def __init__(self):
""" 回测股票权重 验证backtest程序正确与否 """
self.port_name = ""
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
def __init__(self,
fund_pool_name="指数+主动股票+灵活配置60基金",
benchmark_name="基金持仓基准基金池",
alpha_len=750,
alpha_column="RegressAlphaIR",
port_name="股票型基金优选750AlphaIR",
style_deviate=0.10,
position_deviate=0.02,
fund_up_ratio=0.15,
turnover=0.30
):
Data.__init__(self)
self.sub_data_path = r'fund_data\nice_stock_fund'
self.port_name = port_name
self.fund_pool_name = fund_pool_name
self.benchmark_name = benchmark_name
self.alpha_len = alpha_len
self.setup_date_len = self.alpha_len + 50
self.style_deviate = style_deviate
self.position_deviate = position_deviate
self.fund_up_ratio = fund_up_ratio
self.turnover = turnover # double
self.alpha_column = alpha_column
self.data_path = os.path.join(self.primary_data_path, self.sub_data_path, self.port_name)
self.port_path = os.path.join(WindPortUpLoad().path, self.port_name)
if not os.path.exists(self.port_path):
os.makedirs(self.port_path)
if not os.path.exists(self.data_path):
os.makedirs(self.data_path)
def __init__(self):
"""
优化函数母类
主要是共同的取数据函数
"""
Data.__init__(self)
self.port_name = ""
self.benchmark_code = ""
self.stock_pool_name = ""
self.industry_deviate = ""
self.style_deviate = ""
self.stock_deviate = ""
self.double_turnover = ""
self.min_tor = 0.001 # 权重最小容忍值
self.style_columns = []
self.weight_sum = 0.95
self.track_error = 0.05
self.free_mv = None
self.trading_status = None
self.alpha_data = None
self.date_series = None
self.wind_port_path = WindPortUpLoad().path
def __init__(self):
"""
2、max_alpha w为主动权重 考虑基准权重
限制条件：
2、1 风格偏离
2、2 行业偏离
2、3 证券绝对权重上下限
2、4 证券相对权重偏离
2、5 跟踪误差 ***
2、6 换手率
2、7 证券数量约束 ***
"""
OptWeight.__init__(self)
self.port_name = ""
self.benchmark_code = ""
self.stock_pool_name = ""
self.industry_deviate = ""
self.style_deviate = ""
self.stock_deviate = ""
self.double_turnover = ""
self.min_tor = 0.001 # 权重最小容忍值
self.style_columns = []
self.weight_sum = 0.95
self.track_error = 0.05
self.free_mv = None
self.trading_status = None
self.alpha_data = None
self.date_series = None
self.wind_port_path = WindPortUpLoad().path
def generate_wind_file(self, index_name, port_name, end_date):
""" 生成wind文件 """
port_path = os.path.join(WindPortUpLoad().path, port_name)
if not os.path.exists(port_path):
os.makedirs(port_path)
sub_path = os.path.join(self.data_path, 'score_fund', index_name)
filename = os.path.join(
sub_path, '基金得分_' + index_name + '_' + end_date + '.xlsx')
data = pd.read_excel(filename, index_col=[0], encoding='gbk')
data = data[['基金全称', '总得分']]
data = data.dropna()
data = data.iloc[0:min(5, len(data)), :]
end_date = Date().get_trade_date_offset(end_date, 0)
data.index.name = 'Code'
data["Price"] = 0.0
data["Direction"] = "Long"
data["CreditTrading"] = "No"
data['Date'] = end_date
data['Weight'] = 1.0 / len(data)
out_file = os.path.join(port_path, port_name + "_" + end_date + '.csv')
data.to_csv(out_file)
def __init__(self, port_name):
Data.__init__(self)
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.port_name = '天风股票基金'
def __init__(self):
"""
1、max_alpha w为绝对权重 不考虑基准权重
限制条件：
1、1 控制风格偏离
1、2 控制行业偏离
1、3 控制股票上下限
1、4 控制换手率
"""
OptWeight.__init__(self)
self.port_name = ""
self.weight_name = ""
self.benchmark_code = ""
self.stock_pool_name = ""
self.industry_deviate = ""
self.style_deviate = ""
self.double_turnover = ""
self.min_tor = 0.001 # 权重最小容忍值
self.style_columns = []
self.weight_sum = 0.95
self.weight_type = 'fixed'
self.free_mv = None
self.trading_status = None
self.alpha_data = None
self.date_series = None
self.wind_port_path = WindPortUpLoad().path
def get_data_from_out_position(self):
file1 = r'C:\Users\doufucheng\OneDrive\Desktop\普通股票型基金.csv'
file2 = r'C:\Users\doufucheng\OneDrive\Desktop\偏股.csv'
port_name = '天风股票基金仓位'
fund_index_code = "885000.WI"
data1 = pd.read_csv(file1, encoding='gbk')
data2 = pd.read_csv(file2, encoding='gbk')
data = pd.concat([data1, data2], axis=0)
data = data.reset_index(drop=True)
date_series = list(set(data.report_period.values))
date_series.sort()
wind_port_path = WindPortUpLoad().path
sub_path = os.path.join(wind_port_path, port_name)
if not os.path.exists(sub_path):
os.makedirs(sub_path)
for i_date in range(len(date_series)):
date = date_series[i_date]
quarter_date = str(date)
fund_pool = FundPool().get_fund_pool_code(name="基金持仓基准基金池",
date=quarter_date)
stock_ratio = pd.DataFrame(self.stock_ratio.loc[fund_pool,
quarter_date])
ratio = stock_ratio.median().values[0] / 100.0
data_date = data[data.report_period == date]
data_gb = data_date.groupby(by=['stock_code']).sum()['weight']
data_gb = pd.DataFrame(data_gb)
data_gb.columns = ['Weight']
data_gb['Weight'] = data_gb['Weight'] / data_gb['Weight'].sum()
data_date = data_gb
publish_date = Date().get_trade_date_offset(date, 17)
data_date.columns = ['Weight']
data_date['Weight'] *= ratio
data_date.loc["Cash", "Weight"] = 1 - ratio
data_date.index.name = 'Code'
data_date["CreditTrading"] = "No"
data_date["Date"] = publish_date
data_date["Price"] = 0.0
data_date["Direction"] = "Long"
file = os.path.join(sub_path,
'%s_%s.csv' % (port_name, publish_date))
data_date.to_csv(file)
backtest = BackTest()
backtest.set_info(port_name, fund_index_code)
backtest.read_weight_at_all_change_date()
backtest.cal_weight_at_all_daily()
backtest.cal_port_return(beg_date="20040101")
backtest.cal_turnover(annual_number=4)
backtest.cal_summary(all_beg_date="20040101")
def __init__(self, index_code_list, port_name):
"""
跟踪股票基金总指数 基金指数回归各类指数
"""
self.index_code_list = index_code_list
self.port_name = port_name
self.wind_port_path = WindPortUpLoad().path
self.fund_index_code = "885000.WI"
def __init__(self):
Data.__init__(self)
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.port_name = '天风股票基金'
self.fund_index_code = "885000.WI"
def __init__(self):
""" 数据存储位置 """
Data.__init__(self)
self.port_name = "ZZ500_3Factor"
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.beg_date = "20170301"
def __init__(self, fund_code):
Data.__init__(self)
self.port_name = "半年报%s" % fund_code
self.fund_code = fund_code
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
def __init__(self):
Data.__init__(self)
self.port_name = "主动股票基金month"
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.stock_ratio = None
def __init__(self):
Data.__init__(self)
self.port_name = "普通股票型基金_等权季报_报告日"
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.stock_ratio = None
def __init__(self):
Data.__init__(self)
self.port_name = "公募股票基金季报满仓"
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.stock_ratio = None
self.industry_citic1 = None
self.pct = None
self.group = list(range(1, 30))
def __init__(self):
Data.__init__(self)
self.port_name = "普通股票型基金跟随"
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.stock_ratio = None
self.free_mv = None
self.pct = None
self.group = 5
def __init__(self, factor_name="ROETTMDaily"):
""" 生成Alpha因子指数 用来合成Index取回归跟踪股票基金指数 """
self.port_name = factor_name
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.alpha_data = Stock().read_factor_h5(
factor_name,
Stock().get_h5_path("my_alpha"))
self.free_mv = Stock().read_factor_h5(
"Mkt_freeshares",
Stock().get_h5_path("mfc_primary"))
def __init__(self):
Data.__init__(self)
self.sub_data_path = r'4_fund_data\stock_predict'
self.data_path_exposure = os.path.join(self.primary_data_path, self.sub_data_path)
self.wind_port_path = WindPortUpLoad().path
self.stock_pct = Stock().read_factor_h5("Pct_chg").T
self.fund_pct = Fund().get_fund_factor("Repair_Nav_Pct")
self.bold_pct = Index().get_index_factor("885062.WI", attr=['PCT']) * 100
self.bold_pct.columns = ['885062.WI']
self.stock_ratio = Fund().get_fund_factor("Stock_Ratio")
self.regression_len = 60
self.regression_min_len = 12
def __init__(self):
Data.__init__(self)
self.port_name = "主动股票基金ols"
self.wind_port_path = WindPortUpLoad().path
self.data_weight_path = Index().data_path_weight
self.data_factor_path = Index().data_data_factor
self.stock_mv = None
self.index_return = None
self.stock_return = None
self.date_series = None
self.fund_pool = None
def get_data_from_out(self):
file = r'C:\Users\doufucheng\OneDrive\Desktop\普通股票型基金.csv'
port_name = '普通股票基金'
fund_index_code = "885000.WI"
data = pd.read_csv(file, encoding='gbk')
date_series = list(set(data.report_period.values))
date_series.sort()
from quant.source.backtest import BackTest
from quant.source.wind_portfolio import WindPortUpLoad
wind_port_path = WindPortUpLoad().path
sub_path = os.path.join(wind_port_path, port_name)
if not os.path.exists(sub_path):
os.makedirs(sub_path)
for i_date in range(len(date_series)):
date = date_series[i_date]
data_date = data[data.report_period == date]
data_gb = data_date.groupby(by=['stock_code']).sum()['weight']
data_gb = pd.DataFrame(data_gb)
data_gb.columns = ['Weight']
data_gb['Weight'] = data_gb['Weight'] / data_gb['Weight'].sum()
data_date = data_gb
publish_date = Date().get_trade_date_offset(date, 17)
data_date.columns = ['Weight']
data_date.index.name = 'Code'
data_date["CreditTrading"] = "No"
data_date["Date"] = publish_date
data_date["Price"] = 0.0
data_date["Direction"] = "Long"
file = os.path.join(sub_path,
'%s_%s.csv' % (port_name, publish_date))
data_date.to_csv(file)
backtest = BackTest()
backtest.set_info(port_name, fund_index_code)
backtest.read_weight_at_all_change_date()
backtest.cal_weight_at_all_daily()
backtest.cal_port_return(beg_date="20040101")
backtest.cal_turnover(annual_number=4)
backtest.cal_summary(all_beg_date="20040101")
def __init__(self):
Data.__init__(self)
self.wind_data_path = WindPortUpLoad().path
self.stock_pool_ratio = 0.60
self.lasso_stock_pool_number = 150
self.lasso_date_number = 60
self.lasso_date_number_min = 40
self.port_name = '主动股票基金Lasso'
self.stock_mv = None
self.index_return = None
self.stock_return = None
self.date_series = None
self.fund_pool = None
def __init__(self):
Data.__init__(self)
self.sub_data_path = r'4_fund_data\4_fund_holding_predict\ols_stock_weight'
self.data_path_ols_weight = os.path.join(self.primary_data_path,
self.sub_data_path)
self.ols_stock_pool_number = 25
self.ols_date_number = 60
self.ols_date_number_min = 40
self.stock_mv = None
self.fund_return = None
self.stock_return = None
self.fund_holding = None
self.fund_stock_ratio = None
self.date_series = None
self.fund_pool = None
self.wind_port_path = WindPortUpLoad().path
def get_month_wind_portfolio(file_name, date):
project_path = r'E:\Data\portfolio\other_portfolio\exceed_expectation'
wind_port_path = WindPortUpLoad().path
name = "超预期30"
stock_ratio = 0.95
file = os.path.join(project_path, file_name)
data = pd.read_excel(file, index_col=[0])
data.index.name = 'Code'
data['Weight'] = 1 / len(data)
data['Weight'] *= stock_ratio
data.loc['Cash', 'Weight'] = 1 - stock_ratio
data["CreditTrading"] = "No"
data["Date"] = date
data["Price"] = 0.0
data["Direction"] = "Long"
out_sub_path = os.path.join(wind_port_path, name)
out_file = os.path.join(out_sub_path, name + "_" + date + '.csv')
data.to_csv(out_file)
def get_history_wind_portfolio():
project_path = r'E:\Data\portfolio\other_portfolio\exceed_expectation'
wind_port_path = WindPortUpLoad().path
name = "超预期30"
stock_ratio = 0.95
file = os.path.join(project_path, "超预期30组合历史持仓.xlsx")
data = pd.read_excel(file)
data.columns = ["Date", "Code", "Weight", "Industry"]
data["Code"] = data["Code"].map(CodeFormat().stock_code_add_postfix)
data["Date"] = data["Date"].map(Date().change_to_str)
data["Price"] = 0.0
data["Direction"] = "Long"
data["CreditTrading"] = "No"
date_list = list(set(data["Date"]))
date_list.sort()
out_sub_path = os.path.join(wind_port_path, name)
if not os.path.exists(out_sub_path):
os.makedirs(out_sub_path)
for i_date in range(len(date_list)):
date = date_list[i_date]
data_date = data[data["Date"] == date]
data_date.index = data_date['Code']
del data_date['Code']
data_date['Weight'] *= stock_ratio
data_date.loc['Cash', 'Weight'] = 1 - stock_ratio
data_date["Price"] = 0.0
data_date["Direction"] = "Long"
data_date["CreditTrading"] = "No"
data_date['Date'] = date
out_file = os.path.join(out_sub_path, name + "_" + date + '.csv')
data_date.to_csv(out_file)
def upload_all_wind_port(self):
""" 上传wind组合 """
WindPortUpLoad().upload_weight_period(self.port_name)
def upload_all_wind_port(self):
""" 上传wind组合 """
WindPortUpLoad().upload_weight_period("优选沪深300基金")
WindPortUpLoad().upload_weight_period("优选中证500基金")
data['Weight'] = 1 / len(data)
data['Weight'] *= stock_ratio
data.loc['Cash', 'Weight'] = 1 - stock_ratio
data["CreditTrading"] = "No"
data["Date"] = date
data["Price"] = 0.0
data["Direction"] = "Long"
out_sub_path = os.path.join(wind_port_path, name)
out_file = os.path.join(out_sub_path, name + "_" + date + '.csv')
data.to_csv(out_file)
if __name__ == "__main__":
#########################################################
# get_history_wind_portfolio()
# WindPortUpLoad().upload_weight_period("超预期30")
#########################################################
#########################################################
# file_name = "超预期30组合持仓20180928.xlsx"
# date = '20180928'
# get_month_wind_portfolio(file_name, date)
file_name = "超预期30组合持仓20181228.xlsx"
date = '20181228'
get_month_wind_portfolio(file_name, date)
WindPortUpLoad().upload_weight_date('超预期30', date)
#########################################################
port_name = "行业风格平价低风险FOF"
benchmark_code = "885002.WI"
exposure_stock_low = 0.15
exposure_bond_low = 0.50
# 每期优化
####################################################################################################
# date_list = Date().get_normal_date_series("20141230", "20181001", period="Q")
# for end_date in date_list:
# StockFundPortfolio().opt_fund_portfolio_qp(end_date, stock_up, stock_low,
# lamb, port_path, port_name,
# exposure_stock_low, exposure_bond_low)
#
# 上传到wind
###################################################################################################
from quant.source.wind_portfolio import WindPortUpLoad
WindPortUpLoad().upload_weight_period(port_name)
# 本地计算其收益
####################################################################################################
from quant.source.backtest_fund import PortBackTestFund
Index().load_index_factor(benchmark_code)
backtest = PortBackTestFund()
backtest.set_info(port_name, benchmark_code)
backtest.set_weight_at_all_change_date()
backtest.cal_turnover()
backtest.set_weight_at_all_daily()
backtest.cal_port_return()
backtest.cal_summary()
###################################################################################################
def wind_upload(self):
WindPortUpLoad().upload_weight_period(self.port_name)
style_deviate = 0.20
position_deviate = 0.02
fund_up_ratio = 0.15
turnover = 0.25
self = NiceStockFund(fund_pool_name, benchmark_name, alpha_len, alpha_column, port_name,
style_deviate, position_deviate, fund_up_ratio, turnover)
# self.cal_fund_factor_alldate(beg_date, end_date)
# self.opt_alldate(beg_date, end_date, turnover_control=True)
# self.upload_all_wind_port()
# self.backtest(bench_code)
# self.cal_fund_factor_date(end_date)
# self.opt_date(end_date, end_last_date, turnover_control=True)
# self.generate_wind_file(end_date)
WindPortUpLoad().upload_weight_date(port_name, end_date)
# 2、主动股票型基金优选500AlphaIR
fund_pool_name = "基金持仓基准基金池"
benchmark_name = "基金持仓基准基金池"
alpha_len = 500
alpha_column = "RegressAlphaIR"
port_name = "主动股票型基金优选500AlphaIR"
beg_date = "20181201"
end_date = "20190404"
bench_code = "885000.WI"
style_deviate = 0.20
position_deviate = 0.02
fund_up_ratio = 0.15
turnover = 0.25