The RSI indicator has served as the foundation of several popular short-term trading strategies over the years mainly due to its popularity. In the article I compare the results of a basic system based on a two-period RSI to those of a system that is based on a two-day losing streak.

Larry Connors and Cesar Alvarez originally used the RSI(2) in developing trading systems. In this article I show that the basic RSI(2) system has underperformed a simple two-day losing streak indicator during the SPY uptrend since March of 2009 and in GLD with data since inception.

RSI(2) System

Buy at the open of next day if RSI(2) < 10

Sell at the open of next day if RSI(2) > 70

Two-day losing streak system

Buy at the open of next day if Win Rate (2) < 10

Sell at the open of next day if Win Rate (2) > 70

The win Rate (2) indicator has a value of 100 if there are two consecutive winners and the value becomes 0 for two losing trades in a row. Actually, this indicator assumes the values 0, 50 and 100 when the period is 2. Thus, the second system buys if there are two losing days in a row and sells if there are two winning days in a row.

Backtest settings

$100,000 initial capital

Equity is fully invested at each position

$0.01/share commission

Chart example

Below is a chart that plots the RSI(2) and Win Rate (2) indicators.

Backtest results for the period 03/06/2009 – 05/22/2015

Parameter Win Rate(2) System RSI(2) System CAR 14.98% 10.58% Win rate 75% 73.77% Max DD -16.06 -14.97% Trades 120 61 Profit factor 2.54 3.45 Sharpe ratio 2.04 3.02 Payoff ratio 0.85 1.23 No. of losing years 0 0

It may be seen that although the RSI(2) system has higher Sharpe, profit factor and payoff ratio, its CAR is lower by about 4.5%. The reason for that is that the Win Rate (2) system trades more often and although the expectation of the RSI(2) system is higher, it realizes more profit. This is an aspect of trading systems that new trading system developers usually ignore, i.e. systems must be able to deliver quantity in addition to quality.

I wanted to compare these two basic systems in this particular time period because it represents a tough one for traders to match the buy and hold return of SPY before dividends that comes close to 23%.

The conclusion from this first example is that simple systems that obey Occam’s razor usually outperform more complicates systems. In this particular case and when considering only two periods, the RSI(2) is a much more complicated way of trying to gauge mean reversion than a simple indicator based on a 2-day losing streak.

Modified versions of the systems

If the exit signal occurs instead when the close is higher than the 5-day moving average, the Win Rate (2) system still outperforms the RSI(2) system by about the same margin.

Results for GLD with data since inception

This has been a tough market, especially after the downtrend in gold price that started in 2012. The backtest results with GLD data since inception 50 05/22/2015 are shown on the table below:

Parameter Win Rate(2) System RSI(2) System CAR 9.52% 5.61% Win rate 63.6% 66.07% Max DD -33.27% -22.79% Trades 217 112 Profit factor 1.28 1.36 Sharpe ratio 0.83 0.87 Payoff ratio 0.73 0.70 Losing years 2013, 2014 2006, 2008, 2014, 2015

The Win Rate (2) system CAR outperforms the RSI(2) system by 4%. Also, the RSI(2) system has four losing years as compared to only two for the Win Rate (2) system. However, the latter shows a large loss for 2013.

Summary

Most short-term indicators can be effectively approximated by much simpler price patterns. Actually, that was the main idea behind the Price Action Lab software. By adhering to these simpler approximations, we respect Occam’s razor and simplicity over unnecessary complexity. Simplicity was the main reason that the Win Rate (2) indicator outperformed the RSI(2) indicator by such a wide margin. In general, it is true that systems that can deal with the same information but in simpler ways are more robust than those that require higher complexity for achieving the same goal. In essence, this is another reason that machine learning methods, evolutionary algos and neural networks do not usually work and more and more users of such complicated approaches to trading are convinced that the data-mining problems that plague them cannot be easily resolved. Simple and deterministic trading rules lead to less data-mining bias by limiting complexity and randomness and thus keeping the entropy of the trading system development process low.

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Charting program: Amibroker

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