def __init__(self, initial_cash, events_queue, price_handler,
position_sizer, risk_manager):
"""
The PortfolioHandler is designed to interact with the
backtesting or live trading overall event-driven
architecture. It exposes two methods, on_signal and
on_fill, which handle how SignalEvent and FillEvent
objects are dealt with.
Each PortfolioHandler contains a Portfolio object,
which stores the actual Position objects.
The PortfolioHandler takes a handle to a PositionSizer
object which determines a mechanism, based on the current
Portfolio, as to how to size a new Order.
The PortfolioHandler also takes a handle to the
RiskManager, which is used to modify any generated
Orders to remain in line with risk parameters.
"""
self.initial_cash = initial_cash
self.events_queue = events_queue
self.price_handler = price_handler
self.position_sizer = position_sizer
self.risk_manager = risk_manager
self.portfolio = Portfolio(price_handler, initial_cash)
def setUp(self):
"""
Set up the Portfolio object that will store the
collection of Position objects, supplying it with
$500,000.00 USD in initial cash.
"""
ph = PriceHandlerMock()
cash = Decimal("500000.00")
self.portfolio = Portfolio(ph, cash)
def __init__(
self, initial_cash, events_queue,
price_handler, position_sizer, risk_manager
):
"""
The PortfolioHandler is designed to interact with the
backtesting or live trading overall event-driven
architecture. It exposes two methods, on_signal and
on_fill, which handle how SignalEvent and FillEvent
objects are dealt with.
Each PortfolioHandler contains a Portfolio object,
which stores the actual Position objects.
The PortfolioHandler takes a handle to a PositionSizer
object which determines a mechanism, based on the current
Portfolio, as to how to size a new Order.
The PortfolioHandler also takes a handle to the
RiskManager, which is used to modify any generated
Orders to remain in line with risk parameters.
"""
self.initial_cash = initial_cash
self.events_queue = events_queue
self.price_handler = price_handler
self.position_sizer = position_sizer
self.risk_manager = risk_manager
self.portfolio = Portfolio(price_handler, initial_cash)
def test_calculating_statistics(self):
"""
Purchase/sell multiple lots of AMZN, GOOG
at various prices/commissions to ensure
the arithmetic in calculating equity, drawdowns
and sharpe ratio is correct.
"""
# Create Statistics object
price_handler = PriceHandlerMock()
self.portfolio = Portfolio(price_handler, Decimal("500000.00"))
portfolio_handler = PortfolioHandlerMock(self.portfolio)
statistics=SimpleStatistics(portfolio_handler)
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"BOT", "AMZN", 100,
Decimal("566.56"), Decimal("1.00")
)
t="2000-01-01 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("499807.00"))
self.assertEqual(statistics.drawdowns[t], Decimal("193.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("-0.0386"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"BOT", "AMZN", 200,
Decimal
("566.395"), Decimal("1.00")
)
t="2000-01-02 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("499455.00"))
self.assertEqual(statistics.drawdowns[t], Decimal("545.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("-0.0705"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"BOT", "GOOG", 200,
Decimal("707.50"), Decimal("1.00")
)
t="2000-01-03 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("499046.00"))
self.assertEqual(statistics.drawdowns[t], Decimal("954.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("-0.0820"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"SLD", "AMZN", 100,
Decimal("565.83"), Decimal("1.00")
)
t="2000-01-04 00:00:00"
statistics.update(t);
self.assertEqual(statistics.equity[t], Decimal("499164.00"))
self.assertEqual(statistics.drawdowns[t], Decimal("836.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("0.0236"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"BOT", "GOOG", 200,
Decimal("705.545"), Decimal("1.00")
)
t="2000-01-05 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("499146.00"))
self.assertEqual(statistics.drawdowns[t], Decimal("854.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("-0.0036"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"SLD", "AMZN", 200,
Decimal("565.59"), Decimal("1.00")
)
t="2000-01-06 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("499335.00"))
self.assertEqual(statistics.drawdowns[t], Decimal("665.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("0.0379"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"SLD", "GOOG", 100,
Decimal("707.92"), Decimal("1.00")
)
t="2000-01-07 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("499580.00"))
self.assertEqual(statistics.drawdowns[t], Decimal("420.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("0.0490"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"SLD", "GOOG", 100,
Decimal("707.90"), Decimal("0.00")
)
t="2000-01-08 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("499824.00"))
self.assertEqual(statistics.drawdowns[t], Decimal("176.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("0.0488"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"SLD", "GOOG", 100,
Decimal("707.92"), Decimal("0.50")
)
t="2000-01-09 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("500069.50"))
self.assertEqual(statistics.drawdowns[t], Decimal("00.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("0.0491"))
# Perform transaction and test statistics at this tick
self.portfolio.transact_position(
"SLD", "GOOG", 100,
Decimal("707.78"), Decimal("1.00")
)
t="2000-01-10 00:00:00"
statistics.update(t)
self.assertEqual(statistics.equity[t], Decimal("500300.50"))
self.assertEqual(statistics.drawdowns[t], Decimal("00.00"))
self.assertEqual(statistics.equity_returns[t], Decimal("0.0462"))
# Test that results are calculated correctly.
results=statistics.get_results()
self.assertEqual(results["max_drawdown"], Decimal("954.00"))
self.assertEqual(results["max_drawdown_pct"], Decimal("0.1908"))
self.assertEqual(results["sharpe"], Decimal("1.8353"))
class PortfolioHandler(object):
def __init__(self, initial_cash, events_queue, price_handler,
position_sizer, risk_manager):
"""
The PortfolioHandler is designed to interact with the
backtesting or live trading overall event-driven
architecture. It exposes two methods, on_signal and
on_fill, which handle how SignalEvent and FillEvent
objects are dealt with.
Each PortfolioHandler contains a Portfolio object,
which stores the actual Position objects.
The PortfolioHandler takes a handle to a PositionSizer
object which determines a mechanism, based on the current
Portfolio, as to how to size a new Order.
The PortfolioHandler also takes a handle to the
RiskManager, which is used to modify any generated
Orders to remain in line with risk parameters.
"""
self.initial_cash = initial_cash
self.events_queue = events_queue
self.price_handler = price_handler
self.position_sizer = position_sizer
self.risk_manager = risk_manager
self.portfolio = Portfolio(price_handler, initial_cash)
def _create_order_from_signal(self, signal_event):
"""
Take a SignalEvent object and use it to form a
SuggestedOrder object. These are not OrderEvent objects,
as they have yet to be sent to the RiskManager object.
At this stage they are simply "suggestions" that the
RiskManager will either verify, modify or eliminate.
"""
order = SuggestedOrder(signal_event.ticker, signal_event.action)
return order
def _place_orders_onto_queue(self, order_list):
"""
Once the RiskManager has verified, modified or eliminated
any order objects, they are placed onto the events queue,
to ultimately be executed by the ExecutionHandler.
"""
for order_event in order_list:
self.events_queue.put(order_event)
def _convert_fill_to_portfolio_update(self, fill_event):
"""
Upon receipt of a FillEvent, the PortfolioHandler converts
the event into a transaction that gets stored in the Portfolio
object. This ensures that the broker and the local portfolio
are "in sync".
In addition, for backtesting purposes, the portfolio value can
be reasonably estimated in a realistic manner, simply by
modifying how the ExecutionHandler object handles slippage,
transaction costs, liquidity and market impact.
"""
action = fill_event.action
ticker = fill_event.ticker
quantity = fill_event.quantity
price = fill_event.price
commission = fill_event.commission
# Create or modify the position from the fill info
self.portfolio.transact_position(action, ticker, quantity, price,
commission)
def on_signal(self, signal_event):
"""
This is called by the backtester or live trading architecture
to form the initial orders from the SignalEvent.
These orders are sized by the PositionSizer object and then
sent to the RiskManager to verify, modify or eliminate.
Once received from the RiskManager they are converted into
full OrderEvent objects and sent back to the events queue.
"""
# Create the initial order list from a signal event
initial_order = self._create_order_from_signal(signal_event)
# Size the quantity of the initial order
sized_order = self.position_sizer.size_order(self.portfolio,
initial_order)
# Refine or eliminate the order via the risk manager overlay
order_events = self.risk_manager.refine_orders(self.portfolio,
sized_order)
# Place orders onto events queue
self._place_orders_onto_queue(order_events)
def on_fill(self, fill_event):
"""
This is called by the backtester or live trading architecture
to take a FillEvent and update the Portfolio object with new
or modified Positions.
In a backtesting environment these FillEvents will be simulated
by a model representing the execution, whereas in live trading
they will come directly from a brokerage (such as Interactive
Brokers).
"""
self._convert_fill_to_portfolio_update(fill_event)
def update_portfolio_value(self):
"""
Update the portfolio to reflect current market value as
based on last bid/ask of each ticker.
"""
self.portfolio._update_portfolio()
class PriceHandlerMock(object):
def __init__(self):
pass
def get_best_bid_ask(self, ticker):
prices = {
"GOOG": (Decimal("762.15"), Decimal("762.15")),
"AMZN": (Decimal("660.51"), Decimal("660.51")),
}
return prices[ticker]
ph = PriceHandlerMock()
cash = Decimal("500000.00")
port = Portfolio(ph, cash)
port.add_position("BOT", "GOOG", 100, Decimal("761.75"), Decimal("1.00"))
port.add_position("BOT", "AMZN", 100, Decimal("660.86"), Decimal("1.00"))
port.modify_position("BOT", "AMZN", 100, Decimal("660.14"), Decimal("1.00"))
port.modify_position("BOT", "AMZN", 150, Decimal("660.20"), Decimal("1.00"))
port.modify_position("SLD", "AMZN", 300, Decimal("659.713"), Decimal("1.50"))
print("CASH:", port.cur_cash)
print("EQUITY:", port.equity)
print("UPnL:", port.unrealised_pnl)
print("PnL:", port.realised_pnl)
pprint.pprint(port.positions)
print("")
for p in port.positions:
class TestAmazonGooglePortfolio(unittest.TestCase):
"""
Test a portfolio consisting of Amazon and
Google/Alphabet with various orders to create
round-trips for both.
These orders were carried out in the Interactive Brokers
demo account and checked for cash, equity and PnL
equality.
"""
def setUp(self):
"""
Set up the Portfolio object that will store the
collection of Position objects, supplying it with
$500,000.00 USD in initial cash.
"""
ph = PriceHandlerMock()
cash = Decimal("500000.00")
self.portfolio = Portfolio(ph, cash)
def test_calculate_round_trip(self):
"""
Purchase/sell multiple lots of AMZN and GOOG
at various prices/commissions to check the
arithmetic and cost handling.
"""
# Buy 300 of AMZN over two transactions
self.portfolio.transact_position(
"BOT", "AMZN", 100,
Decimal("566.56"), Decimal("1.00")
)
self.portfolio.transact_position(
"BOT", "AMZN", 200,
Decimal("566.395"), Decimal("1.00")
)
# Buy 200 GOOG over one transaction
self.portfolio.transact_position(
"BOT", "GOOG", 200,
Decimal("707.50"), Decimal("1.00")
)
# Add to the AMZN position by 100 shares
self.portfolio.transact_position(
"SLD", "AMZN", 100,
Decimal("565.83"), Decimal("1.00")
)
# Add to the GOOG position by 200 shares
self.portfolio.transact_position(
"BOT", "GOOG", 200,
Decimal("705.545"), Decimal("1.00")
)
# Sell 200 of the AMZN shares
self.portfolio.transact_position(
"SLD", "AMZN", 200,
Decimal("565.59"), Decimal("1.00")
)
# Multiple transactions bundled into one (in IB)
# Sell 300 GOOG from the portfolio
self.portfolio.transact_position(
"SLD", "GOOG", 100,
Decimal("704.92"), Decimal("1.00")
)
self.portfolio.transact_position(
"SLD", "GOOG", 100,
Decimal("704.90"), Decimal("0.00")
)
self.portfolio.transact_position(
"SLD", "GOOG", 100,
Decimal("704.92"), Decimal("0.50")
)
# Finally, sell the remaining GOOG 100 shares
self.portfolio.transact_position(
"SLD", "GOOG", 100,
Decimal("704.78"), Decimal("1.00")
)
# The figures below are derived from Interactive Brokers
# demo account using the above trades with prices provided
# by their demo feed.
self.assertEqual(self.portfolio.cur_cash, Decimal("499100.50"))
self.assertEqual(self.portfolio.equity, Decimal("499100.50"))
self.assertEqual(self.portfolio.unrealised_pnl, Decimal("0.00"))
self.assertEqual(self.portfolio.realised_pnl, Decimal("-899.50"))
class PortfolioHandler(object):
def __init__(
self, initial_cash, events_queue,
price_handler, position_sizer, risk_manager
):
"""
The PortfolioHandler is designed to interact with the
backtesting or live trading overall event-driven
architecture. It exposes two methods, on_signal and
on_fill, which handle how SignalEvent and FillEvent
objects are dealt with.
Each PortfolioHandler contains a Portfolio object,
which stores the actual Position objects.
The PortfolioHandler takes a handle to a PositionSizer
object which determines a mechanism, based on the current
Portfolio, as to how to size a new Order.
The PortfolioHandler also takes a handle to the
RiskManager, which is used to modify any generated
Orders to remain in line with risk parameters.
"""
self.initial_cash = initial_cash
self.events_queue = events_queue
self.price_handler = price_handler
self.position_sizer = position_sizer
self.risk_manager = risk_manager
self.portfolio = Portfolio(price_handler, initial_cash)
def _create_order_from_signal(self, signal_event):
"""
Take a SignalEvent object and use it to form a
SuggestedOrder object. These are not OrderEvent objects,
as they have yet to be sent to the RiskManager object.
At this stage they are simply "suggestions" that the
RiskManager will either verify, modify or eliminate.
"""
order = SuggestedOrder(
signal_event.ticker, signal_event.action
)
return order
def _place_orders_onto_queue(self, order_list):
"""
Once the RiskManager has verified, modified or eliminated
any order objects, they are placed onto the events queue,
to ultimately be executed by the ExecutionHandler.
"""
for order_event in order_list:
self.events_queue.put(order_event)
def _convert_fill_to_portfolio_update(self, fill_event):
"""
Upon receipt of a FillEvent, the PortfolioHandler converts
the event into a transaction that gets stored in the Portfolio
object. This ensures that the broker and the local portfolio
are "in sync".
In addition, for backtesting purposes, the portfolio value can
be reasonably estimated in a realistic manner, simply by
modifying how the ExecutionHandler object handles slippage,
transaction costs, liquidity and market impact.
"""
action = fill_event.action
ticker = fill_event.ticker
quantity = fill_event.quantity
price = fill_event.price
commission = fill_event.commission
# Create or modify the position from the fill info
self.portfolio.transact_position(
action, ticker, quantity,
price, commission
)
def on_signal(self, signal_event):
"""
This is called by the backtester or live trading architecture
to form the initial orders from the SignalEvent.
These orders are sized by the PositionSizer object and then
sent to the RiskManager to verify, modify or eliminate.
Once received from the RiskManager they are converted into
full OrderEvent objects and sent back to the events queue.
"""
# Create the initial order list from a signal event
initial_order = self._create_order_from_signal(signal_event)
# Size the quantity of the initial order
sized_order = self.position_sizer.size_order(
self.portfolio, initial_order
)
# Refine or eliminate the order via the risk manager overlay
order_events = self.risk_manager.refine_orders(
self.portfolio, sized_order
)
# Place orders onto events queue
self._place_orders_onto_queue(order_events)
def on_fill(self, fill_event):
"""
This is called by the backtester or live trading architecture
to take a FillEvent and update the Portfolio object with new
or modified Positions.
In a backtesting environment these FillEvents will be simulated
by a model representing the execution, whereas in live trading
they will come directly from a brokerage (such as Interactive
Brokers).
"""
self._convert_fill_to_portfolio_update(fill_event)