def tear_down(self, code, exception=None):
if code != EXIT_CODE.EXIT_SUCCESS or not self._enabled:
return
# 当 PRE_SETTLEMENT 事件没有被触发当时候，self._total_portfolio 为空list
if len(self._total_portfolios) == 0:
return
strategy_name = os.path.basename(self._env.config.base.strategy_file).split(".")[0]
data_proxy = self._env.data_proxy
summary = {
'strategy_name': strategy_name,
'start_date': self._env.config.base.start_date.strftime('%Y-%m-%d'),
'end_date': self._env.config.base.end_date.strftime('%Y-%m-%d'),
'strategy_file': self._env.config.base.strategy_file,
'run_type': self._env.config.base.run_type.value,
'benchmark': self._env.config.base.benchmark,
}
for account_type, starting_cash in six.iteritems(self._env.config.base.accounts):
summary[account_type] = starting_cash
risk = Risk(
np.array(self._portfolio_daily_returns),
np.array(self._benchmark_daily_returns),
data_proxy.get_risk_free_rate(
self._env.config.base.start_date, self._env.config.base.end_date
),
(self._env.config.base.natural_end_date - self._env.config.base.natural_start_date).days + 1
)
summary.update({
'alpha': self._safe_convert(risk.alpha, 3),
'beta': self._safe_convert(risk.beta, 3),
'sharpe': self._safe_convert(risk.sharpe, 3),
'information_ratio': self._safe_convert(risk.information_ratio, 3),
'downside_risk': self._safe_convert(risk.annual_downside_risk, 3),
'tracking_error': self._safe_convert(risk.annual_tracking_error, 3),
'sortino': self._safe_convert(risk.sortino, 3),
'volatility': self._safe_convert(risk.annual_volatility, 3),
'max_drawdown': self._safe_convert(risk.max_drawdown, 3),
})
summary.update({
'total_value': self._safe_convert(self._env.portfolio.total_value),
'cash': self._safe_convert(self._env.portfolio.cash),
'total_returns': self._safe_convert(self._env.portfolio.total_returns),
'annualized_returns': self._safe_convert(self._env.portfolio.annualized_returns),
'unit_net_value': self._safe_convert(self._env.portfolio.unit_net_value),
'units': self._env.portfolio.units,
})
if self._env.benchmark_portfolio:
summary['benchmark_total_returns'] = self._safe_convert(self._env.benchmark_portfolio.total_returns)
summary['benchmark_annualized_returns'] = self._safe_convert(
self._env.benchmark_portfolio.annualized_returns)
trades = pd.DataFrame(self._trades)
if 'datetime' in trades.columns:
trades = trades.set_index('datetime')
df = pd.DataFrame(self._total_portfolios)
df['date'] = pd.to_datetime(df['date'])
total_portfolios = df.set_index('date').sort_index()
result_dict = {
'summary': summary,
'trades': trades,
'portfolio': total_portfolios,
}
if self._env.benchmark_portfolio is not None:
b_df = pd.DataFrame(self._total_benchmark_portfolios)
df['date'] = pd.to_datetime(df['date'])
benchmark_portfolios = b_df.set_index('date').sort_index()
result_dict['benchmark_portfolio'] = benchmark_portfolios
if not self._env.get_plot_store().empty:
plots = self._env.get_plot_store().get_plots()
plots_items = defaultdict(dict)
for series_name, value_dict in six.iteritems(plots):
for date, value in six.iteritems(value_dict):
plots_items[date][series_name] = value
plots_items[date]["date"] = date
df = pd.DataFrame([dict_data for date, dict_data in six.iteritems(plots_items)])
df["date"] = pd.to_datetime(df["date"])
df = df.set_index("date").sort_index()
result_dict["plots"] = df
for account_type, account in six.iteritems(self._env.portfolio.accounts):
account_name = account_type.lower()
portfolios_list = self._sub_accounts[account_type]
df = pd.DataFrame(portfolios_list)
df["date"] = pd.to_datetime(df["date"])
account_df = df.set_index("date").sort_index()
result_dict["{}_account".format(account_name)] = account_df
positions_list = self._positions[account_type]
positions_df = pd.DataFrame(positions_list)
if "date" in positions_df.columns:
positions_df["date"] = pd.to_datetime(positions_df["date"])
positions_df = positions_df.set_index("date").sort_index()
result_dict["{}_positions".format(account_name)] = positions_df
if self._mod_config.output_file:
with open(self._mod_config.output_file, 'wb') as f:
pickle.dump(result_dict, f)
if self._mod_config.report_save_path:
from .report import generate_report
generate_report(result_dict, self._mod_config.report_save_path)
if self._mod_config.plot or self._mod_config.plot_save_file:
from .plot import plot_result
plot_result(result_dict, self._mod_config.plot, self._mod_config.plot_save_file)
return result_dict
def tear_down(self, code, exception=None):
if code != EXIT_CODE.EXIT_SUCCESS or not self._enabled:
return
strategy_name = os.path.basename(self._env.config.base.strategy_file).split(".")[0]
data_proxy = self._env.data_proxy
summary = {
'strategy_name': strategy_name,
}
for k, v in six.iteritems(self._env.config.base.__dict__):
if k in ["trading_calendar", "account_list", "timezone", "persist_mode",
"resume_mode", "data_bundle_path", "handle_split", "persist"]:
continue
summary[k] = self._safe_convert(v, 2)
risk = Risk(np.array(self._portfolio_daily_returns), np.array(self._benchmark_daily_returns),
data_proxy.get_risk_free_rate(self._env.config.base.start_date, self._env.config.base.end_date),
(self._env.config.base.end_date - self._env.config.base.start_date).days + 1)
summary.update({
'alpha': self._safe_convert(risk.alpha, 3),
'beta': self._safe_convert(risk.beta, 3),
'sharpe': self._safe_convert(risk.sharpe, 3),
'information_ratio': self._safe_convert(risk.information_ratio, 3),
'downside_risk': self._safe_convert(risk.annual_downside_risk, 3),
'tracking_error': self._safe_convert(risk.annual_tracking_error, 3),
'sortino': self._safe_convert(risk.sortino, 3),
'volatility': self._safe_convert(risk.annual_volatility, 3),
'max_drawdown': self._safe_convert(risk.max_drawdown, 3),
})
summary.update({
k: self._safe_convert(v, 3) for k, v in six.iteritems(properties(self._latest_portfolio))
if k not in ["positions", "daily_returns", "daily_pnl"]
})
if self._latest_benchmark_portfolio:
summary['benchmark_total_returns'] = self._latest_benchmark_portfolio.total_returns
summary['benchmark_annualized_returns'] = self._latest_benchmark_portfolio.annualized_returns
trades = pd.DataFrame(self._trades)
if 'datetime' in trades.columns:
trades = trades.set_index('datetime')
df = pd.DataFrame(self._total_portfolios)
df['date'] = pd.to_datetime(df['date'])
total_portfolios = df.set_index('date').sort_index()
result_dict = {
'summary': summary,
'trades': trades,
'total_portfolios': total_portfolios,
}
if ExecutionContext.plots is not None:
plots = ExecutionContext.plots.get_plots()
plots_items = defaultdict(dict)
for series_name, value_dict in six.iteritems(plots):
for date, value in six.iteritems(value_dict):
plots_items[date][series_name] = value
plots_items[date]["date"] = date
df = pd.DataFrame([dict_data for date, dict_data in six.iteritems(plots_items)])
df["date"] = pd.to_datetime(df["date"])
df = df.set_index("date").sort_index()
result_dict["plots"] = df
for account_type, account in six.iteritems(self._env.accounts):
account_name = account_type.name.lower()
portfolios_list = self._sub_portfolios[account_type]
df = pd.DataFrame(portfolios_list)
df["date"] = pd.to_datetime(df["date"])
portfolios_df = df.set_index("date").sort_index()
result_dict["{}_portfolios".format(account_name)] = portfolios_df
positions_list = self._positions[account_type]
positions_df = pd.DataFrame(positions_list)
if "date" in positions_df.columns:
positions_df["date"] = pd.to_datetime(positions_df["date"])
positions_df = positions_df.set_index("date").sort_index()
result_dict["{}_positions".format(account_name)] = positions_df
self._result = result_dict
if self._mod_config.output_file:
with open(self._mod_config.output_file, 'wb') as f:
pickle.dump(result_dict, f)
if self._mod_config.plot:
from rqalpha.plot import plot_result
plot_result(result_dict)
if self._mod_config.plot_save_file:
from rqalpha.plot import plot_result
plot_result(result_dict, False, self._mod_config.plot_save_file)
if self._mod_config.report_save_path:
from rqalpha.utils.report import generate_report
generate_report(result_dict, self._mod_config.report_save_path)