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27.5 Python QSTrader Implementation
Note that the full listings of each of these Python files can be found at the end of
the chapter.
Note also that this strategy contains an implicit lookahead bias and so its performance
will be grossly exaggerated compared to a real implementation. The lookahead bias occurs due to
the use of calculating the hedging ratio across the same sample of data as the trading strategy is
simulated on. In a real implementation two separate sets of data will be needed in order to verify
that any structural relationship persists out-of-sample.
The implementation of the strategy is similar to other QSTrader strategies. It involves the
creation of a subclass of AbstractStrategy in the coint_bollinger_strategy.py file. This
class is then used by the coint_bollinger_backtest.py file to actually simulate the backtest.
coint_bollinger_strategy.py will be described first. NumPy is imported, as are the
necessary QSTrader libraries for handling signals and strategies. PriceParser is brought in
to adjust the internal handling of QSTrader’s price storage mechanism to avoid floating-point
round-off error.
The Python deque double-ended queue class is also imported and used to store a rolling
window of closing price bars. THis is necessary for the moving average and standard deviation
lookback calculations. More on this below.
# coint_bollinger_strategy.py
from __future__ import print_function
from collections import deque
from math import floor
import numpy as np
from qstrader.price_parser import PriceParser
from qstrader.event import (SignalEvent, EventType)
from qstrader.strategy.base import AbstractStrategy
The next step is to define the CointegrationBollingerBandsStrategy subclass of AbstractStrategy,
which carries out the signals generation. As with all strategies it requires a list of tickers that
it acts upon as well as a handle to the events_queue upon which to place the SignalEvent
objects.
This subclass requires additional parameters. lookback is the integer number of bars over
which to perform the rolling moving average and standard deviation calculations. weights is
the set of fixed hedging ratios, or primary Johansen test eigenvector components, to use as the
"unit" of a portfolio of cointegrating assets.
entry_z describes the multiple of z-score entry threshold (i.e. number of standard deviations)
upon which to open a trade. exit_z is the corresponding number of standard deviations upon
which to exit the trade. base_quantity is the integer number of "units" of the portfolio to
trade.
In addition the class also keeps track of the latest prices of all tickers in a separate array in
self.latest_prices. It also contains a double-ended queue consisting of the last lookback val-