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 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 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