BULL MARKET: I will reverse the regime filter, so we will take trades during a bull market. That is, we will be opening new trades when the price is trading above the 200-day simple moving average. I would not expect this to show great results. Shorting the stock index market during a bull market is very difficult.

Winner: Pre-Market is the clear winner. However, it's a meager value, and notice all the other sessions are negative. Shoring during a bull market is difficult!

Testing Mean Reversion Characteristic

BEAR MARKET: Now, I will test our mean-reversion trading system rules over the five different periods. Trades are only opened during a bear market. 

Winner: Post-Market. The Pre-Market is the clear winner. Notice that most other sessions are positive, but only The Open is in the double digits.

BULL MARKET: Let’s now look at our mean-reversion trading strategy during a bull market. 

Winner: Pre-Market. Again, we can see shorting in a bull market is difficult.

Conclusions

I took all the results from above and put them into a single table, below. Now you can sort by Average Net Profit Per Trade by toggling the "triangle" on the column header. 

By sorting the average net profit per trade from highest to lowest, we can see what bubbles up to the top. We can presume that these are the best opportunities for building an intraday short only trading system.

The best performing sessions are.

1. Mean Reversion: Bear: Post-Market
2. Mean Reversion Bear: The Open
3. Mean Reversion Bull: Pre-Market

Mean Reversion is the better strategy as all the top-performing strategies are mean reversion. Furthermore, shorting in a bear market takes the top two positions. The third position is interesting. It's shoring during a bull market.

Notice that shorting the Pre-Market during both a bull and bear market appears to hold an edge. Interesting.

Mean Reversion: Bear Market: Post-Market

Backtested Results: Here we can see the vast bulk of the profit happened on a handful of trades in 2022. So, I would not put too much value into trading this session. It looks like a few excellent trades biased the results. Of course, this good fortune may continue forward into 2022 and beyond.

Mean Reversion: Bear Market: The Open

Backtested Results: In this session we can see we have a climbing equity curve. This tells me we might have an an edge for a mean revering strategy during this session.

.

Mean Reversion: Bull Market: Pre-Market

Backtested Results: This is the best looking equity curve out of the three. This looks like the strongest session where a mean reversion strategy might be developed. Ironically, it's shorting during a bull market!

Conclusions

Remember, the strategies presented in this article are not viable trading systems as they currently stand. The strategies in this article are market studies! You can’t expect these simple trading strategies to produce results with real money.

These are nothing more than indicators to point us in the right direction. However, this article and the previous article demonstrate a way to analyze the market in an attempt to find an edge.

Can an intraday trading system be developed from the information presented in this article? Maybe. It will depend upon many factors. The point here is that this gives us a way to narrow our focus to a place to start.

Good luck, and let us all know, in the comments below, if you found this helpful or if you had success with building a trading system. 

Unless otherwise stated, all the tests performed in this article will be based on the following assumptions:

• Starting account size of  $100,000.
• Dates tested are from 2000 through May 12, 2022
• There are no deductions for commissions and slippage.
• There are no stops.

Not very impressive, with only 26 trades since the year 2000 we only generate 3.51% return on our capital. But this is expected as shorting is very difficult for the stock index markets.

The stock market indexes have an upside bias (trading above the 200-day SMA), so this strategy does not have the opportunity to open many trades. Thus as the strategy stands today, we're not capturing enough profit to make it worth pursuing. This is a similar result I wrote about in this article, “The Death Cross – What You Need To Know.“

While I don’t expect much change, I loaded this strategy on the major futures index markets (NQ, YM, RTY). Below are are the results.

Simple Shorting Strategy

The clear winner is the RTY market. It has a wonderful looking equity curve, pictured below, but of course, it still suffers form lack of trades.

Generating More Trades

The main problem with this strategy is the lack of trades. Can we generate more trades? I think so.

The original rule to open a new position is to look for 4 consecutive up days. So, we're looking for a counter-trend rally but, why four days? Why not three days or five days? Let's make this entry rule a bit more dynamic.

Larry Connors is well known for popularizing the 2-period RSI. The well-known rule to go long the stock index market when the 2-period RSI goes below 20 is a solid buy signal during a bull market. Let's reverse this idea for a bear market.

We will make an important change. Because bear markets tend to be much more volatile, I'm going to double the RSI look back from two to four. I think a 2-period look back will fire off too frequently. In bull markets, we can have strong countertrend rallies and we don't want to short too soon into those rallies.  

So, our sell short rules will now look like this:

• Price must be below its 200 day moving average.

• The 4-period RSI must be above 75.

Let's look at the two best candidates from the original rules (ES and RTY) after applying this new entry rule.

ES With RSI(4)

We can see we did generate more trades. We've gone from 26 to 44. Sill not great, but an improvment.

RTY With RSI(4)

Yes, we've added more trades here as well. We've gone from 31 to 50. We've lost our beautiful-looking equity curve, but we have more trades, and we're still making new equity highs going into 2022.

Simple Shorting Strategy With RSI(4) Entry

Conclusion

Can we make money shorting the market? Yes, but the results are not overly impressive with this strategy. It's great we found a simple technique to increase the number of trades, but it might be best to sidestep that bear market and find other opportunities elsewhere. 

You might be wondering, is this system tradable with real money? Not yet! I think this has potential but remember, there are no stops. With a bit of work on your part, you could trade something very similar to what's presented here.

MyRSI = RSI( Close, RSILookBack );

If ( MyRSI < ShortZone ) Then
begin
   GoShort = true;
   GoLong = false;
End

Else If ( MyRSI > BuyZone ) Then
Begin
   GoShort = false;
   GoLong = true;
End;

If ( MP = 0 ) Then
Begin
   If ( GoShort and BullTrend ) Then Sellshort ("RSI Short") next bar market
   Else If ( GoLong and BearTrend ) Then Buy("RSI Buy") next bar at market;
End;

If you code this basic concept up and test it from 08:30 to 15:00 (central) on the EC market what do you think you’ll get? That’s right, a losing concept with an ugly equity curve.

Clearly, we don’t want to trade our system at any time during the U.S. day session. We want to focus when EC is most likely to show strong trending characteristics. If the price of EC is strong enough to push the RSI value above 80 we want the price to continue climbing. So, what market session is best suited for this RSI trend system? Or are all market sessions hopeless for this simple trading concept? Let’s find out.

The Session Test

When designing a day trading system one of the first steps I want to perform is to test which market session will likely produce the best results. We are all aware that different sessions exist for any given market. For example, when dealing with the S&P E-mini we have a pre-market session - the morning session, lunch time session, and an afternoon session. Sometimes you can see distinct characteristics within each session.

So, when I’m developing a trading system for day trading I don’t want to blindly trade during every session. I want to be more targeted in my approach. There will be most likely a particular session or two where my key idea will do better than the other sessions. In order to test our key idea vs. different sessions I need to create an EasyLanguage function that can isolate these sessions and execute the key idea individually over those sessions only.

This is where my “Session Testing” code comes into play. Session Testing is an EasyLanguage function that I wrote to help me in this task of testing various intraday sessions. Using TradeStation’s optimizer I can test my idea across different combinations of sessions and discover how the key idea holds up across each session. Here are the basic sessions:

1. “Pre-Market” Between 05:30 and 08:30
2. “Open” Between 08:30 and 9:00
3. "Morning" Between 9:00 and 11:30
4. “Lunch” Between 11:30 and 13:15
5. “Afternoon” Between 13:15 and 14:30
6. “Close” Between 14:30 and 15:15
7. “Post-Market” Between 15:00 and 18:00
8. “Night” Between 18:00 and 05:30

From this list of basic sessions I created several more “sessions” based upon combinations of the above.

1. “Pre-Market” + “Open”
2. “Pre-Market” + “Open” + “Morning”
3. “Open” + “Morning”
4. “Open” + “Morning” + “Lunch”
5. “Lunch” + “Afternoon”
6. “Lunch” + “Afternoon” + “Close”
7. “Daily Session”
8. “Night” + “Pre-Market”
9. “Morning” + “Afternoon”

The 17 different sessions I came up with may not please everyone. Perhaps you have a contrasting idea on how to break up the different sessions and with the code provided you can simply change them to your liking. For example, if you are interested in the European markets and trade those times, you can create your own sessions based around the European markets.

Session Test Function

The session testing function is really simple. You place it within your strategy and pass into the function the session number you wish to test. Below is an example of testing session 1 – what I’m calling pre-market.

TradeFlag = SessionTest( 1 );

The variable TradeFlag will be set to a Boolean value based upon if the current time is pre-market (true) or not (false). Now we can use TradeFlag as a switch to enable/disable our trading as demonstrated below.

If ( TradeFlag ) Then
Begin
// Trade Logic goes Here
End;

What’s going on inside of the function? It’s rather simple as it’s nothing more than a bunch of if-then statements testing the current time. Below is a code, snip-it to give you an idea of what it looks like.

If ( SessionToTest = 1 ) And ( Time >= PREMARKET ) And ( Time < THE_OPEN ) Then TradeFlag = true

Else If ( SessionToTest = 2 ) And ( Time >= THE_OPEN ) And ( Time < MORNING ) Then TradeFlag = true

If ( SessionToTest = 3 ) And ( Time >= MORNING ) And ( Time < LUNCH ) Then TradeFlag = true

Else If ( SessionToTest = 4 ) And ( Time >= LUNCH ) And ( Time < AFTERNOON ) Then TradeFlag = true

Here is our code with the session test function applied.

If ( TradeFlag ) Then
Begin

   MyRSI = RSI( Close, RSILookBack );

   If ( MyRSI < ShortZone ) Then
   begin
      GoShort = true;
      GoLong = false;
   End
   Else If ( MyRSI > BuyZone ) Then
   Begin
      GoShort = false;
      GoLong = true;
   End;

   If ( MP = 0 ) Then
   Begin
      If ( GoShort and BullTrend ) Then Sellshort ("RSI Short") next bar market
      Else If ( GoLong and BearTrend ) Then Buy("RSI Buy") next bar at market;
   End;

End;

Testing All Sessions

Next let’s see what happens when I run TradeStation’s optimizer over each of the sessions. In doing so TradeStation will systematically execute my key idea strategy over each market session and record the trading results. This can be done by simply creating an input value to act as the session number to test. Then you simply optimize over the values 1-17.

After all the sessions have been analyzed I can generate a bar graph representing the P&L for each session. Below is the Net Profit graph which depicts the total net profit from our testing. By the way, we were testing this strategy from January 1, 2003 to December 31, 2011. This time-span covers both bull and bear markets. It’s important to test your key idea over a wide range of market conditions. Slippage and commissions are not factors into these results.

In this case we can see session input value number 12 produces the best net profit. This input value is actually a combination of Open, Morning, and Lunch sessions. It’s during these times EC market apparently demonstrates strong trending characteristics that we might be able to take advantage of with our key idea.

Let’s now isolate the session input value to 12 and execute our key idea on this specific time. For this test I will deduct $18.50 per round trip for slippage and commissions. Below is the equity graph of our key idea along with some performance numbers.

There is no optimization here. The code simply trades based off un-optimized RSI signals and the results look promising. Below is the Trade Summary Report.

Please note I have no stops or targets within my test strategy. Instead the strategy simply enters a trade if the proper condition is met and the trade is exited only at the close of the session. That’s it. Remember, I’m not testing a trading strategy. I’m testing a key idea vs. different market sessions. My goal is to locate the best possible market sessions for my key idea. In this case, which session holds the strongest trending characteristic? Once a session(s) has been identified only then will I continue to develop a complete strategy (containing stops, targets, and other rules) tailored to the top session(s).

Conclusion

There you have it. This type of RSI trend following concept should be developed for the Open, Morning, and Lunch sessions. This makes sense. The big volume and big trends often appear during the U.S. day session. This is also corroborated from a previous Best Times To Day Trade, where I explored the hourly range of the Euro Currency market. In that study we can see that the hours between 08:00 and 11:00 (Central) have the highest price movement of the EC market.

I would like to point out again, this is not a trading system. What we did in this article was to validate our key idea. The code presented in this article does not have stops, a regime filter, volatility filters, profit targets, or any number of other checks and balances we might see in a true trading system. In fact, take a look at the average profit per trade. It’s $15 after slippage and commissions which is too low in my opinion. I would like to see this closer to $50 per trade or higher.

• 200-Period ROC calculation must be above zero.
• Buy on close when RSI(2) is below 10.
• Exit when RSI(2) is above 65.

• If TSI >= 1.65 Exit when RSI(2) of the close of current day is above 95

Now let’s take a look at the performance of this strategy.

Conclusion

There you have it. A strategy that adapts to several different market conditions. I did not cover the bear market regime, but you’re certainly free to explore that. Likewise, you can expand this concept to create a strategy that more drastically changes its trading behavior based on which of the four market environments we defined. Maybe during a strongly trending bull market you will want to use a completely different exit method than used during a non-trending market. In closing, this should provide some idea on how you can use two different types of indicators to divide the market into four different market environments.

Double Seven Results

 Based upon the standard rules we get the following results trading the S&P cash market.

Looking at the trading rules, you will also notice it’s a long-only system. Later in this article I will also reverse the rules and test it on the short-side. But for now, let’s look at the performance for the long-only system. At this point I’m going to test it on the S&P cash index ($SPX.X for TradeStation).

Unless otherwise stated, all the tests performed in this article will be based on the following assumptions:

• Starting account size of  $100,000.
• The number of shares traded will be based on volatility estimation and risking no more than $2,000 per trade.
• Volatility is estimated with a five times 20-day ATR calculation. This is done to normalize the amount of risk per trade.
• The P&L is not accumulated to the starting equity.
• There are no deductions for commissions and slippage.
• There are no stops.

Here is the position sizing formula used:

Shares = $2,000 per trade / 5 * ATR(20) * Big_Point_Value ) 

This looks very promising. Keep in mind, the system has no stops.

Is The 7-Day Optimized?

Let’s look at changing the 7-day look-back period for two reasons. First, I would like to see if the default seven value is optimized. Secondly, I would like to know if other nearby values are used, would the system remain viable. In short, I would like to test the robustness of  the look-back period. For example, if we change the 7-day low value to six, I don’t want to see the system’s equity curve drastically change. Likewise, if we increase the 7-day low value to eight, I don’t want to see a drastic change in results. The neighboring values around seven should still produce positive results. In fact, it would be great to see the system  remain profitable over a wide range of values.

I will use TradeStation’s optimization feature to optimize the look-back period over the values 2-20 in increments of one. Keep in mind this single input value controls two look-back periods. The first is the entry look-back and the second is the exit look-back. As the trading system is defined, both these variables use the same value.  The results of the test are in the graph below. You can click the image to see a larger view.  The x-axis contains the look-back period while the y-axis contains the trading systems total P&L.

This looks great. Any value you choose produces positive results. Each change in the look-back period also does not drastically change from neighboring values. The default value of seven is near the peek, which is six, but it’s not the most optimal number. It’s also important to keep in mind we will be using the default value of seven for other instruments as well and it’s highly unlikely the bar graph for those instruments would look exactly like what we have here. In any event, I think this brings a lot of confidence to the look-back period.

Is The Regime Look-Back Optimized?

Just as we did for the look-back period for our entry trigger, let’s perform the same type of test on the regime look-back period. Again, I will use TradeStation’s optimization feature to optimize the look-back period over the values 20-200 in increments of ten. The results are in the graph below. You can click the image to see a larger view.  The x-axis contains the look-back period while the y-axis contains the trading systems total return.

This looks great as well. All values produce positive returns. In general, the longer the look-back period the more profit the system generates. While I did not study the numbers just beyond 200, I feel confident that a 200-period look-back is not optimized.

Taken both these tests we can feel confident that this system does not appear to be optimized and it’s robust given a wide variety of input values.

Going Short

The original system is a long-only system. Let’s try to use the current rules to short the market. We can do this by simply reversing the rules. In other words, we will modify the regime filter to only open trades when price is below the 200-day moving average. Trades will then open when price makes a new 7-day high and close when they make a new 7-day low.

• The instrument must be below its 200 day moving average.
• If the instrument closes at a 7-day high – sell short.
• If a position is open and the instrument closes at a 7-day low – buy to cover.

I created a separate trading system to study only the short-side. The results of the system can be seen in the equity graph below.

This is not so great. Most of the time the equity curve is below the zero line. It’s choppy and ugly.  Clearly the market participants’ psychology during a bear market is different than simply a mirror image of those found in a bull market. One would think shorting new highs in a bear is a good idea, and maybe it is, but this system is not successful at capturing profit. Taking a wild guess simply from past experience, I have found that exiting quickly during bear markets tends to work better than in bull markets. Thus, maybe a modification of the exit rules to only hold a trade until the first profitable day or only hold for a maximum of three days may produce significantly better results. However, such modifications are best left for another day.

To further test our shorting idea, let’s now look at testing a range of look-back periods just like we did during our bull market. If our shorting concept is truly flawed, I would expect to see not much improvement in modification of our look-back period. In fact, since my wild guess is we need to exit quickly to remain profitable, I would expect to see greater losses as we continue to increase our look-back period. Likewise, I would expect to see greater profit as we shorten our look-back period. I will optimize the look-back period over the values 2-20 in increments of one. The results are in the graph below. You can click the image to see a larger view.

Just as I expected. In general the shorter the look-back period, the more profitable. However, since this is our first look at the Double Seven Strategy, I don’t want to over complicate it by introducing a shorting component. We’ll keep it simple for now and apply our long-only strategy to several major market ETFs.

Double Seven Strategy

Confident the long-only trading rules are robust and appear to hold potential, let’s now test this system on several major ETFs. Let’s test, SPY, QQQ, DIA, and IWM. For this test we are going to use all the same trading assumptions and position sizing as we did above except for the following modifications.

• $100,000 starting account
• Risk 2% of account equity per trade
• P&L is not reinvested

Double Seven System

Conclusion

The Double Seven Strategy does produce positive results across the four major market ETFs we tested. Is this system tradable with real money as is? Probably not. Remember, there are no stops. However, this does appear to be a great start to a viable system. I would imagine if some of the larger losing trades could be eliminated by some type of stop method, this system could be viable with real money.

 Get The Book

if ( Average(Close,50) crosses above Average(Close,200) ) then buy(“Golden Cross”) next bar at open;
if ( Average(Close,50) crosses under Average(Close,200) ) then Sell short(“Death Cross”) next bar at open;

Below is the most recent Death Cross. You can see the faster moving average (black) has dipped below the slower moving average (red).

Testing Environment

Before getting into the details of the results, let me say this: all the tests within this article are going to use the following assumptions:

• Amount dedicated to each trade $100,000
• Long only
• The P&L is not accumulated
• $30 was deducted per round-trip for commissions

In order to get a long-term feel for this system I’m going to use the S&P500 cash index going back to 1961. The trading system will trade a fixed dollar amount to each trade. In this case, $100,000.

Before we start looking at some of the numbers I want to break this study into two parts: Shorting a Death Cross vs. going long the Golden Cross. What interests me is how well the system functions going long vs going short. In other words, what does the SMA cross tell us about a bearish cross vs a bullish cross? Are they different?

Shorting The Death Cross

Since we recently experienced a Death Cross, let’s first look at shorting every Death Cross signal. The equity graph is below and it’s not pretty.

What does this tell us? To simply short the Death Cross may not be very profitable. We can see the equity curve hangs around the zero line for decades. In recent times it has climbed from negative territory to just below the zero line. Yet, there is nothing in this picture that suggests we have an edge simply shorting the S&P index upon a Death Cross signal. Below is a graph of the week drawdown experienced while shorting the Death Cross.

This is an ugly picture where you have many trades where drawdowns reached 30% or more. At some points you have to endure drawdowns of up to 65%. That’s massive heat you must take and what is your reward? Not to make any money! Simply using the Death Cross and Golden Cross as a timing signal does not appear to be a good use of your time.

Going Long The Death Cross

Maybe we should be taking the Death Cross as a buying opportunity? Why not? We want to be opposite the crowd, right? Below is the equity graph when we go-long at every Death Cross and exiting our trade when the Golden Cross appears.

Interesting! At times going long actually makes money and notice how the equity graph spends a lot of its time above the zero line. Furthermore, unlike shorting the Death Cross, going long actually produces a positive result. Not much of a result but the equity graph does end in positive territory. Counter intuitive to common knowledge, I would say. Yet, it’s not much of a profit for all those years of trading and it’s certainly not an edge we can take advantage of. All in all this strategy is a wash much like shorting the Death Cross. This graph probably reflects the fact that the S&P 500 has had a bullish bias since 1961.

The equity graph also shows that during those periods imminently after a Death Cross and before a Golden Cross we have continued market weakness. In other words, not many market gains take place when the 50-period SMA is below the 200-day SMA. In general, this appears to be a time of market weakness.

Let’s take a quick look at the drawdown one would have to endure with this type of strategy.

Not as bad as shorting, but there are two spikes of 50%. There is also plenty of time spent around the 20% drawdown mark. That’s still some ugly drawdown that would be very painful to endure.

Going Long The Golden Cross

Next let’s look at going long the Golden Cross and Exiting on on the Death Cross.  Below is the equity graph.

This is a much improved equity graph! We seem to have a clear edge going long the S&P cash index when a Golden Cross is triggered. This graph highlights the fact that when the 50-period SMA is above the 200-day SMA the market often exhibits strong bullish behavior. Below is the drawdown experienced by this system.

Here drawdown is limited to a maximum of around 12%. This is a massive improvement from our previous signals we looked at. A 12% drawdown is much more tolerable. Clearly when a Golden Cross shows up it appears to be the “all-clear” sign in terms of both reward and risk. That is, we see substation market gains and low drawdown.

Summary

Combining what we have now learned I would say it might be wise moving your investing accounts into cash when a Death Cross takes place. This would be done in my opinion to prevent drawdown and thus avoid the pain seeing your profits evaporate. After a Death Cross drawdowns over 50% can take place and we see the upside is limited. It’s certainly can be much more psychologically appealing to be in cash when those big bear markets hit. Shorting a Death Cross does not have much of an edge and you often see massive drawdown with little profit as well.  It’s only after a Golden Cross does the market hold a strong bullish edge.

The table below shows the performance of each system.

By comparing the market behavior during a Death Cross vs a Golden Cross we can come to some generalizations that might help in both your investing accounts as well as your trading accounts (depending upon your timeframe). Of course the results of this article are based upon the S&P cash index, not individual ETFs or stocks. Thus the results could be generalized for a broad U.S. tradable index like SPY. Of course, you must do your own testing!

Death Cross = Move To Cash: During this time the market shows general weakness to the upside. This weakness can be difficult to short and drawdowns can be very substantial.  Moving into cash during this time may prevent you from experiencing these significant drawdowns. Remember, we are not talking about day trading in this article. The Death Cross and Golden Cross are based upon slow moving averages that work best for long term holding periods. A skilled day trader can make a lot of money shorting during a bear market, of course. However, when it comes to using a slow timing method such as the Death Cross, it does not seem to pay off.

Golden Cross = Invest: Upon a Golden Cross signal the market has demonstrated a lot of bullish activity with low drawdown. It appears to be the “all clear” signal to enter the market once again. When a Golden Cross occurs, we should be looking to go long.

In the above bar graph we can see all combinations produced negative return as each bar is below zero (left axis). This tells me a dynamic profit target, without a stop, is not enough to make this system break even. Restricting losses appears to be a critical component to push the strategy into profitability. Notice how there is a valley that’s right around the 1.0 value on the x-axis. This produces the “best” results and represents a profit target equal to the gap size.

Below is the equity graph of the baseline system with the profit target ($200) and stop loss removed.

Looking at the baseline results we see it makes profit. Again the baseline has no stop. Since the baseline makes a profit, we know a static profit target is responsible for the gains.  There seems to be something to the idea of taking a $200 profit and calling it a day as opposed to taking a profit based upon the gap size – at least given the study we have performed here.

This is not to say a dynamic profit target may not work with some gap strategies. Far from it. This is just telling us there is more going on here than meets the eye, and a dynamic profit target applied equally to all gap sizes does not appear to be a quick fix to profits. Yet, we still have more testing to do.

Testing A Dynamic Stop Loss

For the next test I’ll comment out our profit target for both the baseline and the new dynamic stop strategy I’m working on. Why? Once again I want to measure the effect of our static stop loss vs our new dynamic stop loss. To do this, I’m going to create a new input value called StopFactor which will hold our stop loss factor. This will be used to multiply with the gap size. A value of 1 for the StopFactor means we have a stop loss equal to the size of the gap. A StopFactor of .5 will mean we have a stop loss 1/2 the gap size.

With this new input I’ll use TradeStation’s optimization feature to test a range of targetFactors. I’m going to test a range between .1 and 2.0. This will generate 20 different tests.

There appears to be very little change when modifying the stop loss based upon the gap size. This is different than our profit target optimization test above where we can clearly see an optimal range. Here, not so much. All the values tested produced negative returns. This hints to me that given our current strategy, a dynamic stop may not offer much value.

For reference, below is the equity graph of the baseline system with the static stop only, no profit target.

Once again the baseline with the static stop loss makes a profit.  So what does this mean? Maybe profit targets will have more of an impact than a stop. Maybe having a static stop and a dynamic target would be a more powerful combination. However, it really appears that neither a dynamic profit target or a dynamic stop based upon the gap size will provide much of an improvement with our baseline system. We’ll test that next.

Combining Both Dynamic Stop and Target

After studying both the dynamic profit target and stop loss individually, I decided to combine them. I created two input values. The first was prftTgFactor. The second was StopFactor. I then used TradeStation’s optimization feature to optimize these two parameters together. After letting the optimization run there was no worthwhile profitable combination. The vast majority of the tests produced negative results. There was no stable range, there was no improvement. Far from it, in fact!

Conclusion

In the end it appears using a dynamic stop and/or a dynamic profit target based upon the size of the gap does not improve performance given the Simple Gap strategy. While dynamic stops and targets may work with other gap strategies, this particular strategy does much better with static values for both the stop loss and profit target.

In the next article of this series I’m tempted to combine our successful filters and see what we get.

Other Article In This Series

• Improving The Simple Gap Strategy, Part 1
• Improving The Simple Gap Strategy, Part 2
• Improving The Simple Gap Strategy, Part 3
• Testing A Simple Gap Strategy

Here is a closeup view of the last 19 trades, which covers about the last five years.

2-Period RSI System

The trading rules from Larry Connors are very simple and consist of long-only trades. As a reminder, the rules are as follows:

• Price must be above its 200-day moving average.
• Buy on close when cumulative RSI(2) is below 5.
• Exit when price closes above the 5-day moving average.

All the tests within this article are going to use the following assumptions:

• Starting Equity: $100,000.
• Risk Per Trade: $2,000.
• Number of shares is normalized based on a 10-day ATR calculation.
• There are no stops.
• The P&L of each trade is not reinvested.

Is the 2-period RSI indicator losing its edge? Hard to say at this time. It’s not like drawdowns have not happened before, but that’s not really what I want to explore. I want to take a closer look at the 2-period RSI indicator and see if we can improve the basic trading system by Larry Connors.

Days After Opening A Trade

First let’s take a look at how the market behaves after a RSI setup occurs. In short, the 2-period RSI is designed to highlight strong pullbacks. Buying into pullbacks in an uptrend has been a well known and effective trading method and is the essence of the 2-period RSI trading system. We can demonstrate this by looking at how the market behaves after a trade is triggered. I created an EasyLanguage strategy that can hold a position X days after opening a trade. I then tested X for values in the range from 1 through 30. In other words, I want to see how the market behaves 1-30 days after opening a trade. Does the market have a tendency to climb after trade setup occurs or does it tend to drift lower? Below is a bar graph which represents the results. Each bar is the total P&L based upon the number of holding days.

In general the longer the holding period, the more P&L is generated. This shows that after our 2-period RSI indicator triggers a trade, the market tends to climb over the next 30 days. It’s also important to notice all values produce positive results. This shows robustness in this particular parameter.

Moving Average Exit Period

The original trading rules by Larry Connors used a dynamic exit based upon a 5-day moving average. Once price closed above this average, the trade was closed. I wanted to take a closer look at the period used for this moving average exit. Like the bar graph above, I tested values 1-30 for the period used in the moving average.

In this graph we can see increasing profit as we increase the moving average period from one to 14. There is a slow decline after 14 as we continue to our final value of 30. It’s very good to see that all values produce positive results. This shows stability across this parameter and exit method.

RSI Threshold

Let’s look at the threshold value used to determine if the market has pulled back enough to trigger a long trade. Once again, we will create a bar graph as we look at a range of threshold values from 1-30.

We see values below 10 produce the best results. Once again, every value produces positive results and this is a very good sign.

Based upon the information we looked at in this article, let’s modify Connors’ trading rules. We will make the following changes:

• Use a value of 10 as the RSI threshold.
• Use a 10-period simple moving average as our exit signal.

Below is a table showing the difference between the original Connors’ rules and the modified Connors’ rules.

2-Period RSI System

Our increase in net profit comes at the cost of more trades which is due to the fact of lowering the stand on what we consider a viable pullback. By increasing the RSI threshold from 5 to 10 more setups qualify as a valid entry, thus we take more trades. But we also make more money on each trade. This is due to our longer hold period by increasing our moving average period from 5 to 10. In the end, we are willing to take more trades and hold on to those trades for longer periods of time.

Adding Stop Loss

All the results above do not use a stop loss value. Let’s add a $2,000 stop loss on each trade and see how it will change the results. I picked this value because it represents our risk value when scaling the number of shares to trade. Notice we are only risking 2% of our $100,000 account on each trade. The far right column holds the results with the $2,000 hard stop.

2-Period RSI System

This just gets better and better. Often stops will hurt a trading system in several key performance factors but not here. Our $2,000 hard stop improves the system across several performance factors. Unlike the original trading system, this equity curve is producing new highs.

Across Different Markets

Let’s now look at the performance across different markets. I will not modify the trading rules at all.

Notice the number of trades is significantly lower for the above ETFs when compared with SPY. This is due to SPY being around since 1993 while these other ETFs are much newer. Overall, the system holds up nicely across these different markets.

Conclusion

The RSI indicator still appears to be a robust indicator at locating high probability entry points within the major market indices. You can modify the trigger threshold and holding period over a large range of values and still produce positive trading results. I hope this article will give you lots of ideas to explore on your own. Another idea in regards to testing parameters is to independently optimize the parameters over the “portfolio” of market ETFs instead of using just $SPX. There is no doubt in my mind the RSI indicator can be used as a basis for a profitable trading system.

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Update:

This topic has been updated with performance through 2014.