advanced-algorithmic-trading

440
events_queue - A handle to the system events queue
"""
def __init__(
self, tickers, events_queue,
model_pickle_file, lags=5
):
self.tickers = tickers
self.events_queue = events_queue
self.model_pickle_file = model_pickle_file
self.lags = lags
self.invested = False
self.cur_prices = np.zeros(self.lags+1)
self.cur_returns = np.zeros(self.lags)
self.minutes = 0
self.qty = 10000
self.model = joblib.load(model_pickle_file)
def _update_current_returns(self, event):
"""
Updates the array of current returns "features"
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
def calculate_signals(self, event):
"""
Calculate the intraday machine learning
prediction strategy.
"""
if event.type == EventType.BAR:
self._update_current_returns(event)