advanced-algorithmic-trading

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ues of the market value of a "unit" of the portfolio in self.port_mkt_value. A self.invested
flag allows the strategy itself to keep track of whether it is "in the market" or not:
class CointegrationBollingerBandsStrategy(AbstractStrategy):
"""
Requires:
tickers - The list of ticker symbols
events_queue - A handle to the system events queue
lookback - Lookback period for moving avg and moving std
weights - The weight vector describing
a "unit" of the portfolio
entry_z - The z-score trade entry threshold
exit_z - The z-score trade exit threshold
base_quantity - Number of "units" of the portfolio
to be traded
"""
def __init__(
self, tickers, events_queue,
lookback, weights, entry_z, exit_z,
base_quantity
):
self.tickers = tickers
self.events_queue = events_queue
self.lookback = lookback
self.weights = weights
self.entry_z = entry_z
self.exit_z = exit_z
self.qty = base_quantity
self.time = None
self.latest_prices = np.full(len(self.tickers), -1.0)
self.port_mkt_val = deque(maxlen=self.lookback)
self.invested = None
self.bars_elapsed = 0
The following _set_correct_time_and_price method is similar to that found in the subsequent
QSTrader Kalman Filter chapter. The goal of this method is to make sure that the
self.latest_prices array is populated with the latest market values of each ticker. The strategy
will only execute if this array contains a full set of prices, all containing the same time-stamp.
This ensures that if market events arrive "out of order" then the strategy always has enough
pricing information to act upon.
A previous version of this method was fixed for an array of two prices but the code below
works for any number of tickers, which is necessary for cointegrating portfolios that might contain
three or more assets:
def _set_correct_time_and_price(self, event):
"""
Sets the correct price and event time for prices