def __init__(self, sim_params, trading_schedule, env):
self.sim_params = sim_params
self.trading_schedule = trading_schedule
self.asset_finder = env.asset_finder
self.treasury_curves = env.treasury_curves
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_open = self.sim_params.first_open.tz_convert(trading_schedule.tz)
self.day = pd.Timestamp(datetime(first_open.year, first_open.month,
first_open.day), tz='UTC')
self.market_open, self.market_close = trading_schedule.start_and_end(
self.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.trading_days = trading_schedule.trading_dates(
self.period_start, self.period_end
)
self.position_tracker = PositionTracker(
asset_finder=env.asset_finder,
data_frequency=self.sim_params.data_frequency)
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(
index=self.trading_days)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.treasury_curves,
self.trading_schedule
)
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.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.treasury_curves,
self.trading_schedule,
create_first_day_stats=True
)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the cumulative period will be calculated over the entire test.
period_open=self.period_start,
period_close=self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumulative period
serialize_positions=False,
asset_finder=self.asset_finder,
name="Cumulative"
)
self.cumulative_performance.position_tracker = self.position_tracker
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the daily period will be calculated for the market day
period_open=self.market_open,
period_close=self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True,
asset_finder=self.asset_finder,
name="Daily"
)
self.todays_performance.position_tracker = self.position_tracker
self.saved_dt = self.period_start
# one indexed so that we reach 100%
self.day_count = 0.0
self.txn_count = 0
self.account_needs_update = True
self._account = None
class PerformanceTracker(object):
"""
Tracks the performance of the algorithm.
"""
def __init__(self, sim_params, trading_schedule, env):
self.sim_params = sim_params
self.trading_schedule = trading_schedule
self.asset_finder = env.asset_finder
self.treasury_curves = env.treasury_curves
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_open = self.sim_params.first_open.tz_convert(trading_schedule.tz)
self.day = pd.Timestamp(datetime(first_open.year, first_open.month,
first_open.day), tz='UTC')
self.market_open, self.market_close = trading_schedule.start_and_end(
self.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.trading_days = trading_schedule.trading_dates(
self.period_start, self.period_end
)
self.position_tracker = PositionTracker(
asset_finder=env.asset_finder,
data_frequency=self.sim_params.data_frequency)
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(
index=self.trading_days)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.treasury_curves,
self.trading_schedule
)
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.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.treasury_curves,
self.trading_schedule,
create_first_day_stats=True
)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the cumulative period will be calculated over the entire test.
period_open=self.period_start,
period_close=self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumulative period
serialize_positions=False,
asset_finder=self.asset_finder,
name="Cumulative"
)
self.cumulative_performance.position_tracker = self.position_tracker
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the daily period will be calculated for the market day
period_open=self.market_open,
period_close=self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True,
asset_finder=self.asset_finder,
name="Daily"
)
self.todays_performance.position_tracker = self.position_tracker
self.saved_dt = self.period_start
# one indexed so that we reach 100%
self.day_count = 0.0
self.txn_count = 0
self.account_needs_update = True
self._account = None
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, performance_needs_update):
if performance_needs_update:
self.update_performance()
self.account_needs_update = True
return self.cumulative_performance.as_portfolio()
def update_performance(self):
# calculate performance as of last trade
self.cumulative_performance.calculate_performance()
self.todays_performance.calculate_performance()
def get_account(self, performance_needs_update):
if performance_needs_update:
self.update_performance()
self.account_needs_update = True
if self.account_needs_update:
self._update_account()
return self._account
def _update_account(self):
self._account = self.cumulative_performance.as_account()
self.account_needs_update = False
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.
"""
# Default to the emission rate of this tracker if no type is provided
if emission_type is None:
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['daily_perf'] = self.todays_performance.to_dict()
elif emission_type == 'minute':
_dict['minute_perf'] = self.todays_performance.to_dict(
self.saved_dt)
else:
raise ValueError("Invalid emission type: %s" % emission_type)
return _dict
def process_capital_changes(self, capital_change, is_interday):
self.cumulative_performance.subdivide_period(capital_change)
if is_interday:
# Change comes between days
self.todays_performance.adjust_period_starting_capital(
capital_change)
else:
# Change comes in the middle of day
self.todays_performance.subdivide_period(capital_change)
def process_transaction(self, transaction):
self.txn_count += 1
self.cumulative_performance.handle_execution(transaction)
self.todays_performance.handle_execution(transaction)
self.position_tracker.execute_transaction(transaction)
def handle_splits(self, splits):
leftover_cash = self.position_tracker.handle_splits(splits)
if leftover_cash > 0:
self.cumulative_performance.handle_cash_payment(leftover_cash)
self.todays_performance.handle_cash_payment(leftover_cash)
def process_order(self, event):
self.cumulative_performance.record_order(event)
self.todays_performance.record_order(event)
def process_commission(self, commission):
sid = commission['sid']
cost = commission['cost']
self.position_tracker.handle_commission(sid, cost)
self.cumulative_performance.handle_commission(cost)
self.todays_performance.handle_commission(cost)
def process_close_position(self, asset, dt, data_portal):
txn = self.position_tracker.\
maybe_create_close_position_transaction(asset, dt, data_portal)
if txn:
self.process_transaction(txn)
def check_upcoming_dividends(self, next_trading_day, adjustment_reader):
"""
Check if we currently own any stocks with dividends whose ex_date is
the next trading day. Track how much we should be payed on those
dividends' pay dates.
Then check if we are owed cash/stock for any dividends whose pay date
is the next trading day. Apply all such benefits, then recalculate
performance.
"""
if adjustment_reader is None:
return
position_tracker = self.position_tracker
held_sids = set(position_tracker.positions)
# Dividends whose ex_date is the next trading day. We need to check if
# we own any of these stocks so we know to pay them out when the pay
# date comes.
if held_sids:
cash_dividends = adjustment_reader.get_dividends_with_ex_date(
held_sids,
next_trading_day,
self.asset_finder
)
stock_dividends = adjustment_reader.\
get_stock_dividends_with_ex_date(
held_sids,
next_trading_day,
self.asset_finder
)
position_tracker.earn_dividends(
cash_dividends,
stock_dividends
)
net_cash_payment = position_tracker.pay_dividends(next_trading_day)
if not net_cash_payment:
return
self.cumulative_performance.handle_dividends_paid(net_cash_payment)
self.todays_performance.handle_dividends_paid(net_cash_payment)
def handle_minute_close(self, dt, data_portal):
"""
Handles the close of the given minute in minute emission.
Parameters
__________
dt : Timestamp
The minute that is ending
Returns
_______
A minute perf packet.
"""
self.position_tracker.sync_last_sale_prices(dt, False, data_portal)
self.update_performance()
todays_date = normalize_date(dt)
account = self.get_account(False)
bench_returns = self.all_benchmark_returns.loc[todays_date:dt]
# cumulative returns
bench_since_open = (1. + bench_returns).prod() - 1
self.cumulative_risk_metrics.update(todays_date,
self.todays_performance.returns,
bench_since_open,
account.leverage)
minute_packet = self.to_dict(emission_type='minute')
return minute_packet
def handle_market_close(self, dt, data_portal):
"""
Handles the close of the given day, in both minute and daily emission.
In daily emission, also updates performance, benchmark and risk metrics
as it would in handle_minute_close if it were minute emission.
Parameters
__________
dt : Timestamp
The minute that is ending
Returns
_______
A daily perf packet.
"""
completed_date = self.day
if self.emission_rate == 'daily':
# this method is called for both minutely and daily emissions, but
# this chunk of code here only applies for daily emissions. (since
# it's done every minute, elsewhere, for minutely emission).
self.position_tracker.sync_last_sale_prices(dt, False, data_portal)
self.update_performance()
account = self.get_account(False)
benchmark_value = self.all_benchmark_returns[completed_date]
self.cumulative_risk_metrics.update(
completed_date,
self.todays_performance.returns,
benchmark_value,
account.leverage)
# increment the day counter before we move markers forward.
self.day_count += 1.0
# Get the next trading day and, if it is past the bounds of this
# simulation, return the daily perf packet
try:
next_trading_day = self.trading_schedule.next_execution_day(
completed_date
)
except NoFurtherDataError:
next_trading_day = None
# Take a snapshot of our current performance to return to the
# browser.
daily_update = self.to_dict(emission_type='daily')
# 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
# TODO Is this redundant with next_trading_day above?
self.day = self.trading_schedule.next_execution_day(self.day)
self.market_open, self.market_close = \
self.trading_schedule.start_and_end(self.day)
# 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
# If the next trading day is irrelevant, then return the daily packet
if (next_trading_day is None) or (next_trading_day >= self.last_close):
return daily_update
# Check for any dividends, then return the daily perf packet
self.check_upcoming_dividends(
next_trading_day=next_trading_day,
adjustment_reader=data_portal._adjustment_reader
)
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 = pd.Series(
index=self.cumulative_risk_metrics.cont_index,
data=self.cumulative_risk_metrics.benchmark_returns_cont)
ars = pd.Series(
index=self.cumulative_risk_metrics.cont_index,
data=self.cumulative_risk_metrics.algorithm_returns_cont)
acl = self.cumulative_risk_metrics.algorithm_cumulative_leverages
self.risk_report = risk.RiskReport(
ars,
self.sim_params,
benchmark_returns=bms,
algorithm_leverages=acl,
trading_schedule=self.trading_schedule,
treasury_curves=self.treasury_curves,
)
risk_dict = self.risk_report.to_dict()
return risk_dict
def __init__(self, sim_params, trading_calendar, env):
self.sim_params = sim_params
self.trading_calendar = trading_calendar
self.asset_finder = env.asset_finder
self.treasury_curves = env.treasury_curves
self.period_start = self.sim_params.start_session
self.period_end = self.sim_params.end_session
self.last_close = self.sim_params.last_close
self._current_session = self.sim_params.start_session
self.market_open, self.market_close = \
self.trading_calendar.open_and_close_for_session(
self._current_session
)
self.total_session_count = len(self.sim_params.sessions)
self.capital_base = self.sim_params.capital_base
self.emission_rate = sim_params.emission_rate
self.position_tracker = PositionTracker(
asset_finder=env.asset_finder,
data_frequency=self.sim_params.data_frequency)
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(
index=self.sim_params.sessions)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.treasury_curves,
self.trading_calendar
)
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.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.treasury_curves,
self.trading_calendar,
create_first_day_stats=True
)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the cumulative period will be calculated over the entire test.
period_open=self.period_start,
period_close=self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumulative period
serialize_positions=False,
asset_finder=self.asset_finder,
name="Cumulative")
self.cumulative_performance.position_tracker = self.position_tracker
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the daily period will be calculated for the market day
period_open=self.market_open,
period_close=self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True,
asset_finder=self.asset_finder,
name="Daily")
self.todays_performance.position_tracker = self.position_tracker
self.saved_dt = self.period_start
# one indexed so that we reach 100%
self.session_count = 0.0
self.txn_count = 0
self.account_needs_update = True
self._account = None
class PerformanceTracker(object):
"""
Tracks the performance of the algorithm.
"""
def __init__(self, sim_params, trading_calendar, env):
self.sim_params = sim_params
self.trading_calendar = trading_calendar
self.asset_finder = env.asset_finder
self.treasury_curves = env.treasury_curves
self.period_start = self.sim_params.start_session
self.period_end = self.sim_params.end_session
self.last_close = self.sim_params.last_close
self._current_session = self.sim_params.start_session
self.market_open, self.market_close = \
self.trading_calendar.open_and_close_for_session(
self._current_session
)
self.total_session_count = len(self.sim_params.sessions)
self.capital_base = self.sim_params.capital_base
self.emission_rate = sim_params.emission_rate
self.position_tracker = PositionTracker(
asset_finder=env.asset_finder,
data_frequency=self.sim_params.data_frequency)
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(
index=self.sim_params.sessions)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.treasury_curves,
self.trading_calendar
)
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.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.treasury_curves,
self.trading_calendar,
create_first_day_stats=True
)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the cumulative period will be calculated over the entire test.
period_open=self.period_start,
period_close=self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumulative period
serialize_positions=False,
asset_finder=self.asset_finder,
name="Cumulative")
self.cumulative_performance.position_tracker = self.position_tracker
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the daily period will be calculated for the market day
period_open=self.market_open,
period_close=self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True,
asset_finder=self.asset_finder,
name="Daily")
self.todays_performance.position_tracker = self.position_tracker
self.saved_dt = self.period_start
# one indexed so that we reach 100%
self.session_count = 0.0
self.txn_count = 0
self.account_needs_update = True
self._account = None
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.session_count / self.total_session_count
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, performance_needs_update):
if performance_needs_update:
self.update_performance()
self.account_needs_update = True
return self.cumulative_performance.as_portfolio()
def update_performance(self):
# calculate performance as of last trade
self.cumulative_performance.calculate_performance()
self.todays_performance.calculate_performance()
def get_account(self, performance_needs_update):
if performance_needs_update:
self.update_performance()
self.account_needs_update = True
if self.account_needs_update:
self._update_account()
return self._account
def _update_account(self):
self._account = self.cumulative_performance.as_account()
self.account_needs_update = False
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.
"""
# Default to the emission rate of this tracker if no type is provided
if emission_type is None:
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['daily_perf'] = self.todays_performance.to_dict()
elif emission_type == 'minute':
_dict['minute_perf'] = self.todays_performance.to_dict(
self.saved_dt)
else:
raise ValueError("Invalid emission type: %s" % emission_type)
return _dict
def prepare_capital_change(self, is_interday):
self.cumulative_performance.initialize_subperiod_divider()
if not is_interday:
# Change comes in the middle of day
self.todays_performance.initialize_subperiod_divider()
def process_capital_change(self, capital_change_amount, is_interday):
self.cumulative_performance.set_current_subperiod_starting_values(
capital_change_amount)
if is_interday:
# Change comes between days
self.todays_performance.adjust_period_starting_capital(
capital_change_amount)
else:
# Change comes in the middle of day
self.todays_performance.set_current_subperiod_starting_values(
capital_change_amount)
def process_transaction(self, transaction):
self.txn_count += 1
self.cumulative_performance.handle_execution(transaction)
self.todays_performance.handle_execution(transaction)
self.position_tracker.execute_transaction(transaction)
def handle_splits(self, splits):
leftover_cash = self.position_tracker.handle_splits(splits)
if leftover_cash > 0:
self.cumulative_performance.handle_cash_payment(leftover_cash)
self.todays_performance.handle_cash_payment(leftover_cash)
def process_order(self, event):
self.cumulative_performance.record_order(event)
self.todays_performance.record_order(event)
def process_commission(self, commission):
sid = commission['sid']
cost = commission['cost']
self.position_tracker.handle_commission(sid, cost)
self.cumulative_performance.handle_commission(cost)
self.todays_performance.handle_commission(cost)
def process_close_position(self, asset, dt, data_portal):
txn = self.position_tracker.\
maybe_create_close_position_transaction(asset, dt, data_portal)
if txn:
self.process_transaction(txn)
def process_exercise(self, asset, dt, data_portal):
txn = self.position_tracker.\
maybe_create_exercise_transaction(asset, dt, data_portal)
if txn:
self.process_transaction(txn)
def check_upcoming_dividends(self, next_session, adjustment_reader):
"""
Check if we currently own any stocks with dividends whose ex_date is
the next trading day. Track how much we should be payed on those
dividends' pay dates.
Then check if we are owed cash/stock for any dividends whose pay date
is the next trading day. Apply all such benefits, then recalculate
performance.
"""
if adjustment_reader is None:
return
position_tracker = self.position_tracker
held_sids = set(position_tracker.positions)
# Dividends whose ex_date is the next trading day. We need to check if
# we own any of these stocks so we know to pay them out when the pay
# date comes.
if held_sids:
cash_dividends = adjustment_reader.get_dividends_with_ex_date(
held_sids, next_session, self.asset_finder)
stock_dividends = adjustment_reader.\
get_stock_dividends_with_ex_date(
held_sids,
next_session,
self.asset_finder
)
position_tracker.earn_dividends(cash_dividends, stock_dividends)
net_cash_payment = position_tracker.pay_dividends(next_session)
if not net_cash_payment:
return
self.cumulative_performance.handle_dividends_paid(net_cash_payment)
self.todays_performance.handle_dividends_paid(net_cash_payment)
def handle_minute_close(self, dt, data_portal):
"""
Handles the close of the given minute in minute emission.
Parameters
__________
dt : Timestamp
The minute that is ending
Returns
_______
A minute perf packet.
"""
self.position_tracker.sync_last_sale_prices(dt, False, data_portal)
self.update_performance()
todays_date = normalize_date(dt)
account = self.get_account(False)
bench_returns = self.all_benchmark_returns.loc[todays_date:dt]
# cumulative returns
bench_since_open = (1. + bench_returns).prod() - 1
self.cumulative_risk_metrics.update(todays_date,
self.todays_performance.returns,
bench_since_open, account.leverage)
minute_packet = self.to_dict(emission_type='minute')
return minute_packet
def handle_market_close(self, dt, data_portal):
"""
Handles the close of the given day, in both minute and daily emission.
In daily emission, also updates performance, benchmark and risk metrics
as it would in handle_minute_close if it were minute emission.
Parameters
__________
dt : Timestamp
The minute that is ending
Returns
_______
A daily perf packet.
"""
completed_session = self._current_session
if self.emission_rate == 'daily':
# this method is called for both minutely and daily emissions, but
# this chunk of code here only applies for daily emissions. (since
# it's done every minute, elsewhere, for minutely emission).
self.position_tracker.sync_last_sale_prices(dt, False, data_portal)
self.update_performance()
account = self.get_account(False)
benchmark_value = self.all_benchmark_returns[completed_session]
self.cumulative_risk_metrics.update(
completed_session, self.todays_performance.returns,
benchmark_value, account.leverage)
# increment the day counter before we move markers forward.
self.session_count += 1.0
# Get the next trading day and, if it is past the bounds of this
# simulation, return the daily perf packet
try:
next_session = self.trading_calendar.next_session_label(
completed_session)
except NoFurtherDataError:
next_session = None
# Take a snapshot of our current performance to return to the
# browser.
daily_update = self.to_dict(emission_type='daily')
# 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
# If the next trading day is irrelevant, then return the daily packet
if (next_session is None) or (next_session >= self.last_close):
return daily_update
# move the market day markers forward
# TODO Is this redundant with next_trading_day above?
self._current_session = next_session
self.market_open, self.market_close = \
self.trading_calendar.open_and_close_for_session(
self._current_session
)
# 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
# Check for any dividends, then return the daily perf packet
self.check_upcoming_dividends(
next_session=next_session,
adjustment_reader=data_portal._adjustment_reader)
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.session_count),
m=self.total_session_count))
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 = pd.Series(
index=self.cumulative_risk_metrics.cont_index,
data=self.cumulative_risk_metrics.benchmark_returns_cont)
ars = pd.Series(
index=self.cumulative_risk_metrics.cont_index,
data=self.cumulative_risk_metrics.algorithm_returns_cont)
acl = self.cumulative_risk_metrics.algorithm_cumulative_leverages
self.risk_report = risk.RiskReport(
ars,
self.sim_params,
benchmark_returns=bms,
algorithm_leverages=acl,
trading_calendar=self.trading_calendar,
treasury_curves=self.treasury_curves,
)
risk_dict = self.risk_report.to_dict()
return risk_dict
def __init__(self, sim_params, env):
self.sim_params = sim_params
self.env = env
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_open = self.sim_params.first_open.tz_convert(
self.env.exchange_tz)
self.day = pd.Timestamp(datetime(first_open.year, first_open.month,
first_open.day),
tz='UTC')
self.market_open, self.market_close = env.get_open_and_close(self.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
all_trading_days = env.trading_days
mask = ((all_trading_days >= normalize_date(self.period_start)) &
(all_trading_days <= normalize_date(self.period_end)))
self.trading_days = all_trading_days[mask]
self.position_tracker = PositionTracker(
asset_finder=env.asset_finder,
data_frequency=self.sim_params.data_frequency)
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(index=self.trading_days)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params, self.env)
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.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(self.sim_params, self.env,
create_first_day_stats=True)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the cumulative period will be calculated over the entire test.
period_open=self.period_start,
period_close=self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumulative period
serialize_positions=False,
asset_finder=self.env.asset_finder,
name="Cumulative")
self.cumulative_performance.position_tracker = self.position_tracker
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the daily period will be calculated for the market day
period_open=self.market_open,
period_close=self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True,
asset_finder=self.env.asset_finder,
name="Daily")
self.todays_performance.position_tracker = self.position_tracker
self.saved_dt = self.period_start
# one indexed so that we reach 100%
self.day_count = 0.0
self.txn_count = 0
self.account_needs_update = True
self._account = None
def __init__(self, sim_params, trading_calendar, asset_finder):
self.sim_params = sim_params
self.trading_calendar = trading_calendar
self.asset_finder = asset_finder
self.period_start = self.sim_params.start_session
self.period_end = self.sim_params.end_session
self.last_close = self.sim_params.last_close
self._current_session = self.sim_params.start_session
self.market_open, self.market_close = \
self.trading_calendar.open_and_close_for_session(
self._current_session
)
self.total_session_count = len(self.sim_params.sessions)
self.capital_base = self.sim_params.capital_base
self.emission_rate = sim_params.emission_rate
self.position_tracker = PositionTracker(
data_frequency=self.sim_params.data_frequency
)
if self.emission_rate == 'daily':
self.all_benchmark_returns = pd.Series(
index=self.sim_params.sessions
)
self.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.trading_calendar,
)
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.cumulative_risk_metrics = \
risk.RiskMetricsCumulative(
self.sim_params,
self.trading_calendar,
create_first_day_stats=True,
)
# this performance period will span the entire simulation from
# inception.
self.cumulative_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the cumulative period will be calculated over the entire test.
period_open=self.period_start,
period_close=self.period_end,
# don't save the transactions for the cumulative
# period
keep_transactions=False,
keep_orders=False,
# don't serialize positions for cumulative period
serialize_positions=False,
name="Cumulative"
)
self.cumulative_performance.position_tracker = self.position_tracker
# this performance period will span just the current market day
self.todays_performance = PerformancePeriod(
# initial cash is your capital base.
starting_cash=self.capital_base,
data_frequency=self.sim_params.data_frequency,
# the daily period will be calculated for the market day
period_open=self.market_open,
period_close=self.market_close,
keep_transactions=True,
keep_orders=True,
serialize_positions=True,
name="Daily"
)
self.todays_performance.position_tracker = self.position_tracker
self.saved_dt = self.period_start
# one indexed so that we reach 100%
self.session_count = 0.0
self.txn_count = 0
self.account_needs_update = True
self._account = None
