# # Copyright 2013 Quantopian, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ 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 | +-----------------+----------------------------------------------------+ | capital_base | The initial capital assumed for this tracker. | +-----------------+----------------------------------------------------+ | 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`| +-----------------+----------------------------------------------------+ 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 | +-----------------+----------------------------------------------------+ | cost_basis | the volume weighted average price paid per share | +-----------------+----------------------------------------------------+ Performance Period ================== Performance Periods are updated with every trade. When calling code needs a portfolio object that fulfills the algorithm protocol, use the PerformancePeriod.as_portfolio method. See that method for comments on the specific fields provided (and omitted). +---------------+------------------------------------------------------+ | key | value | +===============+======================================================+ | ending_value | the total market value of the positions held at the | | | end of the period | +---------------+------------------------------------------------------+ | cash_flow | the cash flow in the period (negative means spent) | | | from buying and selling securities in the period. | | | Includes dividend payments in the period as well. | +---------------+------------------------------------------------------+ | 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 | +---------------+------------------------------------------------------+ | pnl | Dollar value profit and loss, for both realized and | | | unrealized gains. | +---------------+------------------------------------------------------+ | returns | percentage returns for the entire portfolio over the | | | period | +---------------+------------------------------------------------------+ | cumulative\ | The net capital used (positive is spent) during | | _capital_used | the period | +---------------+------------------------------------------------------+ | max_capital\ | The maximum amount of capital deployed during the | | _used | period. | +---------------+------------------------------------------------------+ | max_leverage | The maximum leverage used during the period. | +---------------+------------------------------------------------------+ | period_close | The last close of the market in period. datetime in | | | pytz.utc timezone. | +---------------+------------------------------------------------------+ | period_open | The first open of the market in period. datetime in | | | pytz.utc timezone. | +---------------+------------------------------------------------------+ | transactions | all the transactions that were acrued during this | | | period. Unset/missing for cumulative periods. | +---------------+------------------------------------------------------+ """ from __future__ import division import logbook import math import numpy as np import pandas as pd from collections import OrderedDict, defaultdict import zipline.protocol as zp import zipline.finance.risk as risk import zipline.finance.trading as trading log = logbook.Logger('Performance') class PerformanceTracker(object): """ Tracks the performance of the algorithm. """ def __init__(self, sim_params): self.sim_params = sim_params self.period_start = self.sim_params.period_start self.period_end = self.sim_params.period_end self.last_close = self.sim_params.last_close first_day = self.sim_params.first_open self.market_open, self.market_close = \ trading.environment.get_open_and_close(first_day) self.total_days = self.sim_params.days_in_period self.capital_base = self.sim_params.capital_base self.emission_rate = sim_params.emission_rate self.emission_rate = sim_params.emission_rate self.perf_periods = [] if self.emission_rate == 'daily': self.all_benchmark_returns = pd.Series( index=trading.environment.trading_days) self.intraday_risk_metrics = None self.cumulative_risk_metrics = \ risk.RiskMetricsCumulative(self.sim_params) elif self.emission_rate == 'minute': self.all_benchmark_returns = pd.Series(index=pd.date_range( self.sim_params.first_open, self.sim_params.last_close, freq='Min')) self.intraday_risk_metrics = \ risk.RiskMetricsCumulative(self.sim_params) self.cumulative_risk_metrics = \ risk.RiskMetricsCumulative(self.sim_params, returns_frequency='daily') self.minute_performance = PerformancePeriod( # initial cash is your capital base. self.capital_base, # the cumulative period will be calculated over the # entire test. self.period_start, self.period_end, # don't save the transactions for the cumulative # period keep_transactions=False, keep_orders=False, # don't serialize positions for cumualtive period serialize_positions=False ) self.perf_periods.append(self.minute_performance) # this performance period will span the entire simulation from # inception. self.cumulative_performance = PerformancePeriod( # initial cash is your capital base. self.capital_base, # the cumulative period will be calculated over the entire test. self.period_start, self.period_end, # don't save the transactions for the cumulative # period keep_transactions=False, keep_orders=False, # don't serialize positions for cumualtive period serialize_positions=False ) self.perf_periods.append(self.cumulative_performance) # this performance period will span just the current market day self.todays_performance = PerformancePeriod( # initial cash is your capital base. self.capital_base, # the daily period will be calculated for the market day self.market_open, self.market_close, keep_transactions=True, keep_orders=True, serialize_positions=True ) self.perf_periods.append(self.todays_performance) self.saved_dt = self.period_start self.returns = [] # one indexed so that we reach 100% self.day_count = 0.0 self.txn_count = 0 self.event_count = 0 def __repr__(self): return "%s(%r)" % ( self.__class__.__name__, {'simulation parameters': self.sim_params}) @property def progress(self): if self.emission_rate == 'minute': # Fake a value return 1.0 elif self.emission_rate == 'daily': return self.day_count / self.total_days def set_date(self, date): if self.emission_rate == 'minute': self.saved_dt = date self.todays_performance.period_close = self.saved_dt def get_portfolio(self): return self.cumulative_performance.as_portfolio() def to_dict(self, emission_type=None): """ Creates a dictionary representing the state of this tracker. Returns a dict object of the form described in header comments. """ if not emission_type: emission_type = self.emission_rate _dict = { 'period_start': self.period_start, 'period_end': self.period_end, 'capital_base': self.capital_base, 'cumulative_perf': self.cumulative_performance.to_dict(), 'progress': self.progress, 'cumulative_risk_metrics': self.cumulative_risk_metrics.to_dict() } if emission_type == 'daily': _dict.update({'daily_perf': self.todays_performance.to_dict()}) elif emission_type == 'minute': _dict.update({ 'intraday_risk_metrics': self.intraday_risk_metrics.to_dict(), 'minute_perf': self.todays_performance.to_dict(self.saved_dt) }) return _dict def process_event(self, event): self.event_count += 1 if event.type == zp.DATASOURCE_TYPE.TRADE: # update last sale for perf_period in self.perf_periods: perf_period.update_last_sale(event) elif event.type == zp.DATASOURCE_TYPE.TRANSACTION: # Trade simulation always follows a transaction with the # TRADE event that was used to simulate it, so we don't # check for end of day rollover messages here. self.txn_count += 1 for perf_period in self.perf_periods: perf_period.execute_transaction(event) elif event.type == zp.DATASOURCE_TYPE.DIVIDEND: for perf_period in self.perf_periods: perf_period.add_dividend(event) elif event.type == zp.DATASOURCE_TYPE.SPLIT: for perf_period in self.perf_periods: perf_period.handle_split(event) elif event.type == zp.DATASOURCE_TYPE.ORDER: for perf_period in self.perf_periods: perf_period.record_order(event) elif event.type == zp.DATASOURCE_TYPE.COMMISSION: for perf_period in self.perf_periods: perf_period.handle_commission(event) elif event.type == zp.DATASOURCE_TYPE.CUSTOM: pass elif event.type == zp.DATASOURCE_TYPE.BENCHMARK: if ( self.sim_params.data_frequency == 'minute' and self.sim_params.emission_rate == 'daily' ): # Minute data benchmarks should have a timestamp of market # close, so that calculations are triggered at the right time. # However, risk module uses midnight as the 'day' # marker for returns, so adjust back to midgnight. midnight = event.dt.replace( hour=0, minute=0, second=0, microsecond=0) else: midnight = event.dt self.all_benchmark_returns[midnight] = event.returns # calculate performance as of last trade for perf_period in self.perf_periods: perf_period.calculate_performance() def handle_minute_close(self, dt): todays_date = dt.replace(hour=0, minute=0, second=0, microsecond=0) minute_returns = self.minute_performance.returns self.minute_performance.rollover() algo_minute_returns = pd.Series({dt: minute_returns}) bench_minute_returns = pd.Series({dt: self.all_benchmark_returns[dt]}) # the intraday risk is calculated on top of minute performance # returns for the bench and the algo self.intraday_risk_metrics.update(dt, algo_minute_returns, bench_minute_returns) bench_since_open = \ self.intraday_risk_metrics.benchmark_period_returns[dt] benchmark_returns = pd.Series({todays_date: bench_since_open}) # if we've reached market close, check on dividends if dt == self.market_close: for perf_period in self.perf_periods: perf_period.update_dividends(todays_date) algorithm_returns = pd.Series({ todays_date: self.todays_performance.returns }) self.cumulative_risk_metrics.update(todays_date, algorithm_returns, benchmark_returns) # if this is the close, save the returns objects for cumulative # risk calculations if dt == self.market_close: todays_return_obj = zp.DailyReturn( todays_date, self.todays_performance.returns ) self.returns.append(todays_return_obj) def handle_intraday_close(self): self.intraday_risk_metrics = \ risk.RiskMetricsCumulative(self.sim_params) # increment the day counter before we move markers forward. self.day_count += 1.0 # move the market day markers forward if self.market_close < trading.environment.last_trading_day: self.market_open, self.market_close = \ trading.environment.next_open_and_close(self.market_open) else: self.market_close = self.sim_params.last_close 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, microsecond=0) self.cumulative_performance.update_dividends(todays_date) self.todays_performance.update_dividends(todays_date) todays_return_obj = zp.DailyReturn( todays_date, self.todays_performance.returns ) self.returns.append(todays_return_obj) # update risk metrics for cumulative performance self.cumulative_risk_metrics.update( todays_return_obj.date, todays_return_obj.returns, self.all_benchmark_returns[todays_return_obj.date]) # increment the day counter before we move markers forward. self.day_count += 1.0 # Take a snapshot of our current performance to return to the # browser. daily_update = self.to_dict() # On the last day of the test, don't create tomorrow's performance # period. We may not be able to find the next trading day if we're # at the end of our historical data if self.market_close >= self.last_close: return daily_update # move the market day markers forward self.market_open, self.market_close = \ trading.environment.next_open_and_close(self.market_open) # Roll over positions to current day. self.todays_performance.rollover() self.todays_performance.period_open = self.market_open self.todays_performance.period_close = self.market_close # The dividend calculation for the daily needs to be made # after the rollover. midnight_between is the last midnight # hour between the close of markets and the next open. To # make sure midnight_between matches identically with # dividend data dates, it is in UTC. midnight_between = self.market_open.replace(hour=0, minute=0, second=0, microsecond=0) self.cumulative_performance.update_dividends(midnight_between) self.todays_performance.update_dividends(midnight_between) return daily_update def handle_simulation_end(self): """ When the simulation is complete, run the full period risk report and send it out on the results socket. """ log_msg = "Simulated {n} trading days out of {m}." log.info(log_msg.format(n=int(self.day_count), m=self.total_days)) log.info("first open: {d}".format( d=self.sim_params.first_open)) log.info("last close: {d}".format( d=self.sim_params.last_close)) bms = self.cumulative_risk_metrics.benchmark_returns ars = self.cumulative_risk_metrics.algorithm_returns self.risk_report = risk.RiskReport( ars, self.sim_params, benchmark_returns=bms) risk_dict = self.risk_report.to_dict() return risk_dict class Position(object): def __init__(self, sid, amount=0, cost_basis=0.0, last_sale_price=0.0, last_sale_date=0.0, dividends=None): self.sid = sid self.amount = amount self.cost_basis = cost_basis # per share self.last_sale_price = last_sale_price self.last_sale_date = last_sale_date self.dividends = dividends or [] def update_dividends(self, midnight_utc): """ midnight_utc is the 0 hour for the current (not yet open) trading day. This method will be invoked at the end of the market close handling, before the next market open. """ payment = 0.0 unpaid_dividends = [] for dividend in self.dividends: if midnight_utc == dividend.ex_date: # if we own shares at midnight of the div_ex date # we are entitled to the dividend. dividend.amount_on_ex_date = self.amount if dividend.net_amount: dividend.payment = self.amount * dividend.net_amount else: dividend.payment = self.amount * dividend.gross_amount if midnight_utc == dividend.pay_date: # if it is the payment date, include this # dividend's actual payment (calculated on # ex_date) payment += dividend.payment else: unpaid_dividends.append(dividend) self.dividends = unpaid_dividends return payment def add_dividend(self, dividend): self.dividends.append(dividend) # Update the position by the split ratio, and return the # resulting fractional share that will be converted into cash. # Returns the unused cash. def handle_split(self, split): if (self.sid != split.sid): raise NameError("updating split with the wrong sid!") ratio = split.ratio log.info("handling split for sid = " + str(split.sid) + ", ratio = " + str(split.ratio)) log.info("before split: " + str(self)) # adjust the # of shares by the ratio # (if we had 100 shares, and the ratio is 3, # we now have 33 shares) # (old_share_count / ratio = new_share_count) # (old_price * ratio = new_price) # ie, 33.333 raw_share_count = self.amount / float(ratio) # ie, 33 full_share_count = math.floor(raw_share_count) # ie, 0.333 fractional_share_count = raw_share_count - full_share_count # adjust the cost basis to the nearest cent, ie, 60.0 new_cost_basis = round(self.cost_basis * ratio, 2) # adjust the last sale price new_last_sale_price = round(self.last_sale_price * ratio, 2) self.cost_basis = new_cost_basis self.last_sale_price = new_last_sale_price self.amount = full_share_count return_cash = round(float(fractional_share_count * new_cost_basis), 2) log.info("after split: " + str(self)) log.info("returning cash: " + str(return_cash)) # return the leftover cash, which will be converted into cash # (rounded to the nearest cent) return return_cash def update(self, txn): if(self.sid != txn.sid): raise Exception('updating position with txn for a ' 'different sid') # we're covering a short or closing a position if(self.amount + txn.amount == 0): self.cost_basis = 0.0 self.amount = 0 else: prev_cost = self.cost_basis * self.amount txn_cost = txn.amount * txn.price total_cost = prev_cost + txn_cost total_shares = self.amount + txn.amount self.cost_basis = total_cost / total_shares self.amount = total_shares def adjust_commission_cost_basis(self, commission): """ A note about cost-basis in zipline: all positions are considered to share a cost basis, even if they were executed in different transactions with different commission costs, different prices, etc. Due to limitations about how zipline handles positions, zipline will currently spread an externally-delivered commission charge across all shares in a position. """ if commission.sid != self.sid: raise Exception('Updating a commission for a different sid?') if commission.cost == 0.0: return # If we no longer hold this position, there is no cost basis to # adjust. if self.amount == 0: return prev_cost = self.cost_basis * self.amount new_cost = prev_cost + commission.cost self.cost_basis = new_cost / self.amount def __repr__(self): template = "sid: {sid}, amount: {amount}, cost_basis: {cost_basis}, \ last_sale_price: {last_sale_price}" return template.format( 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: """ return { 'sid': self.sid, 'amount': self.amount, 'cost_basis': self.cost_basis, 'last_sale_price': self.last_sale_price } class PerformancePeriod(object): def __init__( self, starting_cash, period_open=None, period_close=None, keep_transactions=True, keep_orders=False, serialize_positions=True): self.period_open = period_open self.period_close = period_close self.ending_value = 0.0 self.period_cash_flow = 0.0 self.pnl = 0.0 # sid => position object self.positions = positiondict() self.ending_cash = starting_cash # rollover initializes a number of self's attributes: self.rollover() self.keep_transactions = keep_transactions self.keep_orders = keep_orders # Maps position to following array indexes self._position_index_map = {} # Arrays for quick calculations of positions value self._position_amounts = np.array([]) self._position_last_sale_prices = np.array([]) self.calculate_performance() # An object to recycle via assigning new values # when returning portfolio information. # So as not to avoid creating a new object for each event self._portfolio_store = zp.Portfolio() self._positions_store = zp.Positions() self.serialize_positions = serialize_positions def rollover(self): self.starting_value = self.ending_value self.starting_cash = self.ending_cash self.period_cash_flow = 0.0 self.pnl = 0.0 self.processed_transactions = defaultdict(list) self.orders_by_modified = defaultdict(OrderedDict) self.orders_by_id = OrderedDict() self.cumulative_capital_used = 0.0 self.max_capital_used = 0.0 self.max_leverage = 0.0 def index_for_position(self, sid): try: index = self._position_index_map[sid] except KeyError: index = len(self._position_index_map) self._position_index_map[sid] = index self._position_amounts = np.append(self._position_amounts, [0]) self._position_last_sale_prices = np.append( self._position_last_sale_prices, [0]) return index def add_dividend(self, div): # The dividend is received on midnight of the dividend # declared date. We calculate the dividends based on the amount of # stock owned on midnight of the ex dividend date. However, the cash # is not dispersed until the payment date, which is # included in the event. self.positions[div.sid].add_dividend(div) def handle_split(self, split): if split.sid in self.positions: # Make the position object handle the split. It returns the # leftover cash from a fractional share, if there is any. leftover_cash = self.positions[split.sid].handle_split(split) if leftover_cash > 0: self.handle_cash_payment(leftover_cash) def update_dividends(self, todays_date): """ Check the payment date and ex date against today's date to determine if we are owed a dividend payment or if the payment has been disbursed. """ cash_payments = 0.0 for sid, pos in self.positions.iteritems(): cash_payments += pos.update_dividends(todays_date) # credit our cash balance with the dividend payments, or # if we are short, debit our cash balance with the # payments. # debit our cumulative cash spent with the dividend # payments, or credit our cumulative cash spent if we are # short the stock. self.handle_cash_payment(cash_payments) # recalculate performance, including the dividend # payments self.calculate_performance() def handle_cash_payment(self, payment_amount): self.adjust_cash(payment_amount) def handle_commission(self, commission): # Deduct from our total cash pool. self.adjust_cash(-commission.cost) # Adjust the cost basis of the stock if we own it if commission.sid in self.positions: self.positions[commission.sid].\ adjust_commission_cost_basis(commission) def adjust_cash(self, amount): self.period_cash_flow += amount self.cumulative_capital_used -= amount 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_cash_flow 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 record_order(self, order): if self.keep_orders: dt_orders = self.orders_by_modified[order.dt] if order.id in dt_orders: del dt_orders[order.id] dt_orders[order.id] = order # to preserve the order of the orders by modified date # we delete and add back. (ordered dictionary is sorted by # first insertion date). if order.id in self.orders_by_id: del self.orders_by_id[order.id] self.orders_by_id[order.id] = order def execute_transaction(self, txn): # Update Position # ---------------- position = self.positions[txn.sid] position.update(txn) index = self.index_for_position(txn.sid) self._position_amounts[index] = position.amount self.period_cash_flow -= txn.price * txn.amount # Max Leverage # --------------- # Calculate the maximum capital used and maximum leverage transaction_cost = txn.price * txn.amount 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) # We want to conveye a level, rather than a precise figure. # round to the nearest 5,000 to keep the number easy on the eyes self.max_capital_used = self.round_to_nearest( self.max_capital_used, base=5000 ) # we're adding a 10% cushion to the capital used. self.max_leverage = 1.1 * \ self.max_capital_used / self.starting_cash # add transaction to the list of processed transactions if self.keep_transactions: self.processed_transactions[txn.dt].append(txn) def round_to_nearest(self, x, base=5): return int(base * round(float(x) / base)) def calculate_positions_value(self): return np.dot(self._position_amounts, self._position_last_sale_prices) def update_last_sale(self, event): is_trade = event.type == zp.DATASOURCE_TYPE.TRADE has_price = not np.isnan(event.price) # isnan check will keep the last price if its not present if (event.sid in self.positions) and is_trade and has_price: self.positions[event.sid].last_sale_price = event.price index = self.index_for_position(event.sid) self._position_last_sale_prices[index] = event.price self.positions[event.sid].last_sale_date = event.dt def __core_dict(self): rval = { 'ending_value': self.ending_value, # this field is renamed to capital_used for backward # compatibility. 'capital_used': self.period_cash_flow, 'starting_value': self.starting_value, '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, dt=None): """ Creates a dictionary representing the state of this performance period. See header comments for a detailed description. Kwargs: dt (datetime): If present, only return transactions for the dt. """ rval = self.__core_dict() if self.serialize_positions: positions = self.get_positions_list() rval['positions'] = positions # we want the key to be absent, not just empty if self.keep_transactions: if dt: # Only include transactions for given dt transactions = [x.to_dict() for x in self.processed_transactions[dt]] else: transactions = \ [y.to_dict() for x in self.processed_transactions.itervalues() for y in x] rval['transactions'] = transactions if self.keep_orders: if dt: # only include orders modified as of the given dt. orders = [x.to_dict() for x in self.orders_by_modified[dt].itervalues()] else: orders = [x.to_dict() for x in self.orders_by_id.itervalues()] rval['orders'] = orders 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. """ # Recycles containing objects' Portfolio object # which is used for returning values. # as_portfolio is called in an inner loop, # so repeated object creation becomes too expensive portfolio = self._portfolio_store # maintaining the old name for the portfolio field for # backward compatibility portfolio.capital_used = self.period_cash_flow portfolio.starting_cash = self.starting_cash portfolio.portfolio_value = self.ending_cash + self.ending_value portfolio.pnl = self.pnl portfolio.returns = self.returns portfolio.cash = self.ending_cash portfolio.start_date = self.period_open portfolio.positions = self.get_positions() portfolio.positions_value = self.ending_value return portfolio def get_positions(self): positions = self._positions_store for sid, pos in self.positions.iteritems(): if sid not in positions: positions[sid] = zp.Position(sid) position = positions[sid] position.amount = pos.amount position.cost_basis = pos.cost_basis position.last_sale_price = pos.last_sale_price return positions def get_positions_list(self): positions = [] for sid, pos in self.positions.iteritems(): if pos.amount != 0: positions.append(pos.to_dict()) return positions class positiondict(dict): def __missing__(self, key): pos = Position(key) self[key] = pos return pos