advanced-algorithmic-trading

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• TLT - For the period 3rd August 2009 to 1st August 2016 (link here)
• IEI For the period 3rd August 2009 to 1st August 2016 (link here).
This data will need to placed in the directory specified by the QSTrader settings file if you
wish to replicate the results.
28.2 Python QSTrader Implementation
Since QSTrader handles the position tracking, portfolio management, data ingestion and order
management the only code we need to write involves the Strategy object itself.
The Strategy communicates with the PortfolioHandler via the event queue, making use
of SignalEvent objects to do so. In addition we must import the base abstract strategy class,
AbstractStrategy.
Note that in the current alpha version of QSTrader we must also import the PriceParser
class. This is used to multiply all prices on input by a large multiple (10 8 ) and perform integer
arithmetic when tracking positions. This avoids floating point rounding issues that can accumulate
over the long period of a backtest. We must divide all the prices by PriceParser.PRICE_MULTIPLIER
to obtain the correct values:
from __future__ import print_function
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 create the KalmanPairsTradingStrategy class. The job of this class is
to determine when to create SignalEvent objects based on received BarEvents from the daily
OHLCV bars of TLT and IEI from Yahoo Finance.
There are many different ways to organise this class. I have opted to hardcode all of the
parameters in the class for clarity of the explanation. Notably I have fixed the value of δ =
10 −4 and v t = 10 −3 . They represent the system noise and measurement noise variance in the
Kalman Filter model. This could also be implemented as a keyword argument in the __init__
constructor of the class. Such an approach would allow straightforward parameter optimisation.
The first task is to set the time and invested members to be equal to None, as they will be
updated as market data is accepted and trade signals generated. latest_prices is an array of
length two containing the current prices of TLT and IEI, used for convenience throughout the
class.
The next set of parameters all relate to the Kalman Filter and are explained in depth in the
previous chapters.
The final set of parameters include days, used to track how many days have passed as well
as qty and cur_hedge_qty, used to track the absolute quantities of ETFs to purchase for both
the long and short side. I have set this to be 2,000 units on an account equity of 100,000 USD.