def attribution_one_fund(self):
""" 测试单个基金归因 """
# fund_name = '建行中国人寿多策略管理计划'
# beg_date = '20190101'
# end_date = '20190312'
# period_name = "2019年至今（20190312）"
fund_name = '泰达宏利启富'
beg_date = '20180625'
end_date = '20190411'
period_name = "20180625至20190411"
param = MfcData().get_mfc_fund_info()
param.index = param.Name
param = param.dropna(subset=['Index'])
index_code_ratio = param.loc[fund_name, "Index_Ratio"]
fund_code = param.loc[fund_name, "Code"]
index_code = param.loc[fund_name, "Index"]
type = param.loc[fund_name, "Type"]
trust_fee = param.loc[fund_name, "TrusteeShipFeeRatio"]
mg_fee = param.loc[fund_name, "MgFeeRatio"]
mg_fee_ratio = trust_fee + mg_fee
self.get_info(index_code_ratio, index_code, fund_code, fund_name, type,
beg_date, end_date, period_name, mg_fee_ratio)
self.attribution_fund()
def get_input_data(self, fund_code, fund_name, inside_fund_name,
fund_strategy, asset_allocation_strategy,
comparsion_bench_list, date, fund_manager):
"""
输入基金名称 基金代码 当日日期
得到最近的交易日、季报日、半年报年报日
得到基金净值数据、季报和半年报持仓数据
"""
self.fund_code = fund_code
self.fund_name = fund_name
self.date = date
self.fund_strategy = fund_strategy
self.inside_fund_name = inside_fund_name
self.asset_allocation_strategy = asset_allocation_strategy
self.last_trade_date = Date().get_trade_date_offset(date, 0)
print("参数时间", self.last_trade_date)
if fund_code == fund_name:
self.fund_nav = MfcData().get_mfc_private_fund_nav(fund_name)
self.fund_nav = pd.DataFrame(self.fund_nav['累计复权净值'])
self.fund_nav.columns = ['NAV_ADJ']
else:
self.fund_nav = MfcData().get_mfc_public_fund_nav(self.fund_code)
fund_hold_stock = MfcData().get_fund_security(self.last_trade_date)
fund_hold_stock = fund_hold_stock[fund_hold_stock['基金名称'] ==
self.inside_fund_name]
fund_hold_stock = pd.DataFrame(fund_hold_stock['市值比净值(%)'].values,
index=fund_hold_stock['证券代码'],
columns=['Weight'])
fund_hold_stock.index = fund_hold_stock.index.map(
CodeFormat().stock_code_add_postfix)
fund_hold_stock['Weight'] /= 100.0
self.fund_hold_stock = fund_hold_stock
self.comparsion_bench_list = comparsion_bench_list
self.fund_manager = fund_manager
def attribution_all_fund(self, date_list):
""" 归因所有基准不为空的基金 给定所有时间段 """
param = MfcData().get_mfc_fund_info()
param.index = param.Name
param = param.dropna(subset=['Index'])
print(param.index)
for i_date in range(len(date_list)):
beg_date = date_list[i_date][0]
end_date = date_list[i_date][1]
period_name = date_list[i_date][2]
for i_fund in range(0, len(param)):
fund_name = param.index[i_fund]
index_code_ratio = param.loc[fund_name, "Index_Ratio"]
fund_code = param.loc[fund_name, "Code"]
index_code = param.loc[fund_name, "Index"]
type = param.loc[fund_name, "Type"]
trust_fee = param.loc[fund_name, "TrusteeShipFeeRatio"]
mg_fee = param.loc[fund_name, "MgFeeRatio"]
mg_fee_ratio = trust_fee + mg_fee
self.get_info(index_code_ratio, index_code, fund_code,
fund_name, type, beg_date, end_date, period_name,
mg_fee_ratio)
self.attribution_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 load_holding_data(self):
""" 从网盘下载持仓文件 并修改名字 """
lmd = MfcData()
lmd.load_network_holding_date(date=self.today)
lmd.load_network_stock_pool_date(date=self.today)
lmd.change_holding_date(date=self.today)
def get_input_data(self, fund_code, fund_name, fund_strategy,
asset_allocation_strategy, bench_code, date,
fund_manager):
"""
输入基金名称 基金代码 当日日期
得到最近的交易日、季报日、半年报年报日
得到基金净值数据、季报和半年报持仓数据
"""
self.fund_code = fund_code
self.fund_name = fund_name
self.date = date
self.fund_strategy = fund_strategy
self.asset_allocation_strategy = asset_allocation_strategy
self.last_trade_date = Date().get_trade_date_offset(date, 0)
self.quarter_date = Date().get_last_fund_quarter_date(date)
self.halfyear_date = Date().get_last_fund_halfyear_date(date)
print("参数时间", self.last_trade_date, self.quarter_date,
self.halfyear_date)
self.fund_nav = MfcData().get_mfc_public_fund_nav(self.fund_code)
self.fund_top10_stock = Fund().get_fund_top10_stock_date(
self.fund_code, self.quarter_date)
self.fund_top10_stock /= 100.0
self.fund_all_stock = Fund().get_fund_all_stock_date(
self.fund_code, self.halfyear_date)
self.fund_all_stock /= 100.0
self.bench_code = bench_code
self.fund_manager = fund_manager
def get_fund_asset(self):
"""
得到某只基金资产、指数涨跌幅、基金涨跌幅
总资产包括 净值、基金份额、单位净值；股票、债券、现金、回购资产；买卖金额；股票债券浮动金额、总盈亏金额
"""
fund_asset = MfcData().get_fund_asset_period(self.fund_name,
self.beg_date_pre,
self.end_date)
fund_pct = self.get_fund_pct()
index_pct = self.get_index_pct()
# 数据合并
data = pd.concat([fund_pct, index_pct, fund_asset], axis=1)
data = data.dropna(subset=['基金涨跌幅', '指数涨跌幅'])
print(data.head())
if len(data) <= 0:
data = None
else:
data['单位净值'] = data['净值'] / data['基金份额']
data['昨日净值'] = data['净值'].shift(1)
data['昨日基金份额'] = data['基金份额'].shift(1)
data['昨日单位净值'] = data['单位净值'].shift(1)
return data
def get_mfcteda_all_rank(self, end_date):
""" 计算所有基金今年所有月份排名 """
path = MfcData().data_path
file = os.path.join(path, "static_data", "Fund_Info_For_Month.xlsx")
params = pd.read_excel(file, encoding='gbk')
params = params.dropna(subset=['Rank'])
params = params.reset_index(drop=True)
for i in range(len(params)):
fund_name = params.ix[i, 'Name']
fund_code = params.ix[i, 'Code']
rank_pool = params.ix[i, 'Fund_Pool']
excess = params.ix[i, 'Rank']
print(fund_name)
if i == 0:
return_pd = self.mfcteda_fund_rank(end_date, fund_code,
fund_name, rank_pool,
excess)
else:
return_pd_add = self.mfcteda_fund_rank(end_date, fund_code,
fund_name, rank_pool,
excess)
return_pd = pd.concat([return_pd, return_pd_add], axis=0)
return return_pd
def get_mfcteda_all_excess_return(self, end_date):
""" 计算所有基金今年所有月份超额收益 """
path = MfcData().data_path
file = os.path.join(path, "static_data", "Fund_Info_For_Month.xlsx")
params = pd.read_excel(file, encoding='gbk')
params = params.dropna(subset=['Index_Ratio'])
params = params.reset_index(drop=True)
for i in range(len(params)):
fund_type = params.ix[i, 'Type']
fund_name = params.ix[i, 'Name']
fund_code = params.ix[i, 'Code']
benchmark_code = params.ix[i, 'Index']
benchmark_ratio = params.ix[i, 'Index_Ratio']
print(fund_name)
if i == 0:
return_pd = self.mfcteda_fund_excess_return(
end_date, fund_type, fund_code, fund_name, benchmark_code,
benchmark_ratio)
else:
return_pd_add = self.mfcteda_fund_excess_return(
end_date, fund_type, fund_code, fund_name, benchmark_code,
benchmark_ratio)
return_pd = pd.concat([return_pd, return_pd_add], axis=0)
return return_pd
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 update_data(date):
"""更新数据"""
# 更新日期
beg_date = Date().get_trade_date_offset(date, -5)
end_date = date
# 更新因子数据（原始和alpha）
Stock().load_h5_primary_factor()
AlphaBP().cal_factor_exposure(beg_date, end_date)
AlphaROE().cal_factor_exposure(beg_date, end_date)
AlphaProfitYoY().cal_factor_exposure(beg_date, end_date)
AlphaIncomeYoY().cal_factor_exposure(beg_date, end_date)
# 更新基金净值 和 持仓数据 指数价格数据
# Fund().load_fund_holding_all("19991231", datetime.today())
MfcData().load_mfc_public_fund_nav()
update_end_date = datetime.today().strftime("%Y%m%d")
update_beg_date = Date().get_trade_date_offset(update_end_date, -5)
Index().load_index_factor(index_code='H00985.CSI',
beg_date=update_beg_date,
end_date=update_end_date)
Index().load_index_factor(index_code="885001.WI",
beg_date=update_beg_date,
end_date=update_end_date)
Index().load_index_factor(index_code="000300.SH",
beg_date=update_beg_date,
end_date=update_end_date)
Index().load_index_factor(index_code="000905.SH",
beg_date=update_beg_date,
end_date=update_end_date)
def get_fund_basic_info(self):
""" wind 得到基金的基本信息 类型 成立日期 最新规模等等 """
data = w.wss(self.fund_code, "fund_setupdate,fund_investtype",
"unit=1;tradeDate=%s" % self.last_trade_date)
data_pd = pd.DataFrame(data.Data,
index=data.Fields,
columns=data.Codes).T
data_pd.columns = ['成立日期', '基金类型']
data_pd['成立日期'] = data_pd['成立日期'].map(lambda x: x.strftime("%Y%m%d"))
fund_asset_data = MfcData().get_fund_asset(self.last_trade_date)
fund_asset_data = fund_asset_data[fund_asset_data['基金名称'] ==
self.inside_fund_name]
fund_asset = fund_asset_data['净值'].values[0] / 100000000.0
data_pd['基金规模（亿）'] = np.round(fund_asset, 2)
data_pd = data_pd.T
data_info = pd.DataFrame([self.fund_code, self.fund_name],
columns=[self.fund_code],
index=['基金代码', '基金名称'])
data_concat = pd.concat([data_info, data_pd], axis=0)
data_concat.columns = ['内容']
data_concat['基本信息'] = data_concat.index
data_concat = data_concat[['基本信息', '内容']]
return data_concat
def cal_stock_fee(self, date):
"""
计算股票的 管理托管费（当日净值*管理费率）
交易佣金（当日股票成交额*交易费率，包括新股）
"""
date_pre = Date().get_trade_date_offset(date, -1)
try:
total_asset_pre = MfcData().get_fund_asset(date_pre)
trade = self.get_stock_trading(date)
except Exception as e:
print(e)
return np.nan, np.nan
total_asset_fund = total_asset_pre[total_asset_pre['基金名称'] ==
self.fund_name]
try:
mg_fee = -total_asset_fund['净值'].values[0] * self.mg_fee_ratio
except Exception as e:
print(e)
mg_fee = np.nan
trade_asset = (trade['TradePrice'] * trade['TradeVol'].abs()).sum()
trade_fee = -trade_asset * self.double_fee_ratio / 2.0
return mg_fee, trade_fee
def get_stock_trading(self, date):
""" 读取股票交易数据 """
try:
trade = MfcData().get_trade_statement(date)
trade_data = trade[trade['基金名称'] == self.fund_name]
trade_data = trade_data[trade_data['资产类别'] == '股票资产']
trade_data = trade_data[[
'基金名称', '证券名称', '成交数量', '市场成交均价', '委托方向', '证券代码'
]]
trade_data['证券代码'] = trade_data['证券代码'].map(
CodeFormat().stock_code_add_postfix)
trade_data.index = trade_data['证券代码'].values
trade_data = trade_data[['成交数量', '市场成交均价', '委托方向']]
trade_data.columns = ['TradeVol', 'TradePrice', 'SellOrBuy']
trade_data = trade_data[~trade_data.index.duplicated()]
sell_code_list = list(
trade_data[trade_data['SellOrBuy'] == "卖出"].index)
trade_data.loc[sell_code_list,
'TradeVol'] = -trade_data.loc[sell_code_list,
'TradeVol']
return trade_data
except Exception as e:
print(e)
return None
def load_mfc_holding_data(today):
lmd = MfcData()
# today = datetime.today()
# today = datetime(2018, 6, 27).strftime("%Y%m%d")
lmd.load_holding_date(date=today)
lmd.load_stock_pool_date(date=today)
lmd.change_holding_date(date=today)
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 update_data(self):
""" 下载数据 需要更新 富时指数的数据 """
today = datetime.today()
beg_date = Date().get_trade_date_offset(today, -120)
Index().load_index_factor("000907.CSI", beg_date, today)
MfcData().load_mfc_public_fund_nav(beg_date, today)
def Cal_MfcFund_LastDate_Holding_Exposure(path, report_date, today):
"""
计算部门基金最近一个半年报 的持仓风格暴露
"""
# 参数举例
#####################################################################
# path = 'E:\\3_Data\\4_fund_data\\8_fund_index_exposure_weekly\\'
# report_date = '20171231'
# today = datetime.today().strftime("%Y%m%d")
# 基金池
#####################################################################
fund_pool_file = os.path.join(path, "fund_pool", '重点基金信息.csv')
fund_pool = pd.read_csv(fund_pool_file, index_col=[0], encoding='gbk')
fund_pool = fund_pool[fund_pool['种类'].map(
lambda x: x in ["1部门量化", '1专户量化'])]
date = Date().get_trade_date_offset(report_date, 0)
beg_date = Date().get_trade_date_offset(report_date, -10)
end_date = today
# 计算部门基金 基金最近交易日 的持仓风格暴露
#####################################################################
for i_fund in range(len(fund_pool)):
fund_code = fund_pool.index[i_fund]
fund_type = fund_pool.loc[fund_code, '种类']
if fund_type == '1专户量化':
fund_name = fund_code
else:
fund_name = MfcData().get_mfc_fund_name(fund_code)
MfcData().cal_mfc_holding_barra_exposure_period(
fund_name, beg_date, end_date)
if i_fund == 0:
exposure = MfcData().get_mfc_holding_barra_exposure_date(
fund_name, date)
exposure['CTY'] = MfcData().get_mfc_stock_ratio(fund_name, date)
else:
exposure_add = MfcData().get_mfc_holding_barra_exposure_date(
fund_name, date)
exposure_add['CTY'] = MfcData().get_mfc_stock_ratio(
fund_name, date)
exposure = pd.concat([exposure, exposure_add], axis=0)
#####################################################################
exposure_file = os.path.join(path, "lastyear_holding_exposure",
'FundLastDateExposure_' + date + '.csv')
exposure.to_csv(exposure_file)
def update_data(self):
""" 更新基金净值 和 持仓数据 指数价格数据"""
Fund().update_fund_holding()
MfcData().load_mfc_public_fund_nav()
Index().load_index_factor(index_code='H00985.CSI',
beg_date="20040101",
end_date=datetime.today())
Index().load_index_factor(index_code="885001.WI",
beg_date="20180101",
end_date=datetime.today())
def update_data(self):
""" 更新归因需要的数据 """
today = datetime.today().strftime("%Y%m%d")
today = Date().get_trade_date_offset(today, -1)
beg_date = Date().get_trade_date_offset(today, -25)
Barra().load_barra_data()
MfcData().cal_mfc_private_fund_nav_all()
MfcData().load_mfc_public_fund_nav()
param = MfcData().get_mfc_fund_info()
param.index = param.Name
for i_fund in range(0, len(param)):
fund_name = param.index[i_fund]
MfcData().cal_mfc_holding_barra_exposure_period(
fund_name, beg_date, today)
date_series = Date().get_trade_date_series(beg_date, today)
for date in date_series:
Index().make_weight_mixed(date)
Index().load_index_factor_all(beg_date, today)
Index().cal_index_exposure("000300.SH", beg_date, today)
Index().cal_index_exposure("000905.SH", beg_date, today)
Index().cal_index_exposure("881001.WI", beg_date, today)
Index().cal_index_exposure("中证500+创业板综+中小板综", beg_date, today)
def update_data(self):
""" 更新基金净值 和 持仓数据 指数价格数据"""
Fund().update_fund_holding()
MfcData().load_mfc_public_fund_nav()
update_end_date = datetime.today().strftime("%Y%m%d")
update_beg_date = Date().get_trade_date_offset(update_end_date, -40)
Index().load_index_factor(index_code='H00985.CSI', beg_date=update_beg_date, end_date=update_end_date)
Index().load_index_factor(index_code="885001.WI", beg_date=update_beg_date, end_date=update_end_date)
Index().load_index_factor(index_code="000300.SH", beg_date=update_beg_date, end_date=update_end_date)
Index().load_index_factor(index_code="000905.SH", beg_date=update_beg_date, end_date=update_end_date)
def get_stock_holding(self, date):
""" 读取股票持仓数据"""
try:
security = MfcData().get_fund_security(date)
security_data = security[security['基金名称'] == self.fund_name]
security_data = security_data[security_data['证券类别'] == '股票']
security_data = security_data[['基金名称', '证券名称', '持仓', '证券代码']]
security_data['证券代码'] = security_data['证券代码'].map(
CodeFormat().stock_code_add_postfix)
security_data.index = security_data['证券代码'].values
security_data = security_data[['证券名称', '持仓']]
return security_data
except Exception as e:
print(e)
return None
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 get_report_industry_change(self, fund_name, report_date,
last_report_date):
""" 报告期之间的权重变动 """
# report_date = "20181231"
# last_report_date = "20180930"
# fund_name = "泰达逆向策略"
report_date = Date().get_trade_date_offset(report_date, 0)
last_report_date = Date().get_trade_date_offset(last_report_date, 0)
print(fund_name, report_date, last_report_date)
data_report = MfcData().get_fund_stock_weight(fund_name=fund_name,
date=report_date)
data_report.columns = ['NowHolder']
data_report = data_report[~data_report.index.duplicated()]
data_report_last = MfcData().get_fund_stock_weight(
fund_name=fund_name, date=last_report_date)
data_report_last.columns = ['LastHolder']
data_report_last = data_report_last[~data_report_last.index.duplicated(
)]
industry = Stock().read_factor_h5("industry_citic1")
industry_date = pd.DataFrame(industry[report_date])
industry_date.columns = ['Industry']
data_report_last_industry = pd.concat(
[data_report_last, industry_date], axis=1)
data_report_last_industry = data_report_last_industry.dropna()
data_last_gb = pd.DataFrame(
data_report_last_industry.groupby(
by=['Industry']).sum()['LastHolder'])
data_report_industry = pd.concat([data_report, industry_date], axis=1)
data_report_industry = data_report_industry.dropna()
data_gb = pd.DataFrame(
data_report_industry.groupby(by=['Industry']).sum()['NowHolder'])
data = pd.concat([data_last_gb, data_gb], axis=1)
data.index = data.index.map(
lambda x: Stock().get_industry_citic1_name_ch(x))
data['Diff'] = data['NowHolder'] - data['LastHolder']
data = data.sort_values(by=['Diff'], ascending=False)
data_bigger = data.iloc[0:3, :]
str1 = "本报告期内，本基金增加了" + "、".join(data_bigger.index) + "的配置"
data = data.sort_values(by=['Diff'], ascending=True)
data_smaller = data.iloc[0:3, :]
str2 = "，减少了" + "、".join(data_smaller.index) + '的配置。'
return str1 + str2
def get_fund_pct(self):
""" 产品某一段时间内的涨跌幅，专户公募分开 """
if self.type == "专户":
fund_pct = MfcData().get_mfc_private_fund_nav(self.fund_name)
fund_pct['基金涨跌幅'] = fund_pct['累计复权净值'].pct_change()
fund_pct = fund_pct[['累计复权净值', '基金涨跌幅']]
fund_pct = fund_pct.dropna()
fund_pct = fund_pct.loc[self.beg_date_pre:self.end_date, :]
else:
fund_pct = MfcData().get_mfc_public_fund_nav(self.fund_code)
fund_pct = fund_pct[['NAV_ADJ', 'NAV_ADJ_RETURN1']]
fund_pct['NAV_ADJ_RETURN1'] = fund_pct['NAV_ADJ'].pct_change()
fund_pct.columns = ['累计复权净值', '基金涨跌幅']
fund_pct = fund_pct.dropna()
fund_pct = fund_pct.loc[self.beg_date_pre:self.end_date, :]
return fund_pct
def get_fund_asset_allocation(self):
""" 资产配置（股票、债券、基金、现金占比）"""
fund_asset_data = MfcData().get_fund_asset(self.last_trade_date)
fund_asset_data = fund_asset_data[fund_asset_data['基金名称'] == self.inside_fund_name]
fund_asset_data['现金'] = fund_asset_data['当前现金余额'] + fund_asset_data['回购资产'] + \
fund_asset_data['累计应收金额'] - fund_asset_data['累计应付金额']
asset = fund_asset_data['净值'].values[0] / 100000000.0
stock = fund_asset_data['股票资产'].values[0] / 100000000.0
bond = fund_asset_data['债券资产'].values[0] / 100000000.0
cash = fund_asset_data['现金'].values[0] / 100000000.0
fund = fund_asset_data['基金资产'].values[0] / 100000000.0
data_pd = pd.DataFrame([stock, bond, fund, cash], index = ['股票', '债券', '基金', '现金'],
columns=['占净值比'])
data_pd /= asset
data_pd['资产配置(%s)' % self.last_trade_date] = data_pd.index
data_pd = data_pd[['资产配置(%s)' % self.last_trade_date, '占净值比']]
return data_pd
def get_mfcteda_all_return(self, end_date):
""" 计算所有基金今年所有月份收益 """
path = MfcData().data_path
file = os.path.join(path, "static_data", "Fund_Info_For_Month.xlsx")
params = pd.read_excel(file, encoding='gbk')
for i in range(len(params)):
fund_type = params.ix[i, 'Type']
fund_name = params.ix[i, 'Name']
fund_code = params.ix[i, 'Code']
print(fund_name)
if i == 0:
return_pd = self.mfcteda_fund_return(end_date, fund_type,
fund_code, fund_name)
else:
return_pd_add = self.mfcteda_fund_return(
end_date, fund_type, fund_code, fund_name)
return_pd = pd.concat([return_pd, return_pd_add], axis=0)
return return_pd
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 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 get_futures_holding(self, date, name):
""" 读取期货持仓数据 """
if name == "股指期货多头":
state = "多仓"
else:
state = "空仓"
try:
security = MfcData().get_fund_security(date)
security_data = security[security['基金名称'] == self.fund_name]
security_data = security_data[security_data['证券类别'] == '股指期货']
security_data = security_data[security_data['持仓多空标志'] == state]
security_data = security_data[[
'基金名称', '证券名称', '持仓', '证券代码', '最新价', '市值', '持仓多空标志'
]]
security_data[
'价格'] = security_data['市值'] / security_data['持仓'] / 200.0
security_data.index = security_data['证券代码'].values
security_data = security_data[['证券名称', '市值', '价格', '持仓']]
return security_data
except Exception as e:
print(e)
return None