Futures & Options Algorithmic Trading Strategy

The S&P Crusher

Futures & Options Algorithmic Trading System Video: The S&P Crusher

Futures Trading System Example: The S&P Crusher v2

In this segment of our Algorithmic Trading System videos, we walk you through the details of the The S&P Crusher Package. This package trades the — Treasury Note (TY)Breakout Day Trader (ES), Short Day Trader (ES), Momentum  (ES), Covered Calls (ES Weekly Options), Morning Gap Day Trade (ES) & Iron Condor (ES Weekly Options) Trading Algorithms.

Trading futures involves substantial risk of loss and is not appropriate for all investors. Past performance is not necessarily indicative of futures results.

Results discussed in this video are based on back-tested models that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profits or losses similar to these being shown.


S&P Emini Futures & Options Trading System

Transcript of Video : Okay, in this video I will be reviewing the S&P Crusher V2. This is going to be an overview of our flagship product, and I’ll be talking about all seven strategies this algorithm trades combine, which creates our S&P Crusher portfolio.


Okay, so first, I would like to go over our disclaimer. We are not registered with the CFTC as a commodity trading advisor. We are what’s called a third-party developer. We create algorithms and we license them for use on a personal computer through Trade Station, and they can also be auto-executed through NFA-registered brokers. Keep in mind that trading futures and options involves substantial risk of loss. These algorithms are really not for everyone. They should be traded with risk-capital only in our opinion, and lastly, the data that we show, unless otherwise noted, is based on hypothetical back-tested models, and it does have certain limitations, per the disclaimer here. Feel free to read this. You can pause the video and read it more carefully. The last comment I’ll make is that the data is for educational purposes only. Again, because we’re not registered, we do not control client accounts, and also, I guess I should mention that because we’re not registered, the data that we show has not been audited by any government agencies. Just keep all that in mind as we go through the data in this video. We’re going to be talking about performance. Again, that’s based on hypothetical back-tested models. It does have limitations. At times, we do mention the live returns on the website or in the video, and when we do, that is from live data, and we note it as such. When we do that, just, again, keep in mind that past performance is not indicative of future results, and again, trading futures does involve risk of loss, and it’s not for everyone.


All right, so if you’ve seen our design methodology video, then you’re familiar with this  chart. What this shows is the crusher along with the seven strategies that are traded in it, and what we’ve done is we’ve broken down the average monthly back-tested performance based on what the S&P did for that month. So, for example, if we, in this category here, we have S&P was up, then this shows the average gain for each strategy with all traits combined for each month. This is the average gain that this strategy saw during up months, and you can see the momentum algorithm does the best in up-moving markets. This column here is for sideways-moving markets, and we define that as a loss of four points all the way up to a gain of 30 points on the S&P. We categorize that as sideways. This shows the average monthly performance for each strategy, and then lastly, a down month would be any month where the S&P closed down by at least four points, and this shows how each strategy did. If you’re really interested in this data, I recommend you watch our design methodology video where we talk about this in a lot more detail.



Since I’m talking about the Crusher right now, then I really want to just get into it and start looking at some of the charts. The momentum algorithm is the one that does good when the market is going higher. Let’s take a look at that one. This is the TradeStation platform that I used for the development, and each chart shows one of the strategies. What you’ll see here is this is the momentum algorithm, and it also has the covered calls layered into it. This is the treasury note algorithm. This is the Iron Condors. This is the gap short. This is our breakout day trade algorithm, and this is our short day trade algorithm, the breakdown. If we look at the momentum algorithm, again, this is the one that is designed to do well when the market is going higher. What you’ll see is that first off, this is a chart of the S&P 500. Basically, there’s two candles for each day. This first candle represents the majority of the trading day, when the equity markets open, and then this last candle is  the final hour that the futures market stays open before the futures close. Now, most of you are aware that futures trade, for the most part, 24 hours a day. This does have this session enabled to represent more like the equity market sees. You don’t see the overnight movement on the S&P and these charts. The reason why we do that is because in the evening session, usually the volume is pretty low until Europe opens. A lot of algorithm traders like myself and my company will not trade during those overnight periods. What happens is when the equity market opens, that’s when we launch all of our orders on the futures trades, the stops, the limits, and then we trade until five p.m. Eastern, which is when the futures market closes for about an hour and then it reopens. So, again, this is a chart of the S&P 500. If I zoom out, what you’ll see is all of the trades  layered into the chart, and this bottom chart here is just the VIX. We use that in the covered calls side of the algorithm, but let me  lower that.


If we zoom back in time now, you’ll see 2011. Here’s August of 2011, and we can keep going all the way back. I think this is probably ’07 in here. If you go all the way back to ’03 where we begin the optimization for this algorithm. Now, what happens is when you write an algorithm and you develop it, you’re able to layer it onto the chart, so these are all the back-tested trades that you’re looking at. But again, this is the momentum algorithm only. Keep in mind, this algorithm or this package trades seven algorithms. The reasons why we trade seven is again, so that each algorithm targets a different market condition so that our goal is to have one to two algorithms doing well for each market condition. When the market’s going higher, usually this momentum algorithm will do really well.


But let me look at the performance report from TradeStation. We can begin talking about this. So, this one has the smallest profit factor of any of the algorithms that we use. It’s still above what we like to see, which, minimum, is 1.2, and that’s including commission and slippage already built into it. The percent profitable is 76%. There’s been a thousand trades in the back-testing, 1,083. They’re at a 76% win rate. This is the average trade. Net profit is $56. The average winning trade is $347, and the average losing is 914. Now, remember that when we look at this data, it is subject to limitations.


Back-testing has significant limitations. But since this algorithm is the one that we have for up-moving markets, let’s look at a few periods where the market was going higher so I can show you how it works. If we look at this period back in ’06, market was really going sideways through here, but about right in here is when it started taking off. What you’ll see is that this algorithm had a lot of good winning trades, and that’s to be expected. If the market’s going higher and you go along the S&P and you have a target and a stop, more than likely, unless your stop is super tight, it’s going to do well, and that’s what you see.


ere’s a really good trade where it would have gotten in on the 11th of September in ’06, and then gotten out a few days later. This algorithm is a swing trade. Here’s a case where it held for about five days. Here’s another one where it held for about four days. A lot of times, though, it’ll get in and get out the same day. Or, I’m sorry, not the same day, but it’ll get in towards the close and then get out the next day. But what this is trying to do is just capture these bull market runs where the market just  takes off and goes higher.


Now, of course it’s not perfect. This is an example of a losing trade. Any time you see the red dotted line, that’s a loss that we took. We would have gotten in on the 20th of February and then got stopped out a few days later. We can also look at a few of the more recent trades. This is just actually from this last week where the market was going higher. This algorithm did exactly what it was designed to do. It got in, it got out, and in this week, it had four winning trades. But again, with TradeStation, we have all the performance reports for the back-testing. We have equity curves.


This is an equity curve of how this algorithm did in the back-testing. It has the complete trade list going back to ’03. All kinds of analysis that can be looked at with this algorithm, but what most people look at is number one, the profit factor, and then number two,  how many trades there were. We always want to see… We like to see 200 trades, if that’s possible. This one has a thousand, so the more, the better, and then the win rate, 77%, is really only helpful on the emotional side of the trading. What really matters is the ratio to gain to loss. Is it positive, and does that look good? In this case, it does.


This algorithm, again, it’s designed to do good in up-moving markets. Let’s look now at the other… Let’s see, let’s look at the breakout day trade now. This algorithm is also designed to do well in up markets. You’ll see that it averages 375 when the market’s going up and then 50 when it’s going sideways and 25 down. Really, it’s break even on sideways and down, but it does have positive expectations for up-moving markets. Before I forget, though, with the momentum algorithm, you’ll see that in sideways markets, the performance trails off some to 378, but the real negative on this momentum algorithm that we just looked at is in down markets, it averages 812.


Unfortunately, this is something that’s really hard to get rid of without taking away from these potential gains in the up-moving markets. Again, our design methodology is not to create one algorithm that does good for every market condition, but to have one to two that do good for each market condition. As long as these other algorithms do better in down conditions, which they do, then we have a positive expectation for down-moving markets. Let’s look at the breakout day trade now. So, this right here is the chart of the breakout day trade, and it trades on nine-minute candles. And if I zoom out, you’ll see a few examples more recently of how it does do well in other markets. On the seventh of November this year, it actually got in right here and got out at the close. This is an example of where it got stopped out. It got in right up here and then got stopped out somewhere in here. And then a few days ago, we had a decent breakout trade where it got in at the open and got out at the close. This is a day trade, which is good in that it doesn’t hold overnight, so there’s a little bit less risk with day trades usually. But you’ll see, as the market goes higher, usually this algorithm will have at least one or two good day trades.


Here’s an example where it, I guess it took a small loss, and then here’s one where it got stopped out. Here’s another one where it had a good day. If we look at the performance report for this one, and so what it’s doing is looking at all the trades in the history and then providing this report. It has 507 trades, has a decent profit factor, 1.43, and again, this includes six dollars and 50 cents in commission and a full tick of slippage. Average profit per trade, 57. Now, what you’ll notice is that the win rate on this one is about 51 percent. Half the trades are profitable, half are winners, but what you notice is that the average winning trade is quite a bit bigger than the average losing, which is why this algorithm is still profitable, why we have it in our portfolio. Here’s an equity curve.


You can see, the last few months it hasn’t done quite as well, but that’s normal. You know, you’re going to see these dips in the equity curve. We do expect this one to pop out of that. But the, you know, so you have the trade list, the performance summary, just all kinds of data that TradeStation provides. But this algorithm, again, is designed to do well in upmarket conditions, and to not lose too much in downmarket conditions and then to be, you know, break even during sideways-moving markets.


All right, so now what I would like to do is look at the algorithms that are expected to perform well when the market goes lower, and I’m going to start with the treasury note, and then we’ll look at the morning gap, the breakdown short, and then the covered call. If we look at the treasury note algorithm first, that’s this one here. Now, this one is basically trades the 10-year note, and usually what the 10-year note will do is trade  opposite so what the S&P does. If the S&P’s going higher, then usually this, the T-note, the 10-year note, will usually be going higher. If the S&P’s going lower, I’m sorry, then the 10-year will be going higher. Now, it doesn’t always work that way. It’s not a  100% arbitrage, but in general, that is what happens. I’ll look for this example here, which was… This was in 2014.


Throughout this period, the S&P was going lower, and you can see the 10-year was going higher. What this algorithm does is it’s  a swing trade in that it will get in and hold for usually multiple days. Each one of these candles is 120 minutes, and so, there’s five of these in any given day. This is  a few months’ period that we’re looking at. Let’s start with the performance report, and again, this does model in slippage and commission. And by the way, this algorithm is our best algorithm that we have, in my opinion. It has the highest profit factor, and the profit factor is really not at a point where you would worry that maybe it’s  over-optimized. It has a very high win rate. It has 360 trades, which is decent, given how long it holds, and the average win or the average trade profit is 274. If we look at the equity curve, you can see that it’s really about as good as it gets. It does take losses, and  in here, throughout this equity curve, you’ll see all these little dips. Those are just periods where it got stopped out. Maybe it had a rally, got stopped out again.


Overall, this algorithm is, in my opinion, the best one we have back-tested. It’s traded live also for about a year and a half now, and so it’s done very well in the walk forward results as well. You know, here’s the trade list, and again, all these reports are posted on the websites. You can get them if you’d like them. Here’s the annual breakdown. You’ll see in ’08 the market was in a bear market. This algorithm had its best year with the $18,000 gain back-tested per contract traded. I believe 2011 was also a down year on the S&P and so it did well there, and then I’m pretty sure ’03 was as well. But at any rate, when we do the analysis on a monthly basis, this algorithm has its best performance during months where the S&P closes lower. To give you an idea of what it does, it’s looking for a pullback, and it does some pattern recognition to look for certain patterns. And then when it gets in, it’ll have a stop that’s pretty big, about $2300, but that stop allows quite a bit of room, so that if the market does rally when we get in and then 10-year goes lower, we don’t get whipped out of that trade.


This algo also does good in sideways-moving market conditions, and I think that’s mainly because as the S&P starts going higher, the 10-year note will  start going sideways and start beginning to rally. That’s usually about the time that we get in, and so we usually see some gains, even though the S&P’s going sideways. Once the S&P really starts selling off, it’s not always the case, but many times, the 10-year will just take off on a tear, and it’ll usually have sustained rallies that could last quite a while. If I zoom out even more, I want to show you  some of the more recent trades from this year, beginning with  December of 2015. If you’re paying attention to the market, you know that around December, the market started selling off, the S&P. And then in January the S&P really sold off huge. This algorithm had a really good trade that got into it in December and got out in  mid-January, and it jumped right back in, got out again, jumped back in, got back out, and that’s pretty common with this algorithm. This is actually back in August also of last year when the market sold off, this algorithm rallied. But my point in showing you all this is just to show you that usually when the S&P sells off, the 10-year will take off. Around  mid-February is when the S&P started rallying, and that’s when the 10-year started selling off again. Now, again, it doesn’t always work that way. Sometimes they both rally together. Sometimes they both sell off.


But in general, that’s how they behave. And that’s why we have the 10-year note in our suite of algorithms, because it does so well in downmarket conditions. It also does good in sideways market conditions as well. Let’s look at the morning gap algo. That’s a newer one that we recently added to the S&P Crusher Portfolio and the other ones as well that do the day trades. So, what this algo does is it trades on two-minute candles on the S&P. It’s also a day trade. It’s looking for a big gap up in the morning, and what it’ll do is it’ll go short, and then it’ll hold until the close. But it also does have a limit target that it will get out of its limit, and then it has a stop as well.


This, let’s look at this one’s performance report real quick, and I’ll walk you through its results. It has a pretty high profit factor, 1.7. It has a percent profitability of 57%. It’s a little bit better than a coin flip. The only negative about this algo is it only has 142 trades in the back-testing.


We usually like to see at least 200. With this one, though, we felt that it was okay, mainly because the pattern that this one trades off of is one that we’ve been aware of for quite a while. We just hadn’t actually coded it. It’s the thing that we’ve seen in live,  walk-forward live trades for a while, and that was enough for us to go ahead and add this. It has a pretty good average trade net profit. You can see the winners are quite a bit bigger than the losers. And so that’s why this algorithm exists. It’s also a day trade, meaning it’s a very low… I don’t want to say very low, but it’s lower risk than a swing trade in that we get in and get out the same day.


We don’t have to worry about the overnight risk. It also requires very little margin to trade. And so by adding it, we don’t really lose a lot. Here’s the equity curve or this algorithm. You can see it’s a pretty nice slope upward, and so that’s the morning gap trade. It’s basically looking for a gap, a gap higher in the morning, and then essentially a little bit of weakness, and then it’ll go short the market. Here’s another good trade from July 14th where you had the gap up, it got in, and then got out at the close. Now, of course, you know, none of these are perfect. They will have losers. That was another winner there. Trying to look for a loser. Maybe I passed it. Nope, that was a winner. Anyway, it does have losing trades as well. Oh, there’s a loser. Zoom in and show you that one. All right, so, this top, top left, you’ll eventually see this losing trade it had. So, here was a trade where you had a gap up. It went short. It sold up. We were profitable.


But it didn’t hit its target, and so then we got out at the close. All right, so, if I go back to… To this graphic, that was the gap short that we were looking at, the morning gap day trade. Also does okay in sideways and then doesn’t really lose a lot in upcoming, in up-moving markets. It’s barely profitable on average. The other algorithm I want to look at is this breakdown short day trade. This is our third best performing algorithm in down-moving markets, so I’d like to look at that real quick here. This breakdown short algorithm, this is one that we’ve been trading for over a year and a half now. This one has been around a while, and really, what it does is it looks for weakness in the morning, and then it’ll go short, and it’ll hold until the end of the day. And so, you know, this… Kind of zoom in and try to find a trade.


First, though with this, again, this is the last few weeks of trading in November that we’ve seen. The market was going higher. You’ll see there weren’t any short trades placed there, which is good. That’s really the key to our design methodology is we create algorithms that do well for certain market conditions, and then we just try not to lose a lot or have them maybe even break even or at small gains in the contrary market conditions. This, I guess this was the last trade we had, which was on the 11th of October, and it went short when the market opened, about 18 minutes after the market opened, and then got out at the close. Here’s an example where it didn’t work out so well where you had market weakness. We went short, the market rallied, and we got out at the close. This algorithm, because it’s a short day trade, here’s another trade from September that we had. But because it’s a short day trade, it’s really not expected to do well in bull markets. And, you know, the last year, the market has really been drifting sideways to going higher. Lately, it’s been, I think the NASDAQ at least was hitting new highs. This algorithm hasn’t done so well in the live trading, at least in the months that the market’s going higher. And as we analyze that data, it makes sense to where we never expected this algorithm to do well when the market’s going higher. What we’ve seen is that when the market goes lower, this algorithm does do well, and so it’s why we still have it as part of our portfolio that we trade. If we… You know, so if we quickly look at the data on this one, so it has a profit factor of 1.38. It has a win rate of about 50%, so it’s a coin flip.


The average trade net profit is decent. You know, you have the trade list, periodical returns. What you’ll see here is that in ’08 it did really well. It also did good in 2014, but again, this algorithm is not… It doesn’t exist in our suite to do well in up-moving markets. What we do is expect though is that it won’t lose a lot in these conditions. But, so that’s why it’s part of our suite.


It’s also a day trade. What we believe is that if the market does go into a bear market, this algorithm will really kick in and start doing well. The last one… Not the last one, but the next one I want to talk about is the covered call. The covered call actually does its best in sideways-moving markets, but it’s our second-best performer in down-moving markets. And that should make sense for the most part. One question that we initially had is why does it do better in sideways than down on average, and the reason why is because it places more trades in sideways-moving markets to where the average gain is higher. During down-moving markets, it’s still profitable by a decent amount, and it all adds up when we look at the entire suite.


This is a really good algorithm in our opinion. Another interesting point is that even in up-moving markets, this algorithm still does good. 154 is what it averages. What it’s doing is selling calls when it enters a trade, and it’s selling… I’m sorry, when the momentum algorithm enters a trade. And it’s selling out-of-money calls, which gives us a little bit of a buffer. Here’s an example of a trade that was placed a few weeks ago where it was profitable. On this day, on the ninth, which I believe was a Wednesday, we got into the momentum algorithm, and because we got into the momentum algorithm, we also sold the calls at 2170. What you see is that the momentum algorithm had a target. It hit its targets, but we also sold these calls of 2170, which is right up here. And so when we sell these calls, the covered call strategy, what we want is for the S&P to close Friday below our strike price, which is 2170. In this case, it did. This was Wednesday, Thursday, and then Friday. On Friday, the S&P closed right in here, which was below our call. And so we had a full profit trade on that, on that trade. More recently on this last week is an example of where we were wrong, but it only cost us just a little bit.


We got into the momentum algorithm, and that algorithm, by the way, did really well throughout this last week. But we got in, we sold the 2175 calls, and I believe we collected about 4.5 points when we did that. You can see that the S&P actually rallied above our strike price. The momentum algorithm took some gains out of that, move higher, and then the final close was at about 2179.75.


It closed in the money by about four and a half points, but remember, when we sell the put, or when we sell the call, we collect premiums. It gives us an additional buffer, which is about four and a half points. For this call to have been a loss, S&P would have had to have closed about 2180. And it traded above 2180 for a little while, but then on Friday sold off and closed right at about 2180. Okay, so, I think now what I’d like to do is talk about the last algorithm, which is the Iron Condor. First, let me go back to this slide. Okay, so we’ve talked about the momentum, how it does good in up, sideways, and not so good in down. We talked about these four bear market algorithms that we have. The covered call does good, the short day trade, the gap day trade, and then the treasury note. The Iron Condor is the only one we haven’t talked about yet. This one originally was designed to do well in sideways-moving markets, and that’s why you see that it’s our best performer in sideways-moving markets. To be honest, I was a little bit surprised when we started looking at the data.


It also does good in up-moving markets, and it actually does better. I think the reason why is because it will place more trades in up-moving markets, but I think it’s also the case that when we sell the put and we sell the call, which is what Iron Condor is, we’re creating a bracket, and there’s margin for error because of the premium that we collect. I think it just so happens that, a lot of times, even in these up-moving markets, in those specific weeks, it just doesn’t close way above the call that we sold. And because we collect premium on the put side when we sell it, when you  add it together, it’s still profitable. But it is our best performer in sideways-moving markets, so we really do consider this to be our  sideways market algorithm.


Okay, so here’s a chart of this algorithm, and when you do an options algorithm in TradeStation, the way we do it is we use  a futures trade as a marker to tell us when to get in and when the expiration is, and then we display on the chart where the strike price is, and then we have a tool,  an in-house tool, that looks at the trades and then creates estimations for what the options trades would have looked like. In this, I’ll just look at, I’ll look at the example from the last couple weeks. And by the way, down here is the VIX, and then down here is a 380-minute candle of the S&P. But primarily, this trades on 10-minute increments. When it gets in, it’ll get in towards the close of the day. This was a Tuesday where it triggered. What happens is we sell a call, and we sell a put at strike prices that are out of the money by anywhere between five to 20 points, and the algorithm determines which strike prices we use. In this case, it sold a call that was 20 points out of the money and a put that was 20 points out of the money, so it was trading about 2140. We create this  40-point window of where we would like to see the S&P close by Friday. Again, these are weekly options, so it’s very short-term options trading that we’re doing.


But we’re selling the calls, collecting the premium. Now, in this case, the market did rally above our call, and in the end, it  closed just barely in the money by, I think, a quarter of a point. What happened is we sold on Tuesday. We had Wednesday, Thursday, Friday. This entire time, by the way, the value of the call is decreasing, which is good since we’re short the call. And because we, because the put side expired worthless, meaning the S&P did not close below 2120. it actually closed 2160. It was a full profit on the put that we sold, and then a small loss on the call, except that we collect premium, and so it really had to have closed above our strike price. I believe we collected about four… I think it was actually three and a half points on this one. So, even though it closed just above our call strike, we were still profitable on both options trades, because it didn’t close enough out of the money. And if you look at this one from last week, again, it triggered the position on Monday. And this time, I believe it sold 15 point out of the money call on puts. The put expired completely worthless, so we kept all that premium. The call was in the money, but right at the point where it would have been either a loss or break even. So, again, about four and a half points out of the money. But we collected four and a half points and we sold it. That’s how these Iron Condor trades work.


I can  zoom out and we can look at some of the different trades it’s placed, but generally speaking, this one does really good when the market goes higher, and here’s a good sequence of when the market went sideways, and throughout this period, most of these trades were winners. Here’s an example… This is an example of when it would have taken a loss, although it’s hard to tell by looking at the chart, because, since the volatility was spiking higher, we were collecting more premium on these trades. But it did close below our put strike price here, so the put was probably at a loss. The call would have been at a gain. The good thing about doing Iron Condors, though, is that if the market moves against you, one of the legs of the Iron Condors is going to be profitable by definition.


In order to really take a loss on the trade, it has to go below your strike, below the premium you collected minus the strike, and then also below whatever you collected on the call for that met trade profit to be a loss. And that’s, I think, why I really like Iron Condors, and why we decided to move to the Iron Condor on the weekly options that we trade. The only other comment I’ll make is that, with the Iron Condor, we also buy deeper out of money calls and deeper out of money puts. When we place an Iron Condor trade, it’s really placing four options trades. We’re selling a call, selling a put. We’re also buying a deeper out-of-the-money call and a deeper out-of-the-money put. And the rules that we use is we attempt to buy one that’s trading at basically a quarter of a point. We want to sell, or when we buy the option for the protection, we don’t want to take away from the premium. And so we pretty much do it as far out as we can. However, if doing the quarter point gets us too far out of the money, where it really isn’t giving us a lot of protection at all, then we default to 100 points out of the money, and we’ll pay whatever premium it is. If that’s the case, though, it’s okay, because when we sell the premium, or the premium we collect when we sell is  going to, by definition, be quite a bit higher to where, even though we’re paying more for the protection, it doesn’t really impact the gains by a lot. So, that’s the Iron Condor.


Iron Condors, and any time you saw options, they do have pretty high win rates, and I believe the Iron Condor alone is in the 80% range. But it’s really designed to do good in sideways. It just so happens that it does even better in up-moving markets, and then down-moving markets, it averages at a loss. Okay, so, I think that covers each of the algorithms pretty well. I just want to close out this slide by just reaffirming that this is all based on back-tested models. We also have walk-forward data that we have, but we have to go with lowest common denominator, which is the back-tested model. Keep that in mind.


But our design methodology, and you should watch the design methodology video for more details, is really not that we can predict the market direction with 100% certainty, and so when we think it’s going higher, we load up. When we think it’s going lower, we short. That’s really not how we go about trading. What we do is we create multiple strategies that each do well for different market conditions and then do our best to have those algorithms either shut off or maybe be slightly profitable in the contrary ones. And so when you put it all together, you have these totals here. In the back-testing, for any given month, if the S&P closed higher by 30 points or more, the Iron Condor averages 1,442 per 30k traded. So, you know, when you add all this up, you’re at about 3500 per month on an average, and that’s per $30,000 traded. If you look at the sideways market conditions, they average about 2,661, and then if you look at the downs, it’s about 751, which is still pretty good. We do, you know, again remember that each one of these is a third of the back-testing. The way we set this up is so that each market condition represents  a third of the back-testing months between October ’03 and October 2016. Overall, we think this S&P Crusher package, it’s our flagship portfolio. We think it’ll continue to do well.


We definitely think that our design methodology is valid. We’ve been around as a company for about three years now. And just remember, you know, trading futures options, it does involve substantial risk of loss. It’s not for everyone. The people that really appreciate our algorithms and most of our customers are  day traders or investors that want to get involved with algorithmic or quant trading, because all these trades are based on mathematical models. My background as an electrical design engineer, digital design engineer, is that we use a lot of state machines, and so when I look at the market, I do a lot of that as well. We look at pattern recognition through state machines. I look at a various sequence of events to initiate trades. One of our big philosophies is also that, generally speaking, the hardest trade to make is the right trade to make. A lot of times, we will take comfort in  the uneasy feeling that we all have when we get into some of the trades we do. And a good example of that is when the market gaps higher on  a morning gap, on  a morning breakout. A lot of times it’s really hard to buy the market. This is a great example. When you have a market going lower for a few days and you get a huge gap up, this is always a hard trade to make, going long right here after it feels like it already had its move. You can see that, you know, that trade worked out great. The market just took off. So, when you put all this together, what you have is the S&P Crusher Portfolio.


It requires a minimum of 30,000 to trade with the auto-execution brokers, although that number is just  the initial. Once you’ve already begun, you don’t necessarily have to have the 30,000 in the account. The accounts can… Or built into that 30,000 is a buffer, so that they can take losses with the, you know, in accordance with the max drawdown and still trade through it. I think that summarizes pretty much what I wanted to say when it comes to the S&P Crusher. You should definitely look at our website, and you can take a look at all the stats on this algorithm. I really wanted to just  show you the charts, show you how each algorithm trades within the Crusher. I guess one thing real quick that I want to comment on as  a final wrap-up, remember, we trade seven algorithms on purpose. Again, this momentum algorithm is designed to do well when the market goes higher. What you’ll see is that when the market starts selling off like it did here, a lot of times, it’ll get stopped out once, sometimes even twice, which will take away from some of the gains we saw earlier, but as the market rolls over, it will shut off, and we won’t take losses through these big  sustained down moves. And then as the market starts rallying, we get back in and start having hopefully profitable trades throughout that period. Meanwhile, throughout this  upmove here, I can tell you, the breakout algorithm probably had a trade right here.


It probably had a really good winning trade right here. It’s not that we don’t trade when the market goes lower or when it’s rebounding. It’s that the other algorithms kick in. And then when the market goes lower the 10-year note algorithm will kick in. And it’ll attempt to take advantage of a 10-year note that will typically rally when the S&P goes lower. When the market goes sideways, the covered calls will trade, and we’ll usually have winners on those, and the Iron Condors will as well. So, that’s  our methodology. Seven independent algorithms that trade concurrently, and with a $30,000 account, you can trade all seven of them. It’s not, you know, you don’t need 30,000 times seven in order to trade all these. We already take into account all the overnight margin that’s required. These can be auto-executed using any one of the auto-execution brokers that are supported.


We can also install these on TradeStation, and you can trade them on your own. My company, just to reiterate, we’re a third-party trading system developer. We are not allowed to give advice unique to your situation. We’re not registered with the CFTC as a commodity trading advisor. There’s a self-executing exemption that we claim. And, you know, so keep all that in mind as well. My background, I’m electrical engineer. I worked in the industry for about 13 years as a logic design engineer, and then I traded on the side. And about three years ago, I decided to start this company as an algorithmic trading third-party developer. So, I think that’s it, and once again, I went probably way too long. But I hope you all have a great day, and remember, trading futures and options, it does involve substantial risk of loss. This is not for everyone, but if you have some risk capital that you want to put to work or you’re a trader that is just tired of dealing with the emotions of trading and want to let some algorithm run and just control the trading, you can do that. Again, we don’t control client accounts, but we sell the license to use the algorithms. Once you are using them, you still have the power to turn ’em off, to turn ’em on, to change the number of contracts traded. So I think that’s it, and have a great day, and look forward to hearing from you. Bye-bye.

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AlgorithmicTrading.net provides trading algorithms based on a computerized system, which is also available for use on a personal computer. All customers receive the same signals within any given algorithm package. All advice is impersonal and not tailored to any specific individual's unique situation. AlgorithmicTrading.net, and its principles, are not required to register with the NFA as a CTA and are publicly claiming this exemption. Information posted online or distributed through email has NOT been reviewed by any government agencies — this includes but is not limited to back-tested reports, statements and any other marketing materials. Carefully consider this prior to purchasing our algorithms. For more information on the exemption we are claiming, please visit the NFA website: http://www.nfa.futures.org/nfa-registration/cta/index.html. If you are in need of professional advice unique to your situation, please consult with a licensed broker/CTA.

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With the exception of the statements posted from live accounts on Tradestation and/or Gain Capital, all results, graphs and claims made on this website and in any video blogs and/or newsletter emails are from the result of back-testing our algorithms during the dates indicated. These results are not from live accounts trading our algorithms. They are from hypothetical accounts which have limitations (see CFTC RULE 4.14 below and Hypothetical performance disclaimer above). Actual results do vary given that simulated results could under — or over — compensate the impact of certain market factors. Furthermore, our algorithms use back-testing to generate trade lists and reports which does have the benefit of hind-sight. While back-tested results might have spectacular returns, once slippage, commission and licensing fees are taken into account, actual returns will vary. Posted maximum draw downs are measured on a closing month to closing month basis. Furthermore, they are based on back-tested data (refer to limitations of back-testing below). Actual draw downs could exceed these levels when traded on live accounts.

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