def Analysis(results):
"""
技术指标分析器
:param results:
{
'returns':[0.1,0.1,0.1],
'benchmark':[0.1,0.1,0.1]
'trades':[[2020.01.01 01:00:00,'BUY',6234.10,1]]
}
:return:
"""
res = pnl_res(results["returns"])
bres = pnl_res(results["benchmark"])
return_ratio = empyrical.cum_returns_final(res)
annual_return_ratio = empyrical.annual_return(res)
sharp_ratio = empyrical.sharpe_ratio(res, 0.035 / 252)
return_volatility = empyrical.annual_volatility(res)
max_drawdown = empyrical.max_drawdown(res)
alpha, beta = empyrical.alpha_beta_aligned(res, bres)
pls, wr = pls_ws(results["trades"])
return {
'pls': pls,
'wr': wr,
'return_ratio': return_ratio,
'annual_return_ratio': annual_return_ratio,
'beta': beta,
'alpha': alpha,
'sharp_ratio': sharp_ratio,
'return_volatility': return_volatility,
'max_drawdown': max_drawdown,
}
def calculate_metrics(self):
self.benchmark_period_returns = \
cum_returns(self.benchmark_returns).iloc[-1]
self.algorithm_period_returns = \
cum_returns(self.algorithm_returns).iloc[-1]
if not self.algorithm_returns.index.equals(
self.benchmark_returns.index):
message = "Mismatch between benchmark_returns ({bm_count}) and \
algorithm_returns ({algo_count}) in range {start} : {end}"
message = message.format(bm_count=len(self.benchmark_returns),
algo_count=len(self.algorithm_returns),
start=self._start_session,
end=self._end_session)
raise Exception(message)
self.num_trading_days = len(self.benchmark_returns)
self.mean_algorithm_returns = (
self.algorithm_returns.cumsum() /
np.arange(1, self.num_trading_days + 1, dtype=np.float64))
self.benchmark_volatility = annual_volatility(self.benchmark_returns)
self.algorithm_volatility = annual_volatility(self.algorithm_returns)
self.treasury_period_return = choose_treasury(
self.treasury_curves,
self._start_session,
self._end_session,
self.trading_calendar,
)
self.sharpe = sharpe_ratio(self.algorithm_returns, )
# The consumer currently expects a 0.0 value for sharpe in period,
# this differs from cumulative which was np.nan.
# When factoring out the sharpe_ratio, the different return types
# were collapsed into `np.nan`.
# TODO: Either fix consumer to accept `np.nan` or make the
# `sharpe_ratio` return type configurable.
# In the meantime, convert nan values to 0.0
if pd.isnull(self.sharpe):
self.sharpe = 0.0
self.downside_risk = downside_risk(self.algorithm_returns.values)
self.sortino = sortino_ratio(
self.algorithm_returns.values,
_downside_risk=self.downside_risk,
)
self.information = information_ratio(
self.algorithm_returns.values,
self.benchmark_returns.values,
)
self.alpha, self.beta = alpha_beta_aligned(
self.algorithm_returns.values,
self.benchmark_returns.values,
)
self.excess_return = self.algorithm_period_returns - \
self.treasury_period_return
self.max_drawdown = max_drawdown(self.algorithm_returns.values)
self.max_leverage = self.calculate_max_leverage()
def end_of_bar(self, packet, ledger, dt, session_ix, data_portal):
risk = packet["cumulative_risk_metrics"]
alpha, beta = ep.alpha_beta_aligned(
ledger.daily_returns_array[:session_ix + 1],
self._daily_returns_array[:session_ix + 1],
)
if not np.isfinite(alpha):
alpha = None
if np.isnan(beta):
beta = None
risk["alpha"] = alpha
risk["beta"] = beta
def getStats(cls, returns, benchmark_returns):
_alpha, _beta = alpha_beta_aligned(
returns,
benchmark_returns,
)
_sharpe = sharpe_ratio(
returns
)
_downside_risk = downside_risk(
returns
)
_max_drawdown = max_drawdown(
returns
)
_annual_volatility = annual_volatility(
returns
)
_benchmark_volatility = annual_volatility(
benchmark_returns
)
_annual_return = annual_return(
returns
)
_cum_return = cum_returns(
returns
)
return {
'cum_return' : _cum_return,
'annual_return' : _annual_return,
'annual_volatility' : _annual_volatility,
'benchmark_volatility' : _benchmark_volatility,
'max_drawdown' : _max_drawdown,
'downside_risk' : _downside_risk,
'sharpe ratio' : _sharpe,
'alpha' : _alpha,
'beta' : _beta,
}
def end_of_bar(self,
packet,
ledger,
dt,
session_ix,
data_portal):
risk = packet['cumulative_risk_metrics']
alpha, beta = ep.alpha_beta_aligned(
ledger.daily_returns_array[:session_ix + 1],
self._daily_returns_array[:session_ix + 1],
)
if np.isnan(alpha):
alpha = None
if np.isnan(beta):
beta = None
risk['alpha'] = alpha
risk['beta'] = beta
def update(self, dt, algorithm_returns, benchmark_returns, leverage):
# Keep track of latest dt for use in to_dict and other methods
# that report current state.
self.latest_dt = dt
dt_loc = self.cont_index.get_loc(dt)
self.latest_dt_loc = dt_loc
self.algorithm_returns_cont[dt_loc] = algorithm_returns
self.algorithm_returns = self.algorithm_returns_cont[:dt_loc + 1]
self.num_trading_days = len(self.algorithm_returns)
if self.create_first_day_stats:
if len(self.algorithm_returns) == 1:
self.algorithm_returns = np.append(0.0, self.algorithm_returns)
self.algorithm_cumulative_returns[dt_loc] = cum_returns(
self.algorithm_returns)[-1]
algo_cumulative_returns_to_date = \
self.algorithm_cumulative_returns[:dt_loc + 1]
self.mean_returns_cont[dt_loc] = \
algo_cumulative_returns_to_date[dt_loc] / self.num_trading_days
self.mean_returns = self.mean_returns_cont[:dt_loc + 1]
self.annualized_mean_returns_cont[dt_loc] = \
self.mean_returns_cont[dt_loc] * 252
self.annualized_mean_returns = \
self.annualized_mean_returns_cont[:dt_loc + 1]
if self.create_first_day_stats:
if len(self.mean_returns) == 1:
self.mean_returns = np.append(0.0, self.mean_returns)
self.annualized_mean_returns = np.append(
0.0, self.annualized_mean_returns)
self.benchmark_returns_cont[dt_loc] = benchmark_returns
self.benchmark_returns = self.benchmark_returns_cont[:dt_loc + 1]
if self.create_first_day_stats:
if len(self.benchmark_returns) == 1:
self.benchmark_returns = np.append(0.0, self.benchmark_returns)
self.benchmark_cumulative_returns[dt_loc] = cum_returns(
self.benchmark_returns)[-1]
benchmark_cumulative_returns_to_date = \
self.benchmark_cumulative_returns[:dt_loc + 1]
self.mean_benchmark_returns_cont[dt_loc] = \
benchmark_cumulative_returns_to_date[dt_loc] / \
self.num_trading_days
self.mean_benchmark_returns = self.mean_benchmark_returns_cont[:dt_loc]
self.annualized_mean_benchmark_returns_cont[dt_loc] = \
self.mean_benchmark_returns_cont[dt_loc] * 252
self.annualized_mean_benchmark_returns = \
self.annualized_mean_benchmark_returns_cont[:dt_loc + 1]
self.algorithm_cumulative_leverages_cont[dt_loc] = leverage
self.algorithm_cumulative_leverages = \
self.algorithm_cumulative_leverages_cont[:dt_loc + 1]
if self.create_first_day_stats:
if len(self.algorithm_cumulative_leverages) == 1:
self.algorithm_cumulative_leverages = np.append(
0.0, self.algorithm_cumulative_leverages)
if not len(self.algorithm_returns) and len(self.benchmark_returns):
message = "Mismatch between benchmark_returns ({bm_count}) and \
algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
message = message.format(bm_count=len(self.benchmark_returns),
algo_count=len(self.algorithm_returns),
start=self.start_session,
end=self.end_session,
dt=dt)
raise Exception(message)
self.update_current_max()
self.benchmark_volatility[dt_loc] = annual_volatility(
self.benchmark_returns)
self.algorithm_volatility[dt_loc] = annual_volatility(
self.algorithm_returns)
# caching the treasury rates for the minutely case is a
# big speedup, because it avoids searching the treasury
# curves on every minute.
# In both minutely and daily, the daily curve is always used.
treasury_end = dt.replace(hour=0, minute=0)
if np.isnan(self.daily_treasury[treasury_end]):
treasury_period_return = choose_treasury(
self.treasury_curves,
self.start_session,
treasury_end,
self.trading_calendar,
)
self.daily_treasury[treasury_end] = treasury_period_return
self.treasury_period_return = self.daily_treasury[treasury_end]
self.excess_returns[dt_loc] = (
self.algorithm_cumulative_returns[dt_loc] -
self.treasury_period_return)
self.alpha[dt_loc], self.beta[dt_loc] = alpha_beta_aligned(
self.algorithm_returns,
self.benchmark_returns,
)
self.sharpe[dt_loc] = sharpe_ratio(self.algorithm_returns, )
self.downside_risk[dt_loc] = downside_risk(self.algorithm_returns)
self.sortino[dt_loc] = sortino_ratio(
self.algorithm_returns, _downside_risk=self.downside_risk[dt_loc])
self.max_drawdown = max_drawdown(self.algorithm_returns)
self.max_drawdowns[dt_loc] = self.max_drawdown
self.max_leverage = self.calculate_max_leverage()
self.max_leverages[dt_loc] = self.max_leverage
def risk_metric_period(
cls,
start_session,
end_session,
algorithm_returns,
benchmark_returns,
algorithm_leverages,
):
"""
Creates a dictionary representing the state of the risk report.
Parameters
----------
start_session : pd.Timestamp
Start of period (inclusive) to produce metrics on
end_session : pd.Timestamp
End of period (inclusive) to produce metrics on
algorithm_returns : pd.Series(pd.Timestamp -> float)
Series of algorithm returns as of the end of each session
benchmark_returns : pd.Series(pd.Timestamp -> float)
Series of benchmark returns as of the end of each session
algorithm_leverages : pd.Series(pd.Timestamp -> float)
Series of algorithm leverages as of the end of each session
Returns
-------
risk_metric : dict[str, any]
Dict of metrics that with fields like:
{
'algorithm_period_return': 0.0,
'benchmark_period_return': 0.0,
'treasury_period_return': 0,
'excess_return': 0.0,
'alpha': 0.0,
'beta': 0.0,
'sharpe': 0.0,
'sortino': 0.0,
'period_label': '1970-01',
'trading_days': 0,
'algo_volatility': 0.0,
'benchmark_volatility': 0.0,
'max_drawdown': 0.0,
'max_leverage': 0.0,
}
"""
algorithm_returns = algorithm_returns[
(algorithm_returns.index >= start_session)
& (algorithm_returns.index <= end_session)]
# Benchmark needs to be masked to the same dates as the algo returns
benchmark_returns = benchmark_returns[
(benchmark_returns.index >= start_session)
& (benchmark_returns.index <= algorithm_returns.index[-1])]
benchmark_period_returns = ep.cum_returns(benchmark_returns).iloc[-1]
algorithm_period_returns = ep.cum_returns(algorithm_returns).iloc[-1]
alpha, beta = ep.alpha_beta_aligned(
algorithm_returns.values,
benchmark_returns.values,
)
benchmark_volatility = ep.annual_volatility(benchmark_returns)
sharpe = ep.sharpe_ratio(algorithm_returns)
# The consumer currently expects a 0.0 value for sharpe in period,
# this differs from cumulative which was np.nan.
# When factoring out the sharpe_ratio, the different return types
# were collapsed into `np.nan`.
# TODO: Either fix consumer to accept `np.nan` or make the
# `sharpe_ratio` return type configurable.
# In the meantime, convert nan values to 0.0
if pd.isnull(sharpe):
sharpe = 0.0
sortino = ep.sortino_ratio(
algorithm_returns.values,
_downside_risk=ep.downside_risk(algorithm_returns.values),
)
rval = {
"algorithm_period_return": algorithm_period_returns,
"benchmark_period_return": benchmark_period_returns,
"treasury_period_return": 0,
"excess_return": algorithm_period_returns,
"alpha": alpha,
"beta": beta,
"sharpe": sharpe,
"sortino": sortino,
"period_label": end_session.strftime("%Y-%m"),
"trading_days": len(benchmark_returns),
"algo_volatility": ep.annual_volatility(algorithm_returns),
"benchmark_volatility": benchmark_volatility,
"max_drawdown": ep.max_drawdown(algorithm_returns.values),
"max_leverage": algorithm_leverages.max(),
}
# check if a field in rval is nan or inf, and replace it with None
# except period_label which is always a str
return {
k: (None if k != "period_label" and not np.isfinite(v) else v)
for k, v in rval.items()
}
def risk_metric_period(cls,
start_session,
end_session,
algorithm_returns,
benchmark_returns,
algorithm_leverages):
"""
Creates a dictionary representing the state of the risk report.
Parameters
----------
start_session : pd.Timestamp
Start of period (inclusive) to produce metrics on
end_session : pd.Timestamp
End of period (inclusive) to produce metrics on
algorithm_returns : pd.Series(pd.Timestamp -> float)
Series of algorithm returns as of the end of each session
benchmark_returns : pd.Series(pd.Timestamp -> float)
Series of benchmark returns as of the end of each session
algorithm_leverages : pd.Series(pd.Timestamp -> float)
Series of algorithm leverages as of the end of each session
Returns
-------
risk_metric : dict[str, any]
Dict of metrics that with fields like:
{
'algorithm_period_return': 0.0,
'benchmark_period_return': 0.0,
'treasury_period_return': 0,
'excess_return': 0.0,
'alpha': 0.0,
'beta': 0.0,
'sharpe': 0.0,
'sortino': 0.0,
'period_label': '1970-01',
'trading_days': 0,
'algo_volatility': 0.0,
'benchmark_volatility': 0.0,
'max_drawdown': 0.0,
'max_leverage': 0.0,
}
"""
algorithm_returns = algorithm_returns[
(algorithm_returns.index >= start_session) &
(algorithm_returns.index <= end_session)
]
# Benchmark needs to be masked to the same dates as the algo returns
benchmark_returns = benchmark_returns[
(benchmark_returns.index >= start_session) &
(benchmark_returns.index <= algorithm_returns.index[-1])
]
benchmark_period_returns = ep.cum_returns(benchmark_returns).iloc[-1]
algorithm_period_returns = ep.cum_returns(algorithm_returns).iloc[-1]
alpha, beta = ep.alpha_beta_aligned(
algorithm_returns.values,
benchmark_returns.values,
)
sharpe = ep.sharpe_ratio(algorithm_returns)
# The consumer currently expects a 0.0 value for sharpe in period,
# this differs from cumulative which was np.nan.
# When factoring out the sharpe_ratio, the different return types
# were collapsed into `np.nan`.
# TODO: Either fix consumer to accept `np.nan` or make the
# `sharpe_ratio` return type configurable.
# In the meantime, convert nan values to 0.0
if pd.isnull(sharpe):
sharpe = 0.0
sortino = ep.sortino_ratio(
algorithm_returns.values,
_downside_risk=ep.downside_risk(algorithm_returns.values),
)
rval = {
'algorithm_period_return': algorithm_period_returns,
'benchmark_period_return': benchmark_period_returns,
'treasury_period_return': 0,
'excess_return': algorithm_period_returns,
'alpha': alpha,
'beta': beta,
'sharpe': sharpe,
'sortino': sortino,
'period_label': end_session.strftime("%Y-%m"),
'trading_days': len(benchmark_returns),
'algo_volatility': ep.annual_volatility(algorithm_returns),
'benchmark_volatility': ep.annual_volatility(benchmark_returns),
'max_drawdown': ep.max_drawdown(algorithm_returns.values),
'max_leverage': algorithm_leverages.max(),
}
# check if a field in rval is nan or inf, and replace it with None
# except period_label which is always a str
return {
k: (
None
if k != 'period_label' and not np.isfinite(v) else
v
)
for k, v in iteritems(rval)
}
def calculate_metrics(self):
self.benchmark_period_returns = \
cum_returns(self.benchmark_returns).iloc[-1]
self.algorithm_period_returns = \
cum_returns(self.algorithm_returns).iloc[-1]
if not self.algorithm_returns.index.equals(
self.benchmark_returns.index
):
message = "Mismatch between benchmark_returns ({bm_count}) and \
algorithm_returns ({algo_count}) in range {start} : {end}"
message = message.format(
bm_count=len(self.benchmark_returns),
algo_count=len(self.algorithm_returns),
start=self._start_session,
end=self._end_session
)
raise Exception(message)
self.num_trading_days = len(self.benchmark_returns)
self.mean_algorithm_returns = (
self.algorithm_returns.cumsum() /
np.arange(1, self.num_trading_days + 1, dtype=np.float64)
)
self.benchmark_volatility = annual_volatility(self.benchmark_returns)
self.algorithm_volatility = annual_volatility(self.algorithm_returns)
self.treasury_period_return = choose_treasury(
self.treasury_curves,
self._start_session,
self._end_session,
self.trading_calendar,
)
self.sharpe = sharpe_ratio(
self.algorithm_returns,
)
# The consumer currently expects a 0.0 value for sharpe in period,
# this differs from cumulative which was np.nan.
# When factoring out the sharpe_ratio, the different return types
# were collapsed into `np.nan`.
# TODO: Either fix consumer to accept `np.nan` or make the
# `sharpe_ratio` return type configurable.
# In the meantime, convert nan values to 0.0
if pd.isnull(self.sharpe):
self.sharpe = 0.0
self.downside_risk = downside_risk(
self.algorithm_returns.values
)
self.sortino = sortino_ratio(
self.algorithm_returns.values,
_downside_risk=self.downside_risk,
)
self.information = information_ratio(
self.algorithm_returns.values,
self.benchmark_returns.values,
)
self.alpha, self.beta = alpha_beta_aligned(
self.algorithm_returns.values,
self.benchmark_returns.values,
)
self.excess_return = self.algorithm_period_returns - \
self.treasury_period_return
self.max_drawdown = max_drawdown(self.algorithm_returns.values)
self.max_leverage = self.calculate_max_leverage()
def update(self, dt, algorithm_returns, benchmark_returns, leverage):
# Keep track of latest dt for use in to_dict and other methods
# that report current state.
self.latest_dt = dt
dt_loc = self.cont_index.get_loc(dt)
self.latest_dt_loc = dt_loc
self.algorithm_returns_cont[dt_loc] = algorithm_returns
self.algorithm_returns = self.algorithm_returns_cont[:dt_loc + 1]
self.num_trading_days = len(self.algorithm_returns)
if self.create_first_day_stats:
if len(self.algorithm_returns) == 1:
self.algorithm_returns = np.append(0.0, self.algorithm_returns)
self.algorithm_cumulative_returns[dt_loc] = cum_returns(
self.algorithm_returns
)[-1]
algo_cumulative_returns_to_date = \
self.algorithm_cumulative_returns[:dt_loc + 1]
self.mean_returns_cont[dt_loc] = \
algo_cumulative_returns_to_date[dt_loc] / self.num_trading_days
self.mean_returns = self.mean_returns_cont[:dt_loc + 1]
self.annualized_mean_returns_cont[dt_loc] = \
self.mean_returns_cont[dt_loc] * 252
self.annualized_mean_returns = \
self.annualized_mean_returns_cont[:dt_loc + 1]
if self.create_first_day_stats:
if len(self.mean_returns) == 1:
self.mean_returns = np.append(0.0, self.mean_returns)
self.annualized_mean_returns = np.append(
0.0, self.annualized_mean_returns)
self.benchmark_returns_cont[dt_loc] = benchmark_returns
self.benchmark_returns = self.benchmark_returns_cont[:dt_loc + 1]
if self.create_first_day_stats:
if len(self.benchmark_returns) == 1:
self.benchmark_returns = np.append(0.0, self.benchmark_returns)
self.benchmark_cumulative_returns[dt_loc] = cum_returns(
self.benchmark_returns
)[-1]
benchmark_cumulative_returns_to_date = \
self.benchmark_cumulative_returns[:dt_loc + 1]
self.mean_benchmark_returns_cont[dt_loc] = \
benchmark_cumulative_returns_to_date[dt_loc] / \
self.num_trading_days
self.mean_benchmark_returns = self.mean_benchmark_returns_cont[:dt_loc]
self.annualized_mean_benchmark_returns_cont[dt_loc] = \
self.mean_benchmark_returns_cont[dt_loc] * 252
self.annualized_mean_benchmark_returns = \
self.annualized_mean_benchmark_returns_cont[:dt_loc + 1]
self.algorithm_cumulative_leverages_cont[dt_loc] = leverage
self.algorithm_cumulative_leverages = \
self.algorithm_cumulative_leverages_cont[:dt_loc + 1]
if self.create_first_day_stats:
if len(self.algorithm_cumulative_leverages) == 1:
self.algorithm_cumulative_leverages = np.append(
0.0,
self.algorithm_cumulative_leverages)
if not len(self.algorithm_returns) and len(self.benchmark_returns):
message = "Mismatch between benchmark_returns ({bm_count}) and \
algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
message = message.format(
bm_count=len(self.benchmark_returns),
algo_count=len(self.algorithm_returns),
start=self.start_session,
end=self.end_session,
dt=dt
)
raise Exception(message)
self.update_current_max()
self.benchmark_volatility[dt_loc] = annual_volatility(
self.benchmark_returns
)
self.algorithm_volatility[dt_loc] = annual_volatility(
self.algorithm_returns
)
# caching the treasury rates for the minutely case is a
# big speedup, because it avoids searching the treasury
# curves on every minute.
# In both minutely and daily, the daily curve is always used.
treasury_end = dt.replace(hour=0, minute=0)
if np.isnan(self.daily_treasury[treasury_end]):
treasury_period_return = choose_treasury(
self.treasury_curves,
self.start_session,
treasury_end,
self.trading_calendar,
)
self.daily_treasury[treasury_end] = treasury_period_return
self.treasury_period_return = self.daily_treasury[treasury_end]
self.excess_returns[dt_loc] = (
self.algorithm_cumulative_returns[dt_loc] -
self.treasury_period_return)
self.alpha[dt_loc], self.beta[dt_loc] = alpha_beta_aligned(
self.algorithm_returns,
self.benchmark_returns,
)
self.sharpe[dt_loc] = sharpe_ratio(
self.algorithm_returns,
)
self.downside_risk[dt_loc] = downside_risk(
self.algorithm_returns
)
self.sortino[dt_loc] = sortino_ratio(
self.algorithm_returns,
_downside_risk=self.downside_risk[dt_loc]
)
self.information[dt_loc] = information_ratio(
self.algorithm_returns,
self.benchmark_returns,
)
self.max_drawdown = max_drawdown(
self.algorithm_returns
)
self.max_drawdowns[dt_loc] = self.max_drawdown
self.max_leverage = self.calculate_max_leverage()
self.max_leverages[dt_loc] = self.max_leverage
def generate_analysis_data(self, context):
if self.daily_data_df.shape[0] > 0:
# Calculate returns
daily_returns = self.daily_data_df['net'].pct_change()
# daily return for the first day will always be 0
daily_returns[0] = (
self.daily_data_df['net'][0] /
self.analysis_data.info_data['initial_cash']) - 1
ytd_returns = daily_returns[
daily_returns.index >= datetime.datetime(
daily_returns.index[-1].year, 1, 1).date()]
one_year_daily_returns = daily_returns[daily_returns.index >= (
daily_returns.index[-1] - BDay(252)).date()]
benchmark_returns = context.trading_environment.benchmark_returns.loc[
self.daily_data_df.index[0]:self.daily_data_df.index[-1]]
benchmark_returns.index = benchmark_returns.index.date
daily_returns = daily_returns.drop(
daily_returns.index.difference(benchmark_returns.index))
benchmark_returns = benchmark_returns.drop(
benchmark_returns.index.difference(daily_returns.index))
benchmark_returns = benchmark_returns.loc[~benchmark_returns.index.
duplicated(keep='first')]
ytd_benchmark_returns = benchmark_returns[
benchmark_returns.index >= datetime.datetime(
benchmark_returns.index[-1].year, 1, 1).date()]
one_year_benchmark_returns = benchmark_returns[
benchmark_returns.index >= (benchmark_returns.index[-1] -
BDay(252)).date()]
portfolio_dd = self.rolling_drawdown(daily_returns.values)
benchmark_dd = self.rolling_drawdown(benchmark_returns.values)
report_dict = {}
benchmark_report_dict = {}
report_dict['total_return_pct'] = (daily_returns + 1).prod() - 1
report_dict['total_return'] = self.daily_data_df.iloc[
-1].net - self.daily_data_df.iloc[0].net
report_dict['ytd'] = (ytd_returns + 1).prod() - 1
report_dict['one_year'] = (one_year_daily_returns + 1).prod() - 1
report_dict['max_drawdown'] = portfolio_dd.min()
report_dict['sharpe_ratio'] = empyrical.sharpe_ratio(daily_returns)
report_dict['alpha'], report_dict[
'beta'] = empyrical.alpha_beta_aligned(daily_returns,
benchmark_returns)
cagr = empyrical.cagr(daily_returns)
print(cagr)
report_dict['cagr'] = cagr
report_dict['std'] = daily_returns.std() * 100
benchmark_report_dict['total_return_pct'] = (benchmark_returns +
1).prod() - 1
benchmark_report_dict['total_return'] = self.daily_data_df.iloc[-1].benchmark_net \
- self.daily_data_df.iloc[0].benchmark_net
benchmark_report_dict['ytd'] = (ytd_benchmark_returns +
1).prod() - 1
benchmark_report_dict['one_year'] = (one_year_benchmark_returns +
1).prod() - 1
benchmark_report_dict['max_drawdown'] = benchmark_dd.min()
benchmark_report_dict['sharpe_ratio'] = empyrical.sharpe_ratio(
benchmark_returns)
benchmark_report_dict['alpha'], benchmark_report_dict[
'beta'] = 0, 1
benchmark_report_dict['cagr'] = empyrical.cagr(benchmark_returns)
benchmark_report_dict['std'] = benchmark_returns.std() * 100
self.daily_cagr[daily_returns.index[-1]] = report_dict['cagr']
self.daily_benchmark_cagr[
benchmark_returns.index[-1]] = benchmark_report_dict['cagr']
plot_data_df = pd.concat([daily_returns, benchmark_returns],
axis=1,
keys=['returns', 'benchmark_returns'])
plot_data_df.reset_index(inplace=True)
plot_data_df['drawdown'] = portfolio_dd
plot_data_df['benchmark_drawdown'] = benchmark_dd
plot_data_df.set_index('date', inplace=True)
plot_data_df['cagr'] = self.daily_cagr
plot_data_df['benchmark_cagr'] = self.daily_benchmark_cagr
plot_data_df['positions_count'] = self.daily_positions_df.groupby(
'date').size()
plot_data_df['positions_count'] = plot_data_df[
'positions_count'].fillna(0)
self.analysis_data.chart_data = plot_data_df
self.analysis_data.strategy_report = report_dict
self.analysis_data.benchmark_report = benchmark_report_dict
if len(
self.daily_positions_df.index.get_level_values(
'date').value_counts()) < 30:
self.analysis_data.holdings_data = self.daily_positions_df.reset_index(
)
else:
self.analysis_data.holdings_data = self.daily_positions_df.loc[
self.daily_positions_df.index.get_level_values('date') >=
self.daily_positions_df.index.get_level_values('date').
value_counts().sort_index().index[-30]].reset_index()
if len(self.daily_data_df.index) < 30:
self.analysis_data.monthly_transactions_data = self.transactions_data
else:
self.analysis_data.monthly_transactions_data = self.transactions_data[
self.transactions_data.date >=
self.daily_data_df.index[-30]]
self.analysis_data.holdings_data_historical = self.daily_positions_df.reset_index(
)
self.analysis_data.transactions_data = self.transactions_data