folks in this video we're going to be talking about the relative strength index also known as the relative strength indicator but i'm probably going to refer to it for the most part as rsi by far the rsi is at the top of the list in terms of utilization by most traders who perform technical analysis on whatever they may be trading the problem is is that when i read these books and many of them are old now when i read these books the way it was offered that you should use technical analysis back then when the books were written is outdated and i see many videos on youtube and many of which have over 300 000 views and they're still citing the way to use the rsi is the same today as it was when the books were written back in the 1970s 1980s and nothing could be further from the truth so in this video what we're going to cover is what is the rsi then we're going to do some rsi myth busting now before we go into what is the relative strength indicator or the rsi i'd like to share with you a quick story about me i've been trading stocks options and commodities for over 25 years now and after the dot com bust in march of 2000 i was looking for a new strategy to help me outperform the market but in a way that would reduce my risk and provide me a lot of upside potential and that is when i fell in love with the rsi i use it every single day and on every single chart whether it be daily charts weekly charts monthly charts intraday charts it doesn't matter and what i was able to do was to modify the rsi and i'm going to show you that later on in this video and the modification allowed me to be far more disciplined in my approach to accumulating a stock that met certain criteria but the first and primary criteria was that rsi had to be at a certain level and i'll go into this in greater detail later on in the video because it's my fifth and final strategy called the contrarian trading strategy so please stick around and we'll go over some charts now before we move on i would ask you please leave me a comment are you using rsi and if so how are you doing with it and if you'd like me to do more videos if you get value out of this one what would you like me to do a video on so let's move forward let's get to what is the relative strength indicator now many of you have probably seen this calculation your eyes glazed over and i don't blame you i'll be honest with you in the 25 years that i've been using the relative strength indicator i've never once worried about the formula behind it so let's get rid of that and let's focus on actionable information that could help guide you and i think it's important that we pay credit to j wells wilder jr who was a mathematician and he's the one that developed the relative strength index and that was back in 1978 when it was published in new concepts in technical trading systems and in commodities magazine now 1978 is not all that long ago for many of us i was a child but many of you may not have even been born back then and what j wells wilder was able to do was he was able to measure or provide the secret sauce of how to measure when a stock is moving up the average gain should be larger than the average loss when the stock is going down the average gain is smaller than the average loss it's pretty simple but he was able to develop the rsi and allow us to visualize generally speaking the rate of change of a securities price now the rsi is a leading momentum indicator now there are other leading momentum indicators such as the stochastics but many of you are probably familiar with lagging indicators such as the moving averages or the mac d as well as the adx indicator the value that i get out of the rsi is that there's a saying that i always like to use to members and to viewers is that the market sends you signals and this is what i'm going to show you in this commentary and many times the signal that is provided to us by the markets is illustrated by the relative strength indicator which again is a forward-looking momentum indicator now one of the common questions that i'm asked with regards to the rsi is what can rsi track the momentum of folks it's if you could chart it you can use rsi whether it be stocks or commodities forex or cryptocurrencies i don't care if it's cupcakes if you could get it on the chart you're able to use rsi so it really is a must have in your toolbox or an arrow in your quiver when you're doing technical analysis now what i want to do folks i want to segue over into myth busting and there are three key myths that i want to dismiss to help you become a more powerful trader by being better advised on how to use the rsi in the 21st century the first myth up is 70 equals overbought 30 equals oversold folks this is very outdated information that i've seen written in technical analysis for dummies getting started in technical analysis by jack schwagger which is a very good book but unfortunately it's a little bit out of date now i'll take this blurb out of getting started in stock analysis by michael c tom set and for the most part this is what all of these books define as being overbought and oversold and unfortunately it's being repeated to this day by many on youtube where many people get their information from but unfortunately this information is outdated and i'm going to show you in a moment on the charts what i mean and this passage is from getting started in technical analysis the rsi is a fairly straightforward oscillator and easy to track very true to this day it is based on an index value between zero and 100 which a normal mid-rate range would be above 30 and below 70 if the oscillator reaches or moves above 70 on the top side the stock is overbought if it reaches or moves below 30 the stock is oversold neither of these conditions should be expected to last for very long folks this is where we need to amend the rules on how to use rsi so that we're using rsi more effectively into the 21st century now the first straw we're going to use to illustrate why using 30 as oversold is dangers will be using the chart of best ink ticker symbol b est and you could see here that on september 14th of 2020 the rsi as noted in bloom went below the 30 mark and if you would have went into this trade expecting that the share price would have you may have lost quite a bit of money if you were stopped out why is that because while rsi went down below 30 here on september the 14th it truly didn't bottom out until september the 21st and if you would have bought at the lows on the 14th at $3.35 and sold at the highs when rsi ultimately bottomed out on the 21st you would have still had a net loss and those prices i just gave you our best case scenarios now don't get me wrong there was a point in time where 30 being oversold and 70 being over but were accurate when i first started using rsi that was the case however with the advent of computerized trading algorithms high frequency trading that is no longer the case and we need to modify our behavior in how we use the rsi another example this time we'll go to rsi overbought we'll bring up bmi which is badger meter this is a daily chart and you could see that back here on october the third rsi went above 70 that was right here however the share price would have kept going higher now what if you use the simple rule of thumb of overbought over 70 and you sold your position or you went short you would have lost out on a lot of upside potential because the ultimate high after hitting a high on the eighth which was a high of 69.99 was up here at 82 dollars and 90 cents you would have left a lot of money on the table or if you were short you would have been sitting with a very large loss now these are only two examples but i would strongly encourage you that moving forward that you look at the charts that you're looking to trade and see whether or not in the past on those charts whether or not the examples i've just shown you are more common and i guarantee if you remember what i'm showing you right now you're going to say wow i can't believe how outdated the oversold overbought rule of thumb that my grandfather taught me is out of date so the myth of 30 being oversold and a buy signal and 70 meaning a sell signal and overbought that myth is busted now you may be asking well bob how do you use the rsi in your trading well i'll show you now what i do is this length is periods whether it be days weeks months it's it's it's 14 periods consecutively now at current pretty much all charting softwares default to upper band 70 and lower band 30 which is how it was intended initially and that's fine but what i like to do and this is what gives me the edge is i take this number and i change it to 80 and i change the lower number to 20 these are your settings for the 21st century apply now take a look at this very same chart badger meter some simple bmi same chart as before take a look at this chart and you could see that if you're using my settings that it became overbought the day that it began to top out now this in and of itself does not mean that you're going to see the shares roll over it just implies that you should be watching it very close if you're long set a trailing stop loss order perhaps take some money off the table because this is unsustainable now don't get me wrong rsi i've seen go up to 95 and in rare cases even higher than that but it's at this point here where you need to be saying to yourself my upside is limited and my risk of losing money where to enter the trade has grown therefore i should avoid if you own a stock you should be looking to scale out so myth number one here i think that we've busted now moving on to myth number two and myth number two is changing the formula will give me an edge over other traders and this was brought to my attention by somebody that made a video on youtube and it has over 250 000 views and it's downright scary now what he did in the video was that he modified the length he kept upper at 70 and lower at 30 so we're back to the original settings the default settings but what he did was he modified the length to this i'm going to add another rsi indicator and we'll use this line in purple to illustrate what he did he changed the length to 250 he kept the upper at 70 lower at 30 apply now let's put this to practical use we're going to bring up a chart of am z n which i shorted just prior to the stock market crash during covid now back on february the 11th amazon.com hit an all-time high of 21 85 per share rsi continued to rise validating the price action now what i did back then is i took my crayon out and i watched for a lower high because we knew there was a shock coming to the supply chain and what i did was i used my 14 day rsi with upper 80 lower 20 and i was looking for a lower high on price and a lower high on the rsi and that lower high arrived here on february the 19th this is how i use rsi remember rsi is a momentum indicator and it was clearly showing on this chart that we were losing momentum we were putting in lower highs even though we had put in a new all-time high on february the 19th what did rsi do using my settings 14 day 80 20 it put in the lower high it failed to validate the market sent you a signal that amazon was losing momentum and then once it broke down below rsi 74 spot 51 you knew that you had your lower high and now a lower low but price didn't break down to a new lower low until several days later on february the 21st this is the value of using a 14 day length on your time frame now if you're using a time frame of again 250 periods in this case a daily chart so 250 days without of the box settings of upper band 70 is overbought lower band 30 let's take a look at how this would have helped you while this version of my rsi sent you a signal on the 19th of february that there was trouble with amazon the 250 period view didn't show that there was trouble with amazon until it began to decline a couple of days later on or about the 21st and it did so very calmly and did not give you a sense of urgency that there was something very very wrong with amazon unlike the 14 day time frame so i would strongly encourage you please use the 14 day time frame the value of the rsi is that technical analysts out there use this indicator and you're going to have traders of like mind using that time frame meaning on a 14 day or 14 week or 14 month basis nobody's using 250 periods not enough to make a difference anyway and we were able to short amazon in advance of the rapid sell-off that we saw in the subsequent months so changing the formula will give me an edge over other traders that myth is busted myth number three when rsi is moving lower i need to sell my stock or look to short the stock let's talk about that we're going to use badger meter again symbol bmi and let's take a look at what happened after september the 18th 2020 when it rallied rsi hit a new high but then all of a sudden on the 21st it pulled back so did rsi it then rallied put in a higher low and then broke down yet again to put in a new lower low the key is is that you need to keep in mind what the stock is doing on a longer term time frame weekly time frame it was in an uptrend and the trend remained healthy so you had the probabilities at your back that what we were doing on this pullback was that we were entering a consolidation range and that was evidence with this divergence on price versus rsi this is why you need to use price and rsi i want to talk about this more in a moment in conjunction with each other rsi should never be treated as a standalone reason to buy or sell a stock because take a look at what would have happened if you would have relied solely on rsi so we peeked out here lower high here then what we did is we broke down to a new lower low on the 28th wow if you were just looking at the rsi you're saying i need to short this stock or you need to get out but what was price telling you sometimes you get false negatives this is accurate the 18th through the 21st price pulled back rsi pulled back and then when you fast forward to the 23rd of september the rsi broke down to a new lower low certainly certainly i needed to short this stock certainly i needed to sell this stock but no why because price was not validating what rsi was telling you it did not put in a new lower low and in fact the next day it bounced off of support and rsi recovered to press on higher and then again right here on the 28th you saw rsi pull back but what did it do what did price do it went into a consolidation and then ultimately it broke out so just make sure that if you're going to sell or short a stock based upon lower highs and lower lows on the rsi that you're getting validation on price and i would only be opening up a short position there is a caveat to that and i'll go to it in the final trading strategy on this video so stick around but i would only open a short position when rsi is putting in lower highs lower lows and it's down below 50 why it's because when it's down below 50 rallies tend to fade meaning if you get a rally higher the day following when rsi closes down below 50 and is in decline that rally has a strong probability of fading more on that later now let's talk about spotting divergences using rsi now we've covered strategy number one which is overbought and oversold and in that we discussed the caution about reacting to a declining rsi because it may just be entering a consolidation phase but in this strategy strategy number two i want to talk about spotting divergences because all too often people approach rsi from the perspective of overbought oversold and nothing can be further from the truth now for this example i'm going to talk about xar the s and p500 aerospace and defense spider now you could see that the share price had bottomed out here on september the 24th 2020 and then rsi validated the breakout by moving up in conjunction with the price good stuff and as i pointed out earlier the share price went into a consolidation and you saw a pullback on rsi which is fine what you want to make sure that you're seeing are higher lows it's momentum indicator so the trend is still intact now using rsi to give you an edge over other traders by spotting divergences would have helped you out right here on october the first when the share price hit the upper band of resistance and it pulled back no breakout on price however rsi did break out this is an indicator leading price performance and then the following day you had a buy signal with this outside reversal bar or a bullish engulfing of the prior day's move it's on this breakout that you could have bought the shares and they would have continued higher so in this case here you saw a bullish divergence on the rsi okay so now let's talk about how we spot a bearish divergence using rsi again in conjunction with share price now i was a part of this trade where i had a book profits on the gdxj which is a gold mining etf symbol gdxj now the gdxj put in a high on rsi july the 22nd it consolidated sideways then the share price broke out and while rsi did move up higher it failed to confirm the higher high versus the 22nd now that's no reason to sell however it's reason to keep a close eye on whatever you're trading the share price then dropped back down into what appeared to be a consolidation range rsi confirmed where things get tricky is right here we're going to remove this line where we broke out on august the fourth continuation move higher to new multi-year highs on the gdxj the only problem is is that when you take a look at the rsi to give you a pulse check on how momentum is all you need to do is take a crayon out and do this it was failing to validate we were putting in lower highs despite the fact that price was moving out to new higher highs and not only that what was worse was that we broke down to a new lower low here on the 29th that was over a week before ultimately the price of the gdxj broke down below support levels and went into a brief shallow correction so this is an example of a bearish divergence on the gdxj would i have gone short based upon this probably not however i would have avoided opening up a new position or adding to my existing position and what i did do on this observation was that we began booking some profits this is how you leverage identifying rsi to spot bullish and bearish divergences now strategy number three is the candlestick strategy meaning we're using rsi in conjunction with the candlesticks and if you'd like to see me do more videos on candlestick charting or rsi the stochastics or any other indicator or topic that you might enjoy please leave a comment below and i'll definitely get back to you especially if you have a question with regard to what i'm talking about right now now the candlestick strategy we're going to use the dba is a very powerful way to identify when a stock that has especially been in a long term bull or bear market is tired and a reversal and a change in trend is eminent so let's talk about how we do this now in this analysis we're going to use a little bit of what i already taught you as well we're going to incorporate spotting divergences into candlestick charting it all ties together and this is this is at the root of how i trade now the dba you can go back on youtube i've been talking about the dba for months now and it's been in the long term bear market this is a monthly chart so a lot of data here it's been in the multi-year bear market and we put in an ultimate low on the dba here the month of june of 2020 but the candlesticks we're telling you for the past prior months going back to march april may that you were seeing bottoming action these wicks shadows bottoming tails column cupcakes i don't care what you call them cupcakes again i know it's a thing i have so we saw bottoming action at the 18 dollar and 17 cent mark well that's interesting that's interesting to me you know what that tells me to do are we seeing anything interesting on our indicators because the stock had stopped going down now while we hadn't broken out yet you had begun to see rsi begin to drift up and what we're looking for here because i'm still watching it to buy what we're looking for here is an ultimate breakout so these candlesticks here mark indecision in the dba and when you see indecision as noted by this doji star formation or as i mentioned earlier these bottoming tails that's an indicator that the trend might reverse and after observing the price action on the candlesticks we wanted to take note of what is rsi doing and rsi was drifting up despite the fact that we hadn't yet broken out above resistance see how rsi drifted up here and then ultimately we broke out in august confirmed by rsi and while we're still seeing reluctance on the part of the dba to break out to new higher highs rsi is breaking out this is an indicator leading price performance now there are no guarantees me personally i would wait for the breakout on price but this is a very bullish indicator and this should be on your watch list is one to watch for that breakout and what i like to do this is trend spider i like to set alerts on trend spider this is my primary software for technical analysis in fact there's a 35 discount code below if you watch us on youtube the video description area 35 off or if you watch us on the website it's within the blog post immediately below and it's also included with our silver and gold level memberships but you must use the link below and the discount code c o n 35 to get that discount now using trend spider to set up our alert i want to know when the dba breaks out again above this resistance level a note to myself rsi bullish divergence long entry point and we're going to keep this active for a couple of weeks and all i care about is when we break through and close above monthly resistance and my alert is set and at the contrarian trader we take a rules based approach meaning i don't care what the price of dba does between now and breakout point unless of course it reaches and pulls back through the lower band of support so what i'll do here is set up another alert at the lower band of support we'll keep a little bit of sensitivity on now here's our alert i want to know when the dba touches or bounces off of the support level at $13 97 cents give or take a dime the key is though as noted by my note to self is that rsi must confirm and hold the breakout point right here in white it's okay to pull back test and hold on a pull back to support but ideally we do not break down below this support level if it does i would avoid the trade create alert so again this is our rules based approach using automated technical analysis as provided by trend spider and until one of these alerts are fired off we set it and forget it that's how you take the impulse buying or selling out of your trading and you become a very very powerful trader now the fourth strategy i want to go over is the bolinger band strategy this is a more advanced strategy and i wouldn't suggest it for those who are beginners i would strongly encourage you if you're a beginner rewatch strategy number one which is the overbought oversold module strategy number two which is the spotting divergences module and strategy number three which is the candlestick module now if you're an experienced trader and you're looking to develop new strategies for trading using both the rsi and the bolinger bands well this is for you now this is ideally suited for short-term swing trades or day trading and what you're doing with this strategy is that you're combining the power of the bolinger bands with the rsi now keep in mind that you're out of the box bolinger bands are two standard deviations do not use the two standard deviation what i use are the three standard deviation up and three standard deviation down and when you combine a stock that is trading above its three standard deviation bolinger band along with the stock that is trading above on rsi above my settings of not 70 upper but 80 upper you're dealing with a stock that is at unsustainable price points and that a pullback is eminent does that mean the stock can't go higher of course not yes it can go high it's a very powerful stock but you want to also incorporate in this your candlestick charts to identify topping action we're not there yet we're close and the symbol that we're going to use is cbio catalyst bio and you can see here that we have rsi greater than 80 it can go higher that however here is that we are trading and closed significantly above the third standard deviation bolinger band folks that are watching us in the future i can predict to you that this is not going to end well go back fact check me to make sure i'm right i have no idea i'll scroll this over there's no data looking forward that's how confident i am that while yes we may move a bit higher here that ultimately we are going to correct is it going to crash no it's not going to crash but you are going to get an opportunity to scalp a swing trade to the short side this is why i say that this is a more advanced strategy for those very experienced with trading and if you'd like me to do videos on the bolinger bands or on the bolinger band strategy please leave a comment below let me know what you'd like feedback on and i'll be more than happy to provide it i work for you you guide me and believe me i enjoy the heck out of it so thank you in advance now to give you an example of what i mean by this is not going to end well look at the last time that cbio had been at nosebleed levels as it is right now back in december of 2019 it pierced and closed above its third standard deviation bolinger band hitting a high of seven dollars and ninety five cents rsi on that date hit a high of roughly 80 not quite as overbought as we are at the time of this recording however it's it's it's good enough to illustrate what happens and you could see that after hitting a high on the 18th of december at seven dollars and ninety five cents it finally bottomed out here on january the eighth with a low of six dollars and thirty eight cents now think about if you were looking to buy the stock here and you're very bullish on the shares think about the power that you have at your disposal of being able to identify an extreme overbought level on bolinger bands and an rsi if you had that at your disposal you would have known listen this is not going to end well while i may not want to short the stock i'm good definitely going to hold off until the froth comes off and i'll buy it at a better risk reward entry point and when it hit a low what was rsi doing it was bottoming out and then validated the breakout higher that's the bolinger band strategy the fifth and final strategy i mentioned earlier and it's really what inspired me to create the contrarian trader back in 2005 and we have trained thousands of members on how to learn how to use technical analysis learn how to use discipline to identify opportunities in the marketplace with reduced risk and maximized reward and the strategy i put together was the contrarian trading style and what that combined was primarily the rsi at times the bolinger bands but in all cases candlesticks and volume and the mac d now the mac d is a lagging indicator however it validates what i'm seeing on the leading indicator being the bolinger band and primarily the rsi and as a final bonus i'm going to leave you off with a very popular stock and it's a stock that anyone over the age of 10 has definitely heard of but it is in an rsi bubble and i'm going to leave you off with that so stick around to the end now the first example of an extreme overbought stock which is what the contrarian trading style is all about finding extreme overbought and extreme oversold stocks to trade because the probability of once you have rsi greater than 90 the probability of it being sustainable for the longer term is unlikely you're going to get a reaction to the downside at some point in time i can do far more in-depth detail about this trading style if you're interested please leave a comment below but i don't want to get too in-depth with it right here right now because this is a video regarding the rsi primarily but this is an advanced strategy i would strongly encourage you to not trade this style until you've watched future tutorials from me because there are a lot of nuances on how to trade extreme overbought extreme oversold stocks using the contrarian trading strategy now example number one 10x genomics now oddly enough i was long of this stock not too long ago and i booked profits on or about here where we pierced the third standard deviation bologna ban so we left some money on the table as the shares continued higher but the market became shaky and when i see a stock begin to show me a profit and the market is a bit shaky i invoke my old traders adage first out best out and that's what we did here but in this case we left a little bit of money on the table but we are looking to short 10x genomics based upon rsi candlesticks and let's bring up the mac d and volume now my settings here on rsi to revisit are at 14 day rolling period upper band 80 lower band 20 not the upper band 70 not the lower band 30 as suggested to be used by the books written many many years ago remember we're dealing with rsi now 21st century we need to up our game that's why we're using these new upper bands and i hope that in in updated volumes of getting started in technical analysis which is a very very good book overall i use it to this day i hope that they update in the next volume overbought 280 and oversold 220 now here with rsi you still have rsi validating with a move higher that the shares are very strong the problem is is that what we did on the last trading day which was a friday is that we did break out but we faded along with the rest of the market and while this is becoming an interesting short we're not there quite yet but we're very close now to really drive home how out of date the 70 30 rule is for overbought oversold i'm going to adjust this i'm going to change 80 because i'm open mind to the fact that you know what 80 may be getting a little bit older now and we need to revisit it what about if we change this up to 90 take a look at where rsi is right now we are at 89 spot 89 on the last close and not only that we broke out the last trading day above resistance with rsi well above 70 well above 80 close to 90 this is why again i hate to keep pounding this home but the old way 70 30 rule out the window that myth is now busted i don't want to see anyone lose money now with the catarian trading style what we look for here is not only our primary primary indicator trading above the 80 mark what we're looking for are signs by the market that we're beginning to see price action weakened as you saw this past trading day we had broken out on that day but faded to close well off the highs of the day but that in and of itself is not a short signal we are close we're also looking at volume volume was very light on this particular trading day it was an institutional distribution that may resume the next trading day and we may see higher volume in a break of the support level that would have me much closer to shorting the stock if that were to happen and one of the one of the other indicators which is the lagging indicator which is the mac d what you're seeing here are i don't use the lines i use the histogram the mac d histogram what you're seeing here is momentum which is lagging waning it's declining these are the indicators that we must see declining up volume rsi well over 80 and price action beginning to fade and ideally on an entry day basis we'll use a 30 minute chart on an entry day basis to identify whether or not we're beginning to see signals by the market that we're beginning to put in lower highs and you can see here that we clearly are lower high and what's rsi doing breaking down to lower highs you can see how we close down below it and now we have lower lows so the intraday or the internals of 10x genomics indicate that if you are long you should be booking profits because the rsi on a daily time frame on an intraday time frame is sending you a signal to get out because you're probably going to see shorts lean into the shares and you're going to see some institutional profit selling in the very near term now i promised you that i would leave you off with an extreme over bought stock that everyone knows and it is a bubble and that stock is drum roll please microsoft that's right microsoft is in a bubble this is a daily chart and i need to use stock charts dot com which is a very good software as well but it's falling behind trend spider which again 35 discount code below because trend spider has so many more features now than stock charts dot com anyway i can get time frames on stock charts dot com that is soon coming to trend spider which i'm going to go through in a moment so you may be saying i don't see a bubble here bob what are you talking about the chart looks pretty good yeah it's it's it pulled back a little bit this past trading day on a friday but otherwise we're close to a breakout on microsoft and i won't argue with you however what we do here at the contrarian trader is we use multi time frame analysis meaning if i'm going to buy a stock like microsoft as a potential breakout trade i don't care about the daily chart than many do i care about the higher level charts meaning the daily chart is a view of the battlefield from a 5000 foot view whereas a weekly chart this is your view from a 10 000 foot view of the battlefield and you could see strong uptrend but it's this chart that begins to tell us a story not the daily chart keep an eye on the rsi rsi peaked out on microsoft the week of february the 10th it sold off due to covid rallied back and ultimately broke out to new all time highs there's a problem though as you fast forward to present day and you look back ever since february rsi has been putting in lower highs and now lower lows despite the fact we've been putting in all-time highs that is a bearish divergence we went over bearish divergences earlier but this is not why we have a bubble i'm not done yet we have more let's move back a bit let's go all the way to a quarterly chart now we're talking about the 50 000 foot view of the battlefield and this is where we enter bubble territory rsi and microsoft on a quarterly basis is at 92 spot 86 think about it microsoft a company worth over one trillion dollars has an rsi of 92.86 does this mean i'm going to short microsoft on the next trading day not yet i'm going to wait for the break the charts the price action rsi stochastic they'll tell me when it's time but what i'm doing is i'm using the quarterly chart and rsi as a qualifier it gets better let's further validate using what i taught you earlier the bolinger band strategy on a yearly time frame now we're talking about 100 000 foot view of the battlefield on a yearly time frame we have rsi at 93 spot 76 all-time highs and probably one of the most overbought stocks on the universe and when you take a look at the bolinger bands the bolinger bands i mentioned earlier that out of the box you have a two standard deviation bolinger band that's in green i use a three standard deviation bolinger band that's in red we have traded for the past four years above the third standard deviation bolinger band folks we're not supposed to be here microsoft is a bubble so that i hope that if somebody's watching this a year two years from now you're looking back saying my god the guy was right if you're if you're watching this two years from now this is october of 2020 please leave a comment saying whether or not i was right so to summarize we've covered what the relative strength index or indicator is we've dispelled some myths those myths are 70 is overbought and 30 is oversold we've also dispelled that changing the length of time from 14 to a different time frame does not give you an edge over other traders if you shorten it it becomes too noisy if you draw it out too long as i displayed in a 250 bar time frame it's way too slow for you to react so it does not give you an edge over other traders stick with the 14-day time frame we've also dispelled that when you see the rsi begin to decline that it's a cell signal that's not accurate it could be entering a consolidation range and that you should watch the candlesticks to see whether or not the shares are bottoming out and poise for a new breakout hire because the rsi is in fact a forward looking momentum indicator it generally trades along with the price and with any oscillator as price pulls back so should the oscillator as price breaks out so should the oscillator strategies that we've discussed overbought and oversold i've shown you how to use 80 is overbought you'll need to manually change your settings to 80 and change the lower band to 20 on your charting software if you're using trend spider it's very simple simply click on properties and you can change the settings in here again 35 discount code below in the video description area or on the blog page use that link and the discount code spotting divergences is a very powerful way of identifying whether or not a stock which has been in a long-term downtrend or in a long-term uptrend may in fact begin to see signs as noted by the rsi of beginning to break down or break out prior to being reflected on the share price strategy number three is the candlestick strategy and that's where we combine the power of candlestick charting along with the rsi and you should use that on every single trade that you make without exception the fourth and more advanced strategy that's the bolinger band strategy that's for more short-term swing trades and as i mentioned earlier if you want me to do more videos on indicators trading please leave a comment below let me know what you'd like for me to do and the fifth and final the contrarian trading strategy is the one that i developed going on 15 years now and it's one where we take advantage of short-term extreme overbought and or oversold levels and in the case of microsoft it's not even a short-term overbought condition it's more of a longer-term bubble where i think there's a lot more probable downside than there is upside on microsoft going out a few months and in closing folks i really appreciate you watching all the way through this video i know it's been long but i hope you got value out of it and if you did if you could help support the channel if i could ask you to smash that like button share it with a friend leave a comment below i'll definitely get back to you and please join us every sunday and thursday night as we go live six p.m eastern standard time generally i'll take chart requests from the audience after i go through the requests that have been submitted by members if you're a member of the contrarian trader normally we only go in about an hour but if you're a member i'll stay on until 10 o'clock at night to go through your symbols so we'd love for you to become a member by joining our 14-day free trial offer where you get transpired for free and until the next video everybody have a profitable trading day and week be well


Okay, it's 5.15, so I guess I will call the meeting to order. I didn't know if someone else was going to. But I'm Sandy Payette, and this is Mohamed Javid, and we are going to speak about a project at Cornell called Scholars at Cornell. And the subtitle here is Visualizing the Scholarly Record, and the Viz is kind of our little, kind of potentially maybe a sticker or something could eventually come out of it. So this is the origin of this project. It had its roots in the Cornell Vivo initiative, John Corson-Reichert, the original founder of Vivo. Cornell had the original instance of the Vivo, full installation at Cornell. And we over the years learned a lot at Cornell. I'm new to Cornell University Library again this year. I had been there previously, as you know, at the Infroscience program and department. But now I'm at Cornell University Library. So I'm the director of research IT for scholarship, and this project was kind of reconceived as how can we take Vivo at Cornell into the next generation of what this space means. And so basically, in a nutshell, what we're talking about here from what we're delivering here, data, people, publications, and organizations based on the Vivo ontology exposed as open link data. But we're focusing on this notion of the graph of knowledge. You know, Vivo has many things, and Vivo has been very successful, and is now under the door space umbrella. But we're trying to take a different twist on this at Cornell, and I'll explain why that is. So again, we're really focusing on what can the graph of knowledge of the scholarly record essentially, what can it tell us? What kinds of latent knowledge exists in that graph? So, you know, some of the common questions will be what are the hot research areas at Cornell University? What are the patterns of scholarly collaboration that the scholarly record itself can reveal for us? Who are experts in what areas? Who are co-authors in which areas? And the Vivo project has done a lot of this over the years, but we were turning our attention to visualizations where we really wanted to kind of explore how the semantic knowledge base could meet with the dynamic web. So that's really gonna be the focus of this talk. And the reason that we have reframed and refocused our Vivo work at Cornell is because since Vivo first came out, the landscape is really exploding in terms of people getting a piece of the game here.  And we have the whole Elsevier Pure product. We have symplectic elements, which we are actually using and partnering with. You have all these other players, ResearchGate, academia.edu. Many, many players are kind of entering this space of what you might call research, information management systems. In fact, Lorcan Dempsey noted this several years ago, observing this landscape and reflecting on where's the library's role in this emerging landscape. So that was the question that Javid and I and the team really were setting out to answer. Like, what is the special thing that we at Cornell University Library can do in this space? So again, we have all these players and we have to figure out how we position in this landscape. The other thing I recognize is this notion of research profiles, they're everywhere. I mean, there's like this proliferation of profiles. We started with vivo profiles. There are orchard records, not technically profiles, but they're profiles, even though they're more data-oriented. You have the Pure Isles, you have Pure Profiles, you have the Scopus Profiles, the Mendeley Profiles, the symplectic element profiles, the Open Scholar websites, the Research Gate profiles, the NIH profiles. And as a scholar, where do you begin? And I do wear two hats because I have my scholarly hat in the Department of Communication at Cornell and my librarian hat, but it's like, where do you begin? And so the question is, how do we start to observe this space that is kind of getting a bit unwieldy and yet we're talking about interoperability and linked data and knowledge emerging as this great knowledge network where everything flows and everything's interconnected. And we have a lot of great work going on there, but we're just almost still pretty siloed in some ways with these research information management systems. So again, how to make sense of all this? Is this just the messiness of an emerging knowledge infrastructure? Emerging infrastructure is, by definition, as history will tell us, can be very messy as different players enter the game and things settle down and there's redundancies and some winners and some losers. So yeah, I think it is an example of emerging scholarly knowledge infrastructure. Are these different players focusing more on an institutional perspective around faculty reporting and metrics? Or are they focused on an outward knowledge for the public good, knowledge for everyone, open knowledge, proprietary versus open data? There's tensions there, commercial versus open source versus hybrid technology. So in the technology dimension of this, there are questions. And this notion of, you know, there's a tension between isolated systems and interconnected networks. And so, you know, where do we position? Well, definitely we're in the hybrid network kind of paradigm. Again, thinking of it as scholarly infrastructure, but really trying to think of the piece that we're doing, which is on the far right there, the scholars at Cornell service, which we're kind of conceiving of is more of a data and visualization service, unlike the traditional Vivo perspective. But we said, okay, we're in an ecosystem here and there are upstream data sources that have citations to the scholarly record, Web of Science, CrossFit, PubMed. Then you have a player like Symplectic with their elements product where they basically, you know, have feeds for all of these different upstream sources. Then we also have institutional data in play here. So basically what we bring to the table here with scholars is what we call the scholar's feed machine, whereas basically through this system of systems we will eventually get an automated stream of the scholarly record, okay? But that's not enough because there's tremendous amount of inaccuracies in the data, missing data, incomplete data, even coming from the supposed pure sources upstream to layers up and in the intermediary layer with a product like Symplectic elements that aggregates it all together. But it's still not clean and it's incomplete. There are all kinds of anomalies. We spend a tremendous amount of time working on a piece of code and algorithms that cleanse this data and we try to push as much of the curation through an automated stage but there's always more to be done where manual curation is necessary. So the big point here is the data quality is key to this. And so as the library, we say, what's our special sauce? Data quality, okay? So this, we can do something special in terms of getting a really best of the best kind of record of the scholarly record at Cornell, that is, and we use a Vivo-based internal engine, basically. But then the other big piece that we contributed was how do you bring this data to life and answer real questions and do it in an intuitive way? And that's gonna be the focus of our demo. So how do we motivate this project? Again, we're denying the messiness. We're hanging in there and saying, no, we contain the messiness. We're taking a public perspective on the data unlike some of the pure or Elsevier, focusing more on the institution's needs for reporting and faculty evaluation. Where are the library? That's not our role. Our role is knowledge. So we're taking this public perspective of Cornell's output, which is knowledge. We're taking open data perspective. All of the data that we have will be made open through linked open data in other ways. We're taking a hybrid technology, which means approach, which means we're open to open source and commercial systems coming together to create the best architecture possible for what we want to achieve here. And we assume interconnected networks is the world we're playing in. So in summary, we're kind of starting with a Vivo-based backend taking advantage of the excellent Vivo model, the ontology. And we're building a fresh front-end experience on it with these D3 visualizations. And so basically it's like the best of both worlds is the proposition here where at the backend we have authority records, we can have inference, we have links to external knowledge sources, open access. And on the front-end, we're looking for as much as possible fresh looks and feels, data downloads, moving away from the kind of list views that you often see, there are directories of faculty members, directories of institutions to a more dynamic interactive view. So with no further ado, I'm gonna have Javid show you this because I think the most fun part about this is actually seeing the demo. So I would found an institution where any person can find instruction in any study. That's what Eza Connell said 150 years ago. And that's what Connell is right now. We can find a domain experts in many fields. And if this is so, that means the scholarly contributions of these domain experts also vary significantly. They range from general articles, books, conference papers to newsletters, scripts, reviews, performances, play, and so on. So scholars at Connell's Supreme goal is to record most of these scholarly contributions but for the start we focus on the easy ones, the publications.  So let's go to the main website. So this is our main website, scholars at Connell. As Sandy mentioned, it's about research and scholarship across Connell. And if you look at current Vivo instances, you will see lists everywhere, persons list, organizations list, articles list, grants list, all kinds of lists. And we want a list of good to browse the data, to navigate to the data. But we want the scholars at Connell to be used to discover some data as well. Finding a domain expert by a specific subject area or research interest. Finding a specific article based on a specific subject area with high global impact. Discovering ongoing research grants on a specific topic based on the investigators, based on the funding agencies. We are also using realizations which not only help to navigate through the link data, but they also are useful for demonstrating the implicit knowledge, which you can't see in the list views. And all this data behind these realizations is downloadable. So we are working with specific pilot partners at Connell right now. One of them is College of Engineering. So I will just go, I will just use this list view to go to College of Engineering's page. And I'll select a specific department, biomedical engineering. So in the right hand panel, you see the list, faculty list. You see the list of grant. But in the left-hand panel, we call it this panel, you see some realizations. So these realizations actually are emerged by our continuous discussions with the pilot partners, what their needs are, what kind of information they want. And one of them was to demonstrate the research interest of our faculty members. So we have this person to subject area realization for that specific department if the network's okay. So in the middle, you see the list of faculty members for that department. Across it is the subject areas. So there are two specific questions we have asked, we have heard a number of times. What is the source of the subject areas? And how did you identify this person to subject area mapping? So the answer to the first question is that these are Web of Science subject areas for now. And to find the mapping faculty to subject area mapping. There are three different ways which I know you can do so. First of all is that research faculty member by himself and say, okay, these are my research interests. Second option two is that somebody curate this information for faculty members. I know some people at Cornell, they're doing it. They go to look at the websites of the department. They look at the profile pages. They look into the resumes of faculty members to find the research interest of a faculty member. Option three is that let the data speak. Let the scholarly contribution data speak. And that's what we are doing over here. So Web of Science categorized the journals based on this subject area category. And all the articles which are published in that journal get the same classification. So what we say is that if the articles which are published in that journal get the same classification, can't we infer that the author of that article also have some research interest in the same area? And of course these subject areas are quite broad. Surgery, physics, physiology, pathology and so on. So we can infer this information from the data itself rather than focusing on manual or creative process. So if I hover over the faculty members, you can see in which area they are publishing. You click on that, you see the specific faculty's view. You can also see, okay, who else is working on the same subject area. You click on subject area, you see the subject area view and so on. The same visualization can also be seen as a subject area to person mapping, okay. You can see who is publishing in surgery. Maybe they are the potential collaborators. Maybe already worked together in a collaboration with each other. So if I select here oncology, I see okay, there are five faculty members who have published in that area. And if you want to know a bit more about a faculty member, you can go further ahead. You have to take a one step deeper here. So there's a link off the faculty page. I click there. I go to the faculty members page, okay. So Mr. King is a professor in biomedical engineering department. So in the, again, right hand side, we have the list of applications. But in the list panel, we have this keyword cloud. So normally in the articles, there's a specific section called keywords. So we have aggregated those keywords together and present them in a form of keyword cloud. So it's not only a fancy visualization, but it helps in two ways. First of all, it can be seen as a fingerprints of a faculty member. You can see, okay, he's working on cancer, circulating tumor cells, breast cancer, and so on. It can also be used to filtering the articles of a specific faculty member. For example, I can see what articles he has published on cancer. Click on that. You can see the list of the articles which he has published on that specific topic. Okay, cell deformation. And so on. So, okay, now if I select any one of them, I can go to the articles page. So just moving from one page to another page and it's navigating through the link data. Okay, so on this articles page, I would like to show three main things. Okay, we have this authors list and the journal in which it is published, citation count, volume, published year, and so on. But we also, okay, so first of all here is our traditional metrics, citation count. But if we also have this here, we have automatic donors to see web-based impact. And we have noticed here is that as soon as the article is published, there's no citations which comes up very quickly. But this automatic donors can show the web-based impact of that article straight away. I've seen some articles which are picked by 13 news outlets, but there's zero citation count. So this citation count is not only metric, you can apply on a journal article and so on. We are part of library, so wherever possible, we also linked the journalists to the library catalog page for the same journal so a user can go and see what volumes and issues are available and he can learn specific issue. And wherever we have the DOI, we have this link here to the article page, the full text of the article so somebody can go and get the full contact of the article. Okay, so I'll go back now. So I showed you a department page, a person's page and article space. So let's go back to department level. No, maybe I'll just go to college level. Okay, so another need which we had, we have listened a number of times from our partners about collaborations. Who is collaborating with whom? How often they collaborate? So we have these two civilizations here, interdepartmental collaborations which shows how the departments within one same college are collaborating and cross unit collaboration. So we are at the college of engineering page so engineering goes in the middle and all the colleges around it we can see here. So if I click on art and sciences, now we're just drilling down within the same specific college. So engineering to art and sciences and now we have the faculty departments of art and sciences. You can see okay, who is publishing in that specific department. And then you can see the list of the faculty members who are publishing with collaboration with somebody else out of the college of engineering. Click on that, you can see the output of that collaboration as well. So we can just move once colleges to another college and you can see okay, who is collaborating with whom and how often they collaborate and what is the output of that collaboration. One more thing again, the data is downloadable. So all the data behind this realization is downloadable. It's in JSON format, you just take it and off you go. Okay, so let me go back to the home page. Okay, so we use this realization again, same thing like navigating to the link data and also showing different views, some specialized views at department level, college level, personal level. But we also saw a need to show our Cornell wide view. So we also have, we also have realizations at the home page level. One of them is the research grant realization that shows all the grants we have at Cornell. So it's a big data. And if you hover over, you see the title of the grant, click on that, you see all the title, investigators, funding agency and so on. But main thing over here is that you can filter it. You can just click here, say I need to see the grant from basic Mary. Okay, and here you go. You see the grants, somebody is working on. Similarly, about the funding agencies. Okay, I want to see who is publishing, who is working in the cancer domain. Okay, here are the grants from National Cancer Institute, NIH. And also we can also, of course, we can also filter them based on the activities and so on. So you can see the grants from the weekend. Okay, so now let's go back to the slides. So I would like to highlight two specific things from this demo. One was the VisVivo, the front end. Other was the utilization, the backend thing, which you haven't seen because I'll show the UI thing when the main thing is there. Okay, the first about is the VisVivo. So if you look at the current Vivo implementations, it's based on the Vivo ontology. And this ontology, this link data, these are all the models to, they are all the technologies to model the data. They are backend things, but in current Vivo instances, the front end and backend is coupled very significantly. And in Scholeset Cornell, we are separating them about till a certain level, okay? And that's how we can use the dynamic web technologies like Bootstrap, D3, Node.js, and so on. But again, one more thing is that we're turning data into a knowledge, okay? So if you look at that, this citation data, you can understand, okay, one person who is affiliated with a specific department organization, he has co-authored another article with somebody else. And article is published in specific journal. But when you aggregate those citation data together, you can show some real good knowledge there. Who is collaborating with whom? How often they collaborate? The fingerprints of a faculty member, the domain expertise, the research interests, and categorizing the research grads based on a specific subject area, and so on. So that's what we're doing here. And second thing is the utilization channel. So as Sandy mentioned, we are using elements as a backend thing to record this all the citation data. And elements goes on the web, harvest the data from different upstream sources, and gets the data into elements. And in elements, you define, okay, one of them is my preferred source. For example, in this example, PubMed is my preferred source. So the next step is how you get the data out of elements and convert it into a linked open data. If you use the provided harvest API from elements, you get only one viewpoint. They give you the citation data entry for your preferred source, and that's it. That's not enough for us. So what we do is that we make this data and pass it through our utilization channel, okay? So all this data coming from web of science, PubMed, also whatever source that we have for a specific article, we merge them together into a specific one single record called Uber record. If that Uber record is clean and complete, it can go straight away into scholars at Cornell, converting to an RDF. If it's not clean, then we have to make it clean, make it consistent, so we can use the creation bin. Now this creation bin can be used to clean the data, as well as it can also be used to add additional information on that specific thing. For example, we can add the arcade IDs for the authors. We can add the great IDs for the organizations and so on. So in the end, we are giving, in scholars at Cornell, we are giving the best of the best scholarly record. And that's what the way the value is actually. And we have, in the future, we plan is that if we push this best of the best scholarly record back to the simplistic elements. So now I'll just move back to Sandy. So Sandy will wrap up. So when we set out to do this, it's really in February when we really started this. And we had a number of really key questions. The biggest challenge we were facing at Cornell was that it is a highly decentralized university, and particularly around things like research management. Basically, every department has the right to choose what they want to do to manage their research information. There's a bunch of departments using activity insight. There are a few of them that were actually using Vivo Raw. Using that as the data entry as if it were a full research management information system. Pure has been on campus, has not gotten traction yet, but symplectic elements did. And so we actually are working with the engineering, College of Engineering, the Johnson School of Management, which there's a prospect to have the whole college of business involved, and that. So we have a set of pilot partners. So we really had to approach this as a pilot with those that wanted to go this path with us to see how much could we achieve through this automated feed of the scholarly record. A lot of manual entry was going on in a lot of departments, and it was really kind of a, to tame this was kind of an overwhelming university-wide initiative. The provost's office does not mandate a certain system of record for a scholarly output. The Office of Research doesn't either. So I mean, the question was for us, can the library nudge a university-wide bottom-up coordinated process around managing faculty data, managing faculty citation data, grant data, faculty profiles? And so that's a question, it's still a question. So we're trying to kind of create a system that is compelling enough that when we unveil these pilots over the next six months that more people will be like, ready to come on board. The provost's office is now quite intrigued. A lot of people are getting the word about this demo and what could be possible here. So this is an attempt to have the library do something that the library is not empowered to do to kind of try to get a coordinated response around managing research information. But then you'd have to ask, well, what is the role of the library? What is the role of the academic units? What does the provost's office and the office of research think about this? And these are open questions. Probably I would say this is the classic socio-technical system as a project where the socio part of this in terms of the people, the organizations, the culture, the politics, that is the big challenge here, okay? Less so the technology, because we've been able to do some really cool stuff in a quick amount of time. So data quality, this is the issue here. We took the stance of how much automation can, how far can we push it? How much quality can we infuse into this data through algorithmic means? And a fair amount. I mean, we really have done a lot of remediation of this data, missing ISSNs, journal names that are, messed up in all kinds of authorship problems. I mean, all kinds of duplicates. So we've done a lot, there's still some records that for the College of Engineering, for instance, I mean, we're in the hundreds of records that we have to have manual curation on, which isn't good because it used to be in the thousands, you know, the high thousands. So we've been able to make real progress on this automated curation thing with the Uberization process, the Uber records. Again, this notion of taking the fragments of the various upstream sources and pulling the one that has the best title, the one that has the best journal metadata, the one that has the most accurate author list and kind of mashing it all together and then doing some additional remediation by calling out to authorities in the process. So we've made some really good progress there. Another one of the challenges here in OpenQuestions is, you know, who is the user? I mean, we have many user stories here. Currently, we're engaging at the department chair, university administration level. There seems to be this real need where the questions they'll ask, you know, how many people have collaborated across, you know, the sciences and the social sciences around, you know, social entrepreneurship or, you know, things, questions like this.  So they are really interested in the kinds of things that we can pull out of this knowledge network, this semantic network and the visualizations making some of their job really easy, where it was formerly very manual. The university communications department seemed to love this. I don't, you know, it seems to have this kind of outward facing appeal, you know, of look at what Cornell is doing and you can interactively explore it. So interestingly enough, the departments of communication and outreach seemed to like this. Not surprisingly, the faculty view this stuff is kind of a burden in a way, you know, these research management information systems and they just as well would have administrators put the data in. However, faculty would love to have their websites fed by a clean data source. So when Javad said, every visualization we have, you can get the data through in, you know, all the standard formats, JSON, RDF, XML, and even CSV, whatever. And we're gonna open up the number of formats with the idea that the, you know, faculty websites could use this source as, you know, a trusted source. And, you know, again, this external view of prospective students and faculty that has always been something that Vivo has appealed, had an appeal for, people coming to who's in what departments at the university. But interestingly, we're seeing the most traction right now at this kind of higher administration level and communication for, you know, public views of what's happening at Cornell, you know, what's hot at Cornell. And then the other main question is, what is the investment to sustain this? I mean, one of the big questions we have, and we're finally in a position, we just created a human curation tool. What does it take for a human to curate the bin?  Some people have indicated, the symplectic element people have indicated that a lot of libraries are actually stepping up into that role of the data curator, but is that the department's role? Is it the library's role? Who's is it? How much effort is it? So really we're trying, you know, this month we're gonna do an experiment in terms of how much does it take to figure out what's wrong with a record or why it's a duplicate and to fix it and, you know, let it go into scholars. So, you know, these are the kinds of questions that we're working on. This is a phase one. We just wrapped up phase one, which ran from February to the end of the year and we're gonna do phase two with the idea that we will launch this in July. So, to wrap it up, this could not have been done without the work of an entire team, including our predecessors, which would be John Corson-Reichert and Kathy Chang and several other people, but to me it's a great example of how many pieces of the puzzle need to be done really well at the back end, the front end, the human process, the coordinating with the vendor, of coordinating with the departments. So it really took a whole team to pull this off and so we have a Twitter thing, but we also have kind of a virtual brochure. If you go to about.scholars.cornell.edu, there's kind of a video and a few other things and you can actually, for a limited period of time, we're opening up the demo instance and here's the credentials so you can play around with it and since we're still evolving it, we're gonna leave this open for only a limited period of time but then people of course can contact us always. So, I'll end there and we can take any questions that you have.