mirror of
https://github.com/wassname/catalyst.git
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455 lines
19 KiB
Python
455 lines
19 KiB
Python
"""
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Performance Tracking
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====================
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+-----------------+----------------------------------------------------+
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| key | value |
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+=================+====================================================+
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| period_start | The beginning of the period to be tracked. datetime|
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| | in pytz.utc timezone. Will always be 0:00 on the |
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| | date in UTC. The fact that the time may be on the |
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| | prior day in the exchange's local time is ignored |
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+-----------------+----------------------------------------------------+
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| period_end | The end of the period to be tracked. datetime |
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| | in pytz.utc timezone. Will always be 23:59 on the |
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| | date in UTC. The fact that the time may be on the |
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| | next day in the exchange's local time is ignored |
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+-----------------+----------------------------------------------------+
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| progress | percentage of test completed |
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+-----------------+----------------------------------------------------+
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| cumulative_capti| The net capital used (positive is spent) through |
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| al_used | the course of all the events sent to this tracker |
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+-----------------+----------------------------------------------------+
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| max_capital_used| The maximum amount of capital deployed through the |
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| | course of all the events sent to this tracker |
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+-----------------+----------------------------------------------------+
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| last_close | The most recent close of the market. datetime in |
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| | pytz.utc timezone. Will always be 23:59 on the |
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| | date in UTC. The fact that the time may be on the |
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| | next day in the exchange's local time is ignored |
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+-----------------+----------------------------------------------------+
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| last_open | The most recent open of the market. datetime in |
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| | pytz.utc timezone. Will always be 00:00 on the |
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| | date in UTC. The fact that the time may be on the |
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| | next day in the exchange's local time is ignored |
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+-----------------+----------------------------------------------------+
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| capital_base | The initial capital assumed for this tracker. |
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+-----------------+----------------------------------------------------+
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| returns | List of dicts representing daily returns. See the |
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| | comments for |
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| | :py:meth:`zipline.finance.risk.DailyReturn.to_dict`|
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+-----------------+----------------------------------------------------+
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| cumulative_perf | A dictionary representing the cumulative |
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| | performance through all the events delivered to |
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| | this tracker. For details see the comments on |
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| | :py:meth:`PerformancePeriod.to_dict` |
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+-----------------+----------------------------------------------------+
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| todays_perf | A dictionary representing the cumulative |
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| | performance through all the events delivered to |
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| | this tracker with datetime stamps between last_open|
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| | and last_close. For details see the comments on |
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| | :py:meth:`PerformancePeriod.to_dict` |
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| | TODO: adding this because we calculate it. May be |
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| | overkill. |
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+-----------------+----------------------------------------------------+
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| cumulative_risk | A dictionary representing the risk metrics |
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| _metrics | calculated based on the positions aggregated |
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| | through all the events delivered to this tracker. |
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| | For details look at the comments for |
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| | :py:meth:`zipline.finance.risk.RiskMetrics.to_dict`|
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+-----------------+----------------------------------------------------+
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| timestamp | System time evevent occurs in zipilne |
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+-----------------+----------------------------------------------------+
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Position Tracking
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=================
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+-----------------+----------------------------------------------------+
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| key | value |
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+=================+====================================================+
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| sid | the identifier for the security held in this |
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| | position. |
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+-----------------+----------------------------------------------------+
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| amount | whole number of shares in the position |
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+-----------------+----------------------------------------------------+
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| last_sale_price | price at last sale of the security on the exchange |
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+-----------------+----------------------------------------------------+
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| last_sale_date | datetime of the last trade of the position's |
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| | security on the exchange |
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+-----------------+----------------------------------------------------+
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| timestamp | System time event occurs in zipilne |
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+-----------------+----------------------------------------------------+
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Performance Period
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==================
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+---------------+------------------------------------------------------+
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| key | value |
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+===============+======================================================+
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| ending_value | the total market value of the positions held at the |
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| | end of the period |
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+---------------+------------------------------------------------------+
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| capital_used | the net capital consumed (positive means spent) by |
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| | buying and selling securities in the period |
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+---------------+------------------------------------------------------+
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| starting_value| the total market value of the positions held at the |
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| | start of the period |
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+---------------+------------------------------------------------------+
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| starting_cash | cash on hand at the beginning of the period |
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+---------------+------------------------------------------------------+
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| ending_cash | cash on hand at the end of the period |
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+---------------+------------------------------------------------------+
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| positions | a list of dicts representing positions, see |
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| | :py:meth:`Position.to_dict()` |
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| | for details on the contents of the dict |
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+---------------+------------------------------------------------------+
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| timestamp | System time evevent occurs in zipilne |
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+---------------+------------------------------------------------------+
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"""
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import datetime
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import msgpack
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import pandas
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import math
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import zmq
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import zipline.util as qutil
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import zipline.protocol as zp
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import zipline.finance.risk as risk
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class PerformanceTracker():
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"""
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Tracks the performance of the zipline as it is running in
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the simulator, relays this out to the Deluge broker and then
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to the client. Visually:
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+--------------------+ Result Stream +--------+
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| PerformanceTracker | ----------------> | Deluge |
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+--------------------+ +--------+
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"""
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def __init__(self, trading_environment):
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self.trading_environment = trading_environment
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self.trading_day = datetime.timedelta(hours = 6, minutes = 30)
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self.calendar_day = datetime.timedelta(hours = 24)
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self.period_start = self.trading_environment.period_start
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self.period_end = self.trading_environment.period_end
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self.market_open = self.period_start
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self.market_close = self.market_open + self.trading_day
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self.progress = 0.0
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self.total_days = (self.period_end - self.period_start).days
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self.day_count = 0
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self.cumulative_capital_used = 0.0
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self.max_capital_used = 0.0
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self.capital_base = self.trading_environment.capital_base
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self.returns = []
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self.txn_count = 0
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self.event_count = 0
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self.result_stream = None
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self.last_dict = None
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self.cumulative_performance = PerformancePeriod(
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{},
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self.capital_base,
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starting_cash = self.capital_base
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)
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self.todays_performance = PerformancePeriod(
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{},
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self.capital_base,
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starting_cash = self.capital_base
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)
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def get_portfolio(self):
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return self.cumulative_performance.to_namedict()
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def publish_to(self, zmq_socket, context=None):
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"""
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Publish the performance results asynchronously to a
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socket.
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"""
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ctx = context or zmq.Context.instance()
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sock = ctx.socket(zmq.PUSH)
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sock.connect(zmq_socket)
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self.result_stream = sock
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def to_dict(self):
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"""
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Creates a dictionary representing the state of this tracker.
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Returns a dict object of the form described in header comments.
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"""
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returns_list = [x.to_dict() for x in self.returns]
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return {
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'period_start' : self.period_start,
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'period_end' : self.period_end,
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'progress' : self.progress,
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'cumulative_captial_used' : self.cumulative_capital_used,
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'max_capital_used' : self.max_capital_used,
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'last_close' : self.market_close,
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'last_open' : self.market_open,
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'capital_base' : self.capital_base,
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'returns' : returns_list,
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'cumulative_perf' : self.cumulative_performance.to_dict(),
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'todays_perf' : self.todays_performance.to_dict(),
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'cumulative_risk_metrics' : self.cumulative_risk_metrics.to_dict(),
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'timestamp' : datetime.datetime.now(),
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}
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def process_event(self, event):
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self.event_count += 1
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if(event.dt >= self.market_close):
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self.handle_market_close()
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if not pandas.isnull(event.TRANSACTION):
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self.txn_count += 1
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self.cumulative_performance.execute_transaction(event.TRANSACTION)
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self.todays_performance.execute_transaction(event.TRANSACTION)
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# we're adding a 10% cushion to the capital used,
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# and then rounding to the nearest 5k
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transaction_cost = event.TRANSACTION.price * event.TRANSACTION.amount
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self.cumulative_capital_used += transaction_cost
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if math.fabs(self.cumulative_capital_used) > self.max_capital_used:
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self.max_capital_used = math.fabs(self.cumulative_capital_used)
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cushioned_capital = 1.1 * self.max_capital_used
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self.max_capital_used = self.round_to_nearest(
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cushioned_capital,
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base=5000
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)
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self.max_leverage = self.max_capital_used / self.capital_base
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#update last sale
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self.cumulative_performance.update_last_sale(event)
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self.todays_performance.update_last_sale(event)
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#calculate performance as of last trade
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self.cumulative_performance.calculate_performance()
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self.todays_performance.calculate_performance()
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def handle_market_close(self):
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#add the return results from today to the list of DailyReturn objects.
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todays_date = self.market_close.replace(hour=0, minute=0, second=0)
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todays_return_obj = risk.DailyReturn(
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todays_date,
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self.todays_performance.returns
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)
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self.returns.append(todays_return_obj)
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#calculate risk metrics for cumulative performance
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self.cumulative_risk_metrics = risk.RiskMetrics(
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start_date=self.period_start,
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end_date=self.market_close.replace(hour=0, minute=0, second=0),
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returns=self.returns,
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trading_environment=self.trading_environment
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)
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#move the market day markers forward
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self.market_open = self.market_open + self.calendar_day
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while not self.trading_environment.is_trading_day(self.market_open):
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if self.market_open > self.trading_environment.trading_days[-1]:
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raise Exception("Attempt to backtest beyond available history.")
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self.market_open = self.market_open + self.calendar_day
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self.market_close = self.market_open + self.trading_day
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self.day_count += 1.0
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#calculate progress of test
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self.progress = self.day_count / self.total_days
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# Output Results
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if self.result_stream:
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# TODO: proper framing
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self.result_stream.send_pyobj(self.to_dict())
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#roll over positions to current day.
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self.todays_performance.calculate_performance()
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self.todays_performance = PerformancePeriod(
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self.todays_performance.positions,
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self.todays_performance.ending_value,
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self.todays_performance.ending_cash
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)
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def handle_simulation_end(self):
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#self.risk_report = risk.RiskReport(
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#self.returns,
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#self.trading_environment
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#)
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# Output Results
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#if self.result_stream:
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## TODO: proper framing
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#self.result_stream.send_pyobj(self.risk_report.to_dict())
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if self.result_stream:
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self.result_stream.send_pyobj(None)
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def round_to_nearest(self, x, base=5):
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return int(base * round(float(x)/base))
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class Position():
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def __init__(self, sid):
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self.sid = sid
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self.amount = 0
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self.cost_basis = 0.0 ##per share
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self.last_sale_price = None
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self.last_sale_date = None
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def update(self, txn):
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if(self.sid != txn.sid):
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raise NameError('updating position with txn for a different sid')
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#we're covering a short or closing a position
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if(self.amount + txn.amount == 0):
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self.cost_basis = 0.0
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self.amount = 0
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else:
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prev_cost = self.cost_basis*self.amount
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txn_cost = txn.amount*txn.price
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total_cost = prev_cost + txn_cost
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total_shares = self.amount + txn.amount
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self.cost_basis = total_cost/total_shares
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self.amount = self.amount + txn.amount
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def currentValue(self):
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return self.amount * self.last_sale_price
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def __repr__(self):
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template = "sid: {sid}, amount: {amount}, cost_basis: {cost_basis}, \
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last_sale_price: {last_sale_price}"
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return template.format(
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sid=self.sid,
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amount=self.amount,
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cost_basis=self.cost_basis,
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last_sale_price=self.last_sale_price
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)
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def to_dict(self):
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"""
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Creates a dictionary representing the state of this position.
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Returns a dict object of the form:
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"""
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return {
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'sid' : self.sid,
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'amount' : self.amount,
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'cost_basis' : self.cost_basis,
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'last_sale_price' : self.last_sale_price,
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'last_sale_date' : self.last_sale_date,
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'timestamp' : datetime.datetime.now(),
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}
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class PerformancePeriod():
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def __init__(self, initial_positions, starting_value, starting_cash):
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self.ending_value = 0.0
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self.period_capital_used = 0.0
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self.pnl = 0.0
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#sid => position object
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self.positions = initial_positions
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self.starting_value = starting_value
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#cash balance at start of period
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self.starting_cash = starting_cash
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self.ending_cash = starting_cash
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self.calculate_performance()
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def calculate_performance(self):
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self.ending_value = self.calculate_positions_value()
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total_at_start = self.starting_cash + self.starting_value
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self.ending_cash = self.starting_cash + self.period_capital_used
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total_at_end = self.ending_cash + self.ending_value
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self.pnl = total_at_end - total_at_start
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if(total_at_start != 0):
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self.returns = self.pnl / total_at_start
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else:
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self.returns = 0.0
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def execute_transaction(self, txn):
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if(not self.positions.has_key(txn.sid)):
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self.positions[txn.sid] = Position(txn.sid)
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self.positions[txn.sid].update(txn)
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self.period_capital_used += -1 * txn.price * txn.amount
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def calculate_positions_value(self):
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mktValue = 0.0
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for key,pos in self.positions.iteritems():
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mktValue += pos.currentValue()
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return mktValue
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def update_last_sale(self, event):
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is_trade = event.type == zp.DATASOURCE_TYPE.TRADE
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if self.positions.has_key(event.sid) and is_trade:
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self.positions[event.sid].last_sale_price = event.price
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self.positions[event.sid].last_sale_date = event.dt
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def to_dict(self):
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"""
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Creates a dictionary representing the state of this performance
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period. See header comments for a detailed description.
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"""
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positions = self.get_positions()
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return {
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'ending_value' : self.ending_value,
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'capital_used' : self.period_capital_used,
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'starting_value' : self.starting_value,
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'starting_cash' : self.starting_cash,
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'ending_cash' : self.ending_cash,
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'positions' : positions,
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'timestamp' : datetime.datetime.now(),
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}
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def to_namedict(self):
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"""
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Creates a namedict representing the state of this perfomance period.
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Properties are the same as the results of to_dict. See header comments
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for a detailed description.
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"""
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positions = self.get_positions(namedicted=True)
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positions = zp.namedict(positions)
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return zp.namedict({
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'ending_value' : self.ending_value,
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'capital_used' : self.period_capital_used,
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'starting_value' : self.starting_value,
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'starting_cash' : self.starting_cash,
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'ending_cash' : self.ending_cash,
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'positions' : positions
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})
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def get_positions(self, namedicted=False):
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positions = {}
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for sid, pos in self.positions.iteritems():
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cur = pos.to_dict()
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if namedicted:
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positions[sid] = zp.namedict(cur)
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else:
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positions[sid] = cur
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return positions
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