Is Pairs Trading Still Viable?


Classic pairs trading strategies have suffered deteriorating returns over time. Can a research pipeline that facilitates the identification and selection of ETF pairs help make pairs trading viable again? This post investigates such a pipeline.

The problem: pairs wander away

Source: Ernie Chan, Algorithmic Trading: Winning Strategies and Their Rationale, Wiley, May 28, 2013, chapter 4.

Pairs trading is a classic arbitrage strategy on securities in the same industry (for example, Coke and Pepsi) in which the trader buys one security and sells the other when the spread between them widens, then closes the positions when the spread narrows again.

In his book Algorithmic Trading, Ernie Chan notes that pairs trading of stocks has become more difficult over time. Two stocks may cointegrate in-sample, but they often wander apart out-of-sample as the fortunes of the respective companies diverge. However, Chan finds more fertile ground for pairs trading among ETFs.

I backtest a pairs trading strategy using an ETF pair from Chan’s book, GLD and GDX (the Gold ETF and Gold Miners ETF).

Based on the tendency of pairs to eventually stop cointegrating, I hypothesize that successful pairs trading might require a robust pipeline for continually identifying and selecting new pairs to trade. I attempt to construct such a pipeline using a 3-step process:

  1. For a universe of all liquid US ETFs, I test all possible pairs for cointegration using the Johansen test in an in-sample window.
  2. I run in-sample backtests on all cointegrating pairs and select the 5 best performing pairs.
  3. I run an out-of-sample backtest on a portfolio of the 5 best performing pairs.

Pairs trading strategy

I create a Moonshot pairs trading strategy that replicates the trading rules in Chan’s book. A few code snippets are highlighed here. The strategy calculates daily hedge ratios using the Johansen test:

from statsmodels.tsa.vector_ar.vecm import coint_johansen

# The second and third parameters indicate constant term, with a lag of 1.
# See Chan, Algorithmic Trading, chapter 2.
result = coint_johansen(pair_prices, 0, 1)

# The first column of eigenvectors contains the best weights
hedge_ratios = list(result.evec[0])

The timing of entries and exits is based on Bollinger Bands set one standard deviation away from the spread’s moving average:

# Compute spread and Bollinger Bands
spreads = (pair_prices * hedge_ratios).sum(axis=1)
means = spreads.fillna(method="ffill").rolling(20).mean()
stds = spreads.fillna(method="ffill").rolling(20).std()
upper_bands = means + stds
lower_bands = means - stds

# Long (short) the spread when it crosses below (above) the lower (upper)
# band, then exit when it crosses the mean
long_entries = spreads < lower_bands
long_exits = spreads >= means
short_entries = spreads > upper_bands
short_exits = spreads <= means

See the full source code and read the rest of the article on QuantRocket website.

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