The strategy pays off well; the investor is able to lock in a much higher return over the
relevant period than a plain long investment would provide. Figure 3-8 compares the
cumulative, continuous returns of the index with the cumulative, continuous returns of our
strategy:
In [ 45 ]: sp500[[‘Market’, ‘Strategy’]].cumsum().apply(np.exp).plot(grid=True,
figsize=( 8 , 5 ))
Figure 3-8. The S&P 500 index vs. investor’s wealth
Figure 3-8 shows that especially during market downturns (2003 and 2008/2009) the
shorting of the market yields quite high returns. Although the strategy does not capture the
whole upside during bullish periods, the strategy as a whole outperforms the market quite
significantly.
However, we have to keep in mind that we completely neglect operational issues (like
trade execution) and relevant market microstructure elements (e.g., transaction costs). For
example, we are working with daily closing values. A question would be when to execute
an exit from the market (from being long to being neutral/in cash): on the same day at the
closing value or the next day at the opening value. Such considerations for sure have an
impact on the performance, but the overall result would probably persist. Also, transaction
costs generally diminish returns, but the trading rule does not generate too many signals.
FINANCIAL TIME SERIES
Whenever it comes to the analysis of financial time series, consider using pandas. Almost any time series-related
problem can be tackled with this powerful library.
