From around the globe, it's theCUBE with digital coverage of Women Transforming Technology. Brought to you by VMware. Hi, this is Lisa Martin covering the fifth annual Women Transforming Technology, WT2, from my home in San Jose, California, because this is the first year that WT2 has gone digital. Very excited to welcome next one of the speakers on the executive track. We have Charmaine McClary, president of McClary Group, but also author, C-suite advisor keynote speaker, Charmaine, so nice to chat with you. An absolute pleasure. Thank you for having me. So you have an incredible background. You have been for two decades working with leaders in, I read, 27 industries, five continents, and from some pretty big well-known brands, Coca-Cola, Johnson & Johnson, my particular favorite Starbucks. Tell me a little bit about your background in your career and how you came to be working with potential leaders. Early on in my career, I was working in politics, actually helping politicians understand their constituency and then how to communicate effectively with them. And then went on into marketing. And really what I say is that what I do is a conglomeration of all of my life experience, working with leaders, either in politics, in marketing, in sales, in a variety of industries, including gas and oil. And coming together and helping them understand how to communicate their message effectively, how to have executive presence, and ensuring that they are seen, heard, and remembered. That's one of the things, sorry Charmaine, one of the things that, talking about being remembered, especially now during a crisis that nobody has ever experienced before when there are so much, so much concern and so much uncertainty. I read that you said effective communication is more than just words and phrases, especially in today's climate, what is effective communication? Effective communication is making sure that people hear your value, your value proposition, and that is really essential.  There are a number of competing priorities and what you want your organization to understand is what is it that you see that others don't see and that is a part of your value proposition? How are you going to help your organization innovate through this time? And wanting to do that is really speaking about what is of value? What is it that is going to make the difference for the organization today with this crisis and that will also take it further into the future? So tell me a little bit about this session that you did at Women Transforming Technology the other day, 35 minute interactive session since everything for this year's event was digital. I love the name of your session. Speak up, stand out, be heard. Talk to me a little bit about when you first learned maybe last month that this event was going digital, did you change anything? Were there certain elements of your expertise and your recommendations that are now more, even more important with respect to visibility and value? Yes, so what I changed in was I really wanted to make it as conversational as possible because in this isolation, it's easy to not feel seen or heard. And I wanted people to be able to elevate again their visibility and their ability to add value. So a couple of things that people can do is they can actually rewrite their narrative if they need to. Meaning, I believe that if you do not define yourself, others will and their definition will inevitably be inadequate. So if you know that you are seen as a very quiet person and a person that is in the background and you want to have greater visibility, this is a great opportunity for you to rewrite that narrative and make yourself more visible. Meaning, the expertise that you have or again, the insight that you have, making sure that you bring that to the table. You can do it in a number of formats. You can do it not only on a Zoom call with your colleagues, but you can also, your email is heightened if you're using language, I said the language of leadership, language that really perks people's ear and that creates a visual. So now what you want to do is really make sure that you're using language that is very vivid and allows a person to touch, taste and feel what it is that you're saying. So that's one of the things that you can do. The other is that I say is that I want to make sure that my clients are not well kept secrets. I want to make sure that in this time of isolation that they're finding opportunities to reach out. So most everyone is at home, sheltering in place. So people have more time on their hand in terms of reading your emails. One of the research has found that there is a 26% increase in say your newsletters being read, your emails being read. So now is the time that you could actually heighten that kind of communication. That's fascinating. I love that you said about making what you're communicating in an email, maybe it's even text or over something like Slack, vivid. Say somebody has a great idea and I think, all right, so terms have changed, my job function is different or it's challenging to complete certain tasks. Give me some words that you think, since now you're saying people are actually focusing more on reading what you're saying. What are some vivid words that I could use if I had say an interesting finance project or a marketing project that I wanted to raise the visibility of and get someone to really kind of feel what I'm looking at. So when you speak about a finance project, one of the things that you want to do is think about what is a story that can articulate those numbers, that can tell the story with those numbers. So if you were saying, let's just make it as simple as possible, two plus two equals four. Well, what you want to think about is, what is it that is going to be different when you finish this project or what is it that's going to be, that's going to shift in the marketplace? And so you want to create that visual of what does the future look like and using examples of things that are very basic to our life today as opposed to using really complicated language. Now is the time to have your language simple, have it very clear and have it very vivid. So- I'm glad that you brought up, go ahead, sorry. No, please, go right ahead. I'm glad that you brought up simplicity because so often I think people think maybe I'm managing a project or I'm creating a methodology. And I think it's really, it's just, it's simple. But we often second guess ourselves because I think, and I've included in this, a lot of folks think it can't be that simple. It's got to be more complex. I need to show, you know, like an episode of I'm picturing an episode of the Big Bang Theory and Sheldon's talking about string theory. You need to make it complex to show your value. And sometimes it's the simplest methodology the simplest way of communicating that is the most effective. Do you find them that sometimes folks, regardless of their level of executive-ness are challenged to really step back and look at the simple way to communicate or the simple answer? Absolutely, and simplicity is best, whether it's during this time period or even beyond this pandemic, but particularly now. So I don't know if anybody's ever seen the show, the marvellousness is, I think it's made out. Yes, it's made out. And one of the things that she asked her husband, she goes, well, honey, what do you do? And I think in the first episode, and he says, you know, I sign papers, I do this, I do that.  And I remember an incident with one of my clients and I asked her, what does she do? And she gave me her job title. And I said, okay, how many people work at your company? And she said, 49,000 people work here. I said, how many people do you think have the same title as you? And she goes, well, you know, I'm sure at least a couple of thousand. I said, yes. So what distinguishes you? And so she wanted to talk about the title, which is like talking about acronyms at a company. And I said, so really what do you do? And what we realized is that what she does is that she was responsible for the fastest growing market segment in her company. That articulates her value proposition that made a very vivid and brought it to life. So people were able to understand when someone asked me, what do I do? I don't say that I'm an executive coach because you may have read an article last week that says all executive coaches suck. That defines me. I want to define myself. My value is I help smart people get promoted. When I get promoted, they communicate the big picture. So I help smart people get promoted and communicate the big picture. I provide executive coaching to senior level executives. I articulated my value. You know who I work with? They're smart people. They're not smart. They're not working with me. When they work with me, they get promoted. Why? Because they communicate the big picture. Really simple, one sentence. So what is the value?  And you, I was looking at your website. You have a 98% success rate of folks that have worked with you that had been promoted within the following 18 months. What are some of the both hard and soft skills that you were looking for when you select clients to work with that demonstrate they are ready to be in the C-suite? Well, there are a couple of things. One is the person has to be open and willing and not being volunteered by the organization, meaning saying you have to do this. If it is mandatory that someone work with an executive coach, that's not a winning proposition. The winning proposition is the person is open and open to change and ready to make change. As I say to my clients, if you want everything to remain the same, I am not the coach for you because you're going to see change and you're going to see significant change. So that's one. The other is preparing your organization for the kind of change that's going to take place so that your organization begins to see and hear what you're doing different. So for example, I would say to a client, if you're prepared to really step up and make the commitment to making this shift, you want to let people know what kind of shift that you're making so that they can begin to look forward. People like to look for success. They like to be able to reward you when you're successful, but you've got to let them know that you're there for that shift. So that's one of the things that's really important is that people be open to it and that they be ready to take their spotlight. If you want to do it and remain behind the curtain, that's wonderful. This is not the work for you. It requires a little bit of vulnerability then or maybe a lot of vulnerability to be able to do that. That's not easy unless you're a Brené Brown fan like I am. Talk to me about, especially in this time with COVID-19, the uncertainty in every aspect of our lives, every single aspect is it's dense and it's an emotional challenge. So do you find that it's harder for some folks, whether they're men or women, to do what your title says, speak up, let them know. I'm coming, I'm on my way. How are you advising folks from a psychological perspective to be able to do this? Well, I think that there are a couple of things. One is that with the three questions I ask every client and those three questions are, one, how do you see yourself? How do other people see you? And the third is how do you want to be seen? So when you're able to become introspective and answer those questions from the heart, from your heart, then you can get really clear about what you want the world to know about you and how you want to show up and what does require vulnerability. It requires you to look inward first for you to make that decision on how you want the world to see you. And then once you're able to make that, get that clarity and instead of this process, getting that clarity, then you can speak about that to the world. My thing is, again, if you don't define yourself others well and their definition is inadequate. So when you define yourself, and you know who you are and what you stand for, you can then shout that at the top of your lungs, but you don't really have to because your actions will speak very clearly about what it is and who you say you are and how you want the world to see you. And you're always asking yourself, am I congruent? I love that about defining yourself so that others don't do it incorrectly. Talk to me about how somebody can develop their own communication style. What are some of the steps that they need to recognize? For example, if you see someone and you think they're too bold or they're too brash or maybe dial it back a bit, especially because messages are getting read more now, what's that process internally that I would need to take to develop an effective communication style? What is it that you need to do to develop that effective communication style? One, as I said, being able to define what that looks like for you and what that is may not be appropriate for every organization and every corporate culture. So you need to make sure, either evaluate whether or not you're in the right corporate culture so that you can be successful and or find a new one so that you can be successful. Once you have that, really helping the people in your organization to make it easy for them to come to you. So by extending yourself first, that is one of the things that I would say would be really important in terms of stepping up during this timeframe is saying, I feel really, this is really, let's say someone has been felt really shaken by this. I feel really shaken by this, but I am determined to leverage this as an opportunity to really show up as my best self and show my greatest humanity. And I think that when we let people know where we're going and where we're headed, it's far more easy for people to support you and to provide you with the venues in which to exhibit who you are. This is a great time for you to volunteer as much as possible to have that visibility because I think one of the questions you'd asked me earlier is, how do you become comfortable with this? You get comfortable with it by practicing. Lady Gaga says we're born that way, but we aren't. The only way that it happens with people that are really successful is because they practice. Something that is so interesting is during this time in particular is getting, is accountability, right? It's so easy right now, more than ever to lose accountability. And I like that you said, and that's what I'm hearing when you say, let people know the direction that you're going in. I think for the person, you set that, okay, I publicly said this. I need to hold myself accountable so that I deliver. I think there's a lot of power in that. There is. And when you step up and articulate to the world who you're about, what it is that you're going to deliver, your level of excellence, you hold yourself accountable because the person who is most important for you to be accountable to is yourself. Others come second actually, sort of like being on the airplane in the mask.  And so stepping up to that is so much, there's so much power. And I believe that people provide you with a lot of grace when you do that and people know they can count on you. And that's so important. Knowing, demonstrating your dependability in any situation. Shermaine, I wish we had more time. It's been such a pleasure talking to you. Thank you for sharing your insight. I am going to be visible, show value and be vivid in communication and accountable. Thank you so much for joining me today. Thank you. Have a wonderful day. You as well. And for Shermaine McClary, I'm Lisa Martin. You're watching theCUBE's coverage of the digital version of Women Transforming Technology 2020. Bye for now.


Welcome to Access to Trader, the number one community for those who are committed to taking control of their trading in order to achieve success, profitability, and longevity. Thank you for joining us. Here's Dan Shapiro to help you find your edge, master your process, and own your future. Hey, guys. Good evening, everybody. Welcome to another edition of the Access to Trader.com nightly wrap up show. Hope everybody is doing well. So crazy day, right? I think it's the best way to describe it and just really, really just a crazy exaggerated day today. This is kind of like a telltale of two different markets right now. You have people who don't care. Okay.  And again, they're kind of right because again, I've been saying this for years, indexes mattered 15, 20 years ago. Okay. The idea that the market could be down 300 points and there's absolutely no fear. We'll get to that in a second, is valid, right? On most days, it's valid. But again, we've been talking about something for the last, especially a couple of weeks. That's very, very important. And it actually kind of played out today. Now, again, we've been talking about how I think a lot of the new traders are very, very comfortable, right? They don't care. They'll chase anything in sight. SPCE, Tesla today's stamp today, SDGRO actually over the last couple of days. So the idea that the market is up down 300 points, 250, it's irrelevant. It's the fear of missing out that's being heightened because there is no fear. Because again, these ranges are so, so exaggerated, right? And you could tell when the stock is up and it just keeps on going higher, higher, higher. It's not the question of, oh my God, what's my risk if this thing kind of turns around? The question a lot of people are talking about these days is, well, can this thing get to 50? Well, the stock's at 13. Well, can you get this thing to 70? But the stock's at 20. So the expectations are very, very lofty. And now we've been talking about kind of covering this in nausea that every single day, I wake up and I go, my God, the queues are 20%, 25% now, whatever the hell it is, above the 200-day moving average. That's, that's insane. Okay, that's absolutely insane. So every single day, I'm always on the lookout, okay, everything. I'm always on the lookout of that potential evidence that, you know what, bits can go away and the, you know, the ice was going to crack underneath somebody's foot. That's obvious, okay. And today was a perfect example of what happens when you're arrogance and believing that you can buy anything on, buy the dip. And again, it's another subject we've been talking about for a long, long time that, again, buy the dip only works on a light volume retest on a rising 60-minute support. Nobody actually buys the dip, okay. Retail doesn't buy the dip when you're seeing a $30, $40, $50 candle within three, four minutes. We'll get to that in a second. But that's exactly what happened today. And you saw a lot of traders, especially new traders, talking about, no, every single dip should be bought. This is the easiest thing possible. First of all, there's nothing easy about trading.  There's always a curveball that will humble you to the point of tilt, okay. Not to the point of just giving you a little bit of hickey or giving you a little bit of wealth. It'll put you on tilt. And today was a perfect, perfect example of two things. How incredibly strong the market actually is. And let me show you that in a second. And what happens if you believe that any scenario that you can actually buy a dip with the index, right, 20% above the two-in-a-day moving average, and you feel there's absolutely no consequences to that. And you'll notice what happened here kind of in the middle of the day, right. You kind of notice actually what happened mid-morning.  If you look at the 60-minute views, one by one here, okay. One by one on the 60-minute views, Facebook and Apple, right, not this, but this candle here, and Shop and so forth and so on. These things are all a tribe. These are the only, you know, this is a group of about 10, 12, 15 stocks. They're all a tribe. When one goes, they all go. NVIDIA, you can go through the whole list, right. NVIDIA, all of them, right. They're all a tribe. And when one goes, they all go. And today was a perfect example of what happens when, while stocks don't go up, gravity becomes real. And if you are, quote-unquote, airing enough to think that any dip can be bought, especially again, when 20%, we are at 20% levitating above, you know, above the safety net. Today was a perfect example how, again, you can go from being very, very happy to being un-tilted in a matter of minutes and look what happened. Here's a perfect example what happened. Look at the cues, right. Once the cues, I mean, this is a major, major pull, okay. Incredible major pull. Mid-morning here. We started talking about this in the live webinar. I said, nothing's going up. Like, nothing is going up. You could see it. Nothing was going up this morning. Netflix popped up a little bit, came back in. Facebook popped up a little bit, came back in. I started noticing that Tesla couldn't rally, okay. Tesla couldn't rally. Roku couldn't rally. Amazon couldn't rally. I know that for a fact because we got stopped out of our swing. We had pretty much break even. So I saw that part, right. I saw that part. And next thing I knew, you know, next thing I knew, they all started confirming one by one. And what happened next was pretty extraordinary, like really, really pretty extraordinary. And, you know, I saw people talking on social media. Well, how can I, how can anybody possibly predict? Nobody was predicting it, but we were warned, right. Nobody was predicting it, but we were warned.  And again, if you are not careful, you are going to get absolutely hammered. Here's the good news, right. Here's the good news. Here's the good news and the bad news. The good news is after this major hammering, right, if you look at the Nasdaq 100, if you look at the major, major hammering, and this was like 40, 50 points, for example, on shop, 40 points on Tesla, this is insane.  So not only did the Q's remount, right, the 10-day moving average, they remounted the 5-day moving average. So that is incredibly bullish, very, very bullish. The problem is, right, every single stock right now that's a member of the Nasdaq 100, and you'll kind of see this on the 60-minute channels, everything is in the middle of the range, right. Amazon, Roku, Beyond, Shop, Netflix, even Tesla, right, even Tesla, but everything's in the middle of the channel. So the unfortunate, the good news is the Nasdaq reclaimed, remounted, and went higher. That's very, very bullish. It might not translate into tomorrow's action only because every single stock's in the middle of the range. And now if you believe in the theory that stocks trade from supply to supply and demand to demand, you could see how many trees are standing in the forest. You could see how much supply is literally standing there in every single stock. So the idea that the market will just completely erase what would happen today and just completely go through all the supply, anything's possible in this market. I really believe that. But again, the probability of that happening, unless we get this major gap of tomorrow, you know, 200, 300 points, it probably won't, which is kind of leading us to the reality tomorrow that beta, and again, it's important to kind of understand what happens before it happens. So we might be in a very, very tight channel tomorrow on beta because you could see it, right? They're not breaking down. They're not breaking up. They're just right in the middle of their channels. So tomorrow you're probably going to see the chop factor, right? The distribution, whatever you want to call it, tomorrow you'll see the day most likely that the bulls and bears are going to be trapped in a phone booth and they're trying to hit each other with a pillow fight, right? No damage can be done. But if you don't recognize what potentially could happen tomorrow, at least in these names, right? At least in these names, you're going to chop yourself up. I promise you, I give you my word. I can see it happening before it happens. So tomorrow there's, you know, you have to look at, in my opinion, if you trade beta, tomorrow you have to look at alternative ways to kind of put stakes in the freezer. Remount plays, stocks that have nothing to do with beta, maybe some option flow, aggressive option flow. But you have to get creative. Tomorrow will not be the day, in my opinion, unless these stocks completely roll over and start confirming the downward channels, which is very possible, very unlikely, but very, very possible considering how much they really are off their bottom. Tomorrow will probably be, quote, unquote, a day to kind of put the beta names to the side. Again, anything's possible, anything can happen, different, but I think when most likely our attention should be somewhere else unless one of these things bust through the channel to the upside or confirm to the downside, but most likely, again, we know what we're possibly going to be faced with tomorrow. So again, instead of sitting there trying to pick up a range to fight with a Roku and Netflix and Tesla, maybe Tesla's a different conversation, at least, at least, let's acknowledge what we're up against and kind of move away from that just for one day, maybe mid-morning tomorrow we'll get a little more clarity on the Amazons, on the Roku, on the Netflix of the Worlds, but again, going into it, I kind of want to take my attention away from them, at least from mid-morning, and kind of look at other things. So if you look at today's activity, incredibly aggressive, right? Incredible, they all were aggressive except for this one. This is the only one that didn't work. I bought this ADPT, and it was my fault that I bought it. I bought this ADPT. I didn't realize how thin it was. I just didn't realize how thin it was. I wound up losing about 50 cents on the trade. Again, it's at the end of the world, not at all, but again, I should have been a little bit better to kind of view the liquidity. I really like the chart, but sometimes, again, as I say all the time, not every single trade is for everybody, but forget about that. Let's forget about that. Okay, I took a little loss in there. Forget about that. Let's talk about the demolishment of this market, right? The disaster, the black swan effect that happened. And this was honestly one of the more impressive flushes that I've seen in a very, very long time, and it went one by one by one. So I got short Roku again, 125, 124, ADPT, big support if it builds below it could flush. Now again, nobody in their right mind was thinking, we're going to get 5, 7, 40 points on moves. Nobody in their right mind. Again, when you're taking a pivot, any pivot, you're saying to yourself, well, what's measured potential? And if it gets there, that's great. And if not, I have a break even stop, blah, blah, blah. What happened today was I think pretty much every single one of these beta names, they went through support like a knife through butter, like it wasn't even there, it was incredibly impressive. And let's just go through them one by one by one Roku, right? So we talked about, here's the whole range here, 125, right? 125. And it just, it got destroyed. I mean, this is one candle, it went from 125 all the way down to 121. Again, doesn't sound like a lot. It's $100 stock losing four points in one interval. One interval, that's pretty damn impressive. So very, very impressive move on Roku. Nice move there, nice move there. On Netflix, we talked about 383 is the five day support. If it builds below, it could flush. I thought it was going to get down to 81. I was saying in the live webinar, if it confirms that 81, maybe, I mean, there's a punch of shot 100 million, it gets down to 76. Destroy it.  So here was the 83, right? Here was the 83. Here was the 81. It went through it like it wasn't even there, right? It went 83, 81, and it went down to right to 76.68. Amazing. Absolutely amazing move. Again, congratulations. You guys caught that. Here, here's the Daptek. I lost some money in the Daptek. Coop, nice move. Nice move at the open. It was way too thin for me. I didn't trade this thing. It was trading like 100 sharelots, 50 cents spreads. Just something like this is just not for me. But if you did take a good job, here was Coop. Here is the 175, right? Here's the 175. It went to 178. Nice move. I mean, really, really nice move on Coop. ISRG looks great. Actually, I still like it for tomorrow. It looks really, really good. SPCE basically made Tesla today look like Siri satellite radio. The volatility and the aggression on this stock is off the chains. Absolutely off the chains. I didn't trade this thing into strength. I actually caught this thing on a bounce. But just to give you an idea of how crazy it was. So I bought the washout, that 4070s washout. And it went to like 4150s, right? 4150s. I made some sales along the way up and then I used break even as my stop, right? So I'm talking about in seconds. It goes from 4170s back to 4070s, right? It stops me out of my balance, goes down a dollar. Just to give you an idea. Goes down a dollar, rallies a dollar and a half, right? Rallies a dollar and a half comes back to the original bounce area. Goes down another dollar, rallies $3, right? You saw calls coming in, 44, 45, 46.   Ridiculous.  This is one of the names you kind of wanted to eat. You kind of want to, if you want to manage risk on this thing, you have to buy it into washouts, right? You have to buy into washouts on remounts or else you're going to get destroyed buying this thing. This is one of the names. If you buy this thing into strength, you're going to get destroyed. Absolutely destroyed. Square, again, big breakout yesterday. We talked about the multi-year breakout yesterday at 83. We talked about this pre-market. 85, 80, 86 needs to build. Again, second day in a row, man. Square was just a really big move, right? So here was a sneaky pivot right here, 85, 80, 86.   Square has been a pretty good, you know, pretty good consistent mover here in the last 24 hours. Again, you can see your square take along the way. Coop take along the way. So here was the trade. Here is the move of the day, right? Here is the move of the day. So 891, I saw that Tesla couldn't rally. He just couldn't rally today. 891, if it builds below, can flush. Again, experienced traders only. New traders, you should not even be looking at the symbol. I give you my word. If you're looking at the wrong side, you're dead. It's not a question of being right or wrong. It's a question of being solvent. And again, a lot of you guys, unfortunately, you're so new to this business. Again, when you have such an exaggeration, right? Such an exaggeration on both sides of the market, it can take you out. So again, for new traders, this is not for you. If you see me talk about Tesla and you've been trading less than three years, just close your eyes, close your ears, just move forward. But again, for all you guys that did take, congratulations. 891, if it builds below, can flush. And this was the setup on Tesla. This was the setup on Tesla right here. So this was it right here. So 891, you can see it. 891, right? 891, 893, 893, 891. When this thing took out 891, this is one candle, guys. This is literally one candle. 891 to 859 and one candle. This was an absolute just manslaughter. Absolute manslaughter. Ridiculous. Absolutely ridiculous move on Tesla. Obviously, stay valid. Take along the way. Everything, take along the way that. Take along the way that. Again, I thought Netflix was going to stop at 891. Went right through it. So, yeah, crazy.  This market is nuts. It's absolutely nuts. And it's going to continue to be nuts. And the moral of the story is, again, if you are new to trading, again, baby steps.  Again, this whole business, it's all about being in control.  If you're not in control of your trading, you're going to be statistics. You're going to be a statistic eventually because, again, there is no Mulligan's. There is no Mulligan's. It's most important just to kind of stay upright when you finally figure things out. So let me give you guys some ideas. Let me give you guys some ideas for tomorrow's session. They're all pretty much non-beta. I got a couple of beta names in there. But let me give you guys some ideas for tomorrow that are non-beta. Kind of names that I do like here. Yeah, look at this NLSN. I think it's Nielsen Ratings. I think it's a nice looking chart. For all you guys who don't trade beta, nice looking chart. If this thing starts building about 2240, 2250, nice multi-week, actually almost multi-month breakout potential here. It starts building 2240, 2250. You can move nice looking chart there. AQUA, AQUA, again, pretty basic looking chart here. It starts building 2530, 2540. You could get a move to the 26.5 level. That looks good. I actually like ISRG. ISRG actually held up fairly well. If the market is strong tomorrow, again, this is not really my thing. If all you guys trade options, it's a different thing. I trade the equity side, something like this with no volume. There's no way I could trade properly. But if this thing starts building, if ISRG starts building 620, you could get your next leg up. Tomorrow also, I do like STMP, that SDGR on dips into rising 60-minute support or opening range highs as well. But going into tomorrow, I do believe beta will kind of be off the table for us. At least for the first part of the morning, everything's in the middle of the ranges. Again, we understand we're up against. We have to kind of adjust and kind of, as I say all the time, using a baseball terminology, kind of manufacture runs tomorrow. Get on base any way you can.  Guys, have a great night everybody. Get some rest tomorrow. Probably will not be boring. And with God's help, we'll see you all on the field. Congratulations for putting in the time to take control of your trading. You're one step closer to owning your future and achieving the success you desire. Want daily trade ideas directly from Dan? Straight off his personal watch list? Click the link in the description to get started today.


Hi. In this video, I'm going to go over the data set that's going to be used for the next several hypothesis testing videos and used throughout all the different demonstrations of different forms of hypothesis testing that's coming up. So this data set is, comes from ERCOT and contains the power generation from different sources for two days from the Texas power crisis in February of 2021. Those two days are February 14th and 15th. It's sort of at the height of the crisis. On the 14th, ERCOT started shedding load basically in response to winter storms. And so we can see how power generation fluctuates during that time in this data set. The data are contained in 15 minute intervals throughout that time period. So a lot of data here. And aside from this data set, we know from other sources that the forecasted peak capacity during this time for wind was 6.1 gigawatts. And that forecasted peak capacity for natural gas was 48.4 gigawatts. What this forecasted peak capacity means is this is basically ERCOT's prediction of how much power or energy would be needed from these two different sources wind and natural gas during this timeframe to meet demand during this unseasonably cold winter for Texas. So through in the remainder of this video, I want to go through the processing of this data set. So let's get to it. So we'll start by importing the data. I've got to contain the Google Drive. So I've already mounted my Google Drive. And before I import it, it's always good to click on the CSV and open it up either in Google Sheets or Excel, of course, as we've done before. And so here we see the raw data format of this data set. And the dates in the first column, the fuel type, biomass, coal gas, a lot of different sources here, not just wind and natural gas. And here we see two different forms of natural gas. So there's natural gas and then combined cycle natural gas. Settlement type, total energy, or total power I should say. And then, and then the times that throughout the day. So starting basically at midnight and going out throughout the day, in which power was generated. And then in the cells here, we've got the different amounts of power generated. And we know from ERCOT that these values are in megawatt hours. So let's get down to the processing steps. First off, we'll import the data set using using the usual command here. It's also good to look at just a preview of the data we can do gen.head to see what, see what we have here. Look at the first five rows and last five rows. And we see what we saw in the CSV basically. It looks like everything was imported correctly and also it's not showing all the columns here so we've sticking out, you know, some of the middle columns but we can see that it goes up from, you know, 15 minutes after midnight all the way up to midnight of each day. Let's take a look at the column headers here so gen.columns. It's always good to look at the column names. Again, date, fuel, settlement type, and then all the times throughout the day. Okay, all going good. So our first order of business is to filter out some of these rows. So we want to focus on just those two days, every 14 to 15. And additionally, we only want to focus on two power sources, wind and natural gas. So we can filter based upon those two criteria. So let's make a new data frame called gen fill for filter. We'll call it anything here. And our data frame is currently called gen for saving for generation, power generation that is. And then we want to filter this. So we'll have one filter or set of filters for date. We'll have another set of filters for power source. And so in date, we'll have, we have two dates to filter. We'll separate those by an or statement. So it could be February 14 or February 15. And so here we just need to say gen.date equals. So we need to make sure we get the formatting exactly right here and here it's useful to have this preview and that it's given by head here we know where it takes on this format so month day. Year. And we want to edit this to be out of the 14th of 2021. Or the 15th of 2021. I'll do the power source on the next line here. Organize this a little bit better. And so our power sources as we saw in the file could be wind so our fuel could either equal wind with a capital W, or it could be just gas. Or we have that third type of gas dash CC for combined cycle. Okay, so let's make sure that that worked. Sure we go back to preserve our indenting here. Keep it organized. Oh, we have an error here. Series object is not oops I forgot a or statement here. There we go. Okay, and then we have it so now we are fuels only limited to gas gas CC and wind. Okay, that's all well and good. So what we need to do is that we need to rename one of our columns here. Now this is just a particular set for this data set but turns out that what Urquhart calls in the night 0000. This is going to be they allocate this to to say for example, February 14 when really in reality, you know midnight. It belongs to February 15. Right. So, so let's just change this to to 1159 p.m. or 2359 using military time. So that it's attributed to the right day. The reason for this is that the built in date time tools and in Python are going to think that 0000 should belong to the 15 when really that data value that we have there is attributable to the 14th, for example. We'll just make this quick edit gen fill, and we just need to rename, and we'll rename columns. And so our existing name zero colon 00 should instead become 2359. So even though we're, you know, taking it back one minute that it's really not substantial enough to ruin our data set. So there we have it. Oh, this should be encased in squiggly brackets. There we go. So, moving on here, we've, we're done with that. Now we can remove some superfluous columns here so fuel. I'm sorry not fuel but settlement type in total we don't need. So let's go ahead and get rid of those. So we'll continue just writing over gen fill. And so we'll say, we'll use our drop command to drop from gen fill some columns that are equal to, and in squiggly brackets, settlement type, and total. So we don't need those for the rest of the analysis. And we'll just check on this always get to check and make sure that that worked. And there we go we don't have settlement type we don't have total. Now, this next step is a little bit tricky but essentially we need to think about. So these first steps for, you know, really straightforward we can get rid of some extra stuff here and filter out the things we don't need basically. But ultimately we need to think about how we want this data organized. So, and to do that a good, good, good place to start is to think about what are the real cases here. Well, the case, or each individual case is the power generation. And this is power generation from each source on each day at each time. So, for following our standard format of the data frame having one row for each case, we need to have these generation values all in one column. We're going to have a separate column that indicates the fuel type and a separate column that indicates the date and a separate column that indicates the time. Turns out we're going to combine data times that we don't have. So we have this in one one column here but but basically we need to distinguish each value here by those other columns, the fuel, the date, the time. So in order to get it into that right format to reshape what we currently have here into something that has those columns we're going to use this melt function. All right, so how we do this is, again, we'll just write over gen fill or replace this. And we'll call our data frame gen fill dot melt. So we're going to use our melt function here. And with our ID bars. So the, the bars that are identifying the cases. And so in square brackets, this will be the date and the fuel of our name will be time so that'll be unique for each one. And then our values. So our value name is going to be, sorry, the, the time is going to be a new variable that's going to create. That's going to take all of these current column writers the different time and put them into this new variable called time. And then value name. We can call this generation. And we know from beforehand that these values are in meg watt hours, so we'll keep the units there. So it's going to take all the values that are under these time columns and put them into this generation call. So in other words, just to summarize it so these ID bars are the current ID variables, date and fuel. So those are static we're going to keep those all the other columns are going to go into a new variable called time. And the values underneath those are going to go into a new variable called generation defined by value meeting. Okay, so let's take a look and see how this has worked. And there we have it. You've got our new structure so instead of a wide fat table we've got a skinny neuro table. We've got date, we've got the fuel type, we've got time, and we have generation so each of our cases here as a single row. And that's exactly what we want. Now as I said before we can combine date and time so that we have something called date time that's going to be unique for every case here. So we need to first import a package date time. Then we will create a new variable. We'll just call it date time. We could call it anything we wanted but date time is pretty straightforward. And then we'll say from this pandas package. We need to reduce this function to date time so transform something to the date time object. And the thing we're going to transform is gen fill that date column date here. Plus, and here I'm going to put in a blank space, and another plus, and then our time. So here we're not only pulling in the values under date and under time those current columns here, but we're also specifying the format we're going to take those and we're going to separate them by a single space. So let's see what this looks like here. Go ahead and run it. And there we go we've got a new column date time that's got the date space time. Which is handy. Okay, so, and if we wanted to we could remove date and time from this. But that's not not necessary. One other thing we need to do is we need to convert our generation, which is actually units of energy here so megawatt hours and convert this to power. We're going to convert this in gigawatts. The reason being is because we have our forecasted peak capacity in gigawatts here. What we're going to want to do is compare the actual generation to these peak capacity values. So we need to have them in the same units. So let's go ahead and convert megawatt hours to gigawatts. We're going to be here basically using the state of the known formulas or unit conversions that we're going to enable here. So, we'll start by taking these existing generation values. And we can see the first thing we need to do is we need to get rid of these columns, the commas that are in. So, if we took these directly, Python's not going to recognize them as numbers, we need to remove the commas in order for it to recognize it as a numeric variable. In order to do that, we will replace these strings. We're going to replace the commas with nothing. The way this replace function works is we take our variable, we say dot string, this is referencing a string package, and we're going to use this replace function. We're going to take what we want to replace the commas, and we're going to give it a second argument of what we want to replace it with. In this case, nothing. So that'll just simply remove the commas. We can go ahead and make sure that worked. There you have it. So we can convert this to be numeric. I didn't do the, didn't look at d types before, data types before, but if you did that you'd see that this is actually a character column, character variable. So we'll say to numeric, so we can convert that to a number now. But ultimately, we're going to want to do some quantitative operations on this. So you did as a, as a number. All right, so now that it's a number now we can actually do the unit conversion. So we'll start here instead of generation and megawatt hours and we can say the answer you're not going to be gigawatts. And what we'll do is we'll take our existing variable generation megawatt hours, and we will divide by 0.25 because there's a 15 minutes as a quarter of an hour sort of dividing by these hours to take it on the numerator here. And also additionally we're going to divide by 1000 because there's a 1000 megawatts and a gigawatt. So we can make sure that that worked. And there we go. So, we've got a generation and gigawatts here. And then we can now we can clean up a little bit also we can drop some of these unneeded columns now. So we'll drop columns, we can get rid of date, we can get rid of time, and we can also get rid of generation in megawatt hours. This is going to give me some problems because I already ran that but anyway we can. Here we can put these in a chunk of code. And there we go. So now we have our cleaned up data set. The reason I got this error before is because I'd already run these and so now it can't find. I can't do this replacement because I already replaced it. So, anyway, we've got our cleaned up data set here with just the variables we want the fuel type, the date time and generation. One other thing we need to do is we've got gas and gas combined cycle. Let's combine these into just natural gas because these are two different forms of natural gas. We're going to lump them together because ultimately that forecast to be capacity is considering both forms of natural gas and the generation data is one of those two sources. So, in order to do this, we need to first pivot our data frame. So let's make a new object called genpiv. And what we're going to do now is we're going to, we have something that's, that's tall and skinny here and we're going to make it a bit fatter. We're going to have separate columns for a fuel type here. So we're going to essentially do the reverse of that melt so that we can then do some operations on the two different columns. So let's say we'll define an index here indexes date time. So indexes date time because that's a unique identifier for, for these rows basically will combine with the fuel type I should say, and then we're going to make some new columns that are based on fuel. And the values for this new data frame are going to come from our generation in gigawatts. Okay. So let's take a look at what that looks like. So there we go. Now we have a data frame that has indices so this is not a column right now these are indices of date time. And then we have gas gas combined cycle and wind and the values they're all in generation in units with gigawatts. Okay. And now we can combine gas and gas EC simply by adding those two columns together. So I'll make a new column here let's call it natural gas. And we'll take our gas column and our gas CC column. And we're just simply going to add those two values together. So we're going to aggregate them. And now after we do that, the gas and gas EC are going to be extra. So we can get rid of those will get rid of gas and gas CC since those are no longer needed. And let's see what we have now. Here we go. We've got now just two columns wind and natural gas. And we see that gas gas EC seven and a half 26.2 others together 33.7 that's about right. So so there we go. Now we want to get it back into the form that we had before where fuel was a column not two separate columns. Melt again. Before we melt we need to reestablish date time as a column. So what we're going to say is we're going to say from GenPIV take the index and make it a new column called date time. Let's see what that looks like here. So we still have date times index but we've also separately made it as a column here. And then from GenPIV we can say melt again our ID bars in this case are going to be is going to be just date time. And our values are going to be in generation gigawatts. Go ahead and run that and there we go. That's what we want. So we have a column of date time. And as we're facing the times fuel type and then our generation values. Lastly, let's go ahead and visualize this. This is an important step for hypothesis testing and seeing what the data look like this gives us a good idea of how to set up our subsequent hypotheses. So let's make a line plot here. Where we will have a line one line for each energy source. And plot it over time here. So if you plot, we'll take our data frame GenPIV and we'll have a line where X is going to be our date time. And Y is going to be our generation. We need to get this name exactly right so it matches the column there. And also, we want to specify the color in this case as fuel. So let's see what this looks like. There we go. So we've got our different fuel sources natural gas and red and blue plotted over date time here, we can see natural gas is a lot higher than wins. There's a lot more energy coming from natural gas than win just because there's a larger natural gas infrastructure in Texas. So let's have comparatively large wind infrastructure compared to other states. We will. Let's clean this up a little bit. I don't really like how you can't see the date times here very well so let's adjust our theme and make our access text. Let's make our access a little more readable. We'll say element text and change the angle to be, oh, 75 degrees. You can see the effect of that. There we go. So now we can read the dates here a bit better. We're cutting off the time so let's before we do the theme, I like putting the theme at the end. Before we do that let's adjust the, the date labels. And we can control the formatting of this as such. So we can say we want month and day. So we're only going to extract the month and day because they're all the same year it's all 2021. It's also all the same month we could get rid of the month so let's keep the month and day. And, and we also want to include our and separated by colon, the minute will put a plus here. So let's see the effect of that. There we go so now we've got some time information that we have have the hours and also the minutes displayed. Great. Let's also put in the capacities. So we have the the actual production plotted here. Let's see how we stacked up against our, our capacities. And so we can insert those capacities since those are essentially constant values over the whole time frame here. We'll put those in as horizontal lines. So we're going to do an H line, you need a Y intercept argument so basically what's the Y value for that horizontal line. And so for for natural gas. This is going to be 48.4. And our color here can be red and and our line type will be dashed, make it a dash line. So we're going to do this for wind at 6.1 gigawatts. Let's make this blue. So there we go. Now. Yeah, I don't really like how these are different shades of red different shades of blues I'm pretty unique picky here if we wanted to control that a little bit better we can say, we can define our color scale for for the curves. And we'll set some values. Let's switch this up. Let's say, so we'll have natural gas be the blue and wind be the red. Doesn't really matter. There we go. So now the colors match in perfectly in terms of shade, just how I like it. So, but the main takeaway here is that well, we can see natural gas is consistently below its capacity over this two day time frame. Wind is below its capacity sometimes. And sometimes it's above it. So that's interesting. So let's retain that, that thought as we go forward in the subsequent hypothesis testing. Okay.