def calculate_fund_factor(now_date, out_path):
# 半年计算一次
################################################################################################################
# now_date = "20180715"
# out_path = 'E:\\3_数据\\4_fund_data\\6_index_enhanced_fund_snowball\\'
data = Date().get_normal_date_series("20130622", datetime.today(), period="S")
data.append(now_date)
# 中证500
################################################################################################################
index_code = '000905.SH'
index_name = '中证500'
for i_date in range(10, len(data)):
report_date = data[i_date]
print("正在计算基金指标 %s %s" % (index_name, report_date))
calculate_fund_factor_date(report_date, index_code, index_name, out_path)
# 沪深300
################################################################################################################
index_code = '000300.SH'
index_name = '沪深300'
for i_date in range(10, len(data)):
report_date = data[i_date]
print("正在计算基金指标 %s %s" % (index_name, report_date))
calculate_fund_factor_date(report_date, index_code, index_name, out_path)
def nav(index_code, index_name, now_date):
# 参数
##################################################################################################################
# index_name = '中证500'
# index_code = '000905.SH'
# now_date = "20180715"
# 日期
##################################################################################################################
date_series = Date().get_normal_date_series("20130622",
datetime.today(),
period="S")
date_series.append(now_date)
# 循环计算基金净值
##################################################################################################################
path = 'E:\\4_代码\\pycharmprojects\\31_雪球优选增强基金\\output_data\\'
file_name = path + 'select_fund\\' + index_name + '\\' + '基金每期持仓_' + index_name + '.xlsx'
fund_holding_total = pd.read_excel(file_name,
index_col=[0],
encoding='gbk')
fund_holding_total['换仓日期'] = fund_holding_total['换仓日期'].map(str)
nav_total = pd.DataFrame([])
for i_date in range(0, len(date_series) - 2):
report_date_str = date_series[i_date]
report_date_str_after = date_series[i_date + 1]
print(report_date_str, report_date_str_after)
fund_holding_date = fund_holding_total[fund_holding_total['换仓日期'] ==
report_date_str]
fund_code_list = list(fund_holding_date['基金代码'].values)
fund_data = Fund().get_fund_factor("Repair_Nav", None, None)
fund_data = fund_data.ix[report_date_str:report_date_str_after,
fund_code_list]
fund_data = fund_data / fund_data.iloc[0, :]
fund_data = fund_data.dropna()
nav_array = fund_data.values
weight_array = np.row_stack(fund_holding_date.ix[:, '权重'].values)
fund_data.loc[:, '每日总资产'] = np.dot(nav_array, weight_array)
fund_data['每日涨跌幅'] = fund_data['每日总资产'].pct_change()
fund_data = fund_data.dropna()
nav_total = pd.concat([nav_total, fund_data['每日涨跌幅']], axis=0)
nav_total = nav_total[~nav_total.index.duplicated()]
index_pct = Index().get_index_factor(index_code, None, None, ["PCT"])
result = pd.concat([nav_total, index_pct], axis=1)
result = result.dropna()
result.columns = ['基金组合日涨跌', '指数日涨跌']
result['基金组合累计收益'] = (result['基金组合日涨跌'] + 1).cumprod() - 1
result['指数累计收益'] = (result['指数日涨跌'] + 1).cumprod() - 1
result['超额累计收益'] = result['基金组合累计收益'] - result['指数累计收益']
num_format_pd = pd.DataFrame([], columns=result.columns, index=['format'])
num_format_pd.ix['format', :] = '0.00%'
begin_row_number = 0
begin_col_number = 0
color = "red"
file_name = path + 'nav\\' + index_name + '\\' + '基金回测净值_' + index_name + '.xlsx'
sheet_name = "基金回测净值"
write_pandas(file_name, sheet_name, begin_row_number, begin_col_number,
result, num_format_pd, color)
def backtest(index_name, now_date, path):
# 参数
##################################################################################################################
# index_name = '中证500'
# now_date = "20180715"
# path = 'E:\\3_数据\\4_fund_data\\6_index_enhanced_fund_snowball\\'
# 日期
##################################################################################################################
date_series = Date().get_normal_date_series("20130622",
datetime.today(),
period="S")
# date_series.remove("20180630")
date_series.append(now_date)
# 循环取出持仓基金
##################################################################################################################
out_path = path + 'score_fund\\' + index_name + '\\'
holding_fund_total = pd.DataFrame([])
holding_fund_pct = pd.DataFrame([], index=date_series, columns=['下期超额收益'])
for i_date in range(0, len(date_series)):
report_date = date_series[i_date]
print(report_date)
filename = out_path + '基金得分_' + index_name + '_' + report_date + '.xlsx'
fund_score = pd.read_excel(filename, index_col=[0], encoding='gbk')
fund_score = fund_score[[
'基金名称', '下期超额收益', '总得分', '跟踪误差得分', '超额收益得分', '信息比率得分', '风格暴露得分',
'上期超额收益'
]]
fund_score['基金公司'] = fund_score['基金名称'].map(lambda x: x[0:2])
fund_score['基金代码'] = fund_score.index
fund_score_drop = fund_score.groupby(
['基金公司']).apply(lambda i: i.iloc[0, :] if len(i) >= 1 else np.nan)
fund_score_drop = fund_score_drop.sort_values(by=['总得分'],
ascending=False)
fund_score_drop_0 = fund_score_drop[fund_score_drop['上期超额收益'] > 0.0]
if len(fund_score_drop_0) >= 5:
fund_score_drop = fund_score_drop_0
fund_score_five = fund_score_drop.iloc[0:min(5, len(fund_score)), :]
if len(fund_score_five) > 0.0:
fund_score_five['权重'] = 1.0 / len(fund_score_five)
fund_score_five['换仓日期'] = report_date
holding_fund_total = pd.concat(
[holding_fund_total, fund_score_five], axis=0)
pct = (fund_score_five['权重'] * fund_score_five['下期超额收益']).sum()
holding_fund_pct.ix[report_date, "下期超额收益"] = pct
# 循环取出持仓基金
##################################################################################################################
num_format_pd = pd.DataFrame([],
columns=holding_fund_total.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00'
num_format_pd.ix['format', ['下期超额收益', '上期超额收益']] = '0.00%'
begin_row_number = 0
begin_col_number = 0
color = "red"
file_name = path + 'select_fund\\' + index_name + '\\' + '基金每期持仓_' + index_name + '.xlsx'
sheet_name = "基金每期持仓"
write_pandas(file_name, sheet_name, begin_row_number, begin_col_number,
holding_fund_total, num_format_pd, color)
num_format_pd = pd.DataFrame([],
columns=holding_fund_pct.columns,
index=['format'])
num_format_pd.ix['format', :] = '0.00'
num_format_pd.ix['format', '下期超额收益'] = '0.00%'
begin_row_number = 0
begin_col_number = 0
color = "red"
file_name = path + 'select_fund\\' + index_name + '\\' + '基金每期超额收益_' + index_name + '.xlsx'
sheet_name = "基金每期超额收益"
write_pandas(file_name, sheet_name, begin_row_number, begin_col_number,
holding_fund_pct, num_format_pd, color)
