mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-31 12:10:31 +08:00
350 lines
12 KiB
Python
350 lines
12 KiB
Python
#
|
|
# Copyright 2014 Quantopian, Inc.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
import functools
|
|
import logbook
|
|
import numpy as np
|
|
|
|
import pandas as pd
|
|
from pandas.tseries.tools import normalize_date
|
|
|
|
from six import iteritems
|
|
|
|
from . risk import (
|
|
check_entry,
|
|
choose_treasury
|
|
)
|
|
|
|
from empyrical import (
|
|
alpha_beta_aligned,
|
|
annual_volatility,
|
|
cum_returns,
|
|
downside_risk,
|
|
information_ratio,
|
|
max_drawdown,
|
|
sharpe_ratio,
|
|
sortino_ratio,
|
|
)
|
|
|
|
log = logbook.Logger('Risk Cumulative')
|
|
|
|
|
|
choose_treasury = functools.partial(choose_treasury, lambda *args: '10year',
|
|
compound=False)
|
|
|
|
|
|
class RiskMetricsCumulative(object):
|
|
"""
|
|
:Usage:
|
|
Instantiate RiskMetricsCumulative once.
|
|
Call update() method on each dt to update the metrics.
|
|
"""
|
|
|
|
METRIC_NAMES = (
|
|
'alpha',
|
|
'beta',
|
|
'sharpe',
|
|
'algorithm_volatility',
|
|
'benchmark_volatility',
|
|
'downside_risk',
|
|
'sortino',
|
|
'information',
|
|
)
|
|
|
|
def __init__(self, sim_params, treasury_curves, trading_calendar,
|
|
create_first_day_stats=False):
|
|
self.treasury_curves = treasury_curves
|
|
self.trading_calendar = trading_calendar
|
|
self.start_session = sim_params.start_session
|
|
self.end_session = sim_params.end_session
|
|
|
|
self.sessions = trading_calendar.sessions_in_range(
|
|
self.start_session, self.end_session
|
|
)
|
|
|
|
# Hold on to the trading day before the start,
|
|
# used for index of the zero return value when forcing returns
|
|
# on the first day.
|
|
self.day_before_start = self.start_session - self.sessions.freq
|
|
|
|
last_day = normalize_date(sim_params.end_session)
|
|
if last_day not in self.sessions:
|
|
last_day = pd.tseries.index.DatetimeIndex(
|
|
[last_day]
|
|
)
|
|
self.sessions = self.sessions.append(last_day)
|
|
|
|
self.sim_params = sim_params
|
|
|
|
self.create_first_day_stats = create_first_day_stats
|
|
|
|
cont_index = self.sessions
|
|
|
|
self.cont_index = cont_index
|
|
self.cont_len = len(self.cont_index)
|
|
|
|
empty_cont = np.full(self.cont_len, np.nan)
|
|
|
|
self.algorithm_returns_cont = empty_cont.copy()
|
|
self.benchmark_returns_cont = empty_cont.copy()
|
|
self.algorithm_cumulative_leverages_cont = empty_cont.copy()
|
|
self.mean_returns_cont = empty_cont.copy()
|
|
self.annualized_mean_returns_cont = empty_cont.copy()
|
|
self.mean_benchmark_returns_cont = empty_cont.copy()
|
|
self.annualized_mean_benchmark_returns_cont = empty_cont.copy()
|
|
|
|
# The returns at a given time are read and reset from the respective
|
|
# returns container.
|
|
self.algorithm_returns = None
|
|
self.benchmark_returns = None
|
|
self.mean_returns = None
|
|
self.annualized_mean_returns = None
|
|
self.mean_benchmark_returns = None
|
|
self.annualized_mean_benchmark_returns = None
|
|
|
|
self.algorithm_cumulative_returns = empty_cont.copy()
|
|
self.benchmark_cumulative_returns = empty_cont.copy()
|
|
self.algorithm_cumulative_leverages = empty_cont.copy()
|
|
self.excess_returns = empty_cont.copy()
|
|
|
|
self.latest_dt_loc = 0
|
|
self.latest_dt = cont_index[0]
|
|
|
|
self.benchmark_volatility = empty_cont.copy()
|
|
self.algorithm_volatility = empty_cont.copy()
|
|
self.beta = empty_cont.copy()
|
|
self.alpha = empty_cont.copy()
|
|
self.sharpe = empty_cont.copy()
|
|
self.downside_risk = empty_cont.copy()
|
|
self.sortino = empty_cont.copy()
|
|
self.information = empty_cont.copy()
|
|
|
|
self.drawdowns = empty_cont.copy()
|
|
self.max_drawdowns = empty_cont.copy()
|
|
self.max_drawdown = 0
|
|
self.max_leverages = empty_cont.copy()
|
|
self.max_leverage = 0
|
|
self.current_max = -np.inf
|
|
self.daily_treasury = pd.Series(index=self.sessions)
|
|
self.treasury_period_return = np.nan
|
|
|
|
self.num_trading_days = 0
|
|
|
|
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 to_dict(self):
|
|
"""
|
|
Creates a dictionary representing the state of the risk report.
|
|
Returns a dict object of the form:
|
|
"""
|
|
dt = self.latest_dt
|
|
dt_loc = self.latest_dt_loc
|
|
period_label = dt.strftime("%Y-%m")
|
|
rval = {
|
|
'trading_days': self.num_trading_days,
|
|
'benchmark_volatility':
|
|
self.benchmark_volatility[dt_loc],
|
|
'algo_volatility':
|
|
self.algorithm_volatility[dt_loc],
|
|
'treasury_period_return': self.treasury_period_return,
|
|
# Though the two following keys say period return,
|
|
# they would be more accurately called the cumulative return.
|
|
# However, the keys need to stay the same, for now, for backwards
|
|
# compatibility with existing consumers.
|
|
'algorithm_period_return':
|
|
self.algorithm_cumulative_returns[dt_loc],
|
|
'benchmark_period_return':
|
|
self.benchmark_cumulative_returns[dt_loc],
|
|
'beta': self.beta[dt_loc],
|
|
'alpha': self.alpha[dt_loc],
|
|
'sharpe': self.sharpe[dt_loc],
|
|
'sortino': self.sortino[dt_loc],
|
|
'information': self.information[dt_loc],
|
|
'excess_return': self.excess_returns[dt_loc],
|
|
'max_drawdown': self.max_drawdown,
|
|
'max_leverage': self.max_leverage,
|
|
'period_label': period_label
|
|
}
|
|
|
|
return {k: (None if check_entry(k, v) else v)
|
|
for k, v in iteritems(rval)}
|
|
|
|
def __repr__(self):
|
|
statements = []
|
|
for metric in self.METRIC_NAMES:
|
|
value = getattr(self, metric)[-1]
|
|
if isinstance(value, list):
|
|
if len(value) == 0:
|
|
value = np.nan
|
|
else:
|
|
value = value[-1]
|
|
statements.append("{m}:{v}".format(m=metric, v=value))
|
|
|
|
return '\n'.join(statements)
|
|
|
|
def update_current_max(self):
|
|
if len(self.algorithm_cumulative_returns) == 0:
|
|
return
|
|
current_cumulative_return = \
|
|
self.algorithm_cumulative_returns[self.latest_dt_loc]
|
|
if self.current_max < current_cumulative_return:
|
|
self.current_max = current_cumulative_return
|
|
|
|
def calculate_max_leverage(self):
|
|
# The leverage is defined as: the gross_exposure/net_liquidation
|
|
# gross_exposure = long_exposure + abs(short_exposure)
|
|
# net_liquidation = ending_cash + long_exposure + short_exposure
|
|
cur_leverage = self.algorithm_cumulative_leverages_cont[
|
|
self.latest_dt_loc]
|
|
|
|
return max(cur_leverage, self.max_leverage)
|