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 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 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 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 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 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 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_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 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 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