advanced-algorithmic-trading

405
self.time = event.time
# Set the correct latest prices depending upon
# order of arrival of market bar event
price = event.adj_close_price/float
PriceParser.PRICE_MULTIPLIER
)
if event.time == self.time:
if event.ticker == self.tickers[0]:
self.latest_prices[0] = price
else:
self.latest_prices[1] = price
else:
self.time = event.time
self.days += 1
self.latest_prices = np.array([-1.0, -1.0])
if event.ticker == self.tickers[0]:
self.latest_prices[0] = price
else:
self.latest_prices[1] = price
The core of the strategy is carried out in the calculate_signals method. Firstly we set
the correct times and prices (as described above). Then we check that we have both prices for
TLT and IEI, at which point we can consider new trading signals.
y is set equal to the latest price for IEI, while F is the observation matrix containing the latest
price for TLT, as well as a unity placeholder to represent the intercept in the linear regression.
The Kalman Filter is subsequently updated with these latest prices. Finally we calculate the
forecast error e t and the standard deviation of the predictions, √ Q t . We will run through this
code step-by-step as it looks a little complicated.
The first task is to form the scalar value y and the observation matrix F, containing the prices
of IEI and and TLT respectively. We calculate the variance-covariance matrix R or set it to the
zero-matrix if it has not yet been initialised. Subsequently we calculate the new prediction of
the observation yhat as well as the forecast error et.
We then calculate the variance of the observation predictions Qt as well as the standard
deviation sqrt_Qt. We use the update rules derived in the chapter on State Space Models
to obtain the posterior distribution of the states theta, which contains the hedge ratio/slope
between the two prices:
def calculate_signals(self, event):
"""
Calculate the Kalman Filter strategy.
"""
if event.type == EventType.BAR:
self._set_correct_time_and_price(event)
# Only trade if we have both observations