advanced-algorithmic-trading

Chapter 32
Strategy Decay
In this chapter the issue of when to retire a trading strategy will be considered. It will
present brief reasons why strategies eventually end up underperforming. It will discuss how this
can be measured over time and then describe an implementation in QSTrader that provides this
functionality. The methodology will then be applied to some of the previous strategies presented
within the book in order to gauge their recent effectiveness.
Strategy decay is one of the trickiest aspects to manage within the realm of quantitative
trading. It involves previously well-performing strategies that gradually, and sometimes rapidly,
lose their performance characteristics and end up becoming unprofitable.
Quantitative trading strategies almost unilaterally rely on the concept of forecasting and/or
statistical mispricing. As more and more trading entities–retail or institutional–implement similar
systematic strategies the mispricings give way to price efficiency. The gain derived from such
strategies is eroded and then usually falls to the level of transaction costs required to carry it
out, making them unprofitable.
This means that quantitative trading is not a "set and forget" activity. In reality quant
traders need to have a portfolio of strategies that are slowly "rotated out" over time once any
arbitrage opportunities begin to erode. Thus constant research is required to continually develop
new profitable "edges" that replace those that have been "arbitraged away".
However some systematic strategies often have large periods of mediocre returns and extensive
drawdown. This is particularly prevalent in strategies based on daily data since they tend to
have far fewer positively-expected trading "bets". Thus a major challenge for quant researchers
lies in identifying when a strategy is truly underperforming due to erosion of edge or whether it
is a "temporary" period of poorer performance.
This motivates the need for an effective trailing metric that captures current performance of
the strategy in relation to its previous performance. One of the most widely used measures–at
least in the institutional quant world–is the annualised rolling Sharpe ratio.
The Sharpe ratio of a strategy is designed to provide a measure of mean excess returns of
a strategy as a ratio of the volatility "endured" to achieve those returns. It is a "broad brush"
measure of the reward-to-risk ratio of a strategy. The annualised rolling Sharpe ratio simply
calculates this value on the previous year’s worth of trading data. It provides a continuallyupdated,
albeit rearward-looking view of current reward-to-risk.
A low Sharpe ratio (below 1.0) implies that substantial returns volatility is being endured
for minimal mean return. A negative Sharpe ratio implies that one would have been better off
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