def get_benchmark_net_asset_value(self, period, benchmark):
data_class = GetKlineData()
benchmark_close = None
if period == Period.DAILY.value:
start_time = Environment.benchmark_index[0]
end_time = Environment.benchmark_index[-1]
benchmark_close = data_class.get_market_data(
Environment.index_daily_data,
stock_code=[benchmark],
field=['close'],
start=start_time,
end=end_time)
elif period == Period.ONE_MIN.value:
start_time = millisecond_to_date(
millisecond=Environment.benchmark_index[0],
format='%Y-%m-%d %H:%M:%S')
end_time = millisecond_to_date(
millisecond=Environment.benchmark_index[-1],
format='%Y-%m-%d %H:%M:%S')
benchmark_close = data_class.get_market_data(
Environment.index_daily_data,
stock_code=[benchmark],
field=['close'],
start=start_time,
end=end_time)
benchmark_close = list(benchmark_close)
benchmark_net_asset_value = [
current_close / benchmark_close[0]
for current_close in benchmark_close
]
return benchmark_net_asset_value
def get_kline_data(self):
kline_data = GetKlineData()
all_stock_data = kline_data.cache_all_stock_data()
self.kline = kline_data.get_market_data(all_stock_data,
stock_code=self.stock_list,
field=['close'])
return self.kline
def show_backtesting_indicator(cls, event):
benchmark = event.event_data_dict['strategy_data'].benchmark
account = event.event_data_dict['strategy_data'].account
data_class = GetKlineData()
start_time = Environment.benchmark_index[0]
end_time = Environment.benchmark_index[-1]
account_df = Environment.backtesting_record_account
benchmark_df = data_class.get_market_data(Environment.index_daily_data, stock_code=[benchmark],
field=['close'], ).to_frame(name='close')
account_df = account_df[account_df.index.get_level_values(1) == account[0]]
account_df.reset_index(level='account_id', drop=True, inplace=True)
net_value_analysis_obj = NetValueAnalysis(account_df, benchmark_df, start_time, end_time)
net_analysis_result = net_value_analysis_obj.cal_net_analysis_result()
for i in net_analysis_result:
Environment.logger.info(i, net_analysis_result[i])
# 持仓数据转pandas
position_data_df = Environment.backtesting_record_position
position_data_df = position_data_df[position_data_df.index.get_level_values(1) == account[0]]
position_analysis_obj = PositionAnalysis(position_data_df)
position_analysis_result = position_analysis_obj.cal_position_analysis_result()
for i in position_analysis_result:
Environment.logger.info(i, position_analysis_result[i])
def update_market_data(cls, event):
current_date = event.event_data_dict["strategy_data"].time_tag
data_class = GetKlineData()
stock_code_list = []
for position_data in Environment.bar_position_data_list:
stock_code_list.append(position_data.instrument + "." +
position_data.exchange)
stock_code_list = list(set(stock_code_list))
cls.current_close_price_all = data_class.get_market_data(
Environment.daily_data,
stock_code=stock_code_list,
field=["close"],
start=current_date,
end=current_date)
# 净值分析结果
net_analysis_result=self.net_analysis_result)
doc.save()
if __name__ == '__main__':
factor_name = 'factor_ma10'
path = LocalDataPath.path + LocalDataFolderName.FACTOR.value + '/'
factor_ma5 = get_local_data(path, factor_name + '.h5')
# 指数数据不全，需要删一部分因子数据
factor_ma5 = factor_ma5[factor_ma5.index < datetime.datetime(2020, 1, 1)]
kline_object = GetKlineData()
market_data = kline_object.cache_all_stock_data(
dividend_type=RightsAdjustment.BACKWARD.value, field=['close'])
market_close_data = kline_object.get_market_data(market_data,
field=['close'])
# 指数行情，沪深300代替
all_index_data = kline_object.cache_all_index_data()
benchmark_df = kline_object.get_market_data(
all_index_data, stock_code=['000300.SH'],
field=['close']).to_frame(name='close')
# 沪深300 的日线，有脏数据，后续单独处理
if datetime.datetime(2016, 1, 1) in benchmark_df.index:
benchmark_df = benchmark_df.drop(datetime.datetime(2016, 1, 1))
regression_analysis_obj = RegressionAnalysis(factor_ma5, 'factor_ma5',
market_close_data,
benchmark_df)
regression_analysis_obj.cal_factor_return('float_value_inverse')
regression_analysis_obj.cal_t_value_statistics()
regression_analysis_obj.cal_net_analysis()
self.get_month_ratio_average()
self.get_day_volatility()
self.get_month_volatility()
self.get_selection_timing()
if __name__ == '__main__':
start_time = datetime(2010, 1, 4)
end_time = datetime(2019, 11, 4)
kline_object = GetKlineData()
#
# 指数行情，沪深300代替
all_index_data = kline_object.cache_all_index_data()
benchmark_df = kline_object.get_market_data(
all_index_data,
stock_code=['000300.SH'],
field=['close'],
start=start_time,
end=end_time).to_frame(name='close')
# 策略净值数据,index 为 datetime,取单个账户分析，后续可做多个账户
net_value_df = pd.read_csv('account_data.csv', index_col=0)
net_value_df.index = pd.DatetimeIndex(net_value_df.index)
net_value_single_account_df = pd.DataFrame({})
for i in net_value_df.groupby('account_id'):
net_value_single_account_df = i[1]
break
net_value_analysis_obj = NetValueAnalysis(net_value_single_account_df,
benchmark_df, start_time,
end_time)
net_value_analysis_obj.cal_net_value()
net_value_analysis_obj.cal_profit_ratio()
