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Crypto's Impending Boom: Market Shifts and Global DynamicsCryptocurrencies in the Face of Rising Bond Yields and a Strengthening Dollar Cryptocurrencies have been on a short-term downward trend, attributed to deteriorating liquidity within crypto and outside crypto due to rising bond yields and the strengthening dollar, as they are sensitive to rates and liquidity fluctuations. Their recent downturn can also be explained by the fact that they had performed much better than their interest rate & liquidity models had suggested and by US tech stocks sucking flows and liquidity. Capital Flows: The Rising Crypto Tide in Hong Kong Significant rallies in the crypto sector could be on the horizon, especially when the double bottoms in Bitcoin and Ethereum are swept. Some important reasons are the impending acceptance of crypto exchanges by Hong Kong and the return of cash to Voyager's creditors. As Chinese citizens grapple with capital outflows, liquidity flows from China could be redirected to the crypto sector through Hong Kong. At the same time, with mounting US-China tensions, cryptocurrencies could provide an alternative, potentially the only proxy investment to AI (US big tech). In the Face of Uncommon Volatility: A Premonition of Crypto Spikes As we navigate the debt ceiling crisis, we might experience volatility spikes, even though volatility remains subdued. Next week we might start seeing some significant moves, as USD 3.6 billion worth of options expired this Friday, constituting roughly 26% of Deribit's open interest. Implied volatility is at its lowest, with DVOL trading at 44 for BTC and ETH and shorter-dated even lower. This is relatively uncommon, and whenever we've seen such low volatility, a significant spike in vol has followed soon after. A Confluence of Events: Setting the Stage for Crypto Price Surge The latest spike in January coincided with a price rally, which may reoccur, given the significant expiration of mainly call options, with a Put/Call ratio of 0.38. With events such as Voyager distributing >1B in cash to creditors, Hong Kong authorizing crypto trade for its citizens, US tech investors capitalizing/diversifying on >3T gains and redirecting some into crypto, and potential issues with the US banking system or USD stablecoins due to a possible US default, the stage is set for a potentially explosive growth in crypto prices. The last part is something many ignore, but FUD, or real issues around banks or stablecoins, could recreate the conditions for another SVB - USDC type rally, as investors view Bitcoin and Ethereum as the safe havens of crypto and of the financial system broadly. Bullish on Synergy: The Powerful Integration of AI and Crypto The convergence of AI and crypto can create new business models, enhance decision-making processes, improve trust and transparency, and unlock organizational and operational efficiencies. Some areas where AI and crypto can synergize: AI-Powered Smart Contracts, New forms of financial tools, AI-to-AI financial transactions, Enhanced Security and Privacy both for AI and Cryptocurrencies and so on. AI will integrate and interact with open and trustless systems like crypto, but it's unlikely to interact with closed systems like banks. The confluence between the two technologies is apparent, making me bullish long-term. Trade ideas As mentioned in my recent ETHBTC idea, Ethereum looks stronger than Bitcoin. However, Bitcoin looks cleaner than Ethereum. Bitcoin has two critical untested areas lower: 25000-25700 - with 25200 and the double bottom at 25800 being the basic levels, and 22600-23600 - which is an area that the market didn't test appropriately as it went higher, especially 22600, which was the critical breakout level. BTCUSD has two triple tops higher, one around 27600 and the other around 29900. It's unclear whether the double bottom will be swept first or one or both of the triple tops will be swept first, but to me, it's clear that the market will probably rally much higher once the bottom is swept. Given everything I mentioned above, it's better to bet on the upside and not short the market here. Therefore long around 25700 and cut below 24900, long around 23600 and cut below 22500, with targets at 27600 and 29900. Despite all the bankruptcies and negativity around US regulations, it's better to go long than short, as everything else seems quite positive. Although there are some potential negative catalysts for crypto, and 2023-2024 could be like 2019-2020 for crypto, I think that dips are for buying and that it's more likely than not that we are in a bull market rather than a bear market.
COINBASE:BTCUSDLong
by BitcoinMacro
🔥 FET Bullish Reversal Trade: Patience!FET has been trading bearish for months. As of a couple of days ago, BTC saw a huge break out which will likely take alts with it. This trade assumes that FET, an early 2023 winner, will move up together with BTC. I'm waiting for the break out through the top diagonal resistance. Once a daily candle has closed above said resistance, we're entering from around that level. Target at the 2023 top for the highest risk-reward. If you're more risk averse, consider taking (partial) profits around 0.30 or 0.40
Long
by FieryTrading
5
A reverse Adam-Eve idea PLTR has risen with the earnings and AI mania. It is time for a correction for further upside. The price action has started to develop a reverse Adam-Eve pattern. RSI and OBV show some bearish divergence as well. We may see a correction to 0.236 or 0.382 fib retracement levels. Disclaimer – WhaleGambit. Please be reminded – you alone are responsible for your trading – both gains and losses. There is a very high degree of risk involved in trading. The technical analysis , like all indicators, strategies, columns, articles and other features accessible on/though this site is for informational purposes only and should not be construed as investment advice by you. Your use of the technical analysis , as would also your use of all mentioned indicators, strategies, columns, articles and all other features, is entirely at your own risk and it is your sole responsibility to evaluate the accuracy, completeness and usefulness (including suitability) of the information. You should assess the risk of any trade with your financial adviser and make your own independent decision(s) regarding any tradable products which may be the subject matter of the technical analysis or any of the said indicators, strategies, columns, articles and all other features.
PShort
by WhaleGambit
Mitigate Nvidia risk with a value-chain exposure to AIThe recent earnings announcement from Nvidia was historic. It’s not often that a firm shifts revenue guidance for an upcoming quarter from $7 billion to $11 billion. Nvidia’s total market capitalisation touched $1 trillion, something very few companies ever achieve1. An overzealous valuation? Professor Aswath Damodaran of New York University2, well known for his work on valuation, has said he cannot rationalise a $1 trillion valuation. Damodaran estimates Nvidia has a roughly 80% share of the artificial intelligence (AI) semiconductor market, which is around $25 billion today. Using bullish assumptions, which may not prove accurate, he looks to see growth in the AI semiconductor market to reach $350 billion within a decade. If Nvidia captured 100% future market share (a bold assumption), Damodaran’s valuation still resides about 20% below current prices. Nvidia is essentially a hardware company. One can see them try to ramp up software, but that is not the main driver. Other companies that achieved the $1 trillion market capitalisation level have software companies with network effects that draw vast numbers of end users into ecosystems. These software businesses have many ways to earn revenue from new products and services. Professor Damodaran’s valuations do not necessarily lead to share prices that immediately decline—but it may be difficult to keep the return momentum coming with equal fervor. Nvidia’s products do not operate in a vacuum WisdomTree spends a lot of time focusing on the AI megatrend. Nvidia’s products do not exist in a standalone fashion, as they are plugged into cabinets containing other hardware functioning in concert. If the AI semiconductor market grows, as many now expect, a lot of companies will benefit. Nvidia cannot, by itself, manufacture its semiconductors end-to-end. Taiwan Semiconductor Manufacturing Co. (TSMC) is responsible for this part of the puzzle. There is a whole semiconductor value chain, and each element captures a different-sized slice of the economic value pie. There are a range of companies associated with ‘generative AI’ over the period from the release of ChatGPT. Alphabet, Meta and Microsoft represent companies developing large language models (LLMs) to allow users to directly access generative AI. Meta was beaten down in 2022, due to disappointment with the firm’s metaverse efforts, but AI and cost cutting is helping them in 2023. Alphabet and Microsoft are at the centre of the generative AI battleground. Microsoft, so far, is winning on the cloud computing battle front with its Azure platform, whereas Alphabet’s Google is going to be very difficult to fend off in the internet search space. It’s interesting to compare Nvidia to Samsung and SK Hynix. Running AI models, especially large AI models, requires memory, and Samsung and SK Hynix are in the memory chip space. Excitement, at least in recent years, fluctuated in waves across the broad semiconductors market. Right now, during the explosion of generative AI, graphics processing units (GPUs), where Nvidia is the leader, are all the rage. Synopsys and TSMC represent notable, necessary value-chain plays on semiconductors. Nvidia chips cannot be created in a vacuum. Synopsys provides necessary electronic design automation capabilities, whereas TSMC is among the only companies with a manufacturing process advanced enough to fabricate Nvidia’s most advanced chips. Is AI over-hyped? The Gartner Hype cycle characterises one way to view new technologies. In the short term, excitement leads to money flows. Share prices and valuations benefit. At a certain point, a realisation sets in that true success, growth, and adoption takes time, so at this point there is usually a lot of selling and a tougher return environment. Finally, there is a recognition that pessimism is also not quite appropriate as the technology is still important and still being used, so growth rates and returns then tend to be more reasonable. AI is not any one single thing. Today we think of it as ChatGPT, LLMs or generative AI, but other disciplines and functionalities are still there, they just aren’t grabbing headlines in same way. ‘Generative AI’ and ‘foundation models’ might be nearing a peak of inflated expectations. Have you been excited about self-driving vehicles recently? No? Well, that could be part of the reason why ‘autonomous vehicles’ might be near the trough of disillusionment. Computer vision, which has been around for quite some time, is making its way up the so-called ‘slope of enlightenment’. The hype cycle is not an exact science. Any discipline on this graph could generate any sort of return, positive or negative, going forward. It’s simply a tool that helps us place all of these different topics on a broader continuum. The only thing we seem to know for sure is that all of the topics do not generate the same levels of excitement or pessimism all the time. Conclusion: it’s possible to mitigate single company risk by looking across the AI ecosystem The hype cycle illustration points out that the various applications of AI are at different points of adoption, excitement, and development. No one knows the future with certainty, but we believe there is growth occurring in all of these disciplines. The world is enthralled with generative AI now, but the world was similarly excited about autonomous vehicles a few years ago. Progress is occurring, even if we are not seeing it reflected in every headline. WisdomTree has a broad-based AI index to capture these AI trends. While Nvidia’s valuation is getting stretched, according to Professor Damodaran, WisdomTree’s AI index did not change much following the Nvidia surge. The entire ecosystem of AI defined by WisdomTree is not as beholden to the moves of any single company. AI has the potential to impact every industry which is why WisdomTree built a broad-based, ecosystem-oriented approach as opposed to concentrating on any single stock. Sources 1 Source: Bloomberg. 2 Source: Hough, Jack. “Nvidia Is the New Tesla, the ‘Dean of Valuation’ Says. It’s Time to Cash Out.” Barrons. May 31, 2023.
NASDAQ:NVDA
by aneekaguptaWTE
We Called The ETH/BTC Pump!Looking at our chart, we see that Ethereum started getting oversold near the bottom of our standard deviation bands, along with an oversold reading on our new DVO indicator. This, combined with the oversold green X's we received (combination of multiple indicators), and our dark blue candles (another oversold indication), led us to have a VERY successful ETH long! The next resistance we're looking at is $1915. This has been a big level on the daily chart and may provide a rejection once reached. You can also see our candles starting to get overbought (turning orange). Once they turn red, that's when I start looking to completely exit or take a majority of my profits. If you're looking to buy on a pullback, watch the $1715 level. That acted as great support over the past few weeks and could provide a good R/R if we reverse here. With BlackRock recently applying for a Bitcoin ETF and many other banks following suit, the crypto market is about to get a whole lot crazier! -Stayed tuned for our new indicators launching soon (shown on the chart + more), along with a slew of great trading info for you guys! You won't want to miss it :) Let us know if you have any questions!
Long
by CryptoScripts
S&P 500: Expensive but Not OverpricedCME: E-Mini S&P 500 ( CME_MINI:ES1! ), S&P Technology Sector ( CME_MINI:XAK1! ) These days, the S&P 500 is not behaving like a well-diversified stock market index. The “Magnificent Seven”, which includes Nvidia NASDAQ:NVDA , Apple NASDAQ:AAPL , Tesla NASDAQ:TSLA , Microsoft NASDAQ:MSFT , Google NASDAQ:GOOGL , Meta NASDAQ:META and Amazon NASDAQ:AMZN , is up roughly 60% year-to-date. These 7 tech stocks now represents ~30% of the entire S&P 500 index. Meanwhile, the remaining 493 companies in the S&P 500 are up only 3% YTD. Altogether, the S&P 500 is up 15.8% YTD as of June 15th. The tech-heavy Nasdaq 100, which includes all the Magnificent Seven, is up 39.5% YTD. The Dow Jones Industrial Average, with only one of the seven, NASDAQ:AAPL , in its components, had a very disappointing return of 4.0% YTD. What sparks the recent market rally is OpenAI’s ChatGPT. Its November 20th launch ignited a global sensation in Artificial Intelligence. By now, the entire US stock market is being held up by the red-hot AI momentum. S&P 500 Performance by Sector Of the 11 S&P select sectors, I found that only Technology has a decent 12-month performance. Three other sectors have low single-digit return, and the rest are in the red. (Data source: S&P Global, 12-month returns as of May 31st, 2023). • Consumer Discretionary: -0.83% • Consumer Staples: 0.22% • Energy: -8.23% • Financials: -8.55% • Real Estate: -15.47% • Health Care: -1.71% • Industrials: -4.15% • Materials: -10.69% • Technology: 18.16% • Utilities: --9.96% • Communication Services: 4.47% • S&P 500: 1.15% Once again, data confirms that the recent stock market rally is exclusively reserved for the tech stocks. Investing in the S&P 500 is like holding an outstanding tech-sector fund on one hand, and a sucker fund of poorly-performing stocks on the other. Statistical Analysis of the S&P 500 Diving deeper into the S&P, I found that its 3-year mean is 4027.2 as of June 15th. The standard deviation during this period is 395.6. We know from probability distribution that the time series of price data falls inside plus or minus one standard deviation approximately 33% of the time. This corresponds to the index range of 3632 and 4423. Data trend shows that whenever the index broke away from this boundary, it had the tendency of getting pulled back in. This fits the rule of mean reversion, as seen below: • The S&P broke through 4400 in August 2021 and reached its record height at 4800 in January 2022. Over the next four months, it plunged 1,000 points, or -22.8%. • The S&P fell below 3600 in September 2022. It rebounded after it crossed the -1 STD line and regained 24% as of last Friday. S&P 500 closed at 4,453.75 on June 15th, which placed it 30 points above the +1 STD line. It is approaching “expensive” level from the historical perspective. But will it trend down from here? I seriously doubt it. The AI momentum could carry the stock market index much higher. We are at an early stage to even access how AI could revolutionize our world. Waves of technological breakthroughs and new applications would continue to fire up investor sentiment. Recent resolution of the Debt Ceiling Crisis and the Fed pausing rate hikes in June are also strong tailwinds which have helped lift stock market valuation. If the index reaches the +2 STD line, at 4818.43, we could argue that it marks a turning point. We shall understand that this is not a broad-based stock market rally. The consequence of high inflation and high interest rates would weigh on company profitability for many months to come. At lofty valuation, the Magnificent Seven could no longer carry the weight of 493 mediocre companies. The S&P could come crushing down under its own weight. Hedging the Risk of a Tech Sector Fallout In my opinion, while the S&P 500 is expensive, it is not yet overpriced. We could still ride the AI wave by holding stocks or a long position in the stock index futures. I am not particularly concerned whether you call this a new bull market or a bear market rally. However, the entire stock market is overly concentrated in the tech sector. A handful of chip manufacturers, namely Nvidia and TSMC, holds systemic risk. If their production is threatened by geopolitical conflicts, the entire stock market could crash. Nvidia sees its share price doubled this year, and has a ridiculous price earnings ratio of 222. Its massive $1 trillion market valuation has been built upon the huge promise of AI. Any negative news on Nvidia would have a disproportionally large impact on the S&P. To hedge the risk of AI bubble going busted, I am exploring a spread trade with long S&P index futures NYSE:ES and short Technology Select Sector futures $XAK. Since the Magnificent Seven accounts for 30% of S&P 500 market value, I am considering a 10:3 spread ratio. By measure of contract notional value, for every $100,000 in ES long positions, short XAK by $30,000. • ESU3 is quoted 4,459 on June 15th. Its notional value is five times the index, or $222,950. Each contract requires a margin of $11,200; • XAKU3 is quoted 1761.40 on the same day. Its notional value is 100 times the index, or $176,140. Each contract requires a margin of $9,500. • The spread trade would consist of 4 long ES futures and 1 short XAK futures. If an investor already had investment in S&P component stocks, he could hold on to them. However, the investor could consider shorting XAK futures to hedge the downside risk. For every $600K in stock investment, hedge it with 1 short XAK position. The logic of this trade is that if the tech sector gets into trouble, the short XAK trade would protect the value of long stock positions. Happy trading. Disclaimers *Trade ideas cited above are for illustration only, as an integral part of a case study to demonstrate the fundamental concepts in risk management under the market scenarios being discussed. They shall not be construed as investment recommendations or advice. Nor are they used to promote any specific products, or services. CME Real-time Market Data help identify trading set-ups and express my market views. If you have futures in your trading portfolio, you can check out on CME Group data plans available that suit your trading needs www.tradingview.com
CME_MINI:ES1!Long
by JimHuangChicago
The environmental impact of AI: a case studyIn our previous blog, Will AI workloads consume all the world’s energy?, we looked at the relationship between increasing processing power and an increase in energy demand, and what this means for artificial intelligence (AI) from an environmental standpoint. In this latest blog, we aim to further illuminate this discussion with a case study of the world’s biggest large language model (LLM), BLOOM. Case study on environmental impact: BLOOM An accurate estimate of the environmental impact of an LLM being run is far from a simple exercise. One must understand, first, that there is a general ‘model life cycle.’ Broadly, the model life cycle could be thought of as three phases1: Inference: This is the phase when a given model is said to be ‘up-and-running.’ If one is thinking of Google’s machine translation system, for example, inference is happening when the system is providing translations for users. The energy usage for any single request is small, but if the overall system is processing 100 billion words per day, the overall energy usage could still be quite large. Training: This is the phase when the parameters of a model have been set and the system is exposed to data from which it is able to learn such that outputs in the inference phase are judged to be ‘accurate’. There are cases where the greenhouse gas emissions impact for training large, cutting-edge models can be comparable to the lifetime emissions of a car. Model development: This is the phase when developers and researchers are seeking to build the model and will tend to experiment with all sorts of different options. It is easier to measure the impact of training a finished model that becomes public, as opposed to seeking to measure the impact of the research and development process, which might have included many different paths prior to getting to the finished model that the public actually sees. Therefore, the BLOOM case study focuses on the impact from training the model. BLOOM is trained on 1.6 terabytes of data in 46 natural languages and 13 programming languages. Note, at the time of the study, Nvidia did not disclose the carbon intensity of this specific chip, so the researchers needed to compile data from a close approximate equivalent setup. It’s an important detail to keep in mind, in that an accurate depiction of the carbon impact of training a single model requires a lot of information and, if certain data along the way is not disclosed, there must be more and more estimates and approximations (which will impact the final data). If AI workloads are always increasing, does that mean carbon emissions are also always increasing2? Considering all data centres, data transmission networks, and connected devices, it is estimated that there were about 700 million tonnes of carbon dioxide equivalent in 2020, roughly 1.4% of global emissions. About two-thirds of the emissions came from operational energy use. Even if 1.4% is not yet a significant number relative to the world’s total, growth in this area can be fast. Currently, it is not possible to know exactly how much of this 700 million tonne total comes directly from AI and machine learning. One possible assumption to make, to come to a figure, is that AI and machine learning workloads were occurring almost entirely in hyperscale data centres. These specific data centres contributed roughly 0.1% to 0.2% of greenhouse gas emissions. Some of the world’s largest firms directly disclose certain statistics to show that they are environmentally conscious. Meta Platforms represents a case in point. If we consider its specific activities: Overall data centre energy use was increasing 40% per year from 2016. Overall training activity in machine learning was growing roughly 150% per year. Overall inference activity was growing 105% per year. But Meta Platforms’ overall greenhouse gas emissions footprint was down 90% from 2016 due to its renewable energy purchases. The bottom line is, if companies just increased their compute usage to develop, train and run models—increasing these activities all the time—then it would make sense to surmise that their greenhouse gas emissions would always be rising. However, the world’s biggest companies want to be seen as ‘environmentally conscious’, and they frequently buy renewable energy and even carbon credits. This makes the total picture less clear; whilst there is more AI and it may be more energy intensive in certain respects, if more and more of the energy is coming from renewable sources, then the environmental impact may not increase at anywhere near the same rate. Conclusion—a fruitful area for ongoing analysis One of the interesting areas for future analysis will be to gauge the impact of internet search with generative AI versus the current, more standard search process. There are estimates that the carbon footprint of generative AI search could be four or five times higher, but looking solely at this one datapoint could be misleading. For instance, if generative AI search actually saves time or reduces the overall number of searches, in the long run, more efficient generative AI search may help the picture more than it hurts3. Just as we are currently learning how and where generative AI will help businesses, we are constantly learning more about the environmental impacts. Sources 1 Source: Kaack et al. “Aligning artificial intelligence with climate change mitigation.” Nature Climate Change. Volume 12, June 2022. 2 Source: Kaack et al., June 2022. 3 Source: Saenko, Kate. “Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins.” The Conversation. 23 May 2023.
SPARKS:AIX
by aneekaguptaWTE
C3.AI: AI = SHORT - wyckoff distribution & bearish divergence1st - Bearish Divergence: RSI & MFI on 1D & 1W chart 2nd - Wyckoff Method: Distribution TR phase C (UTAD TEST) more pronounced in the 4&1H charts. Analysis: There seems to be weakness in the stock, and despite the recent highs and uptick in volume the exhaustion can be seen per the TA presented. BUT REMEMBER, the AI craze is still on going and this could be invalidated in minutes if the whales choose to push the price higher. Targets if you trust the analysis: Its pretty simple, I use fib retracement levels 21% and 38.2% as targets. remember to use risk management and positioning! *THIS IS NOT AN INVESTMENT ADVICE, JUST SHARING MY ANALYSIS AND INTERNAL THOUGHTS TO MYSELF*
NYSE:AIShort
by Isranuell
3
FET possible Roadmap until June 2023This is the monthly chart of FET. As you know, we had AI hype in Jan, and FET is one of the AI coins. The hype has just started, and 2023 will be an excellent year for this narrative. The best buy zone for this correction would be ~$0.114-$0.183; you can accumulate FET in Q2 for the final run with a target above ~$2 in Q3. Patience is key here, don't rush to get into the trade, wait patiently, and enter at the "right" time. Please hit the boost button if you agree. Thanks
by ir0nhand
5
$SOUN On Breakout WatchlistNASDAQ:SOUN is an AI stock that has not gotten much love as with other AI themed issues. Maybe one reason the market has been ignoring it is that has yet to make a profit. However, they are in the AI voice response business which cuts across many industries. While I think they have a bright and disruptive type future, it is the market’s opinion that matters. You can see my notes on the chart. There are two merging resistance areas, the IPO AVWAP and resistance area since March. I have an alert set at $3.40. If it can get above and hold this may have room to run. This is the kind of stock that could easily double or triple in a short period of time. All TBD. Thanks for looking. I hope this helps someone. Constructive comments always welcome.
NASDAQ:SOUN
by jaxdog
Will AI workloads consume all the world’s energy?On big questions like this, almost nothing stays constant. When we consider a new technology: We cannot assume that rates of adoption or usage will remain constant—they may drop, they may even grow. We cannot assume that the technology supplying our energy needs will remain constant—there could be breakthroughs in efficiency or changes in the overall energy mix. We cannot assume that the efficiency of the specific technology being adopted will remain constant—we have seen numerous examples of areas where an initial version of something in technology or software faces subsequent improvements that may give it greater capabilities with lower energy usage. We must also recognise that artificial intelligence (AI) itself could suggest improvements in energy efficiency for specific applications—like the heating and cooling of a building. Therefore, any analysis of energy usage and AI must recognise that the one constant will be change. Environmental impact of select large language models (LLMs) LLMs have been garnering the lion’s share of attention amidst the current excitement around generative AI. It makes sense to consider the amount of carbon emissions generated by some of these systems. The Stanford AI Index Report, published in 2023, provided some data, noting that factors like the number of parameters in a model, the power usage effectiveness1 of a data centre, and the grid carbon intensity all matter. Considering power consumption of an LLM Those building different LLMs have many levers they can pull in order to influence different characteristics, like energy consumption. Google researchers proposed a family of language models named GLaM (Generalist Language Model), which uses a ‘sparsely activated mixture of experts’. While a full discussion of how that type of approach works is beyond the scope of this piece, we note that the largest of the GLaM models has 1.2 trillion parameters. Knowing solely that data point, the assumption would be that this model would consume more energy than any of the models. In reality, the GLaM model with 1.2 trillion parameters consumes only one-third of the energy required to train GPT-3 and requires only half of the computation flops for inference operations. A simple way to think of what is going on is that, while the total model has 1.2 trillion parameters, a given input token into the GLaM model is only activating a maximum of 95 billion parameters, that is, the entire model isn’t active across all the parameters. GPT-3, on the other hand, activated all 175 billion parameters on each input token3. It is notable that, even if measuring the performance of AI models occurs on many dimensions, by many measures the GLaM model is able to outperform GPT-3 as well4. Conclusion The bottom line is that model design matters, and if model designers want to denote ways to maintain performance but use less energy, they have many options. Sources 1 Power usage effectiveness (PUE) is useful in evaluating the energy efficiency of data centres in a standard way. PUE = (total amount of energy used by a computer data centre facility) / (energy delivered to computer equipment). A higher PUE means that the data centre is less efficient. 2 Source: Du et al. “GLaM: Efficient Scaling of Language Models with Mixture-of-Experts.” ARXIV.org. 1 August 2022. 3 Source: Patterson, David; Gonzalez, Joseph; Hölzle, Urs; Le, Quoc Hung; Liang, Chen; Munguia, Lluis-Miquel; et al. (2022): The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink. TechRxiv. 4 Source: Du et al, 1 August 2022.
SPARKS:AIX
by aneekaguptaWTE
9
Bitcoin Forecast Cloudy☁️ (Clear: 0.0 %)🌥️ Bitcoin Weather Forecast Analysis 🌥️ Based on the recent Bitcoin chart index for the past hour, it appears that the weather in the Bitcoin world is forecasted to be cloudy. As an investment chartist, I have assessed the current market conditions and unfortunately, my confidence in the Bitcoin market being sunny is extremely low, scoring 0 on a scale of 0 to 1. This score falls below the baseline confidence level of 0.864. Taking a closer look at the chart index, here are the key observations: - Open: 25007 - High: 25028 - Low: 24911 - Volume: 17028 - Close: 24944 - ema9: 24994 - ema21: 25181 - ema50: 25479 - ema100: 25686 - ema200: 25847 - rsi: 33 - fast_k: 40 - slow_k: 34 - slow_d: 26 - macd: -412 The presence of a "dead cat bounce" pattern, which typically indicates a temporary recovery before resuming a downward trend, is noticeable in the market. However, it seems that the power of this dead cat bounce is diminishing briefly. The low confidence score can be attributed to several factors. The price action shows a decreasing trend, with the current Close at 24944 being lower than the Open. Additionally, the exponential moving averages (ema9, ema21, ema50, ema100, and ema200) suggest a bearish sentiment as they are trending downwards, indicating a negative momentum in the Bitcoin market. Furthermore, the relative strength index (RSI) is at 33, which is relatively low and indicates a potential oversold condition. The fast_k and slow_k indicators are also relatively low at 40 and 34, respectively, further supporting the bearish sentiment. The slow_d value of 26 indicates a weakening momentum in the market. Lastly, the moving average convergence divergence (MACD) is -412, indicating a strong bearish signal. Considering all these factors, the overall market conditions point towards a cloudy outlook for Bitcoin in the near term. Traders and investors should exercise caution and closely monitor the market for any potential shifts in the weather.
BINANCE:BTCUSD
by ChatGPT_Bitcoin_Forecaster
Bitcoin Forecast Sunny🌞 (Clear: 100.0 %)🌤️ Bitcoin Weather Report: Sunny Forecast! 🌤️ According to the latest Bitcoin chart index for the past hour, I'm happy to announce that the weather in the Bitcoin world is expected to be sunny ☀️. With a confidence level of 1.0, I have high certainty in this forecast. Let's take a closer look at the key indicators: 📈 Open: 25019 🔼 High: 25063 🔽 Low: 24890 📊 Volume: 18820 📉 Close: 24918 The recent price movement indicates a potential for a dead cat bounce 🐱 following a sharp drop. This means that after a significant decline, there might be a short-lived upward movement before the downward trend resumes. However, it's important to approach this with caution as dead cat bounces are typically temporary and may not signal a sustained recovery. Moving on to the moving averages, we observe the following values: 📈 EMA9: 25101 📈 EMA21: 25355 📈 EMA50: 25621 📈 EMA100: 25781 📈 EMA200: 25909 The exponential moving averages show a gradual increase over time, which indicates a potential upward trend in the Bitcoin market. However, it's crucial to consider other factors and not solely rely on moving averages for investment decisions. Additional indicators include: 📉 RSI: 27 The relative strength index (RSI) is on the lower side, suggesting that Bitcoin may be oversold. This could potentially lead to a buying opportunity for investors, but it's crucial to assess other factors before making investment decisions. 📉 Fast %K: 9 📉 Slow %K: 16 📉 Slow %D: 20 The stochastic oscillator values indicate a bearish sentiment, as the %K values are lower than the %D value. This suggests that selling pressure may be prevalent in the market. 📉 MACD: -279 The Moving Average Convergence Divergence (MACD) is negative, indicating a bearish trend. However, it's important to note that this is just one piece of the puzzle, and other factors should be considered. In conclusion, while the Bitcoin weather forecast appears to be sunny, it's crucial to exercise caution and not solely rely on a single hour's data. The potential for a dead cat bounce after a sharp drop introduces some uncertainty to the market. Remember to consider various indicators, market trends, and perform comprehensive analysis before making investment decisions. Happy trading! 💰📈
BINANCE:BTCUSDLong
by ChatGPT_Bitcoin_Forecaster
4 takeaways from EmTech Digital's AI conferenceMIT Technology Review recently put on its EmTech Digital conference. It will come as no surprise that this year’s focus was generative artificial intelligence (AI). There is a sense that generative AI, in its many different forms, is important and that it will have an economic impact, but it’s not yet clear exactly how this will manifest itself in the coming years. Below we discuss the four key takeaways from the conference. 1. Changing how we interact with Microsoft Office Software It is well known that Microsoft has made significant investments in OpenAI and that there is a close relationship between the two firms—GPT-4 is accessible on certain Microsoft Azure service platforms, as an example. Microsoft had only just mentioned the import and expected impact of AI to its future business results as it reported on the period ended 31 March 2023, so we were curious what more they could add in a short presentation. However, Microsoft mentioned one of the most exciting things across the entire conference. We are all searching for ‘use cases’ and we are also all trying to figure out what it will look like to communicate with Office 365 software in ‘natural language’. Microsoft’s representative noted that he had seen an example case where there was a Word document, and that the technology was able to seamlessly interface with PowerPoint and to go from having a Word document to having a version expressed in slides. In WisdomTree’s research team, taking a source file in text form and converting it to a potential presentation is an important function; some situations require slides, some situations require emails, some situations require Word documents. It takes a really long time to laboriously change a Word document into relevant, impactful slides. If there was a way for the file in Word to communicate with PowerPoint to create at least a rough draft with slides, over the course of the year within WisdomTree’s research team alone this would save a rather large amount of team hours. Since it probably could also work in reverse (PowerPoint back to Word), maybe we are not far away from drafts of blog posts being created off of PowerPoint slides. 2. Did you realise that AI cannot hold a patent? Part of what is sparking the current generative AI revolution has to do with creation. People are excited for the capability to create images, molecules, text, to name just a few things. However, the world is seeking to get a better handle on the legal ramifications. One such example regards Stability AI’s image generation capability. Getty Images, a major holder of rights to photographic content, has alleged that the use of their images in this way runs afoul of its licensing provisions, and that their images are quite valuable for training purposes due to diversity of subject matter and detailed metadata1. The value of access to training data, therefore, is coming to light. Another thing we did not realise was that, if AI is involved in the creation of something novel, AI cannot hold a patent, which could have interesting intellectual property implications in the US. An article in the National Law Review, published on 2 May 2023, affirmed that “Federal Circuit Holds That AI Cannot Be an “Inventor” Under the Patent Act - Only Humans Can Get Patents2.” 3. The magic of defect detection One of the most exciting presentations, in our opinion, regarded ‘defect detection’ from the firm Landing AI. In recent years, we have spent a lot of time thinking about electric vehicles, and WisdomTree as a global business has many funds that focus on different metals, different types of companies—basically all sorts of ways that investors can align an investment with trends they are seeing. The world needs more batteries, that much is clear, but batteries need to be assembled in a way that limits defects. When people mention ‘computer vision’ by itself, without an application, it doesn’t always sound exciting or capture the imagination. Seeing the presentation immediately helped us to picture all of the new factories being built to assemble more battery cells, taking advantage of certain funding provisions in the Inflation Reduction Act in the United States. Picturing a computer vision system, deployed at scale, able to catch defective battery cells in close to real-time, could be immensely valuable. All manufacturing companies could benefit from better defect detection. It was interesting to hear in the presentation how there is so much money in things like ‘Targeted Advertising’ and ‘Internet Search’ that this is where a lot of AI applications are developed, but if a company can serve the totality of need across different manufacturing concerns, it could be a big market as well and immensely valuable if these systems can really catch defective products before they are shipped. It was also particularly powerful to watch a demonstration of how a company might have a series of pictures in a database and use AI to ‘learn’ to recognise a particular attribute, like a crack. This could deploy better defect detection at scale as well as putting model training in the hands of people without PhD’s in data science, both very impactful things. 4. The maths of drug development is prohibitive A few presentations during the event concerned drug discovery, and for good reason. It was mentioned that the development of a given molecule into a drug takes roughly $2 billion, 10 years and has a 96% failure rate along the way. While we need drug therapies, the statistical specification of that journey does not sound compelling, and it makes those drugs that get through extremely expensive. Whether it is Nvidia or Exscientia presenting, so far the critical element is not to say that ‘AI is creating drugs’ but rather ‘AI is improving our chances’. Chemistry and physics are much like languages and there are certain rules that govern how they work. Generative AI does not always craft finished prose, but it is able to put many options to the page quite quickly. Generative AI for drug development is most likely to help researchers make better, higher probability attempts at further study. One thing that was very notable to hear was that we might be at a transition point in how research is done. Human researchers seeking the cure or a new therapy for a particular disease converge quite closely around a lot of similar ideas. For approaches run by humans, this makes sense. But for approaches with machine learning closer to the forefront, there may not be enough diversity across the data from the attempts such that the machine learning algorithm can find notable relationships across the data that human researchers would have been less likely to see. If machine learning algorithms are closer to the forefront, it can change the way certain types of research, like drug discovery, are done such that the systems are getting the appropriate breadth of data from which to draw out patterns and relationships. Conclusion: 2023 as a turning point History is replete with turning points. eCommerce, internet search, smart phones, the app economy, social media—all of these things had a ‘beginning’ where success was far from assured and we could not have predicted exactly where the technologies would go. Even if AI has been developing for many years, maybe 2023 will be seen as somewhat of a beginning, in that it marked the point after which non-technical people were using AI just like it was any other application. Sources 1 Source: Brittain, Blake. “Getty Images lawsuit says Stability AI misused photos to train AI.” Reuters. February 6, 2023. 2 Source: “Federal Circuit Holds that AI Cannot Be an ‘Inventor’ Under the Patent Act—Only Humans Can Get Patents.” The National Law Review. May 6, 2023. Volume XIII, Number 126.
SPARKS:AIX
by aneekaguptaWTE
The end of the SaaSacre and the rise of generative AIWe recently had the pleasure of speaking with Janelle Teng of Bessemer Venture Partners (BVP). Janelle is a vice president at BVP, focused primarily on cloud software, infrastructure and developer platforms. WisdomTree began working with BVP in 2020 to launch WisdomTree’s Cloud Computing Strategy which tracks the BVP Nasdaq Emerging Cloud Index. This blog is a summary of the key takeaways from the discussion. The SaaSacre1 of 2022 We had to start by recognising the feeling of our current environment, which comes largely from what BVP has termed the ‘SaaSacre’ of 2022. What is a SaaSacre? If one pulls up the return of the BVP Nasdaq Emerging Cloud Index during 2022 and sees a figure worse than -40%, then they will see it – that drop is the SaaSacre. The market underwent a complete adjustment to valuations across the board, going from peak levels observed in late 2022 to levels much lower reflecting, among other things, a higher general interest rate environment brought on by the US Federal Reserve (Fed). Investors in software-as-a-service (SaaS) companies tended to see an opposite relationship during 2022, where, as interest rates rose, SaaS valuations fell and vice versa2. While it is logical that companies that expect to deliver cash flows far into the future would see their valuations impacted by interest rates, the relationship is not always so stark. The 3 archetypes of COVID-19 shocks on growth S-curves The S-curve is a commonly used heuristic to help investors relate time, plotted on the horizontal axis, to adoption, plotted on the vertical axis. A steeper S-curve = faster adoption. An S-curve moved vertically upwards = a larger adoption. The COVID-19 pandemic was a shock that changed the position of the S-curves of various SaaS companies. If we can understand at least a few archetypes of how this occurred, it can help us to better evaluate how companies are doing now, largely on the other side of the shock. We show these examples in Figure 13: Temporary exponential growth from illusion of market opportunity: this shock would appear as a bulge upwards in the upper portion of the S-curve—telling us that adoption picked up rapidly for a period of time—before dropping back to the original trend. Unsustainable exponential growth due to acceleration within original market opportunity: this shock would appear as a steeper S-curve, with the rising slope pulled further to the left telling us that adoption was occurring suddenly, faster—with the top level peaking at the same place as originally intended, but just arriving there sooner. Many people are familiar with Zoom Video Communications, and this company’s pandemic experience seems to largely be consistent with this archetype. New growth baseline from expanded market opportunity: while it may be easy for CEOs to tell us all a story about how they now have a ‘new growth baseline’, it is far more difficult to actually deliver and execute on than it is to say. If there is one area where this happened, it was in food delivery, in that after the pandemic the general person thinks differently about using certain services, be it Uber Eats or DoorDash. The difficulty of making predictions In thematic topics, it is frequently difficult to make predictions about growth rates and the ultimate sizes of given markets. In the conversation with Janelle, we talked about an example of some forecasts that Gartner had made regarding Worldwide Public Cloud Service Revenues4. In April of 2019, the prediction for 2022 was $331 billion. In April of 2022, the prediction for 2022 was $495 billion, significantly higher. Initial public offerings (IPO’s) and mergers and acquisitions (M&A) We spent time talking about what we were seeing, or put more accurately weren’t seeing, in 2022, and that was IPOs. A significant benefit of speaking with Janelle and BVP is that there is a sense of history. We can recognise that 2021 was an outlier year, in that the aggregate value of software IPOs priced was in the vicinity of $28 billion. Even without the historic shift in policy at the Fed, Figure 2 shows that matching anything close to 2021’s result was going to be difficult. Within the category of corporate actions, sometimes you see M&As (Adobe’s intended purchase of Figma was a big example) and sometimes you see private equity players making investments. So-called ‘take-privates’ in 2022 were extremely active, and we saw many such examples through the year. Growth vs profitability One of the questions that we hear often regards what is more important, growth or profitability? In recent years, maybe the real answer is, ‘it depends when you ask.’ It’s very clear that those of us following the software space in 2018 and 2019 saw that growth was of the utmost importance. In 2022, on the other hand, we were hearing a lot more about profitability. Janelle was able to walk through some work done by BVP within the 2023 State of the Cloud report, the gist of which was, when considering the impact on valuations5: November 2021: revenue growth was about six times as impactful on valuations as profitability. October 2022: the importance of revenue growth and profitability were roughly equal in their impact on valuations. April 2023: revenue growth was about two times as impactful on valuations as profitability. The true conclusion: It is never all growth and it is never all profitability, but it is important to be aware of how the focus on these measures can ebb and flow across time. Generative AI is going to be everywhere Janelle and I spoke the day after Microsoft reported its quarterly earnings for the period ended March 31, 2023. We briefly touched on this quote from Amy Hood, Executive Vice President and Chief Financial Officer6: “In Azure, we expect revenue growth to be 26% to 27% in constant currency, including roughly 1 point from AI services." We can also note this statement from Satya Nadella, CEO7: “Our Azure OpenAI Service brings together advanced models, including ChatGPT and GPT-4, with the enterprise capabilities of Azure. From Coursera and Grammarly, to Mercedes-Benz and Shell, we now have more than 2,500 Azure OpenAI Service customers, up 10X quarter-over-quarter.” Janelle and I discussed how the big companies, in this case represented by Microsoft, are important, in that they tell us something about broader enterprise consumption and spending, leading to better clarity on the environment that the more ‘emerging’ cloud companies have to operate within. Microsoft is sending a big signal on generative artificial intelligence (AI), and we believe we will continue to see it spreading across many different companies. Bottom line: lots of growth catalysts for those with more time Even if we recognise the uncertainty in the current 2023 economic environment, those investors with a longer time horizon can take advantage, positioning for important growth drivers looking forward. It is rare that companies with the largest market capitalisations in the world are able to announce something that could have a material impact on revenue growth, but that is just what generative AI seems to be as we write these words. Sources 1 SaaSacre is a term from BVP, combining ‘SaaS’ and massacre, to help illustrate in words the tough performance environment observed in 2022. 2 Source: bvp-atlas/state-of-the-cloud-2023 3 Source: nextbigteng.substack the-reckoning-of-pandemic-tech-darlings 4 Source: nextbigteng.substack.com the-reckoning-of-pandemic-tech-darlings 5 Source: bvp atlas/state-of-the-cloud-2023?from=feature 6 Source: Microsoft earnings FY23Q3 7 Source: Microsoft earnings FY23Q3
SPARKS:AIX
by aneekaguptaWTE
Artificial intelligence: signs of acceleration in 2023“One final investment area that I’ll mention, that’s core to setting Amazon up to invent in every area of our business for many decades to come, and where we’re investing heavily, is Large Language Models (“LLMs”) and Generative AI. Machine learning has been a technology with high promise for several decades, but it’s only been the last five to ten years that it’s started to be used more pervasively by companies. This shift was driven by several factors, including access to higher volumes of compute capacity at lower prices than was ever available. Amazon has been using machine learning extensively for 25 years, employing it in everything from personalised ecommerce recommendations, to fulfillment center pick paths, to drones for Prime Air, to Alexa, to the many machine learning services AWS offers (where AWS has the broadest machine learning functionality and customer base of any cloud provider). More recently, a newer form of machine learning, called Generative AI, has burst onto the scene and promises to significantly accelerate machine learning adoption.” Amazon.com CEO Andy Jassy1 When Amazon’s CEO makes such a statement, we pay attention. In 1997, Amazon.com had revenues of $147.8 million; in 2022, this figure was $434 billion for Amazon’s consumer business. Amazon Web Services was conceptualised in 2003, with the first services launched in 2006 and, in 2022, generated $80 billion in revenues. Elsewhere, The Stanford AI Index Steering Committee, Institute for Human-Centered AI (one of the best annual resources on artificial intelligence), have also just released a new report. Artificial intelligence (AI) is, undoubtedly, a big topic in 2023, and this report provides an excellent resource for understanding how it is progressing. The full piece is almost 400 pages, but we wanted to highlight some key points. ChatGPT was not the only big AI development of 2022 On November 30, 2022, ChatGPT was launched, but the Stanford AI Index report helps us remember other notable events in 2022. Our 5 favourites: February 16, 2022: DeepMind trained a reinforcement learning agent to control nuclear fusion plasma in a tokamak2. While this doesn’t mean that fusion powerplants are immediately around the corner, it does show a notable use case for AI to help scientific research in a very, very difficult area. April 5, 2022: Google released its PaLM large language model with 540 parameters. This was an important step, showing that one avenue to improve the performance of these models was to simply train them on more data. As of this writing, we do not know how this figure compares to the number of parameters in use for OpenAI’s GPT-4. May 12, 2022: DeepMind showcased Gato, which is a model that can generalise across such activities as: robotic manipulation, game player, image captioning, and natural language generation. June 21, 2022: GitHub makes Copilot available as a subscription-based service for individual developers. Copilot is a generative AI system that can turn natural language prompts into coding suggestions across multiple languages. July 8, 2022: Nvidia uses reinforcement learning to design better-performing GPUs, accelerating the performance of its latest H100 class of GPU chips. Insights on global corporate investment AI has been one of the hottest areas for corporate investment, but Figure 1 shows the total level of investment shifted downwards, from $276.14 billion to a level of $189.59 billion in 2022 with the market volatility. The two biggest categories comprising the level of AI investment recently has been ‘Merger/Acquisition’ and ‘Private Investment.’ Both of these categories dropped significantly from 2021 to 2022, but this is not surprising in that both of these would be expected to slow in a less certain economic environment with the US Federal Reserve quickly raising the cost of capital. One of the most informative charts in the 400-page report is the specific focal areas of investment, and how they have changed. ‘Medical & Healthcare’ was the biggest focal area in 2022, after being second biggest in 2021, trailing only ‘Data Management, Processing and Cloud.’ ‘Cybersecurity, Data Protection’ was the fourth biggest investment area in 2022 and the largest that saw an acceleration in investment, meaning investment in 2022 was actually larger than in 2021. The Russia/Ukraine conflict in 2022 created a big focus on cybersecurity. There is little question, the first four months of 2023 have seen a massive focus on AI, and a massive focus usually leads to at least some hype and some risk of near-term overvaluation. Sometimes this is the nature of thematic investment—we all want something to get excited about, especially if economic growth and geopolitics are less positive. What is emphasised in the letter from Amazon.com CEO, Andy Jassy, and then measured in the 2023 Stanford AI Index report, is that the AI megatrend is continuing to grow and increase in its impact on society and on businesses. Sources 1 Source: aboutamazon andy-jassy-2022-letter-to-shareholders 2 A tokamak, put simply, is somewhat of a doughnut in shape and is a device used to contain the plasma in a fusion reaction.
SPARKS:AIX
by aneekaguptaWTE
14
AMZN Amazon could lose market share because of ChatGPT If you haven`t sold AMZN here for a profit: Then you should know that the implementation of a chatbot like ChatGPT, powered by OpenAI's advanced natural language processing capabilities, could indeed provide an opportunity for consumers to find the cheapest price online for the same product. In an era where price comparison and cost-saving measures are highly valued by customers, this feature could be a valuable asset for Bing. Amazon's reputation for having expensive and overvalued prices has been a topic of discussion among consumers. While Amazon provides a wide range of products and convenient shopping experiences, some users have expressed concerns about the pricing competitiveness on the platform. This presents an opening for Bing to attract cost-conscious consumers who are actively seeking better deals. Furthermore, the concern over the origin of products, particularly those manufactured in China, has gained attention in recent years. Some consumers prefer to avoid purchasing Chinese-made products due to quality, safety, or political reasons. If Bing can provide a search experience that allows users to filter or identify the origin of products more effectively, it could cater to a segment of consumers who prioritize alternative sourcing. If I had to buy some options, that would be the following puts: 2024-1-19 expiration date $105 strike price $4.10 premium Looking forward to read your opinion about it!
NASDAQ:AMZNShort
by TopgOptions
Tesla poised for a breakoutNASDAQ:TSLA - Tesla the "EV Maker" has seen a massive comeback from the low $100s. Tesla tested the $200 mark in February after which we have seen approximately 30% correction which partially filled the gap created in January. Per the diagram we can see that Tesla NASDAQ:TSLA is forming a classic extended broadening wedge pattern, which can indicate consolidation before continuation to the upside. There is significant hype around AI since ChatGPT since launch. We believe Tesla is well positioned to take advantage of the AI revolution. Tesla has made significant progress in AI specifically with their self driving capabilities. Tesla has an abundance of data due to their extensive fleet of vehicles on the road, collecting data daily. This data is used to train its AI algorithms, enabling Tesla to develop more refined and accurate models over time. As of 2023, Tesla has gathered data from over 35 million Full Self-Driving (FSD) miles driven by its fleet, giving it a significant data advantage over competitors. Remember your AI models is only as good as the data you can train it on. Proprietary datasets are gold in the AI/Machine learning world. Tesla's ultimate ambition is to establish a Robotaxi service, where Tesla cars function autonomously without the need for a human driver. Success in this endeavour is not guaranteed, but should they achieve this goal, it could provide Tesla with a substantial competitive edge in the electric vehicle market. In addition to this, Tesla has Optimus (AI robot), but we shall not dive into that in this thread. Tesla plans to grow vehicle deliveries by more than 50% in 2023. The company also expects to see a significant growth in its energy business and is planning to expand its production capacity. The company is also making progress on its Semi and Cybertruck, with deliveries expected to begin soon. Tesla's new factory in Mexico is also expected to increase the company's production capacity and produce electric vehicles based on Tesla's new vehicle platform. (Low cost vehicle platform. Entry points: Accumulation of entries between $168 to $196. Ultimate confirmation would be a daily/weekly break and close above the wedge line. Targets: TP1: $242 - 0.618 Fib Retracement TP2: $306 - 0.786 Fib Retracement - this also aligns with an order block between $294 & $312. TP3: $376 - Just under 10% away from its previous ATH Stop Loss: $163 - Wide stop loss due to volatile nature of Tesla Leverage: Max 5x - I personally use a combination of spot holdings and 5x leverage to take advantage of shorter term swings.
NASDAQ:TSLALong
by TraderMagz
3