mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-28 12:26:23 +08:00
scottsanderson
603 lines
25 KiB
Python
603 lines
25 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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| started_at | datetime in utc marking the start of this test |
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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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| 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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| exceeded_max_ | True if the simulation was stopped because single |
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| loss | day losses exceeded the max_drawdown stipulated in |
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| | trading_environment. |
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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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| cost_basis | the volume weighted average price paid per share |
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+-----------------+----------------------------------------------------+
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Performance Period
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==================
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Performance Periods are updated with every trade. When calling
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code needs a portfolio object that fulfills the algorithm
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protocol, use the PerformancePeriod.as_portfolio method. See that
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method for comments on the specific fields provided (and
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omitted).
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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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| pnl | Dollar value profit and loss, for both realized and |
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| | unrealized gains. |
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+---------------+------------------------------------------------------+
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| returns | percentage returns for the entire portfolio over the |
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| | period |
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+---------------+------------------------------------------------------+
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| cumulative_ | The net capital used (positive is spent) during |
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| capital_used | the period |
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+---------------+------------------------------------------------------+
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| max_capital_ | The maximum amount of capital deployed during the |
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| used | period. |
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+---------------+------------------------------------------------------+
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| max_leverage | The maximum leverage used during the period. |
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+---------------+------------------------------------------------------+
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| period_close | The last close of the market in period. datetime in |
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| | pytz.utc timezone. |
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+---------------+------------------------------------------------------+
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| period_open | The first open of the market in period. datetime in |
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| | pytz.utc timezone. |
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+---------------+------------------------------------------------------+
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| transactions | all the transactions that were acrued during this |
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| | period. Unset/missing for cumulative periods. |
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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 pytz
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import math
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import zmq
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import zipline.protocol as zp
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import zipline.finance.risk as risk
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log = logbook.Logger('Performance')
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class PerformanceTracker(object):
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UPDATER = True
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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, sid_list):
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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.started_at = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
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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.trading_environment.first_open
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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.trading_environment.days_in_period
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# one indexed so that we reach 100%
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self.day_count = 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.last_dict = None
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self.exceeded_max_loss = False
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self.results_socket = None
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self.results_addr = None
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# this performance period will span the entire simulation.
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self.cumulative_performance = PerformancePeriod(
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# initial positions are empty
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{},
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# initial portfolio positions have zero value
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0,
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# initial cash is your capital base.
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self.capital_base,
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# the cumulative period will be calculated over the entire test.
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self.period_start,
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self.period_end
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)
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# this performance period will span just the current market day
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self.todays_performance = PerformancePeriod(
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# initial positions are empty
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{},
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# initial portfolio positions have zero value
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0,
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# initial cash is your capital base.
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self.capital_base,
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# the daily period will be calculated for the market day
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self.market_open,
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self.market_close,
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# save the transactions for the daily periods
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keep_transactions = True
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)
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for sid in sid_list:
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self.cumulative_performance.positions[sid] = Position(sid)
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self.todays_performance.positions[sid] = Position(sid)
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def update(self, event):
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if event.dt == "DONE":
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event.perf_message = self.handle_simulation_end()
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del event['TRANSACTION']
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return event
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else:
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event.perf_message = self.process_event(event)
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event.portfolio = self.get_portfolio()
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del event['TRANSACTION']
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return event
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def get_portfolio(self):
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return self.cumulative_performance.as_portfolio()
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def open(self, context):
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if self.results_addr:
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sock = context.socket(zmq.PUSH)
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sock.connect(self.results_addr)
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self.results_socket = sock
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else:
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log.warn("Not streaming results because no results socket given")
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def publish_to(self, results_addr):
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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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#assert isinstance(results_addr, basestring), type(results_addr)
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#self.results_addr = results_addr
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self.results_socket = results_addr
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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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return {
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'started_at' : self.started_at,
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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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'capital_base' : self.capital_base,
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'cumulative_perf' : self.cumulative_performance.to_dict(),
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'daily_perf' : self.todays_performance.to_dict(),
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'cumulative_risk_metrics' : self.cumulative_risk_metrics.to_dict()
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}
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def process_event(self, event):
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message = None
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if self.exceeded_max_loss:
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return
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assert isinstance(event, zp.ndict)
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self.event_count += 1
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if(event.dt >= self.market_close):
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message = self.handle_market_close()
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if 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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#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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return message
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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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# increment the day counter before we move markers forward.
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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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# Take a snapshot of our current peformance to return to the
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# browser.
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daily_update = self.to_dict()
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if self.trading_environment.max_drawdown:
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returns = self.todays_performance.returns
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max_dd = -1 * self.trading_environment.max_drawdown
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if returns < max_dd:
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log.info(str(returns) + " broke through " + str(max_dd))
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log.info("Exceeded max drawdown.")
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# mark the perf period with max loss flag,
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# so it shows up in the update, but don't end the test
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# here. Let the update go out before stopping
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self.exceeded_max_loss = True
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return daily_update
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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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# Roll over positions to current day.
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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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self.market_open,
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self.market_close,
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keep_transactions = True
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)
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return daily_update
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def handle_simulation_end(self):
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"""
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When the simulation is complete, run the full period risk report
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and send it out on the results socket.
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"""
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log_msg = "Simulated {n} trading days out of {m}."
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log.info(log_msg.format(n=self.day_count, m=self.total_days))
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log.info("first open: {d}".format(d=self.trading_environment.first_open))
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# the stream will end on the last trading day, but will not trigger
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# an end of day, so we trigger the final market close here.
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# In the case of max drawdown, we needn't close again.
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if not self.exceeded_max_loss:
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self.handle_market_close()
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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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exceeded_max_loss = self.exceeded_max_loss
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)
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risk_dict = self.risk_report.to_dict()
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return risk_dict
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class Position(object):
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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 = 0.0
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self.last_sale_date = 0.0
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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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}
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class PerformancePeriod(object):
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def __init__(
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self,
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initial_positions,
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starting_value,
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starting_cash,
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period_open=None,
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period_close=None,
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keep_transactions=False):
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self.period_open = period_open
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self.period_close = period_close
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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.keep_transactions = keep_transactions
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self.processed_transactions = []
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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.max_leverage = 0.0
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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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# Update Position
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# ----------------
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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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# Max Leverage
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# ---------------
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# Calculate the maximum capital used and maximum leverage
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transaction_cost = txn.price * txn.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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# We want to conveye a level, rather than a precise figure.
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# round to the nearest 5,000 to keep the number easy on the eyes
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self.max_capital_used = self.round_to_nearest(
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self.max_capital_used,
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base=5000
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)
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# we're adding a 10% cushion to the capital used.
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self.max_leverage = 1.1 * self.max_capital_used / self.starting_cash
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# add transaction to the list of processed transactions
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if self.keep_transactions:
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self.processed_transactions.append(txn)
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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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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 __core_dict(self):
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rval = {
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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,
|
|
'ending_cash' : self.ending_cash,
|
|
'portfolio_value' : self.ending_cash + self.ending_value,
|
|
'cumulative_capital_used' : self.cumulative_capital_used,
|
|
'max_capital_used' : self.max_capital_used,
|
|
'max_leverage' : self.max_leverage,
|
|
'pnl' : self.pnl,
|
|
'returns' : self.returns,
|
|
'period_open' : self.period_open,
|
|
'period_close' : self.period_close
|
|
}
|
|
|
|
return rval
|
|
|
|
|
|
def to_dict(self):
|
|
"""
|
|
Creates a dictionary representing the state of this performance
|
|
period. See header comments for a detailed description.
|
|
"""
|
|
rval = self.__core_dict()
|
|
positions = self.get_positions_list()
|
|
rval['positions'] = positions
|
|
|
|
# we want the key to be absent, not just empty
|
|
if self.keep_transactions:
|
|
transactions = [x.as_dict() for x in self.processed_transactions]
|
|
rval['transactions'] = transactions
|
|
|
|
return rval
|
|
|
|
def as_portfolio(self):
|
|
"""
|
|
The purpose of this method is to provide a portfolio
|
|
object to algorithms running inside the same trading
|
|
client. The data needed is captured raw in a
|
|
PerformancePeriod, and in this method we rename some
|
|
fields for usability and remove extraneous fields.
|
|
"""
|
|
portfolio = self.__core_dict()
|
|
# rename:
|
|
# ending_cash -> cash
|
|
# period_open -> backtest_start
|
|
#
|
|
# remove:
|
|
# period_close, starting_value,
|
|
# cumulative_capital_used, max_leverage, max_capital_used
|
|
portfolio['cash'] = portfolio['ending_cash']
|
|
portfolio['start_date'] = portfolio['period_open']
|
|
portfolio['positions_value'] = portfolio['ending_value']
|
|
|
|
del(portfolio['ending_cash'])
|
|
del(portfolio['period_open'])
|
|
del(portfolio['period_close'])
|
|
del(portfolio['starting_value'])
|
|
del(portfolio['ending_value'])
|
|
del(portfolio['cumulative_capital_used'])
|
|
del(portfolio['max_leverage'])
|
|
del(portfolio['max_capital_used'])
|
|
|
|
portfolio['positions'] = self.get_positions(ndicted=True)
|
|
return zp.ndict(portfolio)
|
|
|
|
def get_positions(self, ndicted=False):
|
|
if ndicted:
|
|
positions = zp.ndict({})
|
|
else:
|
|
positions = {}
|
|
|
|
for sid, pos in self.positions.iteritems():
|
|
cur = pos.to_dict()
|
|
if ndicted:
|
|
positions[sid] = zp.ndict(cur)
|
|
else:
|
|
positions[sid] = cur
|
|
|
|
return positions
|
|
|
|
def get_positions_list(self):
|
|
positions = []
|
|
for sid, pos in self.positions.iteritems():
|
|
cur = pos.to_dict()
|
|
positions.append(cur)
|
|
return positions
|