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Rearrange docs & and fix some name conflicts.

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Stephen Diehl
2012-03-15 11:47:28 -04:00
parent 674f445cda
commit f4bdf1fc11
1 changed files with 167 additions and 150 deletions
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zipline/finance/performance.py
+167 -150
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@@ -1,12 +1,121 @@
"""
Performance Tracking
====================
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| period_start | The beginning of the period to be tracked. datetime|
| | in pytz.utc timezone. Will always be 0:00 on the |
| | date in UTC. The fact that the time may be on the |
| | prior day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| period_end | The end of the period to be tracked. datetime |
| | in pytz.utc timezone. Will always be 23:59 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| progress | percentage of test completed |
+-----------------+----------------------------------------------------+
| cumulative_capti| The net capital used (positive is spent) through |
| al_used | the course of all the events sent to this tracker |
+-----------------+----------------------------------------------------+
| max_capital_used| The maximum amount of capital deployed through the |
| | course of all the events sent to this tracker |
+-----------------+----------------------------------------------------+
| last_close | The most recent close of the market. datetime in |
| | pytz.utc timezone. Will always be 23:59 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| last_open | The most recent open of the market. datetime in |
| | pytz.utc timezone. Will always be 00:00 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| capital_base | The initial capital assumed for this tracker. |
+-----------------+----------------------------------------------------+
| returns | List of dicts representing daily returns. See the |
| | comments for |
| | :py:meth:`zipline.finance.risk.DailyReturn.to_dict`|
+-----------------+----------------------------------------------------+
| cumulative_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
+-----------------+----------------------------------------------------+
| todays_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker with datetime stamps between last_open|
| | and last_close. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
| | TODO: adding this because we calculate it. May be |
| | overkill. |
+-----------------+----------------------------------------------------+
| cumulative_risk | A dictionary representing the risk metrics |
| _metrics | calculated based on the positions aggregated |
| | through all the events delivered to this tracker. |
| | For details look at the comments for |
| | :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`|
+-----------------+----------------------------------------------------+
| timestamp | System time evevent occurs in zipilne |
+-----------------+----------------------------------------------------+
Position Tracking
=================
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| sid | the identifier for the security held in this |
| | position. |
+-----------------+----------------------------------------------------+
| amount | whole number of shares in the position |
+-----------------+----------------------------------------------------+
| last_sale_price | price at last sale of the security on the exchange |
+-----------------+----------------------------------------------------+
| last_sale_date | datetime of the last trade of the position's |
| | security on the exchange |
+-----------------+----------------------------------------------------+
| timestamp | System time evevent occurs in zipilne |
+-----------------+----------------------------------------------------+
Performance Period
==================
+---------------+------------------------------------------------------+
| key | value |
+===============+======================================================+
| ending_value | the total market value of the positions held at the |
| | end of the period |
+---------------+------------------------------------------------------+
| capital_used | the net capital consumed (positive means spent) by |
| | buying and selling securities in the period |
+---------------+------------------------------------------------------+
| starting_value| the total market value of the positions held at the |
| | start of the period |
+---------------+------------------------------------------------------+
| starting_cash | cash on hand at the beginning of the period |
+---------------+------------------------------------------------------+
| ending_cash | cash on hand at the end of the period |
+---------------+------------------------------------------------------+
| positions | a list of dicts representing positions, see |
| | :py:meth:`Position.to_dict()` |
| | for details on the contents of the dict |
+---------------+------------------------------------------------------+
| timestamp | System time evevent occurs in zipilne |
+---------------+------------------------------------------------------+
"""
import datetime
import pytz
import math
import msgpack
import pandas
import math
import zmq
from zmq.core.poll import select
import zipline.messaging as qmsg
import zipline.util as qutil
import zipline.protocol as zp
import zipline.finance.risk as risk
@@ -56,69 +165,13 @@ class PerformanceTracker():
def to_dict(self):
"""
Creates a dictionary representing the state of this tracker.
Returns a dict object of the form:
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| period_start | The beginning of the period to be tracked. datetime|
| | in pytz.utc timezone. Will always be 0:00 on the |
| | date in UTC. The fact that the time may be on the |
| | prior day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| period_end | The end of the period to be tracked. datetime |
| | in pytz.utc timezone. Will always be 23:59 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| progress | percentage of test completed |
+-----------------+----------------------------------------------------+
| cumulative_capti| The net capital used (positive is spent) through |
| al_used | the course of all the events sent to this tracker |
+-----------------+----------------------------------------------------+
| max_capital_used| The maximum amount of capital deployed through the |
| | course of all the events sent to this tracker |
+-----------------+----------------------------------------------------+
| last_close | The most recent close of the market. datetime in |
| | pytz.utc timezone. Will always be 23:59 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| last_open | The most recent open of the market. datetime in |
| | pytz.utc timezone. Will always be 00:00 on the |
| | date in UTC. The fact that the time may be on the |
| | next day in the exchange's local time is ignored |
+-----------------+----------------------------------------------------+
| capital_base | The initial capital assumed for this tracker. |
+-----------------+----------------------------------------------------+
| returns | List of dicts representing daily returns. See the |
| | comments for |
| | :py:meth:`zipline.finance.risk.DailyReturn.to_dict`|
+-----------------+----------------------------------------------------+
| cumulative_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
+-----------------+----------------------------------------------------+
| todays_perf | A dictionary representing the cumulative |
| | performance through all the events delivered to |
| | this tracker with datetime stamps between last_open|
| | and last_close. For details see the comments on |
| | :py:meth:`PerformancePeriod.to_dict` |
| | TODO: adding this because we calculate it. May be |
| | overkill. |
+-----------------+----------------------------------------------------+
| cumulative_risk | A dictionary representing the risk metrics |
| _metrics | calculated based on the positions aggregated |
| | through all the events delivered to this tracker. |
| | For details look at the comments for |
| | :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`|
+-----------------+----------------------------------------------------+
Creates a dictionary representing the state of this tracker.
Returns a dict object of the form:
"""
returns_list = [x.to_dict() for x in self.returns]
d = {
return {
'period_start' : self.period_start,
'period_end' : self.period_end,
'progress' : self.progress,
@@ -130,9 +183,9 @@ class PerformanceTracker():
'returns' : returns_list,
'cumulative_perf' : self.cumulative_performance.to_dict(),
'todays_perf' : self.todays_performance.to_dict(),
'cumulative_risk_metrics' : self.cumulative_risk_metrics.to_dict()
'cumulative_risk_metrics' : self.cumulative_risk_metrics.to_dict(),
'timestamp' : datetime.datetime.now(),
}
return d
def update(self, event_frame):
for dt, event_series in event_frame.iteritems():
@@ -158,55 +211,55 @@ class PerformanceTracker():
self.cumulative_capital_used += transaction_cost
if(math.fabs(self.cumulative_capital_used) > self.max_capital_used):
self.max_capital_used = math.fabs(self.cumulative_capital_used)
cushioned_capital = 1.1 * self.max_capital_used
self.max_capital_used = self.round_to_nearest(
cushioned_capital,
cushioned_capital,
base=5000
)
self.max_leverage = self.max_capital_used/self.capital_base
#update last sale
#update last sale
self.cumulative_performance.update_last_sale(event)
self.todays_performance.update_last_sale(event)
#calculate performance as of last trade
self.cumulative_performance.calculate_performance()
self.todays_performance.calculate_performance()
def handle_market_close(self):
#add the return results from today to the list of DailyReturn objects.
todays_date = self.market_close.replace(hour=0, minute=0, second=0)
todays_return_obj = risk.DailyReturn(
todays_date,
todays_date,
self.todays_performance.returns
)
self.returns.append(todays_return_obj)
#calculate risk metrics for cumulative performance
self.cumulative_risk_metrics = risk.RiskMetrics(
start_date=self.period_start,
end_date=self.market_close.replace(hour=0, minute=0, second=0),
start_date=self.period_start,
end_date=self.market_close.replace(hour=0, minute=0, second=0),
returns=self.returns,
trading_environment=self.trading_environment
)
#move the market day markers forward
self.market_open = self.market_open + self.calendar_day
while not self.trading_environment.is_trading_day(self.market_open):
if self.market_open > self.trading_environment.trading_days[-1]:
raise Exception("Attempt to backtest beyond available history.")
self.market_open = self.market_open + self.calendar_day
self.market_close = self.market_open + self.trading_day
self.market_close = self.market_open + self.trading_day
self.day_count += 1.0
#calculate progress of test
self.progress = self.day_count / self.total_days
# Output Results
if self.result_stream:
# TODO: proper framing
self.result_stream.send(str(self.to_dict()))
self.result_stream.send_pyobj(self.to_dict())
#roll over positions to current day.
self.todays_performance.calculate_performance()
@@ -225,26 +278,27 @@ class PerformanceTracker():
# Output Results
if self.result_stream:
# TODO: proper framing
self.result_stream.send(str(self.risk_report.to_dict()))
self.result_stream.send_pyobj(self.risk_report.to_dict())
def round_to_nearest(self, x, base=5):
return int(base * round(float(x)/base))
class Position():
def __init__(self, sid):
self.sid = sid
self.amount = 0
self.cost_basis = 0.0 ##per share
self.last_sale_price = None
self.last_sale_date = None
def update(self, txn):
if(self.sid != txn.sid):
raise NameError('updating position with txn for a different sid')
#throw exception
if(self.amount + txn.amount == 0): #we're covering a short or closing a position
#we're covering a short or closing a position
if(self.amount + txn.amount == 0):
self.cost_basis = 0.0
self.amount = 0
else:
@@ -254,130 +308,93 @@ class Position():
total_shares = self.amount + txn.amount
self.cost_basis = total_cost/total_shares
self.amount = self.amount + txn.amount
def currentValue(self):
return self.amount * self.last_sale_price
def __repr__(self):
template = "sid: {sid}, amount: {amount}, cost_basis: {cost_basis}, \
last_sale_price: {last_sale_price}"
last_sale_price: {last_sale_price}"
return template.format(
sid=self.sid,
amount=self.amount,
cost_basis=self.cost_basis,
sid=self.sid,
amount=self.amount,
cost_basis=self.cost_basis,
last_sale_price=self.last_sale_price
)
def to_dict(self):
"""
Creates a dictionary representing the state of this position.
Returns a dict object of the form:
+-----------------+----------------------------------------------------+
| key | value |
+=================+====================================================+
| sid | the identifier for the security held in this |
| | position. |
+-----------------+----------------------------------------------------+
| amount | whole number of shares in the position |
+-----------------+----------------------------------------------------+
| last_sale_price | price at last sale of the security on the exchange |
+-----------------+----------------------------------------------------+
| last_sale_date | datetime of the last trade of the position's |
| | security on the exchange |
+-----------------+----------------------------------------------------+
"""
state = {
'sid' : self.sid,
'amount' : self.amount,
'cost_basis' : self.cost_basis,
'last_sale_price' : self.last_sale_price,
'last_sale_date' : self.last_sale_date
'last_sale_date' : self.last_sale_date,
'timestamp' : datetime.datetime.now(),
}
return state
class PerformancePeriod():
def __init__(self, initial_positions, starting_value, starting_cash):
self.ending_value = 0.0
self.period_capital_used = 0.0
self.pnl = 0.0
#sid => position object
self.positions = initial_positions
self.positions = initial_positions
self.starting_value = starting_value
#cash balance at start of period
self.starting_cash = starting_cash
self.ending_cash = starting_cash
def calculate_performance(self):
self.ending_value = self.calculate_positions_value()
total_at_start = self.starting_cash + self.starting_value
self.ending_cash = self.starting_cash + self.period_capital_used
total_at_end = self.ending_cash + self.ending_value
self.pnl = total_at_end - total_at_start
if(total_at_start != 0):
self.returns = self.pnl / total_at_start
else:
self.returns = 0.0
def execute_transaction(self, txn):
if(not self.positions.has_key(txn.sid)):
self.positions[txn.sid] = Position(txn.sid)
self.positions[txn.sid].update(txn)
self.period_capital_used += -1 * txn.price * txn.amount
def calculate_positions_value(self):
mktValue = 0.0
for key,pos in self.positions.iteritems():
mktValue += pos.currentValue()
return mktValue
def update_last_sale(self, event):
is_trade = event.type == zp.DATASOURCE_TYPE.TRADE
if self.positions.has_key(event.sid) and is_trade:
self.positions[event.sid].last_sale_price = event.price
self.positions[event.sid].last_sale_price = event.price
self.positions[event.sid].last_sale_date = event.dt
def to_dict(self):
"""
Creates a dictionary representing the state of this performance period
Returns a dict object of the form:
+---------------+-----------------------------------------------------------+
| key | value |
+===============+===========================================================+
| ending_value | the total market value of the positions held at the |
| | end of the period |
+---------------+-----------------------------------------------------------+
| capital_used | the net capital consumed (positive means spent) by |
| | buying and selling securities in the period |
+---------------+-----------------------------------------------------------+
| starting_value| the total market value of the positions held at the |
| | start of the period |
+---------------+-----------------------------------------------------------+
| starting_cash | cash on hand at the beginning of the period |
+---------------+-----------------------------------------------------------+
| ending_cash | cash on hand at the end of the period |
+---------------+-----------------------------------------------------------+
| positions | a list of dicts representing positions, see |
| | :py:meth:`Position.to_dict()` |
| | for details on the contents of the dict |
+---------------+-----------------------------------------------------------+
"""
d = {
return {
'ending_value' : self.ending_value,
'capital_used' : self.period_capital_used,
'starting_value' : self.starting_value,
'starting_cash' : self.starting_cash,
'ending_cash' : self.ending_cash
'ending_cash' : self.ending_cash,
'positions' : self.positions,
'timestamp' : datetime.datetime.now(),
}
position_list = []
for pos in self.positions:
position_list.append(pos)
d['positions'] = position_list
return d