mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 14:09:44 +08:00
fawce
419 lines
16 KiB
Python
419 lines
16 KiB
Python
"""
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Risk Report
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===========
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+-----------------+----------------------------------------------------+
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| key | value |
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+=================+====================================================+
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| trading_days | The number of trading days between self.start_date |
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| | and self.end_date |
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+-----------------+----------------------------------------------------+
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| benchmark_volat\| The volatility of the benchmark between |
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| ility | self.start_date and self.end_date. |
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+-----------------+----------------------------------------------------+
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| algo_volatility | The volatility of the algo between self.start_date |
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| | and self.end_date. |
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+-----------------+----------------------------------------------------+
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| treasury_period\| The return of treasuries over the period. Treasury |
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| _return | maturity is chosen to match the duration of the |
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| | test period. |
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+-----------------+----------------------------------------------------+
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| sharpe | The sharpe ratio based on the _algorithm_ (rather |
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| | than the static portfolio) returns. |
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+-----------------+----------------------------------------------------+
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| beta | The _algorithm_ beta to the benchmark. |
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+-----------------+----------------------------------------------------+
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| alpha | The _algorithm_ alpha to the benchmark. |
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+-----------------+----------------------------------------------------+
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| excess_return | The excess return of the algorithm over the |
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| | treasuries. |
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+-----------------+----------------------------------------------------+
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| max_drawdown | The largest relative peak to relative trough move |
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| | for the portfolio returns between self.start_date |
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| | and self.end_date. |
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+-----------------+----------------------------------------------------+
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"""
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import logbook
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import datetime
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import math
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import numpy as np
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import numpy.linalg as la
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from zipline.utils.date_utils import epoch_now
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log = logbook.Logger('Risk')
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def advance_by_months(dt, jump_in_months):
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month = dt.month + jump_in_months
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years = month / 12
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month = month % 12
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# no remainder means that we are landing in december.
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# modulo is, in a way, a zero indexed circular array.
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# this is a way of converting to 1 indexed months.
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# (in our modulo index, december is zeroth)
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if(month == 0):
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month = 12
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years = years - 1
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return dt.replace(year = dt.year + years, month = month)
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class DailyReturn():
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def __init__(self, date, returns):
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assert isinstance(date, datetime.datetime)
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self.date = date.replace(hour=0, minute=0, second=0)
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self.returns = returns
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def to_dict(self):
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return {
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'dt' : self.date,
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'returns' : self.returns
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}
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def __repr__(self):
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return str(self.date) + " - " + str(self.returns)
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class RiskMetrics():
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def __init__(self, start_date, end_date, returns, trading_environment):
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self.treasury_curves = trading_environment.treasury_curves
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self.start_date = start_date
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self.end_date = end_date
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self.trading_environment = trading_environment
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self.algorithm_period_returns, self.algorithm_returns = \
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self.calculate_period_returns(returns)
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benchmark_returns = [
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x for x in self.trading_environment.benchmark_returns
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if x.date >= returns[0].date and x.date <= returns[-1].date
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]
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self.benchmark_period_returns, self.benchmark_returns = \
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self.calculate_period_returns(benchmark_returns)
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if(len(self.benchmark_returns) != len(self.algorithm_returns)):
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message = "Mismatch between benchmark_returns ({bm_count}) and \
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algorithm_returns ({algo_count}) in range {start} : {end}"
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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=start_date,
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end=end_date
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)
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raise Exception(message)
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self.trading_days = len(self.benchmark_returns)
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self.benchmark_volatility = self.calculate_volatility(self.benchmark_returns)
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self.algorithm_volatility = self.calculate_volatility(self.algorithm_returns)
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self.treasury_period_return = self.choose_treasury()
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self.sharpe = self.calculate_sharpe()
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self.beta, self.algorithm_covariance, self.benchmark_variance, \
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self.condition_number, self.eigen_values = self.calculate_beta()
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self.alpha = self.calculate_alpha()
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self.excess_return = self.algorithm_period_returns - self.treasury_period_return
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self.max_drawdown = self.calculate_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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period_label = self.end_date.strftime("%Y-%m")
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rval = {
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'trading_days' : self.trading_days,
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'benchmark_volatility' : self.benchmark_volatility,
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'algo_volatility' : self.algorithm_volatility,
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'treasury_period_return': self.treasury_period_return,
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'algorithm_period_return' : self.algorithm_period_returns,
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'benchmark_period_return' : self.benchmark_period_returns,
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'sharpe' : self.sharpe,
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'beta' : self.beta,
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'alpha' : self.alpha,
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'excess_return' : self.excess_return,
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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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# check if a field in rval is nan, and replace it with
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# None.
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def check_entry(key, value):
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if key != 'period_label':
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return np.isnan(value)
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else:
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return False
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return {k:None if check_entry(k,v) else v for k,v in rval.iteritems()}
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def __repr__(self):
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statements = []
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metrics = [
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"algorithm_period_returns" ,
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"benchmark_period_returns" ,
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"excess_return" ,
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"trading_days" ,
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"benchmark_volatility" ,
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"algorithm_volatility" ,
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"sharpe" ,
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"algorithm_covariance" ,
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"benchmark_variance" ,
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"beta" ,
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"alpha" ,
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"max_drawdown" ,
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"algorithm_returns" ,
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"benchmark_returns" ,
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"condition_number" ,
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"eigen_values"
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]
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for metric in metrics:
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value = getattr(self, metric)
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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_period_returns(self, daily_returns):
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#TODO: replace this with pandas.
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returns = [
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x.returns for x in daily_returns
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if x.date >= self.start_date and
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x.date <= self.end_date and
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self.trading_environment.is_trading_day(x.date)
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]
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period_returns = 1.0
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for r in returns:
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period_returns = period_returns * (1.0 + r)
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period_returns = period_returns - 1.0
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return period_returns, returns
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def calculate_volatility(self, daily_returns):
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# TODO: we should be using an annualized number for the
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# square root, not the days in the period.
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return np.std(daily_returns, ddof=1) * math.sqrt(self.trading_days)
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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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if self.algorithm_volatility == 0:
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return 0.0
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return ( (self.algorithm_period_returns - self.treasury_period_return) /
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self.algorithm_volatility )
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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.algorithm_returns) < 2:
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return 0.0, 0.0, 0.0, 0.0, []
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returns_matrix = np.vstack([self.algorithm_returns, self.benchmark_returns])
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C = np.cov(returns_matrix)
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eigen_values = la.eigvals(C)
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condition_number = max(eigen_values) / min(eigen_values)
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algorithm_covariance = C[0][1]
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benchmark_variance = C[1][1]
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beta = C[0][1] / C[1][1]
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return (
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beta,
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algorithm_covariance,
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benchmark_variance,
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condition_number,
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eigen_values
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)
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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 self.algorithm_period_returns - (self.treasury_period_return + self.beta * (self.benchmark_period_returns - self.treasury_period_return))
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def calculate_max_drawdown(self):
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compounded_returns = []
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cur_return = 0.0
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for r in self.algorithm_returns:
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try:
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cur_return = math.log(1.0 + r) + cur_return
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#this is a guard for a single day returning -100%
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except ValueError:
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log.debug("{cur} return, zeroing the returns".format(cur=cur_return))
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cur_return = 0.0
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compounded_returns.append(cur_return)
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cur_max = None
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max_drawdown = None
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for cur in compounded_returns:
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if cur_max == None or cur > cur_max:
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cur_max = cur
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drawdown = (cur - cur_max)
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if max_drawdown == None or drawdown < max_drawdown:
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max_drawdown = drawdown
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if max_drawdown == None:
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return 0.0
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return 1.0 - math.exp(max_drawdown)
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def choose_treasury(self):
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td = self.end_date - self.start_date
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if td.days <= 31:
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self.treasury_duration = '1month'
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elif td.days <= 93:
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self.treasury_duration = '3month'
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elif td.days <= 186:
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self.treasury_duration = '6month'
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elif td.days <= 366:
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self.treasury_duration = '1year'
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elif td.days <= 365 * 2 + 1:
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self.treasury_duration = '2year'
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elif td.days <= 365 * 3 + 1:
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self.treasury_duration = '3year'
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elif td.days <= 365 * 5 + 2:
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self.treasury_duration = '5year'
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elif td.days <= 365 * 7 + 2:
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self.treasury_duration = '7year'
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elif td.days <= 365 * 10 + 2:
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self.treasury_duration = '10year'
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else:
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self.treasury_duration = '30year'
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one_day = datetime.timedelta(days=1)
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curve = None
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# in case end date is not a trading day, search for the next market
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# day for an interest rate
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for i in xrange(7):
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if(self.treasury_curves.has_key(self.end_date + i * one_day)):
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curve = self.treasury_curves[self.end_date + i * one_day]
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break
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if curve:
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self.treasury_curve = curve
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rate = self.treasury_curve[self.treasury_duration]
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#1month note data begins in 8/2001, so we can use 3month instead.
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if rate == None and self.treasury_duration == '1month':
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rate = self.treasury_curve['3month']
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if rate != None:
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return rate * (td.days + 1) / 365
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message = "no rate for end date = {dt} and term = {term}. Check \
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that date doesn't exceed treasury history range."
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message = message.format(
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dt=self.end_date,
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term=self.treasury_duration
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)
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raise Exception(message)
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class RiskReport():
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def __init__(
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self,
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algorithm_returns,
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trading_environment,
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exceeded_max_loss=False):
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"""
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algorithm_returns needs to be a list of daily_return objects
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sorted in date ascending order
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"""
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self.algorithm_returns = algorithm_returns
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self.trading_environment = trading_environment
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self.exceeded_max_loss = exceeded_max_loss
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self.created = epoch_now()
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if len(self.algorithm_returns) == 0:
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start_date = self.trading_environment.period_start
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end_date = self.trading_environment.period_end
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else:
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start_date = self.algorithm_returns[0].date
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end_date = self.algorithm_returns[-1].date
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self.month_periods = self.periodsInRange(1, start_date, end_date)
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self.three_month_periods = self.periodsInRange(3, start_date, end_date)
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self.six_month_periods = self.periodsInRange(6, start_date, end_date)
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self.year_periods = self.periodsInRange(12, start_date, end_date)
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def to_dict(self):
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"""
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RiskMetrics are calculated for rolling windows in four lengths::
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- 1_month
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- 3_month
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- 6_month
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- 12_month
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The return value of this funciton is a dictionary keyed by the above
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list of durations. The value of each entry is a list of RiskMetric
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dicts of the same duration as denoted by the top_level key.
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See :py:meth:`RiskMetrics.to_dict` for the detailed list of fields
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provided for each period.
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"""
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return {
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'one_month' : [x.to_dict() for x in self.month_periods],
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'three_month' : [x.to_dict() for x in self.three_month_periods],
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'six_month' : [x.to_dict() for x in self.six_month_periods],
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'twelve_month' : [x.to_dict() for x in self.year_periods],
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'exceeded_max_loss' : self.exceeded_max_loss,
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'created' : self.created
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}
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def periodsInRange(self, months_per, start, end):
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one_day = datetime.timedelta(days = 1)
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ends = []
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cur_start = start.replace(day=1)
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# in edge cases (all sids filtered out, start/end are adjacent)
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# a test will not generate any returns data
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if len(self.algorithm_returns) == 0:
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return ends
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#ensure that we have an end at the end of a calendar month, in case
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#the return series ends mid-month...
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the_end = advance_by_months(end.replace(day=1),1) - one_day
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while True:
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cur_end = advance_by_months(cur_start, months_per) - one_day
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if(cur_end > the_end):
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break
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cur_period_metrics = RiskMetrics(
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start_date=cur_start,
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end_date=cur_end,
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returns=self.algorithm_returns,
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trading_environment=self.trading_environment
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)
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ends.append(cur_period_metrics)
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cur_start = advance_by_months(cur_start, 1)
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return ends
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def find_metric_by_end(self, end_date, duration, metric):
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col = getattr(self, duration + "_periods")
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col = [getattr(x, metric) for x in col if x.end_date == end_date]
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if len(col) == 1:
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return col[0]
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return None
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