advanced-algorithmic-trading

414
if self.R is not None:
self.R = self.C + self.wt
else:
self.R = np.zeros((2, 2))
# Calculate the Kalman Filter update
# ----------------------------------
# Calculate prediction of new observation
# as well as forecast error of that prediction
yhat = F.dot(self.theta)
et = y - yhat
# Q_t is the variance of the prediction of
# observations and hence \sqrt{Q_t} is the
# standard deviation of the predictions
Qt = F.dot(self.R).dot(F.T) + self.vt
sqrt_Qt = np.sqrt(Qt)
# The posterior value of the states \theta_t is
# distributed as a multivariate Gaussian with mean
# m_t and variance-covariance C_t
At = self.R.dot(F.T) / Qt
self.theta = self.theta + At.flatten() * et
self.C = self.R - At * F.dot(self.R)
# Only trade if days is greater than a "burn in" period
if self.days > 1:
# If we’re not in the market...
if self.invested is None:
if et < -sqrt_Qt:
# Long Entry
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