Trading the Odds

A statistical approach to profit in the US equity markets, trading the markets like professional card counters are playing Blackjack or expert poker players are playing Poker.

Trading the RSI as a Static Strategy

A few days ago Michael Stokes at MarketSci made an excellent post concerning RSI(2) readings, the changing frequency of extreme readings, its (the RSI’s) quality of forecast and efficiency of trading short-term mean-reversion in the markets over the course of time since 1950 (Extreme RSI(2) Readings Becoming Less Common, and he already covered the topic here and here).

Although I completely agree to his findings and conclusions which could be summarized as (cit.) ‘I don’t think this says anything about the effectiveness of strategies based on indicators such as RSI(2), but it does say that they might trigger a bit less over time.‘ and (cit.) ‘But I would be hesitant to trade the RSI(2) in the simple form I’ve described here as a static strategy.‘, I was incited by Bill Luby’s comments on Michael’s post concerning the usage of deviating break points (e.g. 95/5 and 98/2 instead of 90/10) and/or different moving averages (e.g. RSI (3) and RSI (4) instead of RSI (2)).

The RSI Relative Strenght Index was developed by J. Welles Wilder and introduced in 1978. The RSI (x days) oscillates between 0 and 100 and compares the magnitude of an assets (e.g. stock or index) recent gains to the magnitude of its recent losses, with low readings indicating oversold and high readings indicating overbought conditions.

My objective was to check if and to what extend the Relative Strength Index (not taking into account any additional indicator and/or condition, so in its pure form) could be utilized for a mechanical (and static) trading strategy, with investigations focused on the following questions:

  • could an RSI based strategy stand the test of time without any adjustments (e.g. break points) and/or adaptions to the then current market conditions (e.g. bull/bear markets),
  • its profitability year by year, in the long run, and its capability to deliver positive returns in bull and bear markets likewise,
  • its capability to outperform the SPY as a tradable proxy for the S&P 500,
  • the possibility to reduce the time in market in comparison to a buy and hold approach (which is always in the market) in order to reduce the risk of being hit by a potential ‘black swan’ event.

As an additional restriction I took into account and allowed for long trades only (the addition of potential short sales will probably be addressed in a future post), and no adjustements  (e.g. break points) are allowed for. No leverage is taken (no position sizing, money management and/or stops except the break points), but the strategy is always ‘all in’ (compounded returns, means any potential profits are always reinvested on the next trade in order to be comparable to a ‘buy and hold’ approach which is evenly always ‘all in’, assumed that no money is taken out). Due to the fact that this is a proof of concept/survey only, performance figures do not account for commissions, fees and slippage.

First of all I figured out that with respect to all the conditions listed above, it is not the 2-day or 3-day or 4-day RSI but the 2.5-day RSI which met those requirements best. Sounds surprisingly, but with respect to the computation of the RSI is makes no difference using 2.5 as a moving average instead of 2/3/4/… /x (even numbers for the number of sessions).

The next step was to evaluate the ideal lower and upper break points. From my perspective the optimal approach would be to determine the distribution of gains and losses (for the SPY) over the course of the then following first two days after a trade would have been entered at the lower break point, broken down by different RSI (2.5) ranges, and respectively at the upper break point in order to ride any upmove to its ideal extent (and before any potential profit would turn into a loss). For the time frame since 01/03/1993, the following tables (Table I for the upper break point,and Table II for the lower break point) show the respective distribution of profits and losses for the SPY over the course of the then following 2 session, broken down by different ranges for the SPY’ RSI (2.5), assumed one would’ve bought the SPY on close of every session when the RSI (2.5) closed anywhere between the lower and the upper break point (no overlapping trades allowed).

In order to maximize profits, the ideal exit is located between 67.5 and 72.5. That would present the ideal upper break point because any exit above (too late) or below (too early) would have reduced any already achieved or have missed any potential further gains due to the fact that the market’s tendency to reverse course significantly increased with an RSI (2.5) above 70. The same principle applies to the lower break point. The highest profitability (sum of all gains minus the sum of all losses, not the profit factor or anything else because the ‘opportunity factor’ plays a decisive role) would have been achieved with a lower break point of 18 (+179.06% during the then following two days if one woud have bought the SPY on close of every day when the RSI (2.5) closed below 18).

Strategy: Buy the SPX on close of a session when the RSI (2.5) closes below the lower break point (18); close the trade on close of a session when the RSI (2.5) closes above the upper break point (70).

(please note that trade performance figures were assigned to the date -and therefore regarded as achieved and realized- when the ‘buy’ was triggered, not on the date the trade was closed; that doesn’t make a difference concerning the total gains/lossed achieved, but due to the deviating distribution of gains/losses -not their total- the equity curve would look a bit different)

Table I


Table II


Table III shows the respective equity curve. Some additional stats:

  • Maximum month-end drawdown: -11.22%
  • % month positive: 74%
  • Month outperformance SPY: 52.85%
  • Time in market: 31.50%


So I completely agree with Michaels bottom line (cit.) ‘I do think RSI(2) has wings in today’s market. … But I would be hesitant to trade the RSI(2) in the simple form I’ve described here as a static strategy.‘, but not mainly concerning the point that it wouldn’t make sense to trade the RSI (2.5) as a static strategy (which would have been considerably profitable, less risky than a ‘buy and hold’ approach and would have almost always outperformed the SPY, but with hindsight only because we determined the ideal break points in 2009 and not in 1993) but in particular with respect to the fact that it will probably be possible to build a strategy around the RSI (2.5) in combination with one or more other indicator/conditions like Michael does in his State of the Market report which would be superior to any static RSI strategy alone.

Successful trading,


P.s.: WordPress recently implemented a Twitter widget, so I’ll regularly make some intraday updates as well using Twitter (as I already did during the last couple of session, but unfortunately there seems to be a connectivity issue between WordPress and Twitter; hope that will be solved soon). If you’re interested in, please have a look at the blog during the trading session as well or subscribe directly to Twitter.


Filed under: Studies/Survey, Trading Strategies

One Response

  1. Peter says:


    Absolutely first-class work. Thank you. Keep it up.

    Your Trading The Odds commentaries are currently running at 65% win rate… enough to make serious money! Thank you … if you ever go subscrition, I will sign-up :-)

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