mirror of
https://github.com/wassname/catalyst.git
synced 2026-07-12 11:57:07 +08:00
Stephen Diehl
@@ -285,17 +285,16 @@ class PerformanceTracker():
|
||||
)
|
||||
|
||||
def handle_simulation_end(self):
|
||||
assert False
|
||||
|
||||
self.risk_report = risk.RiskReport(
|
||||
self.returns,
|
||||
self.trading_environment
|
||||
)
|
||||
#self.risk_report = risk.RiskReport(
|
||||
#self.returns,
|
||||
#self.trading_environment
|
||||
#)
|
||||
|
||||
# Output Results
|
||||
if self.result_stream:
|
||||
# TODO: proper framing
|
||||
self.result_stream.send_pyobj(self.risk_report.to_dict())
|
||||
#if self.result_stream:
|
||||
## TODO: proper framing
|
||||
#self.result_stream.send_pyobj(self.risk_report.to_dict())
|
||||
|
||||
self.result_stream.send_pyobj(None)
|
||||
|
||||
|
||||
+200
-147
@@ -1,69 +1,13 @@
|
||||
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
|
||||
"""
|
||||
|
||||
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)
|
||||
Risk Report
|
||||
===========
|
||||
|
||||
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 |
|
||||
| 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. |
|
||||
@@ -80,7 +24,7 @@ class RiskMetrics():
|
||||
+-----------------+----------------------------------------------------+
|
||||
| beta | The _algorithm_ beta to the benchmark. |
|
||||
+-----------------+----------------------------------------------------+
|
||||
| alpha | The _algorithm_ alpha to the benchmark. |
|
||||
| alpha | The _algorithm_ alpha to the benchmark. |
|
||||
+-----------------+----------------------------------------------------+
|
||||
| excess_return | The excess return of the algorithm over the |
|
||||
| | benchmark. |
|
||||
@@ -89,6 +33,96 @@ class RiskMetrics():
|
||||
| | for the portfolio returns between self.start_date |
|
||||
| | and self.end_date. |
|
||||
+-----------------+----------------------------------------------------+
|
||||
|
||||
"""
|
||||
|
||||
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
|
||||
|
||||
|
||||
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
|
||||
|
||||
return dt.replace(year = dt.year + years, month = month)
|
||||
|
||||
|
||||
class DailyReturn():
|
||||
|
||||
def __init__(self, date, returns):
|
||||
self.date = date
|
||||
self.returns = returns
|
||||
|
||||
def to_dict(self):
|
||||
return {
|
||||
'dt' : self.date,
|
||||
'returns' : self.returns
|
||||
}
|
||||
|
||||
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
|
||||
)
|
||||
|
||||
# TODO: vestigal?
|
||||
#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):
|
||||
"""
|
||||
Creates a dictionary representing the state of the risk report.
|
||||
Returns a dict object of the form:
|
||||
"""
|
||||
return {
|
||||
'trading_days' : self.trading_days,
|
||||
@@ -104,49 +138,73 @@ class RiskMetrics():
|
||||
|
||||
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",
|
||||
metrics = [
|
||||
"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"
|
||||
]:
|
||||
]
|
||||
|
||||
for metric in metrics:
|
||||
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)]
|
||||
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):
|
||||
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
|
||||
|
||||
"""
|
||||
http://en.wikipedia.org/wiki/Sharpe_ratio
|
||||
"""
|
||||
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,
|
||||
"""
|
||||
|
||||
.. math::
|
||||
\beta_a = \frac {\mathrm{Cov}(r_a,r_p)}{\mathrm{Var}(r_p)}
|
||||
|
||||
http://en.wikipedia.org/wiki/Beta_(finance)
|
||||
"""
|
||||
|
||||
#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)
|
||||
@@ -154,12 +212,21 @@ class RiskMetrics():
|
||||
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
|
||||
|
||||
|
||||
return (
|
||||
beta,
|
||||
algorithm_covariance,
|
||||
benchmark_variance,
|
||||
condition_number,
|
||||
eigen_values
|
||||
)
|
||||
|
||||
def calculate_alpha(self):
|
||||
"""
|
||||
http://en.wikipedia.org/wiki/Alpha_(investment)
|
||||
"""
|
||||
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
|
||||
@@ -171,23 +238,23 @@ class RiskMetrics():
|
||||
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:
|
||||
@@ -210,18 +277,18 @@ class RiskMetrics():
|
||||
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):
|
||||
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]
|
||||
@@ -234,25 +301,27 @@ class RiskMetrics():
|
||||
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
|
||||
"""
|
||||
|
||||
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::
|
||||
@@ -260,28 +329,27 @@ class RiskReport():
|
||||
- 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.
|
||||
|
||||
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.
|
||||
provided for each period.
|
||||
"""
|
||||
d = {
|
||||
return {
|
||||
'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],
|
||||
'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
|
||||
|
||||
#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:
|
||||
@@ -289,39 +357,24 @@ class RiskReport():
|
||||
if(cur_end > the_end):
|
||||
break
|
||||
cur_period_metrics = RiskMetrics(
|
||||
start_date=cur_start,
|
||||
end_date=cur_end,
|
||||
returns=self.algorithm_returns,
|
||||
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):
|
||||
|
||||
@@ -346,23 +399,23 @@ class TradingEnvironment(object):
|
||||
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,
|
||||
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
|
||||
|
||||
|
||||
|
||||
@@ -1,3 +1,5 @@
|
||||
from gevent_zeromq import zmq
|
||||
|
||||
import zipline.util as qutil
|
||||
import zipline.messaging as qmsg
|
||||
import zipline.protocol as zp
|
||||
@@ -14,6 +16,12 @@ class TestClient(qmsg.Component):
|
||||
self.received_count = 0
|
||||
self.prev_dt = None
|
||||
|
||||
self.result_streams = []
|
||||
|
||||
# Maximum outgoing result streams, really shouldn't ever
|
||||
# need more than 1.
|
||||
self.max_outgoing = 5
|
||||
|
||||
@property
|
||||
def get_id(self):
|
||||
return "TEST_CLIENT"
|
||||
@@ -25,6 +33,17 @@ class TestClient(qmsg.Component):
|
||||
def open(self):
|
||||
self.data_feed = self.connect_result()
|
||||
|
||||
def result_stream(self, zmq_socket, context=None):
|
||||
"""
|
||||
Asynchronously grab a socket to stream results out on.
|
||||
"""
|
||||
ctx = context or zmq.Context.instance()
|
||||
sock = ctx.socket(zmq.PULL)
|
||||
sock.bind(zmq_socket)
|
||||
|
||||
# Add
|
||||
self.result_streams.append( sock )
|
||||
|
||||
def do_work(self):
|
||||
socks = dict(self.poll.poll(self.heartbeat_timeout))
|
||||
|
||||
|
||||
@@ -5,11 +5,11 @@ def ZmqConsole(sock_typ, socket_addr, sock_conn=None, context=None):
|
||||
|
||||
context = context or zmq.Context.instance()
|
||||
socket = context.socket(zmq.PULL)
|
||||
socket.connect('tcp://127.0.0.1:3141')
|
||||
socket.bind(socket_addr)
|
||||
|
||||
def console():
|
||||
while True:
|
||||
msg = socket.recv()
|
||||
msg = socket.recv_pyobj()
|
||||
print msg
|
||||
import pdb; pdb.set_trace()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user