def rank_fund_manager(end_date, fund_code, rank_pool, fund_name, mage_date):
""" 某个基金经理管理以来排名 """
today = datetime.strptime(end_date, "%Y%m%d")
before_1y = datetime(year=today.year - 1, month=today.month, day=today.day).strftime("%Y%m%d")
before_2y = datetime(year=today.year - 2, month=today.month, day=today.day).strftime("%Y%m%d")
before_3y = datetime(year=today.year - 3, month=today.month, day=today.day).strftime("%Y%m%d")
before_5y = datetime(year=today.year - 5, month=today.month, day=today.day).strftime("%Y%m%d")
date_array = np.array([
["2019年", "20190101", end_date, '20180930'],
["2018年", '20180101', "20181231", '20170930'],
["2017年", "20170101", '20171231', "20160930"],
["2016年", "20160101", '20161231', "20150930"],
["2015年", "20150101", '20151231', "20140930"],
["管理以来", mage_date, end_date, mage_date],
["过去1年", before_1y, end_date, before_1y],
["过去2年", before_2y, end_date, before_2y],
["过去3年", before_3y, end_date, before_3y],
["过去5年", before_5y, end_date, before_3y]])
rank_percent = pd.DataFrame([], index=[fund_name])
rank_str = pd.DataFrame([], index=[fund_name])
for i_date in range(len(date_array)):
label = date_array[i_date, 0]
beg_date = date_array[i_date, 1]
end_date = date_array[i_date, 2]
new_fund_date = date_array[i_date, 3]
if beg_date >= str(int(mage_date)):
# if fund_code in ["162201.OF"]:
# str_rank, pct = MfcManagerMoney().cal_fund_index(
# rank_pool, "FTSE成长", fund_code, beg_date, end_date, "超额收益")
# elif fund_code in ["162202.OF"]:
# str_rank, pct = MfcManagerMoney().cal_fund_index(
# rank_pool, "FTSE周期", fund_code, beg_date, end_date, "超额收益")
# elif fund_code in ["162203.OF"]:
# str_rank, pct = MfcManagerMoney().cal_fund_index(
# rank_pool, "FTSE稳定", fund_code, beg_date, end_date, "超额收益")
# else:
# str_rank, pct = FundRank().rank_fund(fund_code, rank_pool, beg_date, end_date, new_fund_date,
# excess=False)
str_rank, pct = FundRank().rank_fund(fund_code, rank_pool, beg_date, end_date, new_fund_date, excess=False)
rank_percent.loc[fund_name, label] = pct
rank_str.loc[fund_name, label] = str_rank
else:
rank_percent.loc[fund_name, label] = "NAN"
rank_str.loc[fund_name, label] = "NAN"
print(rank_percent)
print(rank_str)
return rank_percent, rank_str
def rank_fund_self(end_date, fund_code, rank_pool, fund_name, mage_date):
""" 某个基金排名 """
today = datetime.strptime(end_date, "%Y%m%d")
before_1y = datetime(year=today.year - 1, month=today.month, day=today.day).strftime("%Y%m%d")
before_2y = datetime(year=today.year - 2, month=today.month, day=today.day).strftime("%Y%m%d")
before_3y = datetime(year=today.year - 3, month=today.month, day=today.day).strftime("%Y%m%d")
before_5y = datetime(year=today.year - 5, month=today.month, day=today.day).strftime("%Y%m%d")
before_10y = datetime(year=today.year - 10, month=today.month, day=today.day).strftime("%Y%m%d")
date_array = np.array([
["2019年", "20190101", end_date, '20180930'],
["2018年", '20180101', "20181231", '20170930'],
["2017年", "20170101", '20171231', "20160930"],
["2016年", "20160101", '20161231', "20150930"],
["2015年", "20150101", '20151231', "20140930"],
["2014年", "20140101", '20141231', "20130930"],
["2013年", "20130101", '20131231', "20120930"],
["2012年", "20120101", '20121231', "20110930"],
["2011年", "20110101", '20111231', "20100930"],
["2010年", "20100101", '20101231', "20090930"],
["2009年", "20090101", '20091231', "20080930"],
["2008年", "20080101", '20081231', "20070930"],
["2007年", "20070101", '20071231', "20060930"],
["成立以来", mage_date, end_date, mage_date],
["过去1年", before_1y, end_date, before_1y],
["过去2年", before_2y, end_date, before_2y],
["过去3年", before_3y, end_date, before_3y],
["过去5年", before_5y, end_date, before_5y],
["过去10年", before_10y, end_date, before_10y],
])
rank_percent = pd.DataFrame([], index=[fund_name])
rank_str = pd.DataFrame([], index=[fund_name])
for i_date in range(len(date_array)):
label = date_array[i_date, 0]
beg_date = date_array[i_date, 1]
end_date = date_array[i_date, 2]
new_fund_date = date_array[i_date, 3]
if beg_date >= str(int(mage_date)):
str_rank, pct = FundRank().rank_fund(fund_code, rank_pool, beg_date, end_date, new_fund_date, excess=False)
rank_percent.loc[fund_name, label] = pct
rank_str.loc[fund_name, label] = str_rank
else:
rank_percent.loc[fund_name, label] = "NAN"
rank_str.loc[fund_name, label] = "NAN"
print(rank_percent)
print(rank_str)
return rank_percent, rank_str
def write_public_fxwy(end_date, save_path):
# 参数
###########################################################################################
fund_name = '泰达宏利复兴伟业'
fund_code = '001170.OF'
fund_type = "公募"
benchmark_code = '885001.WI'
benchmark_name = '偏股混合基金总指数'
benchmark_code_2 = "000300.SH"
benchmark_name_2 = "沪深300"
benchmark_ratio = 0.95
setup_date = '20150421' # 吴华开始管理 也是成立日
today = datetime.strptime(end_date, "%Y%m%d")
before_1y = datetime(year=today.year-1, month=today.month, day=today.day).strftime("%Y%m%d")
before_2y = datetime(year=today.year-2, month=today.month, day=today.day).strftime("%Y%m%d")
before_3y = datetime(year=today.year-3, month=today.month, day=today.day).strftime("%Y%m%d")
before_5y = datetime(year=today.year-5, month=today.month, day=today.day).strftime("%Y%m%d")
date_array = np.array([["2019年", '20190101', end_date, '20180930'],
["2018年", "20180101", '20181231', "20170930"],
["2017年", "20170101", '20171231', "20160930"],
["2016年", '20160101', '20161231', "20150930"],
["成立以来（吴华管理）", setup_date, end_date, setup_date],
["过去1年", before_1y, end_date, before_1y],
["过去2年", before_2y, end_date, before_2y],
["过去3年", before_3y, end_date, before_3y],
])
benchmark_array = np.array([["沪深300", "000300.SH"],
["中证500", "000905.SH"],
["股票型基金总指数", '885012.WI'],
["WIND全A", '881001.WI']])
from quant.fund.fund import Fund
fund_pct = Fund().get_fund_factor("Repair_Nav_Pct")
bench_pct = Fund().get_fund_factor("Fund_Bench_Pct") * 100
# 准备文件
###########################################################################################
file_name = os.path.join(save_path, "OutFile", fund_name + '.xlsx')
sheet_name = fund_name
excel = WriteExcel(file_name)
worksheet = excel.add_worksheet(sheet_name)
# 写入基金表现 和基金排名
###########################################################################################
performance_table = MfcTable().cal_summary_table_sample(fund_name, fund_code, fund_type, date_array, benchmark_array)
rank1 = FundRank().rank_fund_array2(fund_pct, bench_pct, fund_code, date_array, "偏股混合型基金", excess=False)
rank2 = FundRank().rank_fund_array2(fund_pct, bench_pct, fund_code, date_array, "灵活配置型基金_60", excess=False)
rank3 = FundRank().rank_fund_array2(fund_pct, bench_pct, fund_code, date_array, "股票+灵活配置60型基金", excess=False)
performance_table = pd.concat([performance_table, rank1, rank2, rank3], axis=0)
col_number = 1
num_format_pd = pd.DataFrame([], columns=performance_table.columns, index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(performance_table, worksheet, begin_row_number=0, begin_col_number=col_number,
num_format_pd=num_format_pd, color="red", fillna=True)
col_number = col_number + performance_table.shape[1] + 2
# 读取基金和基准时间序列
###########################################################################################
fund_data = MfcData().get_mfc_nav(fund_code, fund_name, fund_type)
benchmark_data = Index().get_index_factor(benchmark_code, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund_data), pd.DataFrame(benchmark_data))
cum_return = fs.get_fund_and_bencnmark_cum_return_series(setup_date, end_date)
benchmark_data = Index().get_index_factor(benchmark_code_2, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund_data), pd.DataFrame(benchmark_data))
cum_return2 = fs.get_bencnmark_cum_return_series(setup_date, end_date)
# 写入基金和基准时间序列
###########################################################################################
cum_return = pd.concat([cum_return, cum_return2], axis=1)
cum_return.columns = [fund_name, benchmark_name, benchmark_name_2]
cum_return = cum_return.dropna()
num_format_pd = pd.DataFrame([], columns=cum_return.columns, index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(cum_return, worksheet, begin_row_number=0, begin_col_number=col_number,
num_format_pd=num_format_pd, color="blue", fillna=True)
# 基金和基准时间序列图
###########################################################################################
chart_name = fund_name + "累计收益（管理以来）"
series_name = [fund_name, benchmark_name, benchmark_name_2]
insert_pos = 'B12'
excel.line_chart_time_series_plot(worksheet, 0, col_number, cum_return,
series_name, chart_name, insert_pos, sheet_name)
excel.close()
###########################################################################################
return True
def write_public_qf(end_date, save_path):
# 参数
###########################################################################################
fund_name = '泰达宏利启富'
fund_code = '003912.OF'
fund_type = "公募"
benchmark_code = '885003.WI'
benchmark_name = '偏债混合型基金总指数'
benchmark_code_2 = "881001.WI"
benchmark_name_2 = "WIND全A"
benchmark_ratio = 0.95
setup_date = '20170315'
date_array = np.array([["2019年", '20190101', end_date, '20180930'],
["2018年", "20180101", '20181231', "20170930"],
["2017年", setup_date, '20171231', setup_date],
["成立以来", setup_date, end_date, setup_date]])
benchmark_array = np.array([["沪深300",
"000300.SH"], ["WIND全A", "881001.WI"],
["中证全债", "H11001.CSI"],
["偏债混合基金指数", '885003.WI']])
from quant.fund.fund import Fund
fund_pct = Fund().get_fund_factor("Repair_Nav_Pct")
bench_pct = Fund().get_fund_factor("Fund_Bench_Pct") * 100
# 准备文件
###########################################################################################
file_name = os.path.join(save_path, "OutFile", fund_name + '.xlsx')
sheet_name = fund_name
excel = WriteExcel(file_name)
worksheet = excel.add_worksheet(sheet_name)
# 写入基金表现 和基金排名
###########################################################################################
performance_table = MfcTable().cal_summary_table(fund_name, fund_code,
fund_type, date_array,
benchmark_array)
rank0 = FundRank().rank_fund_array2(fund_pct,
bench_pct,
fund_code,
date_array,
"灵活配置型基金_30",
excess=False)
rank1 = FundRank().rank_fund_array2(fund_pct,
bench_pct,
fund_code,
date_array,
"wind",
excess=False)
performance_table = pd.concat([performance_table, rank0, rank1], axis=0)
col_number = 1
num_format_pd = pd.DataFrame([],
columns=performance_table.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(performance_table,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="red",
fillna=True)
col_number = col_number + performance_table.shape[1] + 2
# 读取基金和基准时间序列
###########################################################################################
fund_data = MfcData().get_mfc_nav(fund_code, fund_name, fund_type)
benchmark_data = Index().get_index_factor(benchmark_code, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund_data), pd.DataFrame(benchmark_data))
cum_return = fs.get_fund_and_bencnmark_cum_return_series(
setup_date, end_date)
benchmark_data = Index().get_index_factor(benchmark_code_2, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund_data), pd.DataFrame(benchmark_data))
cum_return2 = fs.get_bencnmark_cum_return_series(setup_date, end_date)
# 写入基金和基准时间序列
###########################################################################################
cum_return = pd.concat([cum_return, cum_return2], axis=1)
cum_return.columns = [fund_name, benchmark_name, benchmark_name_2]
cum_return = cum_return.dropna()
num_format_pd = pd.DataFrame([],
columns=cum_return.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(cum_return,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="blue",
fillna=True)
# 基金和基准时间序列图
###########################################################################################
chart_name = fund_name + "累计收益（成立以来）"
series_name = [fund_name, benchmark_name, benchmark_name_2]
insert_pos = 'B12'
excel.line_chart_time_series_plot(worksheet, 0, col_number, cum_return,
series_name, chart_name, insert_pos,
sheet_name)
excel.close()
###########################################################################################
return True
def write_public_zz500_adjust(end_date, save_path):
# 参数
###########################################################################################
fund_name_adjust = '泰达宏利中证500_adjust'
fund_code_adjust = '162216.OF_adjust'
fund_name = '泰达宏利中证500'
fund_code = '162216.OF'
fund_type = "公募"
benchmark_code = '000905.SH'
benchmark_name = '中证500'
benchmark_ratio = 0.95
setup_date = '20141003'
today = datetime.strptime(end_date, "%Y%m%d")
before_1y = datetime(year=today.year - 1, month=today.month,
day=today.day).strftime("%Y%m%d")
before_2y = datetime(year=today.year - 2, month=today.month,
day=today.day).strftime("%Y%m%d")
before_3y = datetime(year=today.year - 3, month=today.month,
day=today.day).strftime("%Y%m%d")
date_array = np.array([
["2019年", '20190101', end_date, '20180930'],
["2018年", "20180101", '20181231', "20170930"],
["2017年", "20170101", '20171231', "20160930"],
["2016年", "20160101", "20161231", "20150930"],
["2015年", "20150101", "20151231", "20150101"],
["管理(20141003)以来", setup_date, end_date, setup_date],
["2015年以来", "20150101", end_date, setup_date],
["过去1年", before_1y, end_date, before_1y],
["过去2年", before_2y, end_date, before_2y],
["过去3年", before_3y, end_date, before_3y],
])
benchmark_array = np.array([["沪深300", "000300.SH"], ["中证500", "000905.SH"],
["WIND全A", '881001.WI']])
from quant.fund.fund import Fund
fund_pct = Fund().get_fund_factor("Repair_Nav_Pct")
bench_pct = Fund().get_fund_factor("Fund_Bench_Pct") * 100
# 准备文件
###########################################################################################
file_name = os.path.join(save_path, "OutFile", fund_name_adjust + '.xlsx')
sheet_name = fund_name_adjust
excel = WriteExcel(file_name)
worksheet = excel.add_worksheet(sheet_name)
# 写入基金表现 和基金排名
###########################################################################################
performance_table = MfcTable().cal_summary_table_sample(
fund_name, fund_code, fund_type, date_array, benchmark_array)
# rank0 = FundRank().rank_fund_array2(fund_pct, bench_pct, fund_code, date_array, "wind", excess=False)
# rank1 = FundRank().rank_fund_array2(fund_pct, bench_pct, fund_code, date_array, "被动指数型基金", excess=True)
rank2 = FundRank().rank_fund_array2(fund_pct,
bench_pct,
fund_code,
date_array,
"中证500基金",
excess=False)
performance_table = pd.concat([performance_table, rank2], axis=0)
col_number = 1
num_format_pd = pd.DataFrame([],
columns=performance_table.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(performance_table,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="red",
fillna=True)
col_number = col_number + performance_table.shape[1] + 2
# 写入增强基金表现
###########################################################################################
performance_table = MfcTable().cal_summary_table_enhanced_fund(
fund_name, fund_code, fund_type, date_array, benchmark_code,
benchmark_name, benchmark_ratio)
num_format_pd = pd.DataFrame([],
columns=performance_table.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(performance_table,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="red",
fillna=True)
col_number = col_number + performance_table.shape[1] + 2
# 读取基金和基准时间序列
###########################################################################################
fund_data = MfcData().get_mfc_nav(fund_code, fund_name, fund_type)
benchmark_data = Index().get_index_factor(benchmark_code, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund_data), pd.DataFrame(benchmark_data),
benchmark_ratio)
# 写入超额收益时间序列
###########################################################################################
excess_cum_return = fs.get_cum_excess_return_series("20150101", end_date)
num_format_pd = pd.DataFrame([],
columns=excess_cum_return.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(excess_cum_return,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="blue",
fillna=True)
# 超额收益图
###########################################################################################
chart_name = fund_name + "累计超额收益（2015年以来）"
insert_pos = 'B12'
excel.line_chart_one_series_with_linear_plot(worksheet, 0, col_number,
excess_cum_return, chart_name,
insert_pos, sheet_name)
col_number = col_number + excess_cum_return.shape[1] + 2
# 写入基金收益时间序列
###########################################################################################
benchmark_data = Index().get_index_factor(benchmark_code, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund_data), pd.DataFrame(benchmark_data))
cum_return = fs.get_fund_and_bencnmark_cum_return_series(
"20150101", end_date)
num_format_pd = pd.DataFrame([],
columns=cum_return.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(cum_return,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="blue",
fillna=True)
# 写入基金收益时间序列图
############################################################################################
series_name = [fund_name, benchmark_name]
chart_name = fund_name + "累计收益（2015年以来）"
insert_pos = 'B26'
excel.line_chart_time_series_plot(worksheet, 0, col_number, cum_return,
series_name, chart_name, insert_pos,
sheet_name)
excel.close()
###########################################################################################
return True
def fund_score(self, fund_code, fund_name, end_date, rank_pool, mg_date, fund_type, my_index_code):
""" 计算基金得分 """
# index_code = "881001.WI"
# fund_code = "162208.OF"
# end_date = "20181231"
# rank_pool = "普通股票型基金"
# mg_date = "20141121"
# fund_type = "行业基金"
# my_index_code = "FTSE成长"
end_date = Date().change_to_datetime(end_date)
before_1y = datetime(year=end_date.year, month=1, day=1).strftime("%Y%m%d")
before_3y = datetime(year=end_date.year-2, month=1, day=1).strftime("%Y%m%d")
before_3y = max(before_3y, "20160101")
before_5y = datetime(year=end_date.year-4, month=1, day=1).strftime("%Y%m%d")
before_5y = max(before_5y, "20160101")
mg_date = max(mg_date, "20160101")
end_date = Date().change_to_str(end_date)
result = pd.DataFrame([], columns=["名称", "1年收益", "1年排名", "1年排名百分比", "1年得分",
"3年收益", "3年排名", "3年排名百分比", "3年得分",
"5年收益", "5年排名", "5年排名百分比", "5年得分"
])
result.loc[fund_code, "名称"] = fund_name
beg_date = before_1y
fund_nav = MfcData().get_mfc_public_fund_nav(fund_code)
fs = FinancialSeries(pd.DataFrame(fund_nav['NAV_ADJ']))
result.loc[fund_code, "1年收益"] = fs.get_interval_return(beg_date, end_date)
str_rank, pct = FundRank().rank_fund(fund_code, rank_pool, beg_date, end_date, beg_date, excess=False)
result.loc[fund_code, "1年排名百分比"] = pct
result.loc[fund_code, "1年排名"] = str_rank
result.loc[fund_code, "1年得分"] = self.score(pct)
beg_date = before_3y
fund_nav = MfcData().get_mfc_public_fund_nav(fund_code)
fs = FinancialSeries(pd.DataFrame(fund_nav['NAV_ADJ']))
result.loc[fund_code, "3年收益"] = fs.get_interval_return(beg_date, end_date)
str_rank, pct = FundRank().rank_fund(fund_code, rank_pool, beg_date, end_date, beg_date, excess=False)
result.loc[fund_code, "3年排名百分比"] = pct
result.loc[fund_code, "3年排名"] = str_rank
result.loc[fund_code, "3年得分"] = self.score(pct)
beg_date = before_5y
fund_nav = MfcData().get_mfc_public_fund_nav(fund_code)
fs = FinancialSeries(pd.DataFrame(fund_nav['NAV_ADJ']))
result.loc[fund_code, "5年收益"] = fs.get_interval_return(beg_date, end_date)
str_rank, pct = FundRank().rank_fund(fund_code, rank_pool, beg_date, end_date, beg_date, excess=False)
result.loc[fund_code, "5年排名百分比"] = pct
result.loc[fund_code, "5年排名"] = str_rank
result.loc[fund_code, "5年得分"] = self.score(pct)
beg_date = mg_date
fund_nav = MfcData().get_mfc_public_fund_nav(fund_code)
fs = FinancialSeries(pd.DataFrame(fund_nav['NAV_ADJ']))
result.loc[fund_code, "管理以来收益"] = fs.get_interval_return(beg_date, end_date)
str_rank, pct = FundRank().rank_fund(fund_code, rank_pool, beg_date, end_date, beg_date, excess=False)
result.loc[fund_code, "管理以来排名"] = str_rank
result.loc[fund_code, "管理以来排名百分比"] = pct
result.loc[fund_code, "管理以来得分"] = self.score(pct)
print(result)
return result
def fund_excess_score(self, fund_code, fund_name, end_date, rank_pool, mg_date, fund_type, my_index_code):
""" 行业基金超额收益得分 """
end_date = Date().change_to_datetime(end_date)
before_1y = datetime(year=end_date.year, month=1, day=1).strftime("%Y%m%d")
before_3y = datetime(year=end_date.year-2, month=1, day=1).strftime("%Y%m%d")
before_3y = max(before_3y, "20160101")
before_5y = datetime(year=end_date.year-4, month=1, day=1).strftime("%Y%m%d")
before_5y = max(before_5y, "20160101")
mg_date = max(mg_date, "20160101")
end_date = Date().change_to_str(end_date)
result = pd.DataFrame([], columns=["名称", "1年超额收益", "1年超额排名", "1年超额排名百分比", "1年超额得分",
"3年超额收益", "3年超额排名", "3年超额排名百分比", "3年超额得分",
"5年超额收益", "5年超额排名", "5年超额排名百分比", "5年超额得分",
"管理以来超额收益", "管理以来超额排名", "管理以来超额排名百分比", "管理以来超额得分"
])
result.loc[fund_code, "名称"] = fund_name
if fund_type == "行业基金":
beg_date = before_1y
excess_return, pct, rank_str = FundRank().rank_excess_fund(fund_pool_name=rank_pool, ge_index_code="881001.WI",
my_index_code=my_index_code, my_fund_code=fund_code,
beg_date=beg_date, end_date=end_date)
result.loc[fund_code, "1年超额收益"] = excess_return
result.loc[fund_code, "1年超额排名"] = rank_str
result.loc[fund_code, "1年超额排名百分比"] = pct
result.loc[fund_code, "1年超额得分"] = self.score(pct)
beg_date = before_3y
excess_return, pct, rank_str = FundRank().rank_excess_fund(fund_pool_name=rank_pool, ge_index_code="881001.WI",
my_index_code=my_index_code, my_fund_code=fund_code,
beg_date=beg_date, end_date=end_date)
result.loc[fund_code, "3年超额收益"] = excess_return
result.loc[fund_code, "3年超额排名"] = rank_str
result.loc[fund_code, "3年超额排名百分比"] = pct
result.loc[fund_code, "3年超额得分"] = self.score(pct)
beg_date = before_5y
excess_return, pct, rank_str = FundRank().rank_excess_fund(fund_pool_name=rank_pool, ge_index_code="881001.WI",
my_index_code=my_index_code, my_fund_code=fund_code,
beg_date=beg_date, end_date=end_date)
result.loc[fund_code, "5年超额收益"] = excess_return
result.loc[fund_code, "5年超额排名百分比"] = pct
result.loc[fund_code, "5年超额排名"] = rank_str
result.loc[fund_code, "5年超额得分"] = self.score(pct)
beg_date = mg_date
excess_return, pct, rank_str = FundRank().rank_excess_fund(fund_pool_name=rank_pool,
ge_index_code="881001.WI",
my_index_code=my_index_code,
my_fund_code=fund_code,
beg_date=beg_date, end_date=end_date)
result.loc[fund_code, "管理以来超额收益"] = excess_return
result.loc[fund_code, "管理以来超额排名百分比"] = pct
result.loc[fund_code, "管理以来超额排名"] = rank_str
result.loc[fund_code, "管理以来超额得分"] = self.score(pct)
return result
def write_public_lh(end_date, save_path):
# 参数
###########################################################################################
fund_name = '泰达宏利量化增强'
fund_code = '001733.OF'
fund_type = "公募"
benchmark_code = '000905.SH'
benchmark_name = '中证500'
benchmark_ratio = 0.95
setup_date = '20160830'
date_array = np.array(
[["2019年", '20190101', end_date, '20180930'],
["2018年", "20180101", '20181231', "20170930"],
["2017年", "20170101", '20171231', "20160930"],
["2016年", setup_date, "20161231", setup_date],
["成立(20160830)以来", setup_date, end_date, setup_date]])
benchmark_array = np.array([["沪深300", "000300.SH"], ["中证500", "000905.SH"],
["股票型基金", '885012.WI'], ["创业板指", '399006.SZ'],
["WIND全A", '881001.WI']])
from quant.fund.fund import Fund
fund_pct = Fund().get_fund_factor("Repair_Nav_Pct")
bench_pct = Fund().get_fund_factor("Fund_Bench_Pct") * 100
# 准备文件
###########################################################################################
file_name = os.path.join(save_path, "OutFile", fund_name + '.xlsx')
sheet_name = fund_name
excel = WriteExcel(file_name)
worksheet = excel.add_worksheet(sheet_name)
# 写入基金表现 和基金排名
###########################################################################################
performance_table = MfcTable().cal_summary_table_sample(
fund_name, fund_code, fund_type, date_array, benchmark_array)
rank0 = FundRank().rank_fund_array2(fund_pct,
bench_pct,
fund_code,
date_array,
"中证500基金",
excess=False)
rank1 = FundRank().rank_fund_array2(fund_pct,
bench_pct,
fund_code,
date_array,
"普通股票型基金",
excess=False)
# rank2 = FundRank().rank_fund_array2(fund_pct, bench_pct, fund_code, date_array, "中证500基金", excess=True)
# rank3 = FundRank().rank_fund_array2(fund_pct, bench_pct, fund_code, date_array, "指数增强型基金", excess=True)
performance_table = pd.concat([performance_table, rank0, rank1], axis=0)
col_number = 1
num_format_pd = pd.DataFrame([],
columns=performance_table.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(performance_table,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="red",
fillna=True)
col_number = col_number + performance_table.shape[1] + 2
# 写入增强基金表现
###########################################################################################
performance_table = MfcTable().cal_summary_table_enhanced_fund(
fund_name, fund_code, fund_type, date_array, benchmark_code,
benchmark_name, benchmark_ratio)
num_format_pd = pd.DataFrame([],
columns=performance_table.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(performance_table,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="red",
fillna=True)
col_number = col_number + performance_table.shape[1] + 2
# 读取基金和基准时间序列
###########################################################################################
fund_data = MfcData().get_mfc_nav(fund_code, fund_name, fund_type)
benchmark_data = Index().get_index_factor(benchmark_code, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund_data), pd.DataFrame(benchmark_data),
benchmark_ratio)
# 写入超额收益时间序列
###########################################################################################
excess_cum_return = fs.get_cum_excess_return_series(setup_date, end_date)
num_format_pd = pd.DataFrame([],
columns=excess_cum_return.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(excess_cum_return,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="blue",
fillna=True)
# 超额收益图
###########################################################################################
chart_name = fund_name + "累计超额收益（成立以来）"
insert_pos = 'B16'
excel.line_chart_one_series_with_linear_plot(worksheet, 0, col_number,
excess_cum_return, chart_name,
insert_pos, sheet_name)
col_number = col_number + excess_cum_return.shape[1] + 2
# 写入基金收益时间序列
###########################################################################################
benchmark_data = Index().get_index_factor(benchmark_code, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund_data), pd.DataFrame(benchmark_data))
cum_return = fs.get_fund_and_bencnmark_cum_return_series(
setup_date, end_date)
num_format_pd = pd.DataFrame([],
columns=cum_return.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(cum_return,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="blue",
fillna=True)
# 写入基金收益时间序列图
############################################################################################
series_name = [fund_name, benchmark_name]
chart_name = fund_name + "累计收益（成立以来）"
insert_pos = 'B32'
excel.line_chart_time_series_plot(worksheet, 0, col_number, cum_return,
series_name, chart_name, insert_pos,
sheet_name)
excel.close()
###########################################################################################
return True