mirror of
https://github.com/wassname/catalyst.git
synced 2026-08-09 11:50:09 +08:00
Eddie Hebert
73573680f1
The method was using a mix of the `dt` parameter and `self.latest_dt`. Use `self.latest_dt` to conform with the rest of the module.
451 lines
16 KiB
Python
451 lines
16 KiB
Python
#
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# Copyright 2014 Quantopian, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import functools
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import logbook
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import math
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import numpy as np
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from zipline.finance import trading
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import zipline.utils.math_utils as zp_math
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import pandas as pd
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from pandas.tseries.tools import normalize_date
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from six import iteritems
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from . risk import (
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alpha,
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check_entry,
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choose_treasury,
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downside_risk,
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sharpe_ratio,
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sortino_ratio,
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)
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log = logbook.Logger('Risk Cumulative')
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choose_treasury = functools.partial(choose_treasury, lambda *args: '10year',
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compound=False)
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def information_ratio(algo_volatility, algorithm_return, benchmark_return):
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"""
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http://en.wikipedia.org/wiki/Information_ratio
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Args:
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algorithm_returns (np.array-like):
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All returns during algorithm lifetime.
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benchmark_returns (np.array-like):
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All benchmark returns during algo lifetime.
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Returns:
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float. Information ratio.
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"""
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if zp_math.tolerant_equals(algo_volatility, 0):
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return np.nan
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return (
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(algorithm_return - benchmark_return)
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# The square of the annualization factor is in the volatility,
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# because the volatility is also annualized,
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# i.e. the sqrt(annual factor) is in the volatility's numerator.
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# So to have the the correct annualization factor for the
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# Sharpe value's numerator, which should be the sqrt(annual factor).
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# The square of the sqrt of the annual factor, i.e. the annual factor
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# itself, is needed in the numerator to factor out the division by
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# its square root.
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/ algo_volatility)
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class RiskMetricsCumulative(object):
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"""
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:Usage:
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Instantiate RiskMetricsCumulative once.
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Call update() method on each dt to update the metrics.
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"""
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METRIC_NAMES = (
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'alpha',
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'beta',
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'sharpe',
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'algorithm_volatility',
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'benchmark_volatility',
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'downside_risk',
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'sortino',
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'information',
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)
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def __init__(self, sim_params,
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returns_frequency=None,
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create_first_day_stats=False):
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"""
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- @returns_frequency allows for configuration of the whether
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the benchmark and algorithm returns are in units of minutes or days,
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if `None` defaults to the `emission_rate` in `sim_params`.
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"""
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self.treasury_curves = trading.environment.treasury_curves
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self.start_date = sim_params.period_start.replace(
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hour=0, minute=0, second=0, microsecond=0
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)
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self.end_date = sim_params.period_end.replace(
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hour=0, minute=0, second=0, microsecond=0
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)
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self.trading_days = trading.environment.days_in_range(
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self.start_date,
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self.end_date)
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# Hold on to the trading day before the start,
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# used for index of the zero return value when forcing returns
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# on the first day.
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self.day_before_start = self.start_date - \
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trading.environment.trading_days.freq
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last_day = normalize_date(sim_params.period_end)
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if last_day not in self.trading_days:
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last_day = pd.tseries.index.DatetimeIndex(
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[last_day]
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)
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self.trading_days = self.trading_days.append(last_day)
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self.sim_params = sim_params
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self.create_first_day_stats = create_first_day_stats
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if returns_frequency is None:
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returns_frequency = self.sim_params.emission_rate
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self.returns_frequency = returns_frequency
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if returns_frequency == 'daily':
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cont_index = self.get_daily_index()
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elif returns_frequency == 'minute':
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cont_index = self.get_minute_index(sim_params)
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self.cont_index = cont_index
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self.algorithm_returns_cont = pd.Series(index=cont_index)
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self.benchmark_returns_cont = pd.Series(index=cont_index)
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self.mean_returns_cont = pd.Series(index=cont_index)
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self.annualized_mean_returns_cont = pd.Series(index=cont_index)
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self.mean_benchmark_returns_cont = pd.Series(index=cont_index)
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self.annualized_mean_benchmark_returns_cont = pd.Series(
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index=cont_index)
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# The returns at a given time are read and reset from the respective
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# returns container.
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self.algorithm_returns = None
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self.benchmark_returns = None
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self.mean_returns = None
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self.annualized_mean_returns = None
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self.mean_benchmark_returns = None
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self.annualized_mean_benchmark_returns = None
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self.algorithm_cumulative_returns = pd.Series(index=cont_index)
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self.benchmark_cumulative_returns = pd.Series(index=cont_index)
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self.excess_returns = pd.Series(index=cont_index)
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self.latest_dt = cont_index[0]
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self.metrics = pd.DataFrame(index=cont_index,
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columns=self.METRIC_NAMES)
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self.drawdowns = pd.Series(index=cont_index)
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self.max_drawdowns = pd.Series(index=cont_index)
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self.max_drawdown = 0
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self.current_max = -np.inf
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self.daily_treasury = pd.Series(index=self.trading_days)
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self.treasury_period_return = np.nan
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self.num_trading_days = 0
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def get_minute_index(self, sim_params):
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"""
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Stitches together multiple days worth of business minutes into
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one continous index.
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"""
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trading_minutes = None
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for day in self.trading_days:
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minutes_for_day = trading.environment.market_minutes_for_day(day)
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if trading_minutes is None:
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# Create container for all minutes on first iteration
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trading_minutes = minutes_for_day
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else:
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trading_minutes = trading_minutes + minutes_for_day
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return trading_minutes
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def get_daily_index(self):
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return self.trading_days
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def update(self, dt, algorithm_returns, benchmark_returns):
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# Keep track of latest dt for use in to_dict and other methods
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# that report current state.
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self.latest_dt = dt
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self.algorithm_returns_cont[dt] = algorithm_returns
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self.algorithm_returns = self.algorithm_returns_cont[:dt]
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self.num_trading_days = len(self.algorithm_returns)
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if self.create_first_day_stats:
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if len(self.algorithm_returns) == 1:
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self.algorithm_returns = pd.Series(
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{self.day_before_start: 0.0}).append(
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self.algorithm_returns)
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self.algorithm_cumulative_returns[dt] = \
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self.calculate_cumulative_returns(self.algorithm_returns)
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algo_cumulative_returns_to_date = \
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self.algorithm_cumulative_returns[:dt]
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self.mean_returns_cont[dt] = \
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algo_cumulative_returns_to_date[dt] / self.num_trading_days
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self.mean_returns = self.mean_returns_cont[:dt]
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self.annualized_mean_returns_cont[dt] = \
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self.mean_returns_cont[dt] * 252
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self.annualized_mean_returns = self.annualized_mean_returns_cont[:dt]
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if self.create_first_day_stats:
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if len(self.mean_returns) == 1:
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self.mean_returns = pd.Series(
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{self.day_before_start: 0.0}).append(self.mean_returns)
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self.annualized_mean_returns = pd.Series(
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{self.day_before_start: 0.0}).append(
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self.annualized_mean_returns)
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self.benchmark_returns_cont[dt] = benchmark_returns
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self.benchmark_returns = self.benchmark_returns_cont[:dt]
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if self.create_first_day_stats:
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if len(self.benchmark_returns) == 1:
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self.benchmark_returns = pd.Series(
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{self.day_before_start: 0.0}).append(
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self.benchmark_returns)
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self.benchmark_cumulative_returns[dt] = \
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self.calculate_cumulative_returns(self.benchmark_returns)
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benchmark_cumulative_returns_to_date = \
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self.benchmark_cumulative_returns[:dt]
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self.mean_benchmark_returns_cont[dt] = \
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benchmark_cumulative_returns_to_date[dt] / self.num_trading_days
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self.mean_benchmark_returns = self.mean_benchmark_returns_cont[:dt]
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self.annualized_mean_benchmark_returns_cont[dt] = \
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self.mean_benchmark_returns_cont[dt] * 252
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self.annualized_mean_benchmark_returns = \
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self.annualized_mean_benchmark_returns_cont[:dt]
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if not self.algorithm_returns.index.equals(
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self.benchmark_returns.index
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):
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message = "Mismatch between benchmark_returns ({bm_count}) and \
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algorithm_returns ({algo_count}) in range {start} : {end} on {dt}"
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message = message.format(
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bm_count=len(self.benchmark_returns),
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algo_count=len(self.algorithm_returns),
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start=self.start_date,
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end=self.end_date,
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dt=dt
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)
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raise Exception(message)
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self.update_current_max()
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self.metrics.benchmark_volatility[dt] = \
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self.calculate_volatility(self.benchmark_returns)
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self.metrics.algorithm_volatility[dt] = \
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self.calculate_volatility(self.algorithm_returns)
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# caching the treasury rates for the minutely case is a
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# big speedup, because it avoids searching the treasury
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# curves on every minute.
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# In both minutely and daily, the daily curve is always used.
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treasury_end = dt.replace(hour=0, minute=0)
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if np.isnan(self.daily_treasury[treasury_end]):
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treasury_period_return = choose_treasury(
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self.treasury_curves,
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self.start_date,
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treasury_end
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)
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self.daily_treasury[treasury_end] = treasury_period_return
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self.treasury_period_return = self.daily_treasury[treasury_end]
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self.excess_returns[self.latest_dt] = (
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self.algorithm_cumulative_returns[self.latest_dt]
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-
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self.treasury_period_return)
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self.metrics.beta[dt] = self.calculate_beta()
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self.metrics.alpha[dt] = self.calculate_alpha()
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self.metrics.sharpe[dt] = self.calculate_sharpe()
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self.metrics.downside_risk[dt] = self.calculate_downside_risk()
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self.metrics.sortino[dt] = self.calculate_sortino()
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self.metrics.information[dt] = self.calculate_information()
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self.max_drawdown = self.calculate_max_drawdown()
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self.max_drawdowns[dt] = self.max_drawdown
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def to_dict(self):
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"""
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Creates a dictionary representing the state of the risk report.
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Returns a dict object of the form:
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"""
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dt = self.latest_dt
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period_label = dt.strftime("%Y-%m")
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rval = {
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'trading_days': self.num_trading_days,
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'benchmark_volatility': self.metrics.benchmark_volatility[dt],
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'algo_volatility': self.metrics.algorithm_volatility[dt],
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'treasury_period_return': self.treasury_period_return,
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# Though the two following keys say period return,
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# they would be more accurately called the cumulative return.
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# However, the keys need to stay the same, for now, for backwards
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# compatibility with existing consumers.
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'algorithm_period_return': self.algorithm_cumulative_returns[dt],
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'benchmark_period_return': self.benchmark_cumulative_returns[dt],
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'beta': self.metrics.beta[dt],
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'alpha': self.metrics.alpha[dt],
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'sharpe': self.metrics.sharpe[dt],
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'sortino': self.metrics.sortino[dt],
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'information': self.metrics.information[dt],
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'excess_return': self.excess_returns[dt],
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'max_drawdown': self.max_drawdown,
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'period_label': period_label
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}
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return {k: (None if check_entry(k, v) else v)
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for k, v in iteritems(rval)}
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def __repr__(self):
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statements = []
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for metric in self.METRIC_NAMES:
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value = getattr(self.metrics, metric)[-1]
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if isinstance(value, list):
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if len(value) == 0:
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value = np.nan
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else:
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value = value[-1]
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statements.append("{m}:{v}".format(m=metric, v=value))
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return '\n'.join(statements)
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def calculate_cumulative_returns(self, returns):
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return (1. + returns).prod() - 1
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def update_current_max(self):
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if len(self.algorithm_cumulative_returns) == 0:
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return
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current_cumulative_return = \
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self.algorithm_cumulative_returns[self.latest_dt]
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if self.current_max < current_cumulative_return:
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self.current_max = current_cumulative_return
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def calculate_max_drawdown(self):
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if len(self.algorithm_cumulative_returns) == 0:
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return self.max_drawdown
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# The drawdown is defined as: (high - low) / high
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# The above factors out to: 1.0 - (low / high)
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#
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# Instead of explicitly always using the low, use the current total
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# return value, and test that against the max drawdown, which will
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# exceed the previous max_drawdown iff the current return is lower than
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# the previous low in the current drawdown window.
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cur_drawdown = 1.0 - (
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(1.0 + self.algorithm_cumulative_returns[self.latest_dt])
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/
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(1.0 + self.current_max))
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self.drawdowns[self.latest_dt] = cur_drawdown
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if self.max_drawdown < cur_drawdown:
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return cur_drawdown
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else:
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return self.max_drawdown
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def calculate_sharpe(self):
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"""
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http://en.wikipedia.org/wiki/Sharpe_ratio
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"""
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return sharpe_ratio(self.metrics.algorithm_volatility[self.latest_dt],
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self.annualized_mean_returns[self.latest_dt],
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self.daily_treasury[self.latest_dt.date()])
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def calculate_sortino(self):
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"""
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http://en.wikipedia.org/wiki/Sortino_ratio
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"""
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return sortino_ratio(self.annualized_mean_returns[self.latest_dt],
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self.daily_treasury[self.latest_dt.date()],
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self.metrics.downside_risk[self.latest_dt])
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def calculate_information(self):
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"""
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http://en.wikipedia.org/wiki/Information_ratio
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"""
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return information_ratio(
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self.metrics.algorithm_volatility[self.latest_dt],
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self.annualized_mean_returns[self.latest_dt],
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self.annualized_mean_benchmark_returns[self.latest_dt])
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def calculate_alpha(self):
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"""
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http://en.wikipedia.org/wiki/Alpha_(investment)
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"""
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return alpha(self.annualized_mean_returns[self.latest_dt],
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self.treasury_period_return,
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self.annualized_mean_benchmark_returns[self.latest_dt],
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self.metrics.beta[self.latest_dt])
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def calculate_volatility(self, daily_returns):
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if len(daily_returns) <= 1:
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return 0.0
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return np.std(daily_returns, ddof=1) * math.sqrt(252)
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def calculate_downside_risk(self):
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return downside_risk(self.algorithm_returns,
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self.mean_returns,
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252)
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def calculate_beta(self):
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"""
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.. math::
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\\beta_a = \\frac{\mathrm{Cov}(r_a,r_p)}{\mathrm{Var}(r_p)}
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http://en.wikipedia.org/wiki/Beta_(finance)
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"""
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# it doesn't make much sense to calculate beta for less than two days,
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# so return none.
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if len(self.annualized_mean_returns) < 2:
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return 0.0
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returns_matrix = np.vstack([self.algorithm_returns,
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self.benchmark_returns])
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C = np.cov(returns_matrix, ddof=1)
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algorithm_covariance = C[0][1]
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benchmark_variance = C[1][1]
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beta = algorithm_covariance / benchmark_variance
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return beta
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