def get_fund_last_performance(self):
""" 基金和对比基准最近业绩表现 """
date_array = self.get_fund_performace_date()
fund_nav = self.fund_nav
result_fund = pd.DataFrame([], columns=['收益率', '超额收益率', '年化跟踪误差'])
# fund
bench_series = Index().get_index_factor(self.bench_code, attr=['CLOSE'])
fund_series = FinancialSeries(pd.DataFrame(fund_nav['NAV_ADJ']), pd.DataFrame(bench_series))
for i in range(len(date_array)):
label = date_array[i][0]
beg_date = date_array[i][1]
end_date = date_array[i][2]
pct = fund_series.get_interval_return(beg_date, end_date, short_handled=True)
bench_pct = fund_series.get_interval_return_benchmark(beg_date, end_date)
te = fund_series.get_interval_tracking_error(beg_date, end_date)
result_fund.loc[label, "收益率"] = pct
try:
result_fund.loc[label, "超额收益率"] = pct - bench_pct * 0.95
except Exception as e:
result_fund.loc[label, "超额收益率"] = ""
result_fund.loc[label, "年化跟踪误差"] = te
columns = list(result_fund.columns)
result_fund['最近表现'] = result_fund.index
columns.insert(0, '最近表现')
result_fund = result_fund[columns]
return result_fund
def mfcteda_fund_excess_return(self, end_date, fund_type, fund_code,
fund_name, benchmark_code, benchmark_ratio):
""" 计算1个基金今年所有月份超额收益 """
date_pd = self.get_date_pd(end_date)
if fund_type == "公募":
fund_data = MfcData().get_mfc_public_fund_nav(fund_code)
fund_data = fund_data['NAV_ADJ']
else:
fund_data = MfcData().get_mfc_private_fund_nav(fund_name)
fund_data = fund_data['累计复权净值']
benchmark_data = Index().get_index_factor(benchmark_code,
attr=["CLOSE"])
return_pd = pd.DataFrame([],
columns=date_pd.columns,
index=[fund_name])
for i in date_pd.columns:
beg_date = date_pd.ix["beg_date", i]
end_date = date_pd.ix["end_date", i]
fs = FinancialSeries(
pd.DataFrame(fund_data),
pd.DataFrame(benchmark_data) * benchmark_ratio)
try:
return_pd.ix[fund_name, i] = fs.get_interval_excess_return(
beg_date, end_date)
except Exception as e:
pass
return return_pd
def rank_excess_fund(self, fund_pool_name, ge_index_code, my_index_code,
my_fund_code, beg_date, end_date):
"""
计算某只基金在基金池的超额收益排名
这只基金指定基准 其他默认为windqa
"""
fund_pool = FundPool().get_fund_pool_all(date="20181231",
name=fund_pool_name)
fund_pool = fund_pool[fund_pool['setupdate'] < beg_date]
fund_pool = list(fund_pool['wind_code'].values)
fund_pool.append(my_fund_code)
result = pd.DataFrame([], index=fund_pool)
data = FundFactor().get_fund_factor("Repair_Nav")
for i in range(0, len(fund_pool)):
fund_code = fund_pool[i]
if fund_code == my_fund_code:
index_code = my_index_code
else:
index_code = ge_index_code
try:
print(fund_code, index_code, beg_date, end_date)
fund = pd.DataFrame(data[fund_code])
index = Index().get_index_factor(index_code, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund), pd.DataFrame(index))
fund_return = fs.get_interval_return(beg_date, end_date)
bench_return = fs.get_interval_return_benchmark(
beg_date, end_date)
result.loc[fund_code, "基准收益"] = bench_return
result.loc[fund_code, "基金收益"] = fund_return
result.loc[fund_code, "超额收益"] = -bench_return + fund_return
except Exception as e:
print(e)
result = result.dropna()
result = result[~result.index.duplicated()]
result = result.sort_values(by=['超额收益'], ascending=False)
result['收益名次'] = range(1, len(result) + 1)
result['收益排名'] = result['收益名次'].map(
lambda x: str(x) + '/' + str(len(result)))
result['收益排名百分比'] = result['收益名次'].map(lambda x: x / len(result))
excess_return = result.loc[my_fund_code, "超额收益"]
pct = result.loc[my_fund_code, "收益排名百分比"]
rank_str = result.loc[my_fund_code, "收益排名"]
result.to_csv(
os.path.join(
self.data_path,
"超额收益_%s_%s_%s.csv" % (my_fund_code, beg_date, end_date)))
return excess_return, pct, rank_str
def return_index(end_date, index_code, index_name, index_ratio, mage_date):
""" 某个指数收益率 """
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],
])
performance_table = pd.DataFrame([], index=[index_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]
if beg_date >= str(int(mage_date)):
index_close = Index().get_index_factor(index_code, attr=['CLOSE'])
print(index_close.tail())
fs = FinancialSeries(pd.DataFrame(index_close))
pct = fs.get_interval_return(beg_date, end_date)
print(pct, index_name)
if type(pct) == np.float64:
pct *= float(index_ratio)
performance_table.loc[index_name, label] = pct
else:
performance_table.ix[index_name, label] = "NAN"
print(performance_table)
return performance_table
def return_fund(end_date, fund_code, fund_name, mage_date):
""" 某个基金收益率 """
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],
])
performance_table = 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]
if beg_date >= str(int(mage_date)):
fund_nav = MfcData().get_mfc_public_fund_nav(fund_code)
fs = FinancialSeries(pd.DataFrame(fund_nav['NAV_ADJ']))
performance_table.ix[fund_name, label] = fs.get_interval_return(beg_date, end_date)
else:
performance_table.ix[fund_name, label] = "NAN"
print(performance_table)
return performance_table
def mfcteda_fund_return(self, end_date, fund_type, fund_code, fund_name):
""" 计算1个基金今年所有月份收益 """
date_pd = self.get_date_pd(end_date)
fund_data = MfcData().get_mfc_nav(fund_code, fund_name, fund_type)
return_pd = pd.DataFrame([],
columns=date_pd.columns,
index=[fund_name])
for i in date_pd.columns:
beg_date = date_pd.ix["beg_date", i]
end_date = date_pd.ix["end_date", i]
fs = FinancialSeries(pd.DataFrame(fund_data))
try:
return_pd.ix[fund_name,
i] = fs.get_interval_return(beg_date, end_date)
except Exception as e:
pass
return return_pd
def mfcteda_benchmark_return(self, end_date, benchmark_code,
benchmark_name):
""" 计算1个指数今年所有月份收益 """
date_pd = self.get_date_pd(end_date)
benchmark_data = Index().get_index_factor(benchmark_code,
attr=["CLOSE"])
return_pd = pd.DataFrame([],
columns=date_pd.columns,
index=[benchmark_name])
for i in date_pd.columns:
beg_date = date_pd.ix["beg_date", i]
end_date = date_pd.ix["end_date", i]
fs = FinancialSeries(pd.DataFrame(benchmark_data))
try:
return_pd.ix[benchmark_name,
i] = fs.get_interval_return(beg_date, end_date)
except Exception as e:
pass
return return_pd
def cal_fund_index(self, fund_pool_name, my_index_code, my_fund_code, beg_date, end_date):
"""
计算某只基金所在基金池的各项指标（包括基金收益、基金基准收益、超额收益、跟踪误差及信息比率） 剔除新基金
"""
fund_pool = Fund().get_fund_pool_all(date="20181231", name=fund_pool_name)
fund_pool = fund_pool[fund_pool['setupdate'] < beg_date]
fund_pool = list(fund_pool['wind_code'].values)
fund_pool.append(my_fund_code)
result = pd.DataFrame([], index=fund_pool)
data = Fund().get_fund_factor("Repair_Nav")
for i in range(0, len(fund_pool)):
fund_code = fund_pool[i]
if fund_code == my_fund_code:
index_code = my_index_code
else:
index_code = "881001.WI"
print(fund_code, index_code, beg_date, end_date)
try:
fund = pd.DataFrame(data[fund_code])
index = Index().get_index_factor(index_code, attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(fund), pd.DataFrame(index))
fund_return = fs.get_interval_return_annual(beg_date, end_date)
bench_return = fs.get_interval_return_benchmark(beg_date, end_date)
excess_return = fs.get_interval_excess_return(beg_date, end_date)
tracking_error = fs.get_interval_tracking_error(beg_date, end_date)
ir = excess_return / tracking_error
result.loc[fund_code, "基准收益"] = bench_return
result.loc[fund_code, "基金收益"] = fund_return
result.loc[fund_code, "超额收益"] = - bench_return + fund_return
result.loc[fund_code, "跟踪误差"] = tracking_error
result.loc[fund_code, "信息比率"] = ir
except Exception as e:
print(e)
result = result.dropna()
result = result[~result.index.duplicated()]
result = result.sort_values(by=['基金收益'], ascending=False)
result['收益名次'] = range(1, len(result) + 1)
result['收益排名'] = result['收益名次'].map(lambda x: str(x) + '/' + str(len(result)))
file = "%s_%s_%s_%s.csv" % (fund_pool_name, my_fund_code, beg_date, end_date)
file = os.path.join(self.data_path, 'data', file)
result.to_csv(file)
def get_fund_last_performance(self):
""" 基金和对比基准最近业绩表现 """
date_array = self.get_fund_performace_date()
fund_nav = self.fund_nav
result_fund = pd.DataFrame([], columns=['收益率'])
# fund
fund_series = FinancialSeries(pd.DataFrame(fund_nav['NAV_ADJ']))
for i in range(len(date_array)):
label = date_array[i][0]
beg_date = date_array[i][1]
end_date = date_array[i][2]
pct = fund_series.get_interval_return(beg_date,
end_date,
short_handled=True)
result_fund.loc[label, "收益率"] = pct
# benchmark
for i_bench in range(len(self.comparsion_bench_list)):
bench_name = self.comparsion_bench_list[i_bench][0]
bench_code = self.comparsion_bench_list[i_bench][1]
index_close = Index().get_index_factor(bench_code, attr=['CLOSE'])
index_series = FinancialSeries(pd.DataFrame(index_close['CLOSE']))
for i in range(len(date_array)):
label = date_array[i][0]
beg_date = date_array[i][1]
end_date = date_array[i][2]
pct = index_series.get_interval_return(beg_date,
end_date,
short_handled=True)
result_fund.loc[label, bench_name] = pct
columns = list(result_fund.columns)
result_fund['最近表现'] = result_fund.index
columns.insert(0, '最近表现')
result_fund = result_fund[columns]
return result_fund
def cal_summary_table_sample(fund_name, fund_code, fund_type, date_array,
benchmark_array):
"""
主动股票型基金表现总结（简单版）
分区间计算 基金表现 累计收益
分区间计算 分基准 计算基准累计收益
"""
fund_data = MfcData().get_mfc_nav(fund_code, fund_name, fund_type)
performance_table = pd.DataFrame([], columns=date_array[:, 0])
fs = FinancialSeries(pd.DataFrame(fund_data),
pd.DataFrame([], columns=['nav']))
for i_date in range(date_array.shape[0]):
label = date_array[i_date, 0]
bd = Date().change_to_str(date_array[i_date, 1])
ed = Date().change_to_str(date_array[i_date, 2])
print("Cal Interval Return ", bd, ed)
performance_table.ix[fund_name + "累计收益",
label] = fs.get_interval_return(bd, ed)
for i_benchmark in range(benchmark_array.shape[0]):
benchmark_name = benchmark_array[i_benchmark, 0]
benchmark_code = benchmark_array[i_benchmark, 1]
benchmark_data = Index().get_index_factor(benchmark_code,
attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(benchmark_data),
pd.DataFrame([], columns=['nav']))
for i_date in range(date_array.shape[0]):
label = date_array[i_date, 0]
bd = Date().change_to_str(date_array[i_date, 1])
ed = Date().change_to_str(date_array[i_date, 2])
performance_table.ix[benchmark_name + "累计收益",
label] = fs.get_interval_return(bd, ed)
return performance_table
def cal_summary_table(fund_name, fund_code, fund_type, date_array,
benchmark_array):
"""
主动股票型基金表现总结
分区间计算 基金表现（累计收益 年化收益 年化波动 最大回撤 收益波动比）
分区间计算 分基准 计算基准表现（累计收益 年化收益 年化波动 最大回撤 收益波动比）
"""
# 分类读取基金数据
fund_data = MfcData().get_mfc_nav(fund_code, fund_name, fund_type)
performance_table = pd.DataFrame([], columns=date_array[:, 0])
fs = FinancialSeries(pd.DataFrame(fund_data),
pd.DataFrame([], columns=['nav']))
for i_date in range(date_array.shape[0]):
label = date_array[i_date, 0]
bd = Date().change_to_str(date_array[i_date, 1])
ed = Date().change_to_str(date_array[i_date, 2])
performance_table.ix[fund_name + "累计收益",
label] = fs.get_interval_return(bd, ed)
performance_table.ix[fund_name + "年化收益",
label] = fs.get_interval_return_annual(
bd, ed)
performance_table.ix[fund_name + "年化波动",
label] = fs.get_interval_std_annual(bd, ed)
performance_table.ix[fund_name + "最大回撤",
label] = fs.get_interval_max_drawdown(bd, ed)
performance_table.ix[fund_name + "收益波动比",
label] = fs.get_interval_return_std_ratio(
bd, ed)
for i_benchmark in range(benchmark_array.shape[0]):
benchmark_name = benchmark_array[i_benchmark, 0]
benchmark_code = benchmark_array[i_benchmark, 1]
benchmark_data = Index().get_index_factor(benchmark_code,
attr=["CLOSE"])
fs = FinancialSeries(pd.DataFrame(benchmark_data),
pd.DataFrame([], columns=['nav']))
for i_date in range(date_array.shape[0]):
label = date_array[i_date, 0]
bd = Date().change_to_str(date_array[i_date, 1])
ed = Date().change_to_str(date_array[i_date, 2])
performance_table.loc[benchmark_name + "累计收益",
label] = fs.get_interval_return(bd, ed)
performance_table.loc[benchmark_name + "年化收益",
label] = fs.get_interval_return_annual(
bd, ed)
performance_table.loc[benchmark_name + "年化波动",
label] = fs.get_interval_std_annual(
bd, ed)
performance_table.loc[benchmark_name + "最大回撤",
label] = fs.get_interval_max_drawdown(
bd, ed)
return performance_table
def cal_summary_table_enhanced_fund(fund_name,
fund_code,
fund_type,
date_array,
benchmark_code,
benchmark_name,
benchmark_ratio=1.0):
"""
指数型基金表现总结
分区间计算 基金和基准表现（累计收益 年化收益 超额收益 跟踪误差 信息比率 超额收益最大回撤等）
"""
# 分类读取基金数据
fund_data = MfcData().get_mfc_nav(fund_code, fund_name, fund_type)
benchmark_data = Index().get_index_factor(benchmark_code,
attr=["CLOSE"])
enhanced_table = pd.DataFrame([], columns=date_array[:, 0])
fs = FinancialSeries(pd.DataFrame(fund_data),
pd.DataFrame(benchmark_data), benchmark_ratio)
for i_date in range(date_array.shape[0]):
label = date_array[i_date, 0]
bd = Date().change_to_str(date_array[i_date, 1])
ed = Date().change_to_str(date_array[i_date, 2])
enhanced_table.loc[fund_name + "累计收益",
label] = fs.get_interval_return(bd, ed)
enhanced_table.loc[benchmark_name + "累计收益",
label] = fs.get_interval_return_benchmark(
bd, ed)
bench_return = fs.get_interval_return_benchmark_ratio(bd, ed)
enhanced_table.loc[benchmark_name + "*%s累计收益" % benchmark_ratio,
label] = bench_return
enhanced_table.loc[fund_name + "超额收益",
label] = fs.get_interval_excess_return(bd, ed)
enhanced_table.loc[fund_name + "超额年化收益",
label] = fs.get_interval_excess_return_annual(
bd, ed)
enhanced_table.loc[fund_name + "跟踪误差",
label] = fs.get_interval_tracking_error(bd, ed)
enhanced_table.loc[fund_name + "信息比率",
label] = fs.get_interval_mean_ir(bd, ed)
enhanced_table.loc[
fund_name + "超额收益最大回撤",
label] = fs.get_interval_excess_return_max_drawdown(bd, ed)
enhanced_table.loc[fund_name + "标准差",
label] = fs.get_interval_std_annual(bd, ed)
enhanced_table.loc[fund_name + "夏普比率",
label] = fs.get_interval_shape_ratio(
bd, ed, 0.03)
enhanced_table.loc[fund_name + "最大回撤",
label] = fs.get_interval_max_drawdown(bd, ed)
enhanced_table.loc[fund_name + "年化收益",
label] = fs.get_interval_return_annual(bd, ed)
enhanced_table.loc[
benchmark_name + "年化收益",
label] = fs.get_interval_return_annual_benchmark(bd, ed)
return enhanced_table
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 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 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
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 write_quant12(end_date, save_path):
# 参数
###########################################################################################
fund_name = '光大量化组合12号'
fund_code = fund_name
fund_type = "专户"
benchmark_code = "000905.SH"
benchmark_name = "中证500"
setup_date = "20160714"
date_array = np.array([["2019年", '20190101', end_date],
["2018年", "20180101", '20181231'],
["2017年", "20170101", '20171231'],
["成立(20160714)至2016年末", setup_date, '20161231'],
["成立(20160714)以来", setup_date, end_date]])
benchmark_array = np.array([["沪深300", "000300.SH"],
["中证500", "000905.SH"],
["股票型基金", '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)
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)
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))
# 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 = '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(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 = '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 cal_summary_period(self, beg_date=None, end_date=None):
"""
计算组合在区间内 收益率、波动率等表现情况
并画图并存储图片
"""
self.get_port_return()
if beg_date is None:
beg_date = self.port_return.index[0]
if end_date is None:
end_date = self.port_return.index[-1]
fs = FinancialSeries(
pd.DataFrame(self.port_return["CumPortReturn"] + 1.0),
pd.DataFrame(self.port_return["CumIndexReturn"] + 1.0))
port_return = self.port_return.loc[beg_date:end_date, :]
result = pd.DataFrame([])
beg_date = port_return.index[0]
end_date = port_return.index[-1]
label = str(beg_date)[0:4] + '年'
result.loc[label, '开始时间'] = beg_date
result.loc[label, '结束时间'] = end_date
result.loc[label, "收益率"] = fs.get_interval_return(beg_date, end_date)
result.loc[label, "年化收益率"] = fs.get_interval_return_annual(
beg_date, end_date)
result.loc[label,
"年化波动率"] = fs.get_interval_std_annual(beg_date, end_date)
result.loc[label, "年化跟踪误差"] = fs.get_interval_tracking_error(
beg_date, end_date)
result.loc[label,
"最大回撤率"] = fs.get_interval_max_drawdown(beg_date, end_date)
result.loc[label, "超额收益率"] = fs.get_interval_excess_return(
beg_date, end_date)
result.loc[label, "年化超额收益率"] = fs.get_interval_excess_return_annual(
beg_date, end_date)
result.loc[label,
"超额收益率最大回撤"] = fs.get_interval_excess_return_max_drawdown(
beg_date, end_date)
result.loc[label, "基准收益率"] = fs.get_interval_return_benchmark(
beg_date, end_date)
result.loc[label, "基准年化收益率"] = fs.get_interval_return_annual_benchmark(
beg_date, end_date)
result.loc[label, "基准年化波动率"] = fs.get_interval_std_annual_benchmark(
beg_date, end_date)
result.loc[label, "基准最大回撤率"] = fs.get_interval_max_drawdown_benchmark(
beg_date, end_date)
self.plot_port_return_period(beg_date, end_date)
return result.T
def write_zlhl2018(end_date, save_path):
# 参数
###########################################################################################
fund_name = '广州农商行梓霖惠利1号'
fund_code = fund_name
fund_type = "专户"
benchmark_code = '885007.WI'
benchmark_name = "混合债券二级基金指数"
benchmark_code_2 = "H11001.CSI"
benchmark_name_2 = "中证全债指数"
setup_date = '20180628'
date_array = np.array([["2019年", '20190101', end_date],
["2018年", "20180101", '20181231'],
["20180628以来", '20180628', end_date]])
benchmark_array = np.array([["沪深300", "000300.SH"], ["中证500", "000905.SH"],
["股票型基金", '885012.WI'],
["混合债券二级基金指数", '885007.WI'],
["中证全债指数", "H11001.CSI"]])
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 + '2018年.xlsx')
sheet_name = fund_name
excel = WriteExcel(file_name)
worksheet = excel.add_worksheet(sheet_name)
# 写入增强基金表现 相对基准
###########################################################################################
col_number = 1
performance_table = MfcTable().cal_summary_table_enhanced_fund(
fund_name, fund_code, fund_type, date_array, benchmark_code,
benchmark_name)
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 相对指数
###########################################################################################
performance_table = MfcTable().cal_summary_table_enhanced_fund(
fund_name, fund_code, fund_type, date_array, benchmark_code_2,
benchmark_name_2)
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
# 写入基金绝对表现
###########################################################################################
performance_table = MfcTable().cal_summary_table(fund_name, fund_code,
fund_type, date_array,
benchmark_array)
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)
# 写入基金和基准收益时间序列 相对基准
###########################################################################################
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)
# 基金和基准收益图 相对基准
###########################################################################################
series_name = [fund_name, benchmark_name]
chart_name = fund_name + "相对" + benchmark_name + " 累计超额收益（成立以来）"
insert_pos = 'B16'
excel.line_chart_time_series_plot(worksheet, 0, col_number, cum_return,
series_name, chart_name, insert_pos,
sheet_name)
col_number = col_number + cum_return.shape[1] + 2
# 写入基金和基准收益时间序列 相对指数
###########################################################################################
benchmark_data = Index().get_index_factor(benchmark_code_2, 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)
# 基金和基准收益图 相对指数
###########################################################################################
series_name = [fund_name, benchmark_name_2]
chart_name = fund_name + "相对" + benchmark_name_2 + " 累计超额收益（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
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_rs_500(end_date, save_path):
###########################################################################################
fund_name = '建行中国人寿中证500管理计划'
fund_code = fund_name
fund_type = "专户"
benchmark_code = "中证500全收益指数80%+固定收益1%"
benchmark_name = "中证500全收益指数80%+固定收益1%"
benchmark_code_2 = 'H00905.CSI'
benchmark_name_2 = "中证500全收益指数"
setup_date = '20151021'
date_array = np.array([["2019年", '20190101', end_date],
["2018年", "20180101", '20181231'],
['20171110至今', "20171110", end_date],
["2017年", "20170101", '20171231'],
["2016年", "2016001", '20161231'],
["2016年以来", "20160101", end_date],
["成立以来", setup_date, end_date]])
benchmark_array = np.array(
[["中证500全收益指数80%+固定收益1%", "中证500全收益指数80%+固定收益1%"],
["中证500", "000905.SH"], ["中证500全收益", 'H00905.CSI']])
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)
# 写入增强基金表现 相对基准
###########################################################################################
col_number = 1
performance_table = MfcTable().cal_summary_table_enhanced_fund(
fund_name, fund_code, fund_type, date_array, benchmark_code,
benchmark_name)
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 相对指数
###########################################################################################
performance_table = MfcTable().cal_summary_table_enhanced_fund(
fund_name, fund_code, fund_type, date_array, benchmark_code_2,
benchmark_name_2)
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)
# 写入基金和基准收益时间序列 相对基准
###########################################################################################
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)
# 基金和基准收益图 相对基准
###########################################################################################
series_name = [fund_name, benchmark_name]
chart_name = fund_name + "相对基准（全收益80%+1%）累计超额收益（成立以来）"
insert_pos = 'B16'
excel.line_chart_time_series_plot(worksheet, 0, col_number, cum_return,
series_name, chart_name, insert_pos,
sheet_name)
col_number = col_number + cum_return.shape[1] + 2
daliy_return = fs.get_fund_benchmark_daily_return_series(
setup_date, end_date)
num_format_pd = pd.DataFrame([],
columns=daliy_return.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00%'
excel.write_pandas(daliy_return,
worksheet,
begin_row_number=0,
begin_col_number=col_number,
num_format_pd=num_format_pd,
color="blue",
fillna=True)
col_number = col_number + cum_return.shape[1] + 2
# 写入基金和基准收益时间序列 相对基准
###########################################################################################
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(
"20160101", 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)
# 基金和基准收益图 相对基准
###########################################################################################
series_name = [fund_name, benchmark_name]
chart_name = fund_name + "相对基准（全收益80%+1%）累计超额收益（2016年以来）"
insert_pos = 'B32'
excel.line_chart_time_series_plot(worksheet, 0, col_number, cum_return,
series_name, chart_name, insert_pos,
sheet_name)
col_number = col_number + cum_return.shape[1] + 2
# 写入基金和基准收益时间序列 相对指数
###########################################################################################
benchmark_data = Index().get_index_factor(benchmark_code_2, 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)
# 基金和基准收益图 相对指数
###########################################################################################
series_name = [fund_name, benchmark_name_2]
chart_name = fund_name + "相对500全收益指数累计超额收益（成立以来）"
insert_pos = 'B48'
excel.line_chart_time_series_plot(worksheet, 0, col_number, cum_return,
series_name, chart_name, insert_pos,
sheet_name)
excel.close()
###########################################################################################
return True