mirror of
https://github.com/wassname/catalyst.git
synced 2026-08-19 12:00:15 +08:00
357 lines
14 KiB
Python
357 lines
14 KiB
Python
import datetime
|
|
import math
|
|
import pytz
|
|
import numpy as np
|
|
import numpy.linalg as la
|
|
import zipline.util as qutil
|
|
import zipline.protocol as zp
|
|
from pymongo import ASCENDING, DESCENDING
|
|
|
|
class DailyReturn():
|
|
|
|
def __init__(self, date, returns):
|
|
self.date = date
|
|
self.returns = returns
|
|
|
|
def to_dict(self):
|
|
d = {
|
|
'dt': self.date,
|
|
'returns': self.returns
|
|
}
|
|
|
|
return d
|
|
|
|
def __repr__(self):
|
|
return str(self.date) + " - " + str(self.returns)
|
|
|
|
class RiskMetrics():
|
|
def __init__(self, start_date, end_date, returns, trading_environment):
|
|
|
|
self.treasury_curves = trading_environment.treasury_curves
|
|
self.start_date = start_date
|
|
self.end_date = end_date
|
|
self.trading_environment = trading_environment
|
|
self.algorithm_period_returns, self.algorithm_returns = self.calculate_period_returns(returns)
|
|
benchmark_returns = [x for x in self.trading_environment.benchmark_returns if x.date >= returns[0].date and x.date <= returns[-1].date]
|
|
|
|
self.benchmark_period_returns, self.benchmark_returns = self.calculate_period_returns(benchmark_returns)
|
|
if(len(self.benchmark_returns) != len(self.algorithm_returns)):
|
|
message = "Mismatch between benchmark_returns ({bm_count}) and \
|
|
algorithm_returns ({algo_count}) in range {start} : {end}"
|
|
message.format(
|
|
bm_count=len(self.benchmark_returns),
|
|
algo_count=len(self.algorithm_returns),
|
|
start=start_date,
|
|
end=end_date
|
|
)
|
|
|
|
raise Exception(messge)
|
|
self.trading_days = len(self.benchmark_returns)
|
|
self.benchmark_volatility = self.calculate_volatility(self.benchmark_returns)
|
|
self.algorithm_volatility = self.calculate_volatility(self.algorithm_returns)
|
|
self.treasury_period_return = self.choose_treasury()
|
|
self.sharpe = self.calculate_sharpe()
|
|
self.beta, self.algorithm_covariance, self.benchmark_variance, \
|
|
self.condition_number, self.eigen_values = self.calculate_beta()
|
|
self.alpha = self.calculate_alpha()
|
|
self.excess_return = self.algorithm_period_returns - self.treasury_period_return
|
|
self.max_drawdown = self.calculate_max_drawdown()
|
|
|
|
def to_dict(self):
|
|
"""
|
|
+-----------------+----------------------------------------------------+
|
|
| key | value |
|
|
+=================+====================================================+
|
|
| trading_days | The number of trading days between self.start_date |
|
|
| | and self.end_date |
|
|
+-----------------+----------------------------------------------------+
|
|
| benchmark_volat\| The volatility of the benchmark between |
|
|
| ility | self.start_date and self.end_date. |
|
|
+-----------------+----------------------------------------------------+
|
|
| algo_volatility | The volatility of the algo between self.start_date |
|
|
| | and self.end_date. |
|
|
+-----------------+----------------------------------------------------+
|
|
| treasury_period\| The return of treasuries over the period. Treasury |
|
|
| _return | maturity is chosen to match the duration of the |
|
|
| | test period. |
|
|
+-----------------+----------------------------------------------------+
|
|
| sharpe | The sharpe ratio based on the _algorithm_ (rather |
|
|
| | than the static portfolio) returns. |
|
|
+-----------------+----------------------------------------------------+
|
|
| beta | The _algorithm_ beta to the benchmark. |
|
|
+-----------------+----------------------------------------------------+
|
|
| alpha | The _algorithm_ alpha to the benchmark. |
|
|
+-----------------+----------------------------------------------------+
|
|
| excess_return | The excess return of the algorithm over the |
|
|
| | benchmark. |
|
|
+-----------------+----------------------------------------------------+
|
|
| max_drawdown | The largest relative peak to relative trough move |
|
|
| | for the portfolio returns between self.start_date |
|
|
| | and self.end_date. |
|
|
+-----------------+----------------------------------------------------+
|
|
"""
|
|
d = {
|
|
'trading_days' : self.trading_days,
|
|
'benchmark_volatility' : self.benchmark_volatility,
|
|
'algo_volatility' : self.algo_volatility,
|
|
'treasury_period_return': self.treasury_period_return,
|
|
'sharpe' : self.sharpe,
|
|
'beta' : self.beta,
|
|
'alpha' : self.alpha,
|
|
'excess_return' : self.excess_return,
|
|
'max_drawdown' : self.max_drawdown
|
|
}
|
|
|
|
def __repr__(self):
|
|
statements = []
|
|
for metric in [
|
|
"algorithm_period_returns",
|
|
"benchmark_period_returns",
|
|
"excess_return",
|
|
"trading_days",
|
|
"benchmark_volatility",
|
|
"algorithm_volatility",
|
|
"sharpe",
|
|
"algorithm_covariance",
|
|
"benchmark_variance",
|
|
"beta",
|
|
"alpha",
|
|
"max_drawdown",
|
|
"algorithm_returns",
|
|
"benchmark_returns",
|
|
"condition_number",
|
|
"eigen_values"
|
|
]:
|
|
value = getattr(self, metric)
|
|
statements.append("{m}:{v}".format(m=metric, v=value))
|
|
|
|
return '\n'.join(statements)
|
|
|
|
def calculate_period_returns(self, daily_returns):
|
|
#TODO: replace this with pandas.
|
|
returns = [x.returns for x in daily_returns if x.date >= self.start_date and x.date <= self.end_date and self.trading_environment.is_trading_day(x.date)]
|
|
period_returns = 1.0
|
|
for r in returns:
|
|
period_returns = period_returns * (1.0 + r)
|
|
period_returns = period_returns - 1.0
|
|
return period_returns, returns
|
|
|
|
def calculate_volatility(self, daily_returns):
|
|
#qutil.LOGGER.debug("trading days {td}".format(td=self.trading_days))
|
|
return np.std(daily_returns, ddof=1) * math.sqrt(self.trading_days)
|
|
|
|
def calculate_sharpe(self):
|
|
return (self.algorithm_period_returns - self.treasury_period_return) / self.algorithm_volatility
|
|
|
|
def calculate_beta(self):
|
|
#it doesn't make much sense to calculate beta for less than two days,
|
|
#so return none.
|
|
if len(self.algorithm_returns) < 2:
|
|
return 0.0, 0.0, 0.0, 0.0, []
|
|
returns_matrix = np.vstack([self.algorithm_returns, self.benchmark_returns])
|
|
C = np.cov(returns_matrix)
|
|
eigen_values = la.eigvals(C)
|
|
condition_number = max(eigen_values) / min(eigen_values)
|
|
algorithm_covariance = C[0][1]
|
|
benchmark_variance = C[1][1]
|
|
beta = C[0][1] / C[1][1]
|
|
|
|
return beta, algorithm_covariance, benchmark_variance, condition_number, eigen_values
|
|
|
|
def calculate_alpha(self):
|
|
return self.algorithm_period_returns - (self.treasury_period_return + self.beta * (self.benchmark_period_returns - self.treasury_period_return))
|
|
|
|
def calculate_max_drawdown(self):
|
|
compounded_returns = []
|
|
cur_return = 0.0
|
|
for r in self.algorithm_returns:
|
|
if(r != -1.0):
|
|
cur_return = math.log(1.0 + r) + cur_return
|
|
#this is a guard for a single day returning -100%
|
|
else:
|
|
qutil.LOGGER.warn("negative 100 percent return, zeroing the returns")
|
|
cur_return = 0.0
|
|
compounded_returns.append(cur_return)
|
|
|
|
cur_max = None
|
|
max_drawdown = None
|
|
for cur in compounded_returns:
|
|
if cur_max == None or cur > cur_max:
|
|
cur_max = cur
|
|
|
|
drawdown = (cur - cur_max)
|
|
if max_drawdown == None or drawdown < max_drawdown:
|
|
max_drawdown = drawdown
|
|
|
|
if max_drawdown == None:
|
|
return 0.0
|
|
|
|
return 1.0 - math.exp(max_drawdown)
|
|
|
|
|
|
def choose_treasury(self):
|
|
td = self.end_date - self.start_date
|
|
if td.days <= 31:
|
|
self.treasury_duration = '1month'
|
|
elif td.days <= 93:
|
|
self.treasury_duration = '3month'
|
|
elif td.days <= 186:
|
|
self.treasury_duration = '6month'
|
|
elif td.days <= 366:
|
|
self.treasury_duration = '1year'
|
|
elif td.days <= 365 * 2 + 1:
|
|
self.treasury_duration = '2year'
|
|
elif td.days <= 365 * 3 + 1:
|
|
self.treasury_duration = '3year'
|
|
elif td.days <= 365 * 5 + 2:
|
|
self.treasury_duration = '5year'
|
|
elif td.days <= 365 * 7 + 2:
|
|
self.treasury_duration = '7year'
|
|
elif td.days <= 365 * 10 + 2:
|
|
self.treasury_duration = '10year'
|
|
else:
|
|
self.treasury_duration = '30year'
|
|
|
|
|
|
one_day = datetime.timedelta(days=1)
|
|
|
|
curve = None
|
|
# in case end date is not a trading day, search for the next market
|
|
# day for an interest rate
|
|
for i in range(7):
|
|
if(self.treasury_curves.has_key(self.end_date + i * one_day)):
|
|
curve = self.treasury_curves[self.end_date + i * one_day]
|
|
break
|
|
|
|
if curve:
|
|
self.treasury_curve = curve
|
|
rate = self.treasury_curve[self.treasury_duration]
|
|
#1month note data begins in 8/2001, so we can use 3month instead.
|
|
if rate == None and self.treasury_duration == '1month':
|
|
rate = self.treasury_curve['3month']
|
|
if rate != None:
|
|
return rate * (td.days + 1) / 365
|
|
|
|
message = "no rate for end date = {dt} and term = {term}. Using zero."
|
|
message = message.format(dt=self.end_date,term=self.treasury_duration)
|
|
raise Exception(message)
|
|
|
|
class RiskReport():
|
|
|
|
def __init__(self, algorithm_returns, trading_environment):
|
|
""" algorithm_returns needs to be a list of daily_return objects
|
|
sorted in date ascending order
|
|
"""
|
|
|
|
self.algorithm_returns = algorithm_returns
|
|
self.trading_environment = trading_environment
|
|
|
|
start_date = self.algorithm_returns[0].date
|
|
end_date = self.algorithm_returns[-1].date
|
|
|
|
self.month_periods = self.periodsInRange(1, start_date, end_date)
|
|
self.three_month_periods = self.periodsInRange(3, start_date, end_date)
|
|
self.six_month_periods = self.periodsInRange(6, start_date, end_date)
|
|
self.year_periods = self.periodsInRange(12, start_date, end_date)
|
|
|
|
def to_dict(self):
|
|
"""
|
|
RiskMetrics are calculated for rolling windows in four lengths::
|
|
- 1_month
|
|
- 3_month
|
|
- 6_month
|
|
- 12_month
|
|
|
|
The return value of this funciton is a dictionary keyed by the above
|
|
list of durations. The value of each entry is a list of RiskMetric
|
|
dicts of the same duration as denoted by the top_level key.
|
|
|
|
See :py:meth:`RiskMetrics.to_dict` for the detailed list of fields
|
|
provided for each period.
|
|
"""
|
|
d = {
|
|
'1_month' : [x.to_dict() for x in self.month_periods],
|
|
'3_month' : [x.to_dict() for x in self.three_year_periods],
|
|
'6_month' : [x.to_dict() for x in self.six_month_periods],
|
|
'12_month' : [x.to_dict() for x in self.month_periods]
|
|
}
|
|
|
|
return d
|
|
|
|
def periodsInRange(self, months_per, start, end):
|
|
one_day = datetime.timedelta(days = 1)
|
|
ends = []
|
|
cur_start = start.replace(day=1)
|
|
#ensure that we have an end at the end of a calendar month, in case
|
|
#the return series ends mid-month...
|
|
the_end = advance_by_months(end.replace(day=1),1) - one_day
|
|
while True:
|
|
cur_end = advance_by_months(cur_start, months_per) - one_day
|
|
if(cur_end > the_end):
|
|
break
|
|
cur_period_metrics = RiskMetrics(
|
|
start_date=cur_start,
|
|
end_date=cur_end,
|
|
returns=self.algorithm_returns,
|
|
trading_environment=self.trading_environment
|
|
)
|
|
|
|
ends.append(cur_period_metrics)
|
|
cur_start = advance_by_months(cur_start, 1)
|
|
|
|
return ends
|
|
|
|
def find_metric_by_end(self, end_date, duration, metric):
|
|
col = getattr(self, duration + "_periods")
|
|
col = [getattr(x, metric) for x in col if x.end_date == end_date]
|
|
if len(col) == 1:
|
|
return col[0]
|
|
return None
|
|
|
|
def advance_by_months(dt, jump_in_months):
|
|
month = dt.month + jump_in_months
|
|
years = month / 12
|
|
month = month % 12
|
|
|
|
# no remainder means that we are landing in december.
|
|
# modulo is, in a way, a zero indexed circular array.
|
|
# this is a way of converting to 1 indexed months.
|
|
# (in our modulo index, december is zeroth)
|
|
if(month == 0):
|
|
month = 12
|
|
years = years - 1
|
|
|
|
r = dt.replace(year = dt.year + years, month = month)
|
|
return r
|
|
|
|
class TradingEnvironment(object):
|
|
|
|
def __init__(self, benchmark_returns, treasury_curves):
|
|
self.trading_days = []
|
|
self.trading_day_map = {}
|
|
self.treasury_curves = treasury_curves
|
|
self.benchmark_returns = benchmark_returns
|
|
for bm in benchmark_returns:
|
|
self.trading_days.append(bm.date)
|
|
self.trading_day_map[bm.date] = bm
|
|
|
|
def normalize_date(self, test_date):
|
|
return datetime.datetime(
|
|
year=test_date.year,
|
|
month=test_date.month,
|
|
day=test_date.day,
|
|
tzinfo=pytz.utc
|
|
)
|
|
|
|
def is_trading_day(self, test_date):
|
|
dt = self.normalize_date(test_date)
|
|
return self.trading_day_map.has_key(dt)
|
|
|
|
def get_benchmark_daily_return(self, test_date):
|
|
date = self.normalize_date(test_date)
|
|
if self.trading_day_map.has_key(date):
|
|
return self.trading_day_map[date].returns
|
|
else:
|
|
return 0.0
|
|
|