Price.values))
direction_str = ','.join(list(data_pd.Direction.values))
credit_str = ','.join(list(data_pd.CreditTrading.values))
# 上传组合
##########################################################
print("Direction=%s;CreditTrading=%s;Owner=%s;type=%s" %
(direction_str, credit_str, self.owner, "weight"))
print(port_name, change_date, code_str, weight_str, price_str)
res = w.wupf(port_name, change_date, code_str, weight_str, price_str,
"Direction=%s;CreditTrading=%s;Owner=%s;type=%s" %
(direction_str, credit_str, self.owner, "weight"))
self.detect_error(res, "UpLoad Weight ", port_name, change_date)
def cal_all_wind_file(self):
""" 计算 所有季报日 公募主动股票基金 基金平均持仓 还要考虑股票仓位 并生成wind文件"""
date_series = Date().get_normal_date_series("20040101", datetime.today(), "Q")
for i_date in range(len(date_series)):
quarter_date = date_series[i_date]
fund_pool = FundPool().get_fund_pool_code(name="普通股票型基金", date=quarter_date)
stock_ratio = pd.DataFrame(self.stock_ratio.loc[fund_pool, quarter_date])
ratio = stock_ratio.median().values[0] / 100.0
stock_data_weight = self.cal_weight_date(quarter_date)
stock_data_weight.columns = ["Weight"]
stock_data_weight /= stock_data_weight.sum()
publish_date = Date().get_trade_date_offset(quarter_date, 16)
# quarter_next_date = Date().get_trade_date_offset(quarter_date, 1)
#
#
# pct_period = self.pct.T.loc[quarter_next_date:publish_date, :]
# multi = pd.DataFrame((pct_period / 100.0 + 1.0).cumprod().iloc[-1, :])
# multi.columns = ['Multi']
#
# stock_data_weight = pd.concat([stock_data_weight, multi], axis=1)
# stock_data_weight = stock_data_weight.dropna()
# stock_data_weight['Weight'] = stock_data_weight['Weight'] * stock_data_weight['Multi']
# stock_data_weight['Weight'] /= stock_data_weight['Weight'].sum()
print(len(stock_data_weight))
stock_data_weight.index.name = "Code"
stock_data_weight *= ratio
stock_data_weight.loc['Cash', 'Weight'] = 1.0 - ratio
stock_data_weight["CreditTrading"] = "No"
stock_data_weight["Date"] = publish_date
stock_data_weight["Price"] = 0.0
stock_data_weight["Direction"] = "Long"
sub_path = os.path.join(self.wind_port_path, self.port_name)
if not os.path.exists(sub_path):
os.makedirs(sub_path)
file = os.path.join(sub_path, '%s_%s.csv' % (self.port_name, publish_date))
stock_data_weight.to_csv(file)
def ChangeQuarterToDaliy(data_path, name):
"""
将基金季报数据转化为日度数据
"""
# paramter
##################################################################################################
# data_path = r"E:\SoftWare\anaconda\anaconda\Lib\site-packages\quant\project\fund\fund_factor\stockfactor_holderbyins\data"
# name = "HolderRatioByQfii"
# read xlsx data
##################################################################################################
xlsx_file = os.path.join(data_path, name + '.xlsx')
h5_file = os.path.join(data_path, name + '.h5')
csv_file = os.path.join(data_path, name + '.csv')
xlsx_data = pd.read_excel(xlsx_file, index_col=[0], encoding='gbk')
xlsx_data = xlsx_data.drop('证券简称', axis=1)
xlsx_data = xlsx_data.T
quarter_date_series = Date().get_trade_date_series("20040101", "20181231",
"Q")
quarter_date_series_publish_date = list(
map(lambda x: Date().get_stcok_same_publish_date(x),
quarter_date_series))
xlsx_data.index = quarter_date_series_publish_date
xlsx_data = xlsx_data.dropna(how='all')
xlsx_data = xlsx_data.fillna('miss')
# change to daily
##################################################################################################
date_series = Date().get_trade_date_series(xlsx_data.index[0],
xlsx_data.index[-1])
h5_data = xlsx_data.loc[date_series, :]
h5_data = h5_data.fillna(method='pad')
# h5_data.applymap(lambda x: np.nan if x is None else x)
h5_data = h5_data.replace('miss', np.nan)
# date_series = Date().get_trade_date_series(xlsx_data.index[0], xlsx_data.index[-1])
h5_data = h5_data.loc[date_series, :]
h5_data = h5_data.T
# save h5 data
##################################################################################################
HdfMfc().write_hdf_factor(h5_file, name, h5_data)
h5_data.to_csv(csv_file)
def cal_mfc_private_fund_nav(self, fund_name, begin_date_int,
end_date_int):
""" 分红信息 和基金资产 计算泰达专户基金的复权净值（由专户投资经理给出）"""
self.load_mfc_fund_div()
file = os.path.join(self.data_path, "static_data", "dividend.csv")
did_file = pd.read_csv(file, index_col=[0], encoding='gbk')
did_file['DATETIME'] = did_file['DATETIME'].map(str)
did_data = did_file[did_file.index == fund_name]
sub_path = os.path.join(self.data_path, 'mfc_holding\\基金资产')
date_list = self.get_file_tradedate(sub_path, begin_date_int,
end_date_int)
trade_date_list = Date().get_trade_date_series(begin_date_int,
end_date_int)
date_list = list(set(date_list) & set(trade_date_list))
date_list.sort()
cum_nav_period = pd.DataFrame([],
index=date_list,
columns=['单位净值', '基金份额', '净值'])
for i in range(len(date_list)):
date_int = date_list[i]
try:
asset = MfcGetData().get_fund_asset(date_int)
asset.index = asset['基金名称']
cum_nav_period.ix[date_int, '基金份额'] = asset.ix[fund_name,
'基金份额']
cum_nav_period.ix[date_int, '净值'] = asset.ix[fund_name, '净值']
cum_nav_period[
'单位净值'] = cum_nav_period['净值'] / cum_nav_period['基金份额']
except Exception as e:
pass
did_date = pd.DataFrame(did_data['DIVD_ASSET'].values,
index=did_data['DATETIME'],
columns=['分红资产'])
all_data = pd.concat([cum_nav_period, did_date], axis=1)
all_data['分红资产'] = all_data['分红资产'].fillna(0.0)
all_data['分红净值'] = all_data['分红资产'] / all_data['基金份额']
all_data['累计分红净值'] = all_data['分红净值'].cumsum()
all_data['累计净值'] = all_data['累计分红净值'] + all_data['单位净值']
all_data['累计净值涨跌幅'] = all_data['累计净值'].pct_change()
all_data['累计复权分红净值'] = (all_data['累计分红净值'] *
all_data['累计净值涨跌幅']).cumsum()
all_data['累计复权净值'] = all_data['累计净值'] + all_data['累计复权分红净值']
all_data = all_data.dropna(subset=["单位净值"])
all_data[['累计复权净值', '累计净值']] = all_data[['累计复权净值', '累计净值']].fillna(1.0)
all_data['累计净值涨跌幅'] = all_data['累计净值涨跌幅'].fillna(0.0)
print("Cal Mfc fund %s Nav " % fund_name)
file = os.path.join(self.data_path, "nav\private_fund",
fund_name + "_Nav.csv")
all_data.to_csv(file)
def get_trade_status_date(self, date):
""" 得到股票交易状态 """
date = Date.change_to_str(date)
out_path = os.path.join(self.data_path_static, "trade_status")
out_file = os.path.join(out_path, 'trade_status_' + date + '.csv')
trade_status = pd.read_csv(out_file, index_col=[0], encoding='gbk')
return trade_status
def get_balance_data(self, beg_date, end_date):
""" 一段时间内的数据 """
beg_date = Date().get_trade_date_offset(beg_date, 0)
end_date = Date().get_trade_date_offset(end_date, 0)
print(beg_date, end_date)
data = Stock().read_factor_h5("Margin_TradeBalance")
data = data.fillna(0.0)
data /= 100000000.0
concat_data = pd.concat([data[beg_date], data[end_date]], axis=1)
concat_data.columns = ['BegMv', 'EndMv']
concat_data['DiffMv'] = concat_data['EndMv'] - concat_data['BegMv']
concat_data = concat_data.dropna()
return concat_data
def get_audit_category_date(self, date):
""" 下载 最近年报审计意见 """
year_date = Date().get_last_stock_year_report_date(date)
file = os.path.join(self.data_path_static, "stmnote_audit_category.csv")
data = pd.read_csv(file, encoding='gbk', index_col=[0])
data_date = pd.DataFrame(data[year_date])
return data_date
def get_fund_holder_exposure_date(self, fund, date, type_list=["STYLE"]):
date = Date().get_normal_date_month_end_day(date)
exposure = self.get_fund_holder_exposure(fund, type_list)
exposure_date = exposure.ix[date, :]
exposure_date = pd.DataFrame(exposure_date.values,
index=exposure_date.index,
columns=[fund]).T
return exposure_date
def get_index_weight_riskparity(self, end_date, type):
# 利用风险平价的方法求解不同指数的权重
####################################################################
beg_date = Date().get_trade_date_offset(end_date, - self.period_risk_parity)
data = self.get_index_return_daily(beg_date, end_date, type)
cov = data.cov() * 250
weight = AssetAllocation().cal_risk_parity_weights(cov)
return weight
def get_free_market_value_date(self, date):
""" 得到股票自由流通市值 """
date = Date.change_to_str(date)
out_path = os.path.join(self.data_path_static, "Free_Market_Value")
out_file = os.path.join(out_path, 'Free_Market_Value_' + date + '.csv')
free_market_value = pd.read_csv(out_file, index_col=[0], encoding='gbk')
return free_market_value
def equal_top10stock_holding_all():
date_series = Date().get_normal_date_series("20040101",
datetime.today(),
period="Q")
for i_date in range(0, len(date_series) - 1):
report_date = date_series[i_date]
equal_top10stock_holding_date(report_date)
print(report_date)
def cal_quarter_holding_allfund_allquarter(self):
"""
计算 季报日 普通股票+偏股混合基金 基金平均持仓
"""
date_series = Date().get_normal_date_series("20040101",
datetime.today(), "Q")
for i_date in range(len(date_series)):
quarter_date = date_series[i_date]
self.cal_quarter_holding_allfund_quarter(quarter_date)
def weight_top10stock_good_all():
date_series = Date().get_normal_date_series("20040101",
datetime.today(),
period="Q")
for i_date in range(0, len(date_series) - 2):
report_date = date_series[i_date]
weight_top10stock_good_date(report_date)
print(report_date)
def cal_fund_holder_risk_alpha_return_quarter(self, fund, end_date):
""" 根据季报持仓风格暴露进行收益拆分 """
beg_date = "20040101"
type_list = ['STYLE', 'COUNTRY', 'INDUSTRY']
fund_exposure = FundHolderExposureQuarter(
).get_fund_holder_exposure_quarter_daily(fund, beg_date, end_date)
barra_riskfactor_return = Barra().get_factor_return(
beg_date, end_date, type_list=type_list)
date_series = Date().get_trade_date_series(beg_date, end_date)
fund_pct = FundFactor().get_fund_factor("Repair_Nav_Pct",
fund_pool=[fund],
date_list=date_series)
fund_pct.columns = ["FundReturn"]
if fund_exposure is None:
return None
fund_riskfactor_return = barra_riskfactor_return.mul(fund_exposure)
fund_return = pd.concat([fund_pct, fund_riskfactor_return], axis=1)
fund_return = fund_return.dropna()
barra_factor_name = list(
Barra().get_factor_name(type_list=["STYLE"])["NAME_EN"].values)
fund_return["StyleReturn"] = fund_return[barra_factor_name].sum(axis=1)
barra_factor_name = list(
Barra().get_factor_name(type_list=["INDUSTRY"])["NAME_EN"].values)
fund_return["IndustryReturn"] = fund_return[barra_factor_name].sum(
axis=1)
barra_factor_name = list(
Barra().get_factor_name(type_list=["COUNTRY"])["NAME_EN"].values)
fund_return["CountryReturn"] = fund_return[barra_factor_name].sum(
axis=1)
barra_factor_name = ["StyleReturn", "IndustryReturn", "CountryReturn"]
fund_return["SumReturn"] = fund_return[barra_factor_name].sum(axis=1)
fund_return["AlphaReturn"] = fund_return["FundReturn"] - fund_return[
"SumReturn"]
data_new = fund_return.dropna()
# 合并新数据
####################################################################
out_path = os.path.join(self.data_path_exposure,
'fund_holding_risk_alpha_return_quarter')
out_file = os.path.join(
out_path, 'Fund_Holder_Risk_Alpha_Return_Quarter_' + fund + "_" +
end_date + '.csv')
print(out_file)
if os.path.exists(out_file):
data_old = pd.read_csv(out_file, index_col=[0], encoding='gbk')
data_old.index = data_old.index.map(str)
params = FactorOperate().pandas_add_row(data_old, data_new)
else:
params = data_new
params.to_csv(out_file)
return data_new
def equal_allstock_halfyear_all():
date_series = Date().get_normal_date_series("20040101",
datetime.today(),
period="S")
for i_date in range(26, len(date_series) - 1):
report_date = date_series[i_date]
equal_allstock_halfyear_date(report_date)
print(report_date)
def generate_excel_history(self, end_date):
""" 陆股通信息 输出到Excel """
# 一段时间内增减持额时间序列
from quant.stock.index import Index
index_data = Index().get_index_factor(index_code="000300.SH")
date_series = Date().get_trade_date_series("20170301", end_date, "M")
result = pd.DataFrame([])
ed_date = end_date
for i in range(len(date_series) - 1):
bg_date = Date().get_trade_date_offset(ed_date, -20)
print("Hk Inflow Period %s %s" % (bg_date, ed_date))
result_add = self.hk_inflow_period(bg_date, ed_date)
result_add.loc[ed_date, '沪深300'] = index_data.loc[ed_date, "CLOSE"]
result = pd.concat([result, result_add], axis=0)
ed_date = bg_date
result = result.sort_index()
result['累计净流入(亿元)'] = result['期间净流入(亿元)'].cumsum()
# 数据存贮位置
sub_path = os.path.join(self.data_path, end_date)
if not os.path.exists(sub_path):
os.makedirs(sub_path)
# 信息写入文件
filename = os.path.join(sub_path, '陆股通北上资金历史.xlsx')
excel = WriteExcel(filename)
sheet_name = "陆股通北上资金"
worksheet = excel.add_worksheet(sheet_name)
num_format_pd = pd.DataFrame([],
columns=result.columns,
index=['format'])
num_format_pd.loc['format', :] = '0.00'
num_format_pd.loc['format', "净流入占比"] = '0.00%'
excel.write_pandas(result,
worksheet,
begin_row_number=0,
begin_col_number=1,
num_format_pd=num_format_pd,
color="orange",
fillna=True)
excel.close()
def cal_weight_at_all_daily(self):
"""
计算在每个交易日的股票权重
"""
self.get_weight_at_all_change_date()
beg_date = self.port_hold.columns[0]
end_date = datetime.today().strftime("%Y%m%d")
date_series = Date().get_trade_date_series(beg_date, end_date)
date_series = list(set(date_series) & set(self.asset_return.columns))
date_series.sort()
date_change_date_list = list(self.port_hold.columns)
for i_date in range(len(date_series)):
date = date_series[i_date]
date_before = Date().get_trade_date_offset(date, -1)
if date in date_change_date_list:
self.port_hold_daily[date] = self.port_hold[date]
print(" Calculating Weight of Portfolio %s At Date %s " %
(self.port_name, date))
else:
print(" Calculating Weight of Portfolio %s At Date %s " %
(self.port_name, date))
weight_before = self.port_hold_daily[date_before]
pct_date = self.asset_return[date]
concat_data = pd.concat([weight_before, pct_date], axis=1)
concat_data.columns = ["WeightBefore", "PctCur"]
if "Cash" in concat_data.index:
concat_data.loc['Cash', "PctCur"] = 0.0
concat_data = concat_data.dropna(subset=["WeightBefore"])
average_pct = concat_data["PctCur"].median()
concat_data["PctCur"] = concat_data["PctCur"].fillna(
average_pct)
concat_data["Weight"] = concat_data["WeightBefore"] * (
1.0 + concat_data["PctCur"] / 100.0)
concat_data["Weight"] = concat_data["Weight"] / concat_data[
"Weight"].sum()
self.port_hold_daily[date] = concat_data["Weight"]
sub_path = os.path.join(self.save_path, self.port_name)
self.port_hold_daily.to_csv(
os.path.join(sub_path, self.port_name + '_PortHoldDaily.csv'))
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# param
term = 60
effective_term = 30
# read data
pct = Stock().read_factor_h5("Pct_chg").T
# calculate data daily
date_series = Date().get_trade_date_series(beg_date, end_date)
date_series = list(set(date_series) & set(pct.index))
date_series.sort()
res = pd.DataFrame([])
for i in range(0, len(date_series)):
current_date = date_series[i]
data_beg_date = Date().get_trade_date_offset(
current_date, -(term - 1))
data_period = pct.loc[data_beg_date:current_date, :]
data_period = data_period.dropna(how='all')
data_period /= 100.0
if len(data_period) == term:
print('Calculating factor %s at date %s' %
(self.raw_factor_name, current_date))
momentum = ((data_period + 1.0).cumprod() -
1.0).loc[current_date, :]
vaild = data_period.count() >= effective_term
momentum[~vaild] = np.nan
momentum = -pd.DataFrame(momentum)
momentum.columns = [current_date]
res = pd.concat([res, momentum], axis=1)
else:
print('Calculating factor %s at date %s is null' %
(self.raw_factor_name, current_date))
res = res.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_factor_exposure(self, beg_date, end_date):
""" 计算因子暴露 """
# param
term = 150
effective_term = 120
# read data
pct = Stock().read_factor_h5("Pct_chg")
# calculate data daily
date_series = Date().get_trade_date_series(beg_date, end_date)
date_series = list(set(pct.columns) & set(date_series))
date_series.sort()
res = pd.DataFrame()
for i in range(0, len(date_series)):
current_date = date_series[i]
data_beg_date = Date().get_trade_date_offset(
current_date, -(term - 1))
pct_before = pct.ix[:, data_beg_date:current_date]
pct_stock = pct_before.T.dropna(how='all')
if len(pct_stock) > effective_term:
print('Calculating factor %s at date %s' %
(self.raw_factor_name, current_date))
skew_date = -pct_stock.skew()
effective_number = pct_stock.count()
skew_date[effective_number <= effective_term] = np.nan
skew_date = pd.DataFrame(skew_date.values,
columns=[current_date],
index=skew_date.index)
else:
print('Calculating factor %s at date %s is null' %
(self.raw_factor_name, current_date))
skew_date = pd.DataFrame([],
columns=[current_date],
index=pct.index)
res = pd.concat([res, skew_date], axis=1)
res = res.T.dropna(how='all').T
self.save_alpha_factor_exposure(res, self.raw_factor_name)
def cal_all_date(self):
""" 计算多期的数据 """
data = Date().get_normal_date_series("20130622",
datetime.today(),
period="S")
for i_date in range(0, len(data)):
end_date = data[i_date]
halfyear_date = Date().get_last_fund_halfyear_date(end_date)
print("Cal Factor %s %s" % (end_date, halfyear_date))
# self.calculate_fund_factor("000300.SH", "沪深300", end_date)
# self.calculate_fund_factor("000905.SH", "中证500", end_date)
# self.fund_index_exposure("000300.SH", "沪深300", end_date, halfyear_date, False)
# self.fund_index_exposure("000905.SH", "中证500", end_date, halfyear_date, False)
# self.fund_score_date("000300.SH", "沪深300", end_date, halfyear_date)
# self.fund_score_date("000905.SH", "中证500", end_date, halfyear_date)
self.generate_wind_file("沪深300", "优选沪深300基金", end_date)
self.generate_wind_file("中证500", "优选中证500基金", end_date)
