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,
period=period)
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_benchmark_net_asset_value(self, event):
period = event.event_data_dict["strategy"].period
data_class = GetData()
benchmark = event.event_data_dict["strategy"].benchmark
if period == Period.DAILY.value:
start_time = millisecond_to_date(millisecond=Environment.benchmark_index[0], format="%Y-%m-%d")
end_time = millisecond_to_date(millisecond=Environment.benchmark_index[-1], format="%Y-%m-%d")
benchmark_close = data_class.get_market_data(Environment.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.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 show_backtesting_indicator(cls, event):
period = event.event_data_dict['strategy'].period
benchmark = event.event_data_dict['strategy'].benchmark
indicator_dict = {}
# （１）基准净值
benchmark_net_asset_value = cls().get_benchmark_net_asset_value(
period, benchmark)
print('benchmark_net_asset_value', benchmark_net_asset_value)
# （２）策略净值
strategy_net_asset_value = cls().get_strategy_net_asset_value()
print('strategy_net_asset_value', strategy_net_asset_value)
# （３）基准年化收益率
benchmark_year_yield = cls().get_year_yield(benchmark_net_asset_value)
print('benchmark_year_yield', benchmark_year_yield)
# （４）策略年化收益率
strategy_year_yield = cls().get_year_yield(strategy_net_asset_value)
print('strategy_year_yield', strategy_year_yield)
# （５）beta
beta = cls().get_beta(benchmark_net_asset_value,
strategy_net_asset_value)
print('beta', beta)
indicator_dict['beta'] = beta
# （６）alpha
alpha = cls().get_alpha(benchmark_year_yield, strategy_year_yield,
beta)
print('alpha', alpha)
indicator_dict['alpha'] = alpha
# （７）volatility
volatility = cls().get_volatility(strategy_net_asset_value)
print('volatility', volatility)
indicator_dict['volatility'] = volatility
# （８）sharpe
sharpe = cls().get_sharp(strategy_year_yield, volatility)
print('sharpe', sharpe)
indicator_dict['sharpe'] = sharpe
# （９）downside_risk
downside_risk = cls().get_downside_risk(strategy_year_yield)
print('downside_risk', downside_risk)
indicator_dict['downside_risk'] = downside_risk
# （１０）sortino_ratio
sortino_ratio = cls().get_sortino_ratio(strategy_year_yield,
downside_risk)
print('sortino_ratio', sortino_ratio)
indicator_dict['sortino_ratio'] = sortino_ratio
# （１１）tracking_error
tracking_error = cls().get_tracking_error(benchmark_net_asset_value,
strategy_net_asset_value)
print('tracking_error', tracking_error)
indicator_dict['tracking_error'] = tracking_error
# （１２）information_ratio
information_ratio = cls().get_information_ratio(
benchmark_year_yield, strategy_year_yield, tracking_error)
print('information_ratio', information_ratio)
indicator_dict['information_ratio'] = information_ratio
# （１３）max_drawdown
max_drawdown = cls().get_max_drawdown(strategy_net_asset_value)
print('max_drawdown', max_drawdown)
indicator_dict['max_drawdown'] = max_drawdown
# 展示到html
period = event.event_data_dict['strategy'].period
if period == Period.DAILY.value:
time_tag_date = Environment.benchmark_index
else:
time_tag_date = [
millisecond_to_date(millisecond=i, format='%Y-%m-%d %H:%M:%S')
for i in Environment.benchmark_index
]
def show_backtesting_indicator(cls, event):
indicator_dict = {}
indicator_dict = collections.OrderedDict()
# （１）基准净值
benchmark_net_asset_value = cls().get_benchmark_net_asset_value(event)
# print(benchmark_net_asset_value, "benchmark_net_asset_value" * 3)
# （２）策略净值
strategy_net_asset_value = cls().get_strategy_net_asset_value()
# print(strategy_net_asset_value, "strategy_net_asset_value")
# （３）基准年化收益率
benchmark_year_yield = cls().get_year_yield(benchmark_net_asset_value)
# （４）策略年化收益率
strategy_year_yield = cls().get_year_yield(strategy_net_asset_value)
# print(benchmark_year_yield, strategy_year_yield)
# （５）beta
beta = cls().get_beta(benchmark_net_asset_value, strategy_net_asset_value)
# print(beta)
indicator_dict["beta"] = beta
# （６）alpha
alpha = cls().get_alpha(benchmark_year_yield, strategy_year_yield, beta)
# print(alpha)
indicator_dict["alpha"] = alpha
# （７）volatility
volatility = cls().get_volatility(strategy_net_asset_value)
# print(volatility)
indicator_dict["volatility"] = volatility
# （８）sharpe
sharpe = cls().get_sharp(strategy_year_yield, volatility)
# print(sharpe)
indicator_dict["sharpe"] = sharpe
# （９）downside_risk
downside_risk = cls().get_downside_risk(strategy_year_yield)
# print(downside_risk)
indicator_dict["downside_risk"] = downside_risk
# （１０）sortino_ratio
sortino_ratio = cls().get_sortino_ratio(strategy_year_yield, downside_risk)
# print(sortino_ratio)
indicator_dict["sortino_ratio"] = sortino_ratio
# （１１）tracking_error
tracking_error = cls().get_tracking_error(benchmark_net_asset_value, strategy_net_asset_value)
# print(tracking_error)
indicator_dict["tracking_error"] = tracking_error
# （１２）information_ratio
information_ratio = cls().get_information_ratio(benchmark_year_yield, strategy_year_yield, tracking_error)
# print(information_ratio)
indicator_dict["information_ratio"] = information_ratio
# （１３）max_drawdown
max_drawdown = cls().get_max_drawdown(strategy_net_asset_value)
# print(max_drawdown)
indicator_dict["max_drawdown"] = max_drawdown
# 展示到html
period = event.event_data_dict["strategy"].period
if period == Period.DAILY.value:
timetag_date = [millisecond_to_date(millisecond=i, format="%Y-%m-%d") for i in Environment.benchmark_index]
else:
timetag_date = [millisecond_to_date(millisecond=i, format="%Y-%m-%d %H:%M:%S") for i in
Environment.benchmark_index]
page = Page("strategy backtesting indicator")
line_net_asset_value = Line("net_asset_value", width=1300, height=400, title_pos="8%")
line_net_asset_value.add("benchmark_net_asset_value", timetag_date,
[round(i, 4) for i in benchmark_net_asset_value],
tooltip_tragger="axis", legend_top="3%", is_datazoom_show=True)
line_net_asset_value.add("strategy_net_asset_value", timetag_date,
[round(i, 4) for i in strategy_net_asset_value],
tooltip_tragger="axis", legend_top="3%", is_datazoom_show=True)
page.add(line_net_asset_value)
line_year_yield = Line("year_yield", width=1300, height=400, title_pos="8%")
line_year_yield.add("benchmark_year_yield", timetag_date,
[round(i, 4) for i in benchmark_year_yield],
tooltip_tragger="axis", legend_top="3%", is_datazoom_show=True)
line_year_yield.add("strategy_year_yield", timetag_date,
[round(i, 4) for i in strategy_year_yield],
tooltip_tragger="axis", legend_top="3%", is_datazoom_show=True)
page.add(line_year_yield)
for indicator_name, indicator in indicator_dict.items():
cls().add_to_page(page, indicator, indicator_name, timetag_date)
# print(indicator_name)
millisecond_timetag = str(int(time.time()) * 1000)
page.render(path=sys.argv[0][sys.argv[0].rfind(os.sep) + 1:][
:-3] + "_" + "strategy backtesting indicator" + millisecond_timetag + ".html") # 生成本地 HTML 文件
def run(self, save_trade_record=False):
self.initialize()
# 初始化 account_data
if self.account:
for account in self.account:
Environment.current_account_data = AccountData()
Environment.current_account_data.account_id = generate_random_id.generate_random_id(
account)
Environment.current_account_data.total_balance = self.capital[
account]
Environment.current_account_data.available = self.capital[
account]
Environment.bar_account_data_list.append(
Environment.current_account_data)
if self.run_mode == RunMode.TRADE.value:
self.end = self._get_data.get_end_timetag(
benchmark=self.benchmark, period=Period.DAILY.value)
# 缓存数据开关，和bar_index的计算
if self.period == Period.DAILY.value:
self.daily_data_cache = True
elif self.period == Period.ONE_MIN.value:
self.one_min_data_cache = True
stock_list = copy.copy(self.universe)
stock_list.append(self.benchmark)
stock_list = list(set(stock_list))
if self._daily_data_cache:
Environment.daily_data = self._get_data.get_all_market_data(
stock_code=stock_list,
field=["open", "high", "low", "close", "volumn", "amount"],
end=self.end,
period=Period.DAILY.value)
if self.one_min_data_cache:
Environment.one_min_data = self._get_data.get_all_market_data(
stock_code=stock_list,
field=["open", "high", "low", "close", "volumn", "amount"],
end=self.end,
period=Period.ONE_MIN.value)
if self.period == Period.DAILY.value:
Environment.benchmark_index = [
data_transfer.date_to_millisecond(str(int(i)), '%Y%m%d') for i
in Environment.daily_data["open"].ix[self.benchmark].index
if i >= data_transfer.date_str_to_int(self.start)
]
elif self.period == Period.ONE_MIN.value:
Environment.benchmark_index = [
data_transfer.date_to_millisecond(str(int(i)), '%Y%m%d') for i
in Environment.one_min_data["open"].ix[self.benchmark].index
if i >= data_transfer.date_str_to_int(self.start)
]
# print(self.benchmark, self.start, self.end, self.period, self.rights_adjustment, self.run_mode)
self.bar_index = 0
while True:
try:
self.timetag = Environment.benchmark_index[self.bar_index]
except IndexError:
if self.run_mode == RunMode.BACKTESTING.value:
if save_trade_record:
run_backtesting_analysis_engine(self)
break
elif self.run_mode == RunMode.TRADE.value:
'''读取最新tick, 更新最新的分钟或者日线
if 读取最新tick, 更新最新的分钟或者日线 == done:
daily_data.append(new_day_data)
self.bar_index += 1
benchmark_index.append(new_day_timetag)
'''
pass
else:
date = int(
data_transfer.millisecond_to_date(millisecond=self.timetag,
format="%Y%m%d"))
run_bar_engine(self)
@abstractmethod
def initialize(self):
pass
@abstractmethod
def handle_bar(self, event):
pass
