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 weight_top10stock_good_date(report_date):
report_date = Date().change_to_str(report_date)
data = Fund().get_fund_holding_stock_date(report_date)
data = data[['FundCode', 'Weight', 'StockCode']]
pool = Fund().get_fund_pool_code(report_date, "基金持仓基准基金池")
fund_code = list(set(pool))
fund_code.sort()
weight = Fund().get_fund_factor("Total_Asset", date_list=[report_date]).T
weight = weight.dropna()
# 根据业绩 筛选股票池 其他还和原来一样
###############################################################################################
end_0 = int(report_date)
begin_0 = int(report_date) - 10000
end_1 = int(report_date) - 10000
begin_1 = int(report_date) - 20000
end_2 = int(report_date) - 20000
begin_2 = int(report_date) - 30000
print(end_0, end_1, end_2)
code_str = ','.join(fund_code)
data0 = w.wss(code_str, "NAV_adj_return",
"startDate=" + str(begin_0) + ";endDate=" + str(end_0))
data0 = pd.DataFrame(data0.Data, columns=data0.Codes, index=[str(end_0)]).T
data1 = w.wss(code_str, "NAV_adj_return",
"startDate=" + str(begin_1) + ";endDate=" + str(end_1))
data1 = pd.DataFrame(data1.Data, columns=data1.Codes, index=[str(end_1)]).T
data2 = w.wss(code_str, "NAV_adj_return",
"startDate=" + str(begin_2) + ";endDate=" + str(end_2))
data2 = pd.DataFrame(data2.Data, columns=data2.Codes, index=[str(end_2)]).T
performance = pd.concat([data0, data1, data2], axis=1)
performance = performance.dropna()
rank = performance.rank(ascending=False)
rank = rank.dropna()
rank /= len(rank)
rank['rank_mean'] = rank[[str(end_0), str(end_1), str(end_2)]].mean(axis=1)
rank['rank_std'] = rank[[str(end_0), str(end_1), str(end_2)]].std(axis=1)
rank['rank_mean_plus_std'] = rank['rank_mean'] + rank['rank_std']
rank = rank.sort_values(by=['rank_mean_plus_std'], ascending=True)
size = max(2, np.ceil(0.15 * len(rank)))
rank_good = rank[0:int(size)]
rank_good = pd.concat([performance, rank_good], axis=1)
rank_good = rank_good.dropna()
rank_good = rank_good.sort_values(by=['rank_mean_plus_std'])
fund_code = list(rank_good.index)
print(fund_code)
###############################################################################################
if len(fund_code) == 0:
pass
else:
fund_code.sort()
for i_fund in range(len(fund_code)):
fund = fund_code[i_fund]
print(fund)
data_fund = data[data['FundCode'] == fund]
data_fund = data_fund.dropna(subset=['Weight'])
data_fund = data_fund.sort_values(by=['Weight'], ascending=False)
try:
asset = weight.ix[fund, str(report_date)]
asset /= 100000000
except Exception as e:
asset = 1.0
if i_fund == 0:
data_fund_top10 = data_fund.iloc[:10, :]
data_fund_top10[
"Asset_Weight"] = data_fund_top10['Weight'] * asset
top10_weight = data_fund_top10['Weight'].sum()
if top10_weight < 30:
data_fund_top10 = pd.DataFrame([],
columns=data_fund.columns)
else:
data_fund_top10_add = data_fund.iloc[:10, :]
data_fund_top10_add[
"Asset_Weight"] = data_fund_top10_add['Weight'] * asset
top10_weight = data_fund_top10_add['Weight'].sum()
if top10_weight < 30:
data_fund_top10_add = pd.DataFrame(
[], columns=data_fund.columns)
data_fund_top10 = pd.concat(
[data_fund_top10, data_fund_top10_add], axis=0)
stock_code = list(set(data_fund_top10['StockCode'].values))
stock_code.sort()
weight_sum = data_fund_top10['Asset_Weight'].sum()
weight_code = pd.DataFrame([],
index=stock_code,
columns=['Asset_Weight'])
for i_stock in range(len(stock_code)):
stock = stock_code[i_stock]
data_stock = data_fund_top10[data_fund_top10['StockCode'] == stock]
stock_weight_sum = data_stock['Asset_Weight'].sum()
weight_code.ix[stock,
'Asset_Weight'] = stock_weight_sum / weight_sum
weight_code.index = weight_code.index.map(lambda x: x[0:6] + '-CN')
out_path = os.path.join(Fund().data_path_holder,
"fund_holding_benchmark")
out_path = os.path.join(out_path, "weight_quarter_top10_good")
if not os.path.exists(out_path):
os.makedirs(out_path)
out_file = os.path.join(
out_path, "weight_quarter_top10_good_" + report_date + '.csv')
print(out_file)
weight_code.to_csv(out_file, header=None)
def weight_allstock_holding_date(report_date):
report_date = Date().change_to_str(report_date)
data = Fund().get_fund_holding_stock_date(report_date)
data = data[['FundCode', 'Weight', 'StockCode']]
pool = Fund().get_fund_pool_code(report_date, "基金持仓基准基金池")
fund_code = list(set(pool))
fund_code.sort()
weight = Fund().get_fund_factor("Total_Asset", date_list=[report_date]).T
weight = weight.dropna()
for i_fund in range(len(fund_code)):
fund = fund_code[i_fund]
data_fund = data[data['FundCode'] == fund]
data_fund = data_fund.dropna(subset=['Weight'])
data_fund = data_fund.sort_values(by=['Weight'], ascending=False)
try:
asset = weight.ix[fund, report_date]
asset /= 100000000
except Exception as e:
asset = 1.0
if i_fund == 0:
data_fund_all = data_fund.copy()
data_fund_all["Asset_Weight"] = data_fund_all['Weight'] * asset
all_weight = data_fund_all['Weight'].sum()
if all_weight < 60:
data_fund_all = pd.DataFrame([], columns=data_fund.columns)
else:
data_fund_all_add = data_fund.copy()
data_fund_all_add[
"Asset_Weight"] = data_fund_all_add['Weight'] * asset
all_weight = data_fund_all_add['Weight'].sum()
if all_weight < 60:
data_fund_all_add = pd.DataFrame([], columns=data_fund.columns)
data_fund_all = pd.concat([data_fund_all, data_fund_all_add],
axis=0)
stock_code = list(set(data_fund_all['StockCode'].values))
stock_code.sort()
weight_sum = data_fund_all['Asset_Weight'].sum()
weight_code = pd.DataFrame([], index=stock_code, columns=['Asset_Weight'])
for i_stock in range(len(stock_code)):
stock = stock_code[i_stock]
data_stock = data_fund_all[data_fund_all['StockCode'] == stock]
stock_weight_sum = data_stock['Asset_Weight'].sum()
weight_code.ix[stock, 'Asset_Weight'] = stock_weight_sum / weight_sum
weight_code.index = weight_code.index.map(lambda x: x[0:6] + '-CN')
out_path = os.path.join(Fund().data_path_holder, "fund_holding_benchmark")
out_path = os.path.join(out_path, "weight_halfyear_all")
if not os.path.exists(out_path):
os.makedirs(out_path)
out_file = os.path.join(out_path,
"weight_halfyear_all_" + report_date + '.csv')
print(out_file)
weight_code.to_csv(out_file, header=None)
