advanced-algorithmic-trading

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print("LONG: %s" % event.time)
self.cur_hedge_qty = int(floor(self.qty*self.theta[0]))
self.events_queue.put(
SignalEvent(self.tickers[1], "BOT", self.qty)
)
self.events_queue.put(
SignalEvent(self.tickers[0], "SLD", self.cur_hedge_qty)
)
self.invested = "long"
elif e > sqrt_Q:
# Short Entry
print("SHORT: %s" % event.time)
self.cur_hedge_qty = int(floor(self.qty*self.theta[0]))
self.events_queue.put(
SignalEvent(self.tickers[1], "SLD", self.qty)
)
self.events_queue.put(
SignalEvent(self.tickers[0], "BOT", self.cur_hedge_qty)
)
self.invested = "short"
# If we are in the market...
if self.invested is not None:
if self.invested == "long" and e > -sqrt_Q:
print("CLOSING LONG: %s" % event.time)
self.events_queue.put(
SignalEvent(self.tickers[1], "SLD", self.qty)
)
self.events_queue.put(
SignalEvent(self.tickers[0], "BOT", self.cur_hedge_qty)
)
self.invested = None
elif self.invested == "short" and e < sqrt_Q:
print("CLOSING SHORT: %s" % event.time)
self.events_queue.put(
SignalEvent(self.tickers[1], "BOT", self.qty)
)
self.events_queue.put(
SignalEvent(self.tickers[0], "SLD", self.cur_hedge_qty)
)
self.invested = None
This is all of the code necessary for the Strategy object. We also need to create a backtest
file to encapsulate all of our trading logic and class choices. The particular version is very similar
to those used in the examples directory and replaces the equity of 500,000 USD with 100,000
USD.