advanced-algorithmic-trading

428
used by the machine learning model for prediction.
"""
# Adjust the feature vector to move all lags by one
# and then recalculate the returns
for i, f in reversed(list(enumerate(self.cur_prices))):
if i > 0:
self.cur_prices[i] = self.cur_prices[i-1]
else:
self.cur_prices[i] = event.close_price / float(
PriceParser.PRICE_MULTIPLIER
)
if self.minutes > (self.lags + 1):
for i in range(0, self.lags):
self.cur_returns[i] = ((
self.cur_prices[i]/self.cur_prices[i+1]
)-1.0)*100.0
As with all QSTrader Strategy subclasses the signal generation happens in the calculate_signals
method. Firstly, the current returns are updated upon receipt of a new market data bar. If
enough minutes have passed (lags+2) then the model begins making predictions based on the
current returns data.
Note that in newer versions of Scikit-Learn it is necessary to reshape one-dimensional arrays
into two-dimensional arrays containing a single row otherwise a deprecation warning is given.
This explains the reshape((1,-1)) method call. Finally, since model.predict returns an array
of a single scalar rather than the scalar itself it is necessary to use the index element operator
([0]) to extract the value.
The remaining logic is tasked with going long or exiting depending upon whether the strategy
is already invested and whether the prediction is +1 or -1. As usual, SignalEvent objects are
sent to the events_queue to be utilised by the Portfolio object:
def calculate_signals(self, event):
"""
Calculate the intraday machine learning
prediction strategy.
"""
if event.type == EventType.BAR:
self._update_current_returns(event)
self.minutes += 1
# Allow enough time to pass to populate the
# returns feature vector
if self.minutes > (self.lags + 2):
pred = self.model.predict(self.cur_returns.reshape((1, -1)))[0]
# Long only strategy
if not self.invested and pred == 1:
print("LONG: %s" % event.time)
self.events_queue.put(