Trading the Golden Cross 50 & 200 SMA - Is it Profitable? | Beyond the Charts: EP001

Trading the Golden Cross 50 & 200 SMA - Is it Profitable? | Beyond the Charts: EP001

Show Video

Hi everyone, my name is evan and welcome to our brand new series. Beyond the charts. Beyond the charts, is designed to give you insights, into. The trade risks, research, process. We start by simply, asking, a question, and the question can be anything from. Is there really such a thing as a santa claus rally, to something. Much more specific. Like. Buying a breakout, after a very particular, type of consolidation. No matter what the question is we follow the same process, each and every time that you might actually remember, from middle school, as the good old-fashioned. Scientific. Method. We start by simply, observing, the market. Forming a hypothesis. And asking, a question. Next we gather our data, we write some code and we conduct the experiment. Finally, we analyze, those results, and we compare, them to what we thought might happen. And draw some conclusions. So the goal of this series, is three parts, the first is to simply, ask questions, and to test our assumptions, about the market. Second, is to learn how to write code we're going to be looking at lots of code throughout the series. And hopefully, some new tools, as well, to answer those questions. Third, is hopefully, we get some good, results, or some meaningful, conclusions. From those experiments. And they'll actually, impact the way we view the markets. And invest or trade our own dollars. So all of this might not be for everyone, if looking at code makes your eyes glaze over then we're going to leave some timestamps, in the description, of all these videos, so you can jump around, and. Jump to the end if you just want to see those conclusions. Or. In the middle if you're interested. In, learning how to program yourself. We're also going to have this available, as a blog post so you can read through this instead of watching the video, and all of the code that we're going to go through, in this series, is going to be made public on our github account, for you to download, and test out yourself. So hopefully, this is interesting to you we're super excited to get started with it so without, further, let's jump into. Today's, experiment. Today we're here asking the question. Is trading, the golden, cross. A profitable. Trading, strategy. And if it is. Is it worth trading. Now if you're not familiar, with the golden, cross, it's a fancy, term. For. The intersection. Or the cross, of. Two, moving averages, the two most commonly. Cited moving averages, in markets. Which is the 50-day. And the 200-day. Moving average. When the 50-day, which is the short-term, moving average, crosses. Over. The long-term, moving average, the 200-day. You have a golden, cross.

And A lot of people will go as far as saying, you're in a bull market. If that happens. And when things go the other way when the 50 crosses, below the 200-day. You have what we call, or what is called. A, death cross. When the 50-day, moving average crosses below the 200-day, and a lot of people will call that a bear, market, now there's strong opinions on both sides it is, very, uh, well, cited when this happens. Some people. Believe in it and, make decisions. Off of it, and other people, uh dismiss, it as being noise, or not important. So, what we're going to do is, write the code today to, actually, run this test. Over historical, market data and find out, if it's actually meaningful. If it's worth trading, and if it's worth paying attention, to, the tools we're going to use today, are, ninjatrader. And, microsoft, excel that's all we're going to need, and we're going to jump into it, right now. All right here we go we've got ninjatrader. 8 open. This is the latest and greatest, of ninjatrader. 8, at the time of recording, this, so june, 2020. And, we're basically, looking, at a. Default. Um. Trading strategy, right now so this, looks maybe a little intense already with lots of code in here, but this is basically, the boilerplate. Starting, template. That ninjatrader. Gives you when you unlock a strategy. And start to. Code with it, manually. Now. The only thing we've added so far is just some documentation. In here, and we've given it a name, and description. But basically, from here on out we're going to be. Writing. The code, to test. Our. Golden, cross. Trading strategy, on historical. Market, data, now this won't be a total tutorial. 101. Of ninjatrader. We're going to just kind of jump right in at this point, if you're. Starting out you want more help just really getting started with the platform, leave a comment below if i see enough of enough interest, here i'll do a, real kind of 101. Tutorial. For this platform. Now. Again what you're seeing here, are the default sort of settings, uh for our strategy. And most of the work, uh most of the coding that we're going to do and it's a pretty easy strategy, today. Um. But most of that is going to be contained, in. The um, on bar, update. Method. So every single strategy. Will always have an on on bar update, method, and this is basically, the driver. Of the strategy. So when you're running the strategy, on historical, market data or if you're running a strategy, live. What this method basically, does is with each new bar of data that comes, in. You'll, start to execute, some code, and, the code that you execute. Are is going to be contained. Within, this method. So this is where, um again the driver, of, this strategy, and this is where we'll be spending. Most of our time so, basically. We're going to start in here right now, the only thing we have in here is this number 73, this if condition. This basically, says if the current bar is less than the bars required, to trade. Um, escape, this method. So, um, in this case are bars, required, to trade i guess i lied i did update this already, is 200. And this basically, just tells us we need at least 200 bars of data before we can even run the strategy. The reason for that is because we're using a 200 period simple moving average, so there needs to be 200 bars of data. For that average to be calculated. And then we can actually. Start with the logic, of the strategy. So hopefully you're following me. So far and again this is a little bit dense but hopefully as we do more and more tutorials, this will start to make more sense because a lot of this is going to be, basically, the same thing, with each new experiment, that we run, there's always the same sort of ingredients.

And Setup, that we need. So, we're going to continue on here we're going to assume, you know we have enough bars of data, so the next thing that we're going to do here, is that we're going to get started, with our. Buy cell rules, and what we're going to do is we're actually going to just write a new method. Called, buy cell rules. That. Will contain, the rules. To our strategy. So we could easily, just. Write all of the code, in the onbar, update method, and if you have a very simple strategy, or a very simple test that you're running, that's totally fine. But generally, speaking some good practice, is to sort of modularize. Your code. And. Write out the different, parts of your strategy. In different methods, that way it's just easier to debug. And easier to sort of know what's going on. This strategy is pretty trivial, but, we'll try and follow some good convention, here so, the buy cell rules, method. Is something we now need to define. And, we can simply do it, by. This line here, it's not going to return, anything, the only thing it's going to be doing is just monitoring. If there's an actual, trade or if there's an actual. Execution. That should be happening. And the way it's going to do that. Is by using a built-in. Ninjatrader. Method. Called. Cross. Above. And cross, above. Takes. Two parameters. Which, are. You know really. Arrays. Of data. And in this case, the arrays, of data, are going to be, moving averages. So you'll see that i just defined it as sma, fast and sma, slow. We'll need to define those in our. In our strategy, above, but basically. Uh what this will do is. It will be monitoring. These two arrays, so our, our fast moving average and our slow moving average, and as the name implies, cross above. It's going to be waiting for those to cross and as soon as that cross, happens. It's going to return, true to us, and in which case. If that cross actually happens, if it's a true. Cross, then we want to enter, our, long position, we want to put our buy order. Into the market. So what we're going to do is, we have to compute the number of shares, which, we'll write another, function, for. Called compute share size. Uh and then we just have to give this buy signal, a name, so in our case we're gonna we're gonna call it, a golden, cross. Buy, and basically, these two lines of code right here, uh are all we need to get us into the market, on a cross. Above. Now one thing we need to do is define that sma fast and sma, slow, so we're going to go up to the top of our strategy. And, these are going to be. Sma, so simple moving average objects. And we're going to call, we're going to have an sma, fast. And then we're going to have. An sma. Slow. So, those two objects we're going to we're going to initialize, them a little bit later. But that'll make this, happy here for our, cross, above. Now we need to handle the other side, which is basically getting out of the market. And to do that. We're going to just do an else if. And we're going to use a cross below, which is, as its name implies, is basically. The exact, opposite, of. Our, original. Cross above so if the sma, crosses below the sma, slow. We're not going to short anything in this experiment, we're going to just exit the long. And the only, and the way we do that is to use exit long, and all we have to do is reference, the name. That we gave, our. Long. Uh, order, or execution, which is golden cross by. So there we have it that is uh that's in fact, um. The. Four lines of code that you actually, need for our, buy and sell rules. Now we're obviously not done we have a few other things to do, uh one of the things is to. Compute the share size. And, the way we can do that is um. By, creating another method. This case it's going to return an integer, so this is the integer which is the number of shares. That we. Need to purchase. And it's going to be called compute, share size. And it's a, one line. Method, here. Which is. It's a little, bit nuanced. But bear. With me. We're gonna have to define. Um.

Current Balance. In just a moment. But basically, here what we're doing, is, um. Our current balance, which is just going to be our running, account, size. Is going to be divided, by the closing, price, of. Whatever, this bar is, so that's the, zero. Um. That's the zero that you see there means the current bar, uh so we're going to divide our current balance by the c by the closing, price of today. And we are going to essentially, be truncating, it we're going to be removing the decimal, even though fractional, shares, are getting sort of popular, nowadays. Um we're going to be just chopping off and assuming, that. We can only purchase. Whole share sizes. Rounded, down. And, that will give us, a basically, a hundred percent. Uh, invested. Portfolio. Each and every signal it's going to take our total account balance. And try and use all of that money, uh to to take our buy signal. And uh that's it i mean that that'll compute the share size, uh according to our, um, you know when it needs it from the buy sell rules. Method. Now we're not done yet because we do have, um. We have a few more things that we need to um, to, to code up here, and i'm going to just sort of pause and kind of copy these in because they're a little more nuanced. And then i'll just explain, them a little bit after the fact. All right so i just copied in a few new methods here just to finalize. Our strategy. The calculate, performance, method. And the output, performance, method, so if we look at the calculate, performance, method first, you can see this is basically, just tracking. We defined, and then are tracking. Three different variables, the unrealized. P l, the realized, p l, and then the current balance. Of. Our, account, equity, so we're just keeping track of that every, single bar that comes in, just to make sure, uh and for reporting, purposes. Um. What uh how, the strategy, is actually performing. The second, method we copied in here is the output performance. So basically. Uh we're just printing, out, the dates, and the current balance, to. Our, output, screen which we'll look at in just a moment when we run the strategy.

Just For again, reporting, and analytical. Analytics, purposes. So those two got filled in, added in some documentation. Uh, and included, these two other methods, in our. Onbar, update method, so again you can see we're now on every single bar that comes in we're calculating, performance. We're running our buy cell rules, and then we're outputting. The performance. One last thing we did was. Included, this block of code in here, which is basically, just plotting. The. Simple moving averages so the 50 and the 200 sma, when we go to run this strategy. With our strategy, analyzer, we can now, we'll be able to see the actual. Moving averages get plotted. And then we can visually, just sort of identify. Okay the cross actually happened. Yes we got filled. And everything, um, you know makes sense on on terms of strategy, logic. Uh and then if we scroll up a little bit further you can see i just defined, some of those variables, the starting balance the current balance. Starting with ten thousand dollars. And uh letting it kind of rip. Fully invested. Just to see how well the strategy. Actually, performs. So that's it that is the total strategy, again this is uh this file as you see it right here is available. Uh, to download, so you can start. With the final version you don't have to retype this all yourself. If you want to duplicate, this. Into your own workstation. So the next thing we're going to do now, is to actually, open up. The strategy. Analyzer. And this is actually going to be where we run, the code. On our. Historical, market data. Now to take a moment and talk about data, data, is, super, important. And it's super important to have, quality. Data. That is clean, that is accurate. And that actually, represents. Either a total return, index, or, the actual fills that you could get in the market. It is very important. To. Not. Skimp, on data there's lots of free sources, out there but. Generally. Speaking. You get what you pay for and if you're serious, about. Really doing some research, or potentially, putting. These types of. Findings, or, building your own strategies, and trading them live. You absolutely, want to invest in data so we use. Nor gate, for all of the stock market data that we have, they're basically. Uh the best you're gonna find, for the uh you know for the average retail, investor. Um, you know unless you want to start paying. Um, you know mid six uh mid five figures, for data, um. Nor gate's gonna be sort of your best bang for your buck, uh we have a nice, referral, program with them so if you're interested.

In, Supporting, the show. And if you're interested, in in picking up some of the data, yourself, and investing, in it uh we have a link down below i would definitely check them out, if this is a path that you want to go down, to do some research, and to test things out yourself. So. With that said, all of nor gates data is currently, imported. Into ninjatrader. And what we're going to do is we're going to run, this. Uh we're going to run our strategy. On. The, dow jones. Uh that is uh that is going to be our benchmark. Of choice so the good old diamonds. We're using total return, data. And, we're going to use. We're going to use a start date of. Let's go back to, 2005. And we're going to end, uh, in december. 31st. 2018.. So we're going to run basically. Um. A little over 12 years 13-year, back test here from 05, to 2018. And we're going to run uh our, episode, 001. Which is a strategy. That we just, looked at, so we're going to click on the run button here. And. We've got some results, we've got a total of six trades we've got profit. Things look pretty good, um we're going to pump the brakes before we look at this screen. I want to first just jump right into the chart. To just visually. Kind of check in on. This trading signals that we just generated. So if we just go all the way back to the very very beginning. You can see here where in, the. 2006. Time frame right now, and, we got our, first, buy signal, here in november. Of 2005. In fact, so if we look at our chart you can see the light blue line is the 50 period simple moving average, the yellow line is the 200 sma. And you can see we got that crossover. Right, in here. And the strategy, ended up buying 134. Shares, at 74.. So if we continue to follow this forward. You can see the blue line just continues, to hold above the yellow line, so there's no reason to sell, we continue, to just stay, involved. In the market. And as we keep scrolling to the right here you can see, that blue line's moving all over the place but it's still holding above the yellow line until. Here which is 2008. And we know what kind of year 2008. Was. But the sell signal, comes in. From, january. 2008. At 93, dollars. And then just to dial it back again. We originally. Bought. In 2005.. So we were holding. Long, diamonds, for about. Two, years little over two years. Before, the sell signal, came, in 2008. And this all checks out if we look at the moving averages and we look how the strategy, is performing. Uh that looks correct. If we zoom forward here we've got this big bear market that. You know kind of developed, throughout the next two years or so and then we got our next. Our next buy signal our next, entry spot into the market. In june, of uh this has to be 2009. Yes it is uh so june 2009. We get a golden, cross here with the 50 period simple moving average crosses, over the 200 period, the strategy, buys, and again if we just zoom forward here, you can see it stays in, for um about a year's worth of time, before a little more whip saw, so all of this looks pretty, uh spot, on of course. You would want to go through the actual trade history. You can see exactly, the entry times the exit times you can see the percentage, of profit. Uh so on and so forth, so the question becomes now. We've, looked. At, the actual. Chart to see that the strategy, is behaving as we expect that it would, uh it does, seem to be. So now we actually want to compare. Did the strategy. Outperform. Just buy and hold is it worth. Trading. Now this is where, um. I get a little bit of, i have some gripes with ninjatrader. It's a great platform, for lots of things. For, the actual. Result. Analysis. I tend to like to just do things myself. I've noticed some interesting, behavior, that i won't necessarily, get into right now with ninjatrader. And analyzing. Results. So. Notice. When we originally, coded up our strategy. That last method, of, outputting, performance. Is um where we were printing, out the performance, to a log statement, or to a log window. For us to analyze. And we're actually going to use that right now so if we um if we run this strategy, again. And i pull up. This, window, here. I've got the output, of just the, every single date that the strategy, just ran, and then on the second window, here, i have our current account, equity. Printing, every single day, and so what i'm going to do is i'm going to take this information. And i'm going to plug it into. A microsoft, excel template that i've built out, to analyze. The performance. So what i'll do is i'm going to take, the. I'm just going to select, all and copy. The. Dates that i printed out here, and i'm going to just put it right into. This column. And, that's fine. All right so we're starting in 10 8 10 18 2005. And i'm going to go to our second window here, i'm going to select, all.

And I'm going to put this in our strategy. Equity. Column. All right. So now you can see we've got the strategy, now. Populating. What we need. At this point. Is, a benchmark. And so there's lots of different benchmarks, we can use. Um. We're going to just use the diamonds, as a total buy and hold 100, diamonds buy and hold strategy, as our. Benchmark. So, um i have, a. Buy and hold, strategy. Built out. And. We're going to want to. Just double check here so the starting date should be 10. 18.05. So if you recall. Remember, that we started, the strategy. In january. Of 2005. But because. Uh we have a warm-up, period of 200, days to get that moving average calculated. The first real buy signal the strategy, necessarily, activate. Until, 10 18, which is 10 months in, from january. So that makes a lot of sense here and that's why. Um. When we run our benchmark, we want to start from 10 18, 2005. We're going to run. This, strategy. And then we're going to. Copy. From our, output window again. And we're going to paste that. Right in here. So, we now have the strategy. And the and the benchmark. Got a couple of rows here just at the end to clean, up so let's just delete, those to make sure that. Our performance, tracks correctly. And now, we're gonna scroll all the way up here. And this is what we have. So, what we're looking at, is, um. The benchmark, which is in orange, and our strategy, which is in blue. And so what you'll notice here. Is that. If we just look at the historical. Equity curve. Of our. Um. Of our strategy. You can see that it actually comes in pretty much exactly. In line. With buy and hold. For this time period, from, approximately. Um, october, 2005. To the end of 2018.. Now notice, a couple of, important periods, in here, just notice how, the strategy, went completely. Flat, in here. Throughout. The bear market, of 2007. And 8.. The strategy, in fact actually picked up a lot of edge here a lot of alpha, maybe if you want to call it that. Inside, of this time period, so notice we had this big drawdown. In. The dow jones. Our strategy, did not have. That same. Experience, that same drawdown, it basically, went sideways, picked itself back up here at the end of uh 28, 2008. And then it proceeded, to, um you know kind of push higher and track, just like the dao was doing, now notice though another interesting, point in time here is, 2014. To 15.. All right so notice that, while, uh the dow jones, pretty much went sideways. In this general, area. Notice that the, bot that the uh, timing strategy, here our golden cross strategy. Started to draw down pretty significantly. In 2014. To 2016. Area, and if we scroll down to this second, pen. This second pane here this is another chart, of our, drawdown. So basically, you can see the amount that um. Each uh the the actual timing strategy, and then the benchmark. Is drawing, down. From its all-time, highs, so notice here in 2015, to 2016. Uh how uh the, uh golden cross strategy. Tends to out, tends to underperform. And starts to draw down, uh as much as 20 percent, here, in this time period. So, a couple of you know interesting, time slices, to to certainly take note of, if we scroll over and just kind of take a look at. Some of the uh, final, output, metrics. Now if some of these metrics, are foreign to you, that we're going to look at we've got a glossary, that we wrote up, on the trade risk website we'll link it up below. And. We'll share the link now appearing. That, you can look up the definitions, to see how some of these things are calculated. But we're going to just run through now and look at, this back test so we're looking at 13, years of data.

And Again this is the strategy, here so this is this is the golden cross. And you can see we started with ten thousand dollars we ended with twenty nine thousand dollars for 196. Percent return. If we compare, that to the benchmark, which started with 10 000, and ended with 30 000 you can see the benchmark, ended up producing a 207. Return, so they're very similar, the benchmark, did outperform, just by a little bit but, approximately. Pretty much the same thing so that's actually a pretty good um. A pretty good win for our strategy. If we look here at this next. Row this is um. The compound, annual. Growth rate so kagar, for short. 8.6. For the strategy, 8.9. For the benchmark, so again you can see very similar, here, but this next one which is max drawdown. Is where the strategy. Really shines. So, 20. Was the drawdown, that was in that 2015. Period. Versus, a benchmark, at 51. Of a drawdown. So you can see now that this is where the benefit, of, a trend following strategy, like, the 5200. Sma cross. Actually, adds, value, it produced, very similar. Buy and hold returns. But the drawdown, was significantly. Lower. Another, row here i want to, pay attention to is sharpe ratio, which is a way to measure sort of risk risk-adjusted. Returns, or. The number the really the distribution. Of returns. Uh over, your growth rate and you can see 0.7, for our strategy, 0.48. For the benchmark. So again you can see on a risk-adjusted. Basis. Our strategy, did pretty good here. In terms of timing, and producing, returns, and stepping aside. During, the bad times, a couple of other stats here time in market was approximately, 80 percent. Uh correlation, was 0.65. So, you might be thinking to yourself wow this is uh this is actually, pretty, good and, it is. For this time period, right, so, notice. I sort of cherry pick this time period. Um, intentionally. To, sort of um, kind of pull a fast one uh over, everyone, just to start, i intentionally. Chose 2006. To 2018. Because this is a time period where, um a 5200, sma, cross. Actually did pretty well you got all of the market return. Without. Nearly as much drawdown, and volatility. And i'd say that's a pretty good win. For the 5200. Sma, cross. But here's the thing what if we went back even further, and what if we actually took it to current, times. So i've already gone ahead and did that and if i go to the next, slide, here. We're looking at a slightly different story. And the different story, is, we're now looking at 20 years of data, we're going back to the 2000. 2000, on the dot. All the way to. March, or no actually, june, of 2020., we basically took as much data, as we could sort of get our hands on we did almost a 21-year, back test. And then we compared. The strategy. To, the benchmark. And what you'll notice is again the benchmark, is the orange, line and the strategy, is the blue line, and you can see as we got to the very, end of this test. That the golden, 50. Golden 250. Cross, did a lot. Worse. Than. The. Benchmark. So the question is well. Even if it did worse that might be fine as long as it's stepped aside from a lot of the volatility. So let's go to our, chart over here again. So now we're looking at a 20 almost a 21-year. Back test, we started with 10 000, for our strategy, we ended with twenty one, eight, so we had a hundred and eighteen, percent, return.

Uh If we look at our benchmark. That started with ten thousand, it ended up having a thirty six thousand dollar final value, and a two hundred and sixty three percent return. So right off the bat you can see there's a drastic, difference. In the end result, here, from, the 50, 50 200 sma cross strategy, to the benchmark. It lagged, pretty, dramatically. Over, this much longer, back test. Notice the khagar, which is our again our compound, annual, growth rate, is now only 3.9. Percent for the strategy. And it's six and a half percent. For the benchmark. So again. Um, almost get cut in half. From what it was before so remember, our first. Uh, test that we ran here, uh actually did get cut more than half it was 8.6. Uh. Compound, annual growth rate you can see down to 3.9. The benchmark, at 6.5. And if we look at drawdown. Here, you can see that the drawdown, for this strategy, actually, doubled, its worst drawdown. Is now, 39. Instead of the 20. It was, in our first period. So all around we basically got returns, cut in half, and we got our drawdown. That doubled, so you basically went in the wrong direction, on both. Um, you know on both sides, of, um, the, performance, profile. Sharp ratio, dropped down to 0.3. Uh benchmark, at 0.33. Time in market. Reduced, a little bit to 71, percent. So, all around, here when we ran a 20-year, back test on the dow jones, for, the golden, cross. You can see returns, just really did, dramatically. Fall, you can see in the 2000. To 2003. Period, this strategy, did not perform, well, you can see it basically, just sold off with the market. It didn't step, aside, from that bear market. Now of course you could run this on the nasdaq, you can see what the difference is there. But the bottom line is you know. With the market selling off with such wide movements, here, it just continued, to get whipsawed, and it continued, to kind of by the top and then gets, sold off at the bottom, and it just rinse and repeated, that multiple, times here, and got chopped up in this period. It still did excellent, during the. 2007-2010. Period 2009. Period so that's good, um, and then if we look at, the 2020. Period. This really had a tough time, with the latest, sell-off, in, january. Or february, i guess. Into march, it basically. Um. Sold out at the complete, low. Or pretty much at the low, of march. And then, it. I don't think it even at the time of recording this has got back into the market, yet, and we know that the market went on a total v-shape. So at the time of certainly recording this and running this test. Through the month of june. It basically, hasn't got back into the market yet, it sold the bottom, and it's just kind of sitting there. So, if we take a step back and we think about the two questions, that we set out. To, answer, with this experiment, we asked. Is trading, the golden, 50 200 sma, profitable. And i think we can say yes yes it is in fact if we looked at this this 20-year, back test we ran on the dow jones. Uh it did in fact turn a profit 120. Approximately. For a 3.9. Kgar, so. Yes i think the answer, is definitive, on the first question. Now the second, question. Um. Is it worth trading. If i'm looking, at. The. Performance. Compared, to a benchmark. I would say, personally, for myself. This isn't, all that attractive. Of a strategy. To trade. The drawdown. Isn't. All that much better. Than uh trading just holding the benchmark, outright. And the returns, are significantly. Worse, uh you know basically. Less than you know, almost half the benchmark. With. Nearly as much drawdown. So. For me, you know looking at this. Uh, this specific, type of test. It's not all that interesting, to trade, now a couple of other kind of final thoughts here is that, this is not a complete. Comprehensive. Test of course we would want to test this on other strategies.

We Would want to get more data i mean test this on more time frames and markets, we would want to get more data. We'd want to be maybe a little more rigorous, with our buy sell rules. Um. So on and so forth so, um, for, you know a real. In-depth. Analysis. Here there's plenty more that we could do but, for, the simple test that we set out to build, and test, i think we've got a good kind of picture here on how this strategy, performs. Uh it really does depend, on, the type of market, environment, you expect. So again if we went to our first test here when we looked from 2005. To. 2018. This actually did pretty good, so if you think that the market environment. Going forward, is going to look very similar to this type of environment. Then. The strategy, gets better and better in terms of, wanting to treat it, but, if you're going to look holistically. At a much broader, view and, you don't have a strong opinion of what the market environment is going to look like in the future. Then you should probably, consider, that, this might be, a pretty good summation, here of how the strategy, might perform. And in which case. Probably. Something that you could find. You know you could find something better for your money, but again, everyone needs to figure that out for themselves. So, hopefully. This was informative, hopefully this was helpful. We got to look at some code we got to. Look at some new tools. And we got to answer a question here, and get some actual data behind. A 50, 200 period simple moving average, so now, next time, cnbc. Is touting it or. Twitter, is blowing up over the 5200. Sma cross, can easily point to this video, and, uh basically, have some data. Behind the significance. Of those crosses. So that's it for me thanks so much as, always, for tuning in and watching our videos hopefully you enjoyed, and we'll see you, in the next, video, update. Hey it's evan here again and i'd love to know what you learned, from this episode, was there anything that stood out or surprised, you, leave a comment below, and let me know your thoughts, thanks again for tuning in and. Watching. You.

2020-07-22 09:31

Show Video

Other news