advanced-algorithmic-trading

Chapter 28
Kalman Filter-Based Pairs Trading
using QSTrader
In previous chapters of the book we considered the mathematical underpinnings of State Space
Models and Kalman Filters, as well as the application of the PyKalman library to a pair of ETFs
to dynamically adjust a hedge ratio as a basis for a mean reverting trading strategy.
In this chapter we will discuss a trading strategy originally due to Ernest Chan[32] and tested
by Aidan O’Mahony over at Quantopian[73]. We will make use of the QSTrader backtesting
framework in order to provide a new implementation of the strategy. QSTrader will carry out
the "heavy lifting" of the position tracking, portfolio handling and data ingestion, while we
concentrate here solely on the code that generates the trading signals.
28.1 The Trading Strategy
The pairs-trading strategy is applied to a couple of Exchange Traded Funds (ETF) that both
track the performance of varying duration US Treasury bonds. They are:
• TLT - iShares 20+ Year Treasury Bond ETF
• IEI - iShares 3-7 Year Treasury Bond ETF
The goal is to build a mean-reverting strategy from this pair of ETFs.
The synthetic "spread" between TLT and IEI is the time series that we are actually interested
in longing or shorting. The Kalman Filter is used to dynamically track the hedging ratio between
the two in order to keep the spread stationary.
To create the trading rules it is necessary to determine when the spread has moved too far
from its expected value. How do we determine what "too far" is? We could utilise a set of
fixed absolute values, but these would have to be empirically determined. This would introduce
another free parameter into the system that would require optimisation (and additional danger
of overfitting).
One approach to creating these values is to consider a multiple of the standard deviation of
the spread (as in the previous chapter) and use these as the bounds. For simplicity we can set
the coefficient of the multiple to be equal to one.
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