ElliottWave-Forecast has built its reputation on accurate technical analysis and a winning attitude. By successfully incorporating the Elliott Wave Theory with Market Correlation, Cycles, Proprietary Pivot System, we provide precise forecasts for 78 instruments including Forex, Commodities, Indices and a number of stocks & etfs from around the World. Technical Videos, Elliott Wave Setup .
#Learningmondays CRYPTO GLOSSARY What is Technical Analysis? Technical analysis is a trading discipline to examine, predict and identify trading opportunities in price trends and patterns seen on charts in the financial markets Also known as identification of patterns on a chart
Does technical analysis of charts apply the same concepts across different types of trading? Such as forex, stocks and crypto
I just watched my first video learning about technical analysis. I'm looking to invest in crypto and invest long term and also potentially day trade. What I want to know, is if this knowledge and the techniques being shown here are universal for all trading? I assume there will be small differences, but is the general jist the same across all types of trading? Here is the video i watched and i will be watching a lot more of their videos over the next few days/weeks/months. The Chart Guys https://www.youtube.com/watch?v=rlZRtQkfK04
Looking to start a discord server for traders for futures, crypto, natural gas, and crude oil and many more looking for members and admins for some channels ultimate goal would be consistent trades and sharing ideas and chart analysis
Hey guys looking for a admin and member for my stock trading discord it’s a fresh server and my ultimate goal is to make consistent trades. We have a focus on crude oil, futures, natural gas and crypto. I’m looking to build a group of traders that are willing to learn and share ideas and chart analysis if you want to help build this come join the server. https://discord.gg/8FH9eN7
#IDAP stands ahead of other #crypto #exchanges by providing freedom to its users with #derivatives-based #trading. Read all about crypto derivatives instruments and advanced charting and #analysis features that enable seamless trading in our #whitepaper https://buff.ly/2LGTLx7
Necessary Disclaimer: no rule breaking intended. No price manipulation intended. I only want to share verifiable facts/links and my analysis. If I am doing anything against the rules please let me know and I will do my best to fix it ASAP. I trade crypto, including LINK, and I am currently short on LINK. This is not financial advice; this is just for my own record and to start a discussion for anyone who might want more transparency around LINK.
I believe there is a lot of misinformation, uncertainty, and unanswered questions about the LINK token, the Chainlink ecosystem, the SmartContract parent company. I also believe that LINK's current price is unjustified based on fundamental factors like usage/business case/current customers/future potential. So I'm raising some points and asking some questions. What is this post? Why should I care? How do I use it? Read or skim it. It's about the LINK token, the Chainlink ecosystem, and the parent company SmartContract. It's about why I believe the price of the LINK token may be currently driven mostly by hype and not backed by standard market fundamentals like usage/economics. Update 9 AUG: reorganizing, rewriting this post and moving supporting data/sources into "appendix" comments below on this post. The previous versions of this post and my comments elsewhere were too emotionally charged and caused more division rather than honest, evidence-based, productive discussion and I sincerely apologize for that. I have now rewritten it and will continue to update it.
Threshold signatures, staking, on-chain SLAs: How real are these, is there a roadmap, how will this benefit users, is there any evidence of users currently *wanting* to use chainlink but needing these features and actively waiting for Chainlink to launch these? Staking: for there to be a valid incentive for users to stake LINK, it has to return around 5% annually because anything substantially under that would have users putting their money elsewhere (https://www.stakingrewards.com/cryptoassets) (not counting speculative capital gains in terms of LINK's price, but price gain per token/coin applies to all other crypto projects as well). Currently, for stakable cryptos, around 30-80% of their total supply is staked, and a good adjusted reward is on the order of 5% as well (some actually negative, some 10%+). The promise of staking incentivises people to buy and hold more LINK tokens (again, many other crypto projects have staking already live). That 5% reward will ultimately have to come from the customers who pay Chainlink oracle nodes to use their services, so it's an extra 5% fee for them. Of course, in the near future, the staking rewards *could* be subsidized by the founders' reserve wallets. Threshold signatures: addressed below in a comment. On-chain SLAs: [TODO] Here's supposedly Chainlink's agile/project planning board. (TODO: verify that it is indeed Chainlink's, and then analyse it) https://www.pivotaltracker.com/n/projects/2129823
I manually traced EVERY single inbound transaction/source of funds for the above 4 (not counting #1 as 10 LINK is negligible). 2 & 3 are 99.99%+ genesis-funded, being ACTIVELY topped up by a genesis wallet, last tx 4 days ago, 500,000 LINK. #4 has been funded 36 times over the past year and a half (that's 36 manual exports and I did them all). They all come from the 0x27158..., 0x2f0acb..., and https://etherscan.io/token/0x514910771af9ca656af840dff83e8264ecf986ca?a=0x1f9e26f1c050b5c018ab0e66fcae8e4394eb0165 (another address like the 0x2f0acb that I went through and checked EVERY SINGLE inbound source of funds, and it's also >99.9% genesis-funded - one tx from Binance for 6098 LINK out of a total ~6,560,000 inbound LINK from genesis wallets), and two other addresses linked to Binance (0x1b185c8611d157a67d9a9d5261b0d2bd52c0bb78, 10,000 LINK and 0x039ac18afe298747c51c85e7c8f0d67c327f3883, 1,000,000 LINK) The 0x039ac... address funded the "Chainlink: Aggregator" address with 127,900 LINK, and the 0x1b185... with about ~9,600 LINK). So yes, it's technically possible that someone not related to Chainlink paid for the ETH / USD price feed because some funds do come from Binance. However, they only come from two distinct addresses. Surely for "240+" claimed partnerships, more than TWO would pay to use Chainlink's MOST POPULAR price feed? That is, unless they don't pay directly but to another address and then Chainlink covers this one from their own wallets. I will check if that's in line with Chainlink's whitepaper, but doesn't that throw doubt on the whole model of end-users paying to use oracles/aggregators, even if it's subsidized? I provide you this much detail not to bore you but to show you that I went through BY HAND and checked every single source (detailed sources in Appendix B) of funds for the OFFICIAL, Chainlink-listed "ETH/USD" aggregator that's supposedly sponsored by 10 DeFi partners (Synthetix, LoopSpring, OpenLaw, 1inch, ParaSwap, MCDEX, FuturesSwap, DMM, Aave, The Force Protocol). Yet where are the transactions showing that those 10 partners have EVER paid for this ETH/USD oracle? Perhaps the data is there so what am I missing? This ETH/USD aggregator has transferred out ~76,000 LINK to I guess the data providers in increments of .33 LINK. It has 21 data providers responding. I will begin investigating the data providers themselves soon. And those middle addresses like 0x1f9e26... and 0x2f0acb...? They have transferred out hundreds of thousands if not millions of LINK to exchanges. And that's just ONE price pair aggregator. Chainlink has around 40 of these (albeit this one's one of the more popular ones). SNX / ETH aggregator is funded 100% by genesis-sourced wallets, only 3 inbound transactions: https://etherscan.io/token/0x514910771af9ca656af840dff83e8264ecf986ca?a=0xe23d1142de4e83c08bb048bcab54d50907390828 Some random examples (for later, ignore these for now) *********** https://etherscan.io/token/0x514910771af9ca656af840dff83e8264ecf986ca?a=0x039ac18afe298747c51c85e7c8f0d67c327f3883 bought 1,000,000 LINK from Binance in Sept 12 & 15, 2019. (one of the possible funding sources for the ETH / USD aggregator example above) This address got 500,000 LINK from 0x27158... and has distributed them into ~5-10,000 LINK wallets that haven't had any out transactions yet https://etherscan.io/token/0x514910771af9ca656af840dff83e8264ecf986ca?a=0x5bcf3edc0bb7119e35f322ba40793b99d4620f1e ************** Another example with an unnamed aggregator-node-like wallet that was only spun up 5 days ago, Aug 5: https://etherscan.io/token/0x514910771af9ca656af840dff83e8264ecf986ca?a=0x2cbfd29947f774b8cf338f776915e6fee052f236 It was funded 2,000 LINK SOLELY by the 0x27158... wallet and has so far paid out ~500 LINK in 0.43 LINK amounts to 9 wallets at a time. For example, this is one of the wallets it cashes out to: https://etherscan.io/token/0x514910771af9ca656af840dff83e8264ecf986ca?a=0x64fe692be4b42f4ac9d4617ab824e088350c11c2#tokenAnalytics That wallet extremely consistently collects small amounts of LINK since Oct 2019. It must be a data provider because a lot of Chainlink named wallets pay it small amounts of LINK regularly. It has transferred out 20 times. The most recent transfer out: https://etherscan.io/token/0x514910771af9ca656af840dff83e8264ecf986ca?a=0xc8c30fa803833dd1fd6dbcdd91ed0b301eff87cf which then immediately transferred to the named "1inch.exchange" wallet, so I assume this was a "cash-out" transaction. It has cashed out via this address a lot. Granted, it also has transfer-out transactions that haven't (yet) ended up in an exchange wallet, eg https://etherscan.io/token/0x514910771af9ca656af840dff83e8264ecf986ca?a=0x88e5353a73f38f25a9611e6083de6f361f9b537b with a current balance of 3000 LINK. This could be a user's exchange wallet, ready to be sold, or could be something else. No way for me to tell as there are no out txs from it.
LINK overall transaction, volume, and tx fees
This is to understand how much $ moves through the LINK ecosystem through: nodes, data providers, reserve wallets, wallets linked to exchanges, others. A typical aggregator node tx (payout?): https://etherscan.io/tx/0xef9e8e6dd94ebe9bbac8866f18c2ea0a07408ced1aa77fa04826043eaa55e772 This is their ETH/USD aggregator paying out 1 LINK to each of 21 addresses. Value of 21 LINK ~= $210. Total eth tx fees: .233 ETH (~$88.5, ~42% of the total tx value. If LINK was $4.2 instead of $10, the tx fees would be 100% of the value of the tx). Transactions like this happen every few minutes, and the payout amounts are most often 0.16, 0.66, 1.0, and 2.0 Link. Chainlink’s node/job listing site, https://market.link, lists 86 nodes, 195 feeds, 801 jobs, ~1,080,000 job runs (I can’t tell if this is over the past 2 months or 1.5 years). Only 20 nodes have over 1000 job runs, and 62 nodes have ZERO runs. Usual job cost is listed as 0.1 link, but the overall payout to the nodes is 10-20 times this. The nodes then cash out usually through a few jump addresses to exchanges. Some quick maths: (being generous and assuming it’s 1mil jobs every 2 months = ~6mil link/year = $60,000,000 revenue a year. This is the most generous estimate towards link’s valuation I’ve found so far. If we ignore the below examples where on multi-node payouts the tx fees are more than the node revenue itself, then it’s almost in line with an over-valued (but real) big tech company. For example, one of the latest CHF/USD job runs paid 0.1 LINK to 9 addresses (data providers?) - total $14.4 payout - and paid 0.065 ETH ($24.5) in fees. That’s a $10.1 LOSS on a $14.4 revenue: https://etherscan.io/tx/0xa6351bab810b6864bfebb0f6e1e3bde3c8856f8aac3ba769dd2e6d1a39c0d23f Linkpool’s (one of the biggest node operators) “ETH-USD CryptoCompare” job costs 0.1 link and has 33 runs in the past 24 hours (once every ~44min), total ~78,000 runs since May 30 2019 (once every ~8min). https://market.link/jobs/64bb0845-c4e1-4681-8853-0b5aa7366101/runs (PS cryptocompare has a free API that does this. Not sure why it costs $1 at current link prices to access an API once)
Top 100 wallets (0.05% of ~186,000 total) hold 83% of tokens. 8 wallets each hold over 1% of total, 58 hold over 0.1%. Of these 58, 9 are named exchange/lending pool wallets. For comparison, for Tether (TUSD), the top 100 wallets (0.006% of ~1,651,000 total) hold 35.9% of the supply. 3 addresses hold over 1% of the supply and 135 hold over 0.1%. Of these 135, at least 15 are named exchange/lending pool wallets. LINK’s market cap is $3.5B (or $10B fully diluted, if we count the foundedev-controlled tokens, which we should as there's nothing preventing them from being moved at a moment's notice). Tether’s is $6.9B. Tether has 10 times more addresses and less distribution inequality. Both LINK and Tether are ERC20 tokens, and even if we temporarily ignore any arguments related to management/roadmap/teams etc, Tether has a clear, currently functional, single use case: keep 1 USDT = $1 USD by printing/burning USDT (and yet as of April 2019, only 74% of Tether's market cap is backed by real funds - https://en.wikipedia.org/wiki/Tether_(cryptocurrency))). Given that Chainlink's market cap is now 50% bigger than Tether's, surely by now there's AT LEAST one clear, currently functional use case for LINK? What is it? Can we *see* it happening on-chain?
Chainlink’s actual deliverable products
"What do I currently get for my money if I buy LINK 1) as an investor and 2) as a tech business/startup thinking of using oracles?” Codebase (Chainlink’s github has around 140-200,000 lines of code (not counting html/css). What else is not counted in this? Town crier? Proprietary code that we don't know about yet? How much CODING has Chainlink done other than what's on github? Current network of oracles - only ~20 active nodes - are there many more than the ones listed on market.link and reputation.link? If so, would be nice to know about these if we're allowed! Documentation - they have what seems like detailed instructions on how to launch and use oracle nodes (and much more, I haven't investigated yet) (TODO this part more - what else do they offer to me as an end consumer, and eg as a tech startup needing oracle services that I can’t code myself?)
Network utilization statistics:
Etherscan.io allows csv export of the first 5000 txs from each day. From Jul 31 to Aug 6 2020, I thus downloaded 30,000 tx from midnight every day to an average of 7:10am (so 24 hour totals are 3.34x these numbers if we assume the same network utilization throughout the day). (Summary of all LINK token activity on the ETH blockchain from 31.07 to 06.08, first 5000 txs of each day (30k total) shown Appendix A comment below this post.) If we GENEROUSLY assume that EVERY SINGLE transaction under 10.0 LINK is ACTUAL chainlink nodes doing ACTUAL work, that’s still under 0.1% of the LINK network’s total volume being used for ACTUAL ecosystem functioning. The rest is speculation, trading, node funding by foundedev wallets, or dumping to exchanges (anything I missed?) Assuming the above, the entire turnover of the actual LINK network is currently (18,422 LINK) * ($10/LINK) * (3.34 as etherscan.io’s data only gives first 5000 tx per day which averages to 7:10am) * (52 wk/year) = USD $31,995,329 turnover a year. Note: the below paragraph is old analysis using traditional stock market Price/Earnings ratios which several users have now pointed out isn't really applicable in crypto. I leave it for the record. Assuming all of that is profit (which it’s not given tx fees at the very least), LINK would need a PE ratio (Price/Earnings) of 100 times to justify its current (undiluted) valuation of $3.5 billion of 300 if you count the other 65% of tokens that haven’t been dumped by the founders/devs yet. For comparison, common PE ratios are 32 (facebook), 29 (google), 37 (uber), 20 (twitter on a good year), 10 (good hedge fund returning 10% annual).
Thoughts on DeFi & yield-farming - [TODO]
Why would exchanges who do their due diligence list LINK, let alone at a leverage? 1) that's their business, they take a cut of every transaction, overhyped or not, 2) they're not safe from listing openly bearish tokens like EIDOS (troll token that incentivized users to make FAKE transactions, response to EOS) https://www.coindesk.com/defi-yield-farming-comp-token-explained The current ANNUAL yield on liquidity/yield farming is something like 2% on STABLE tokens like USDC and TETHER which at least have most of their supply backed by real-world assets. If Chainlink LINK staking is to be successful, they'll have to achieve at LEAST that same 2% at end-state. IF LINK is in bubble territory and drops, that's a lot of years at 2% waiting to recoup losses.
SmartContract Team & Past Projects
Normally I don't like focussing on people because it leads too easily to ad-hominem attacks on personality rather than on technology/numbers as I've done above, but I came across this and didn't like what I saw. Steve Ellis, SmartContract's current CTO, co-founded and worked in "Secure Asset Exchange" from 2014 to 2016. They developed the NXT blockchain, issued 1,000,000,000 NXT tokens (remind you of anything?), NXT was listed end of 2013 and saw 3 quick 500%-1000% pumps and subsequent dumps in early in mid 2014, and then declined to . SecureAE officially shut down in Jan 2016. Then at some point a company called Jelurida acquired the rights to NXT (presumably after SecureAE?), then during the 2017 altcoin craze NXT pumped 300 times to a market cap of $1.8 BILLION and then dumped back down 100 times and now it's a dead project with a market cap of $13 million. https://www.linkedin.com/in/steveellis0606/ https://trade.secureae.com/ https://coinmarketcap.com/currencies/nxt/ https://www.jelurida.com/news/lawsuit-against-apollo-license-violations As an investor or business owner, would you invest/hire a company whose co-founders/CTO's last project was a total flop with a price history chart that's textbook pump-and-dump behaviour? (and in this case, we KNOW the end result - 99% losses for investors) If you're Google/Oracle/SWIFT/Intel, would you partner with them?
Open questions for the Chainlink community and investors:
Network activity: Are there any other currently active chainlink nodes other than those listed on market.link and reputation.link? If so, is there a list of them with usage statistics? Do they use some other token than LINK and thus making simple analytics of the LINK ERC20 token not an accurate representation of Chainlink’s actual activity? If the nodes listed on the two sites above ARE currently the main nodes, then
PR, partnership announcements: Why is the google tweet still pinned to the top of Chainlink’s twitter? Due to the frequently circulated Chainlink promotion material (https://chainlinkecosystem.com/) that lists Google as one of the key partners, this tweet being pinned is potentially misleading as there isn't anything in there to merit calling Google a "collaborator" or "partner" - just that blockchains/oracles *can* use Google's APIs (but so can most software in the world). Is there something else going on with the SmartContract-Google relationship that warrants calling Google a partner that we're simply not aware of yet?
By buying LINK, what backs YOUR money: If you have bought and currently hold LINK tokens, how comfortable are you that the future promise of your investment growing is supported on verifiable business and technological grounds versus pure, parabolic hype? If after reading this post you still are, I kindly ask you to reply and show how even one of the points I provided is either incorrect or not applicable, and I will edit my post and include your feedback in the relevant section as I have already done from other users.
What have I missed? Of course not 100% of what I've said is infallible truth. I am a real human, and I have plenty of biases and blind spots. Even if what I've provided is technically correct, there may be other much more important info that I've missed that eclipses what I've provided here. Ask yourself: if the current hype around LINK is indeed valid and points to a $100/$1000 future LINK price, then Where’s Chainlink’s missing financial/performance/usage evidence to justify LINK’s current valuation of $10+?
For your consideration, I have provided evidence with links that you can follow and verify, and draw your own conclusions. I have made my case as to why I believe the LINK token is currently priced much higher than evidence supports, and I ask you to peer-review my analysis and share your thoughts with me and with the wider LINK/crypto community. Thank you for your time, I realize this is a long post. All questions and feedback welcome, feel free to comment or PM. I won't delete/censoblock (except for personal threats, safety considerations etc). I am a real human but I am not revealing my true identity for obvious privacy/harassment reasons. (If anyone is wondering about my credentials ability to add 2+2 and work with basic spreadsheets: I have previously won a math competition in a USA state, I won an English-speaking country's physics olympiad, my university education is in mathematical physics/optimization engineering, and I worked for a few years in a global manufacturing company doing data analytics, obviously I'm not posting my CV here to verify that but I promise you it's the truth) I’m not looking to spread neither FUD, nor blind faith, nor pure hype, and I want an honest transparent objective discussion. I personally believe more that LINK is overvalued, but my beliefs have evolved and may continue to do so as I research more and understand more about Chainlink, LINK, Ethereum, DeFi, and other related topics, and as I incorporate YOUR feedback. If you think I haven't disclosed something, ask. As always, this is not financial advice and I am not liable for anything that may happen as a result of you reading this!
A Physicist's Bitcoin Trading Strategy. No leverage, no going short, just spot trading. Total cumulative outperformance 2011-2020: 13,000,000%.
https://www.tradingview.com/script/4J5psNDo-A-Physicist-s-Bitcoin-Trading-Strategy/ 3. Backtest Results Backtest results demonstrate significant outperformance over buy-and-hold . The default parameters of the strategy/indicator have been set by the author to achieve maximum (or, close to maximum) outperformance on backtests executed on the BTCUSD ( Bitcoin ) chart. However, significant outperformance over buy-and-hold is still easily achievable using non-default parameters. Basically, as long as the parameters are set to adequately capture the full character of the market, significant outperformance on backtests is achievable and is quite easy. In fact, after some experimentation, it seems as if underperformance hardly achievable and requires deliberately setting the parameters illogically (e.g. setting one parameter of the slow indicator faster than the fast indicator). In the interest of providing a quality product to the user, suggestions and guidelines for parameter settings are provided in section (6). Finally, some metrics of the strategy's outperformance on the BTCUSD chart are listed below, both for the default (optimal) parameters as well as for a random sample of parameter settings that adhere to the guidelines set forth in section (6). Using the default parameters, relative to buy-and-hold strategy, backtested from August 2011 to August 2020,
Total cumulative outperformance (total return of strategy minus total return of buy-n-hold): 13,000,000%.
Rolling 1-year outperformance: mean 318%, median 84%, 1st quartile 55%, 3rd quartile, 430%.
Rolling 1-month outperformance: mean 2.8% (annualized, 39%), median -2.1%, 1st quartile -7.7%, 3rd quartile 13.2%, 10th percentile -13.9%, 90th percentile 24.5%.
Using the default parameters, relative to buy-and-hold strategy, during specific periods,
Cumulative outperformance during the past year (August 2019-August 2020): 37%.
12/17/2016 - 12/17/2017 (2017 bull market) absolute performance of 2563% vs buy-n-hold absolute performance of 2385%
11/29/2012 - 11/29/2013 (2013 bull market) absolute performance of 14033% vs buy-n-hold absolute performance of 9247%
Using a random sample (n=20) of combinations of parameter settings that adhere to the guidelines outlined in section (6), relative to buy-and-hold strategy, backtested from August 2011 to August 2020,
Average total cumulative outperformance, from August 2011 to August 2020: 2,000,000%.
Median total cumulative outperformance, from August 2011 to August 2020: 1,000,000%.
EDIT (because apparently not everybody bothers to read the strategy's description): 7. General Remarks About the Indicator Other than some exponential moving averages, no traditional technical indicators or technical analysis tools are employed in this strategy. No MACD , no RSI , no CMF , no Bollinger bands , parabolic SARs, Ichimoku clouds , hoosawatsits, XYZs, ABCs, whatarethese. No tea leaves can be found in this strategy, only mathematics. It is in the nature of the underlying math formula, from which the indicator is produced, to quickly identify trend changes. 8. Remarks About Expectations of Future Results and About Backtesting 8.1. In General As it's been stated in many prospectuses and marketing literature, "past performance is no guarantee of future results." Backtest results are retrospective, and hindsight is 20/20. Therefore, no guarantee can, nor should, be expressed by me or anybody else who is selling a financial product (unless you have a money printer, like the Federal Reserve does). 8.2. Regarding This Strategy No guarantee of future results using this strategy is expressed by the author, not now nor at any time in the future. With that written, the author is free to express his own expectations and opinions based on his intimate knowledge of how the indicator works, and the author will take that liberty by writing the following: As described in section (7), this trading strategy does not include any traditional technical indicators or TA tools (other than smoothing EMAs). Instead, this strategy is based on a principle that does not change, it employs a complex indicator that is based on a math formula that does not change, and it places trades based on five simple rules that do not change. And, as described in section (2.1), the indicator is designed to capture the full character of the market, from a macro/global scope down to a micro/local scope. Additionally, as described in section (3), outperformance of the market for which this strategy was intended during backtesting does not depend on luckily setting the parameters "just right." In fact, all random combinations of parameter settings that followed the guidelines outperformed the intended market in backtests. Additionally, no parameters are included within the underlying math formula from which the indicator is produced; it is not as if the formula contains a "5" and future outperformance would depend on that "5" being a "6" instead. And, again as described, it is in the nature of the formula to quickly identify trend changes. Therefore, it is the opinion of the author that the outperformance of this strategy in backtesting is directly attributable to the fundamental nature of the math formula from which the indicator is produced. As such, it is also the opinion of the author that continued outperformance by using this strategy, applied to the crypto ( Bitcoin ) market, is likely, given that the parameter settings are set reasonably and in accordance with the guidelines. The author does not, however, expect future outperformance of this strategy to match or exceed the outperformance observed in backtests using the default parameters, i.e. it probably won't outperform by anything close to 13,000,000% during the next 9 years. Additionally, based on the rolling 1-month outperformance data listed in section (3), expectations of short-term outperformance should be kept low; the median 1-month outperformance was -2%, so it's basically a 50/50 chance that any significant outperformance is seen in any given month. The true strength of this strategy is to be out of the market during large, sharp declines and capitalizing on the opportunities presented at the bottom of those declines by buying the dip. Given that such price action does not happen every month, outperformance in the initial months of use is approximately as likely as underperformance.
How many people really understand what they’re buying, especially when it comes to highly specialized hardware companies? Most NVidia investors seem to be relying on a vague idea of how the company should thrive “in the future”, as their GPUs are ostensibly used for Artificial Intelligence, Cloud, holograms, etc. Having been shocked by how this company is represented in the media, I decided to lay out how this business works, doing my part to fight for reality. With what’s been going on in markets, I don’t like my chances but here goes: Let’s start with… How does NVDA make money? NVDA is in the business of semiconductor design. As a simplified image in your head, you can imagine this as designing very detailed and elaborate posters. Their engineers create circuit patterns for printing onto semiconductor wafers. NVDA then pays a semiconductor foundry (the printer – generally TSMC) to create chips with those patterns on them. Simply put, NVDA’s profits represent the difference between the price at which they can sell those chips, less the cost of printing, and less the cost of paying their engineers to design them. Notably, after the foundry prints the chips, NVDA also has to pay (I say pay, but really it is more like “sell at a discount to”) their “add-in board” (AIB) partners to stick the chips onto printed circuit boards (what you might imagine as green things with a bunch of capacitors on them). That leads to the final form in which buyers experience the GPU. What is a GPU? NVDA designs chips called GPUs (Graphical Processing Units). Initially, GPUs were used for the rapid processing and creation of images, but their use cases have expanded over time. You may be familiar with the CPU (Central Processing Unit). CPUs sit at the core of a computer system, doing most of the calculation, taking orders from the operating system (e.g. Windows, Linux), etc. AMD and Intel make CPUs. GPUs assist the CPU with certain tasks. You can think of the CPU as having a few giant very powerful engines. The GPU has a lot of small much less powerful engines. Sometimes you have to do a lot of really simple tasks that don’t require powerful engines to complete. Here, the act of engaging the powerful engines is a waste of time, as you end up spending most of your time revving them up and revving them down. In that scenario, it helps the CPU to hand that task over to the GPU in order to “accelerate” the completion of the task. The GPU only revs up a small engine for each task, and is able to rev up all the small engines simultaneously to knock out a large number of these simple tasks at the same time. Remember the GPU has lots of engines. The GPU also has an edge in interfacing a lot with memory but let’s not get too technical. Who uses NVDA’s GPUs? There are two main broad end markets for NVDA’s GPUs – Gaming and Professional. Let’s dig into each one: The Gaming Market: A Bit of Ancient History (Skip if impatient) GPUs were first heavily used for gaming in arcades. They then made their way to consoles, and finally PCs. NVDA started out in the PC phase of GPU gaming usage. They weren’t the first company in the space, but they made several good moves that ultimately led to a very strong market position. Firstly, they focused on selling into OEMs – guys like the equivalent of today’s DELL/HP/Lenovo – , which allowed a small company to get access to a big market without having to create a lot of relationships. Secondly, they focused on the design aspect of the GPU, and relied on their Asian supply chain to print the chip, to package the chip and to install in on a printed circuit board – the Asian supply chain ended up being the best in semis. But the insight that really let NVDA dominate was noticing that some GPU manufacturers were focusing on keeping hardware-accelerated Transform and Lighting as a Professional GPU feature. As a start-up, with no professional GPU business to disrupt, NVidia decided their best ticket into the big leagues was blowing up the market by including this professional grade feature into their gaming product. It worked – and this was a real masterstroke – the visual and performance improvements were extraordinary. 3DFX, the initial leader in PC gaming GPUs, was vanquished, and importantly it happened when funding markets shut down with the tech bubble bursting and after 3DFX made some large ill-advised acquisitions. Consequently 3DFX, went from hero to zero, and NVDA bought them for a pittance out of bankruptcy, acquiring the best IP portfolio in the industry. Some more Modern History This is what NVDA’s pure gaming card revenue looks like over time – NVDA only really broke these out in 2005 (note by pure, this means ex-Tegra revenues): 📷 https://hyperinflation2020.tumblr.com/private/618394577731223552/tumblr_Ikb8g9Cu9sxh2ERno So what is the history here? Well, back in the late 90s when GPUs were first invented, they were required to play any 3D game. As discussed in the early history above, NVDA landed a hit product to start with early and got a strong burst of growth: revenues of 160M in 1998 went to 1900M in 2002. But then NVDA ran into strong competition from ATI (later purchased and currently owned by AMD). While NVDA’s sales struggled to stay flat from 2002 to 2004, ATI’s doubled from 1Bn to 2Bn. NVDA’s next major win came in 2006, with the 8000 series. ATI was late with a competing product, and NVDA’s sales skyrocketed – as can be seen in the graph above. With ATI being acquired by AMD they were unfocused for some time, and NVDA was able to keep their lead for an extended period. Sales slowed in 2008/2009 but that was due to the GFC – people don’t buy expensive GPU hardware in recessions. And then we got to 2010 and the tide changed. Growth in desktop PCs ended. Here is a chart from Statista: 📷https://hyperinflation2020.tumblr.com/private/618394674172919808/tumblr_OgCnNwTyqhMhAE9r9 This resulted in two negative secular trends for Nvidia. Firstly, with the decline in popularity of desktop PCs, growth in gaming GPUs faded as well (below is a chart from Jon Peddie). Note that NVDA sells discrete GPUs, aka DT (Desktop) Discrete. Integrated GPUs are mainly made by Intel (these sit on the motherboard or with the CPU). 📷 https://hyperinflation2020.tumblr.com/private/618394688079200256/tumblr_rTtKwOlHPIVUj8e7h You can see from the chart above that discrete desktop GPU sales are fading faster than integrated GPU sales. This is the other secular trend hurting NVDA’s gaming business. Integrated GPUs are getting better and better, taking over a wider range of tasks that were previously the domain of the discrete GPU. Surprisingly, the most popular eSports game of recent times – Fortnite – only requires Intel HD 4000 graphics – an Integrated GPU from 2012! So at this point you might go back to NVDA’s gaming sales, and ask the question: What happened in 2015? How is NVDA overcoming these secular trends? The answer consists of a few parts.Firstly, AMD dropped the ball in 2015. As you can see in this chart, sourced from 3DCenter, AMD market share was halved in 2015, due to a particularly poor product line-up: 📷 https://hyperinflation2020.tumblr.com/private/618394753459994624/tumblr_J7vRw9y0QxMlfm6Xd Following this, NVDA came out with Pascal in 2016 – a very powerful offering in the mid to high end part of the GPU market. At the same time, AMD was focusing on rebuilding and had no compelling mid or high end offerings. AMD mainly focused on maintaining scale in the very low end. Following that came 2017 and 2018: AMD’s offering was still very poor at the time, but cryptomining drove demand for GPUs to new levels, and AMD’s GPUs were more compelling from a price-performance standpoint for crypto mining initially, perversely leading to AMD gaining share. NVDA quickly remedied that by improving their drivers to better mine crypto, regaining their relative positioning, and profiting in a big way from the crypto boom. Supply that was calibrated to meet gaming demand collided with cryptomining demand and Average Selling Prices of GPUs shot through the roof. Cryptominers bought top of the line GPUs aggressively. A good way to see changes in crypto demand for GPUs is the mining profitability of Ethereum: 📷 https://hyperinflation2020.tumblr.com/private/618394769378443264/tumblr_cmBtR9gm8T2NI9jmQ This leads us to where we are today. 2019 saw gaming revenues drop for NVDA. Where are they likely to head? The secular trends of falling desktop sales along with falling discrete GPU sales have reasserted themselves, as per the Jon Peddie research above. Cryptomining profitability has collapsed. AMD has come out with a new architecture, NAVI, and the 5700XT – the first Iteration, competes effectively with NVDA in the mid-high end space on a price/performance basis. This is the first real competition from AMD since 2014. NVDA can see all these trends, and they tried to respond. Firstly, with volumes clearly declining, and likely with a glut of second-hand GPUs that can make their way to gamers over time from the crypto space, NVDA decided to pursue a price over volume strategy. They released their most expensive set of GPUs by far in the latest Turing series. They added a new feature, Ray Tracing, by leveraging the Tensor Cores they had created for Professional uses, hoping to use that as justification for higher prices (more on this in the section on Professional GPUs). Unfortunately for NVDA, gamers have responded quite poorly to Ray Tracing – it caused performance issues, had poor support, poor adoption, and the visual improvements in most cases are not particularly noticeable or relevant. The last recession led to gaming revenues falling 30%, despite NVDA being in a very strong position at the time vis-à-vis AMD – this time around their position is quickly slipping and it appears that the recession is going to be bigger. Additionally, the shift away from discrete GPUs in gaming continues. To make matters worse for NVDA, AMD won the slots in both the New Xbox and the New PlayStation, coming out later this year. The performance of just the AMD GPU in those consoles looks to be competitive with NVidia products that currently retail for more than the entire console is likely to cost. Consider that usually you have to pair that NVidia GPU with a bunch of other expensive hardware. The pricing and margin impact of this console cycle on NVDA is likely to be very substantially negative. It would be prudent to assume a greater than 30% fall in gaming revenues from the very elevated 2019 levels, with likely secular decline to follow. The Professional Market: A Bit of Ancient History (again, skip if impatient) As it turns out, graphical accelerators were first used in the Professional market, long before they were employed for Gaming purposes. The big leader in the space was a company called Silicon Graphics, who sold workstations with custom silicon optimised for graphical processing. Their sales were only $25Mn in 1985, but by 1997 they were doing 3.6Bn in revenue – truly exponential growth. Unfortunately for them, from that point on, discrete GPUs took over, and their highly engineered, customised workstations looked exorbitantly expensive in comparison. Sales sank to 500mn by 2006 and, with no profits in sight, they ended up filing for bankruptcy in 2009. Competition is harsh in the semiconductor industry. Initially, the Professional market centred on visualisation and design, but it has changed over time. There were a lot of players and lot of nuance, but I am going to focus on more recent times, as they are more relevant to NVidia. Some More Modern History NVDA’s Professional business started after its gaming business, but we don’t have revenue disclosures that show exactly when it became relevant. This is what we do have – going back to 2005: 📷 https://hyperinflation2020.tumblr.com/private/618394785029472256/tumblr_fEcYAzdstyh6tqIsI In the beginning, Professional revenues were focused on the 3D visualisation end of the spectrum, with initial sales going into workstations that were edging out the customised builds made by Silicon Graphics. Fairly quickly, however, GPUs added more and more functionality and started to turn into general parallel data processors rather than being solely optimised towards graphical processing. As this change took place, people in scientific computing noticed, and started using GPUs to accelerate scientific workloads that involve very parallel computation, such as matrix manipulation. This started at the workstation level, but by 2007 NVDA decided to make a new line-up of Tesla series cards specifically suited to scientific computing. The professional segment now have several points of focus:
GPUs used in workstations for things such as CAD graphical processing (Quadro Line)
GPUs used in workstations for computational workloads such as running engineering simulations (Quadro Line)
GPUs used in workstations for machine learning applications (Quadro line.. but can use gaming cards as well for this)
GPUs used by enterprise customers for high performance computing (such as modelling oil wells) (Tesla Line)
GPUs used by enterprise customers for machine learning projects (Tesla Line)
GPUs used by hyperscalers (mostly for machine learning projects) (Tesla Line)
In more recent times, given the expansion of the Tesla line, NVDA has broken up reporting into Professional Visualisation (Quadro Line) and Datacenter (Tesla Line). Here are the revenue splits since that reporting started: 📷 https://hyperinflation2020.tumblr.com/private/618394798232158208/tumblr_3AdufrCWUFwLgyQw2 📷 https://hyperinflation2020.tumblr.com/private/618394810632601600/tumblr_2jmajktuc0T78Juw7 It is worth stopping here and thinking about the huge increase in sales delivered by the Tesla line. The reason for this huge boom is the sudden increase in interest in numerical techniques for machine learning. Let’s go on a brief detour here to understand what machine learning is, because a lot of people want to hype it but not many want to tell you what it actually is. I have the misfortune of being very familiar with the industry, which prevented me from buying into the hype. Oops – sometimes it really sucks being educated. What is Machine Learning? At a very high level, machine learning is all about trying to get some sort of insight out of data. Most of the core techniques used in machine learning were developed a long time ago, in the 1950s and 1960s. The most common machine learning technique, which most people have heard of and may be vaguely familiar with, is called regression analysis. Regression analysis involves fitting a line through a bunch of datapoints. The most common type of regression analysis is called “Ordinary Least Squares” OLS regression, and that type of regression has a “closed form” solution, which means that there is a very simple calculation you can do to fit an OLS regression line to data. As it happens, fitting a line through points is not only easy to do, it also tends to be the main machine learning technique that people want to use, because it is very intuitive. You can make good sense of what the data is telling you and can understand the machine learning model you are using. Obviously, regression analysis doesn’t require a GPU! However, there is another consideration in machine learning: if you want to use a regression model, you still need a human to select the data that you want to fit the line through. Also, sometimes the relationship doesn’t look like a line, but rather it might look like a curve. In this case, you need a human to “transform” the data before you fit a line through it in order to make the relationship linear. So people had another idea here: what if instead of getting a person to select the right data to analyse, and the right model to apply, you could just get a computer to do that? Of course the problem with that is that computers are really stupid. They have no preconceived notion of what data to use or what relationship would make sense, so what they do is TRY EVERYTHING! And everything involves trying a hell of a lot of stuff. And trying a hell of a lot of stuff, most of which is useless garbage, involves a huge amount of computation. People tried this for a while through to the 1980s, decided it was useless, and dropped it… until recently. What changed? Well we have more data now, and we have a lot more computing power, so we figured lets have another go at it. As it happens, the premier technique for trying a hell of a lot of stuff (99.999% of which is garbage you throw away) is called “Deep Learning”. Deep learning is SUPER computationally intensive, and that computation happens to involve a lot of matrix multiplication. And guess what just happens to have been doing a lot of matrix multiplication? GPUs! Here is a chart that, for obvious reasons, lines up extremely well with the boom in Tesla GPU sales: 📷 https://hyperinflation2020.tumblr.com/private/618394825774989312/tumblr_IZ3ayFDB0CsGdYVHW Now we need to realise a few things here. Deep Learning is not some magic silver bullet. There are specific applications where it has proven very useful – primarily areas that have a very large number of very weak relationships between bits of data that sum up into strong relationships. An example of ones of those is Google Translate. On the other hand, in most analytical tasks, it is most useful to have an intuitive understanding of the data and to fit a simple and sensible model to it that is explainable. Deep learning models are not explainable in an intuitive manner. This is not only because they are complicated, but also because their scattershot technique of trying everything leaves a huge amount of garbage inside the model that cancels itself out when calculating the answer, but it is hard to see how it cancels itself out when stepping through it. Given the quantum of hype on Deep learning and the space in general, many companies are using “Deep Learning”, “Machine Learning” and “AI” as marketing. Not many companies are actually generating significant amounts of tangible value from Deep Learning. Back to the Competitive Picture For the Tesla Segment So NVDA happened to be in the right place at the right time to benefit from the Deep Learning hype. They happened to have a product ready to go and were able to charge a pretty penny for their product. But what happens as we proceed from here? Firstly, it looks like the hype from Deep Learning has crested, which is not great from a future demand perspective. Not only that, but we really went from people having no GPUs, to people having GPUs. The next phase is people upgrading their old GPUs. It is much harder to sell an upgrade than to make the first sale. Not only that, but GPUs are not the ideal manifestation of silicon for Deep Learning. NVDA themselves effectively admitted that with their latest iteration in the Datacentre, called Ampere. High Performance Computing, which was the initial use case for Tesla GPUs, was historically all about double precision floating point calculations (FP64). High precision calculations are required for simulations in aerospace/oil & gas/automotive. NVDA basically sacrificed HPC and shifted further towards Deep Learning with Ampere, announced last Thursday. The FP64 performance of the A100 (the latest Ampere chip) increased a fairly pedestrian 24% from the V100, increasing from 7.8 to 9.7 TF. Not a surprise that NVDA lost El Capitan to AMD, given this shift away from a focus on HPC. Instead, NVDA jacked up their Tensor Cores (i.e. not the GPU cores) and focused very heavily on FP16 computation (a lot less precise than FP64). As it turns out, FP16 is precise enough for Deep Learning, and NVDA recognises that. The future industry standard is likely to be BFloat 16 – the format pioneered by Google, who lead in Deep Learning. Ampere now does 312 TF of BF16, which compares to the 420 TF of Google’s TPU V3 – Google’s Machine Learning specific processor. Not quite up to the 2018 board from Google, but getting better – if they cut out all of the Cuda cores and GPU functionality maybe they could get up to Google’s spec. And indeed this is the problem for NVDA: when you make a GPU it has a large number of different use cases, and you provide a single product that meets all of these different use cases. That is a very hard thing to do, and explains why it has been difficult for competitors to muscle into the GPU space. On the other hand, when you are making a device that does one thing, such as deep learning, it is a much simpler thing to do. Google managed to do it with no GPU experience and is still ahead of NVDA. It is likely that Intel will be able to enter this space successfully, as they have widely signalled with the Xe. There is of course the other large negative driver for Deep Learning, and that is the recession we are now in. Demand for GPU instances on Amazon has collapsed across the board, as evidenced by the fall in pricing. The below graph shows one example: this data is for renting out a single Tesla V100 GPU on AWS, which isthe typical thing to do in an early exploratory phase for a Deep Learning model: 📷 https://hyperinflation2020.tumblr.com/private/618396177958944768/tumblr_Q86inWdeCwgeakUvh With Deep Learning not delivering near-term tangible results, it is the first thing being cut. On their most recent conference call, IBM noted weakness in their cognitive division (AI), and noted weaker sales of their power servers, which is the line that houses Enterprise GPU servers at IBM. Facebook cancelled their AI residencies for this year, and Google pushed theirs out. Even if NVDA can put in a good quarter due to their new product rollout (Ampere), the future is rapidly becoming a very stormy place. For the Quadro segment The Quadro segment has been a cash cow for a long time, generating dependable sales and solid margins. AMD just decided to rock the boat a bit. Sensing NVDA’s focus on Deep Learning, AMD seems to be focusing on HPC – the Radeon VII announced recently with a price point of $1899 takes aim at NVDAs most expensive Quadro, the GV100, priced at $8999. It does 6.5 TFLOPS of FP64 Double precision, whereas the GV100 does 7.4 – talk about shaking up a quiet segment. Pulling things together Let’s go back to what NVidia fundamentally does – paying their engineers to design chips, getting TSMC to print those chips, and getting board partners in Taiwan to turn them into the final product. We have seen how a confluence of several pieces of extremely good fortune lined up to increase NVidia’s sales and profits tremendously: first on the Gaming side, weak competition from AMD until 2014, coupled with a great product in form of Pascal in 2016, followed by a huge crypto driven boom in 2017 and 2018, and on the Professional side, a sudden and unexpected increase in interest in Deep Learning driving Tesla demand from 2017-2019 sky high. It is worth noting what these transient factors have done to margins. When unexpected good things happen to a chip company, sales go up a lot, but there are no costs associated with those sales. Strong demand means that you can sell each chip for a higher price, but no additional design work is required, and you still pay the printer, TSMC, the same amount of money. Consequently NVDA’s margins have gone up substantially: well above their 11.9% long term average to hit a peak of 33.2%, and more recently 26.5%: 📷 https://hyperinflation2020.tumblr.com/private/618396192166100992/tumblr_RiWaD0RLscq4midoP The question is, what would be a sensible margin going forward? Obviously 33% operating margin would attract a wall of competition and get competed away, which is why they can only be temporary. However, NVidia has shifted to having a greater proportion of its sales coming from non-OEM, and has a greater proportion of its sales coming from Professional rather than gaming. As such, maybe one can be generous and say NVDA can earn an 18% average operating margin over the next cycle. We can sense check these margins, using Intel. Intel has a long term average EBIT margin of about 25%. Intel happens to actually print the chips as well, so they collect a bigger fraction of the final product that they sell. NVDA, since it only does the design aspect, can’t earn a higher EBIT margin than Intel on average over the long term. Tesla sales have likely gone too far and will moderate from here – perhaps down to a still more than respectable $2bn per year. Gaming resumes the long-term slide in discrete GPUs, which will likely be replaced by integrated GPUs to a greater and greater extent over time. But let’s be generous and say it maintains $3.5 Bn Per year for the add in board, and let’s assume we keep getting $750mn odd of Nintendo Switch revenues(despite that product being past peak of cycle, with Nintendo themselves forecasting a sales decline). Let’s assume AMD struggles to make progress in Quadro, despite undercutting NVDA on price by 75%, with continued revenues at $1200. Add on the other 1.2Bn of Automotive, OEM and IP (I am not even counting the fact that car sales have collapsed and Automotive is likely to be down big), and we would end up with revenues of $8.65 Bn, at an average operating margin of 20% through the cycle that would have $1.75Bn of operating earnings power, and if I say that the recent Mellanox acquisition manages to earn enough to pay for all the interest on NVDAs debt, and I assume a tax rate of 15% we would have around $1.5Bn in Net income. This company currently has a market capitalisation of $209 Bn. It blows my mind that it trades on 139x what I consider to be fairly generous earnings – earnings that NVidia never even got close to seeing before the confluence of good luck hit them. But what really stuns me is the fact that investors are actually willing to extrapolate this chain of unlikely and positive events into the future. Shockingly, Intel has a market cap of 245Bn, only 40Bn more than NVDA, but Intel’s sales and profits are 7x higher. And while Intel is facing competition from AMD, it is much more likely to hold onto those sales and profits than NVDA is. These are absolutely stunning valuation disparities. If I didn’t see NVDA’s price, and I started from first principles and tried to calculate a prudent price for the company I would have estimated a$1.5Bn normalised profit, maybe on a 20x multiple giving them the benefit of the doubt despite heading into a huge recession, and considering the fact that there is not much debt and the company is very well run. That would give you a market cap of $30Bn, and a share price of $49. And it is currently $339. Wow. Obviously I’m short here!
This is a new post after some interest in a comment why I believed the S&P is going to 1700. Update 3: I am going to limit my answers in the comments guys; as the post becomes more popular it is becoming more diluted with snark etc. I don't expect anyone to follow my opinions; I just want to share one aspect of why I am making the trades I am. I maybe wrong. Random walk and all that.. Original Disclaimer: This is based on historical precedence and we are in unprecedented times but, with history as our guide a strong argument can be made for the S&P to decline to a level that is currently inconceivable.I have disclosed all my positions near the bottom. Update 1: Slightly long; happy to be challenged in the comments, it is late in the UK (2am) so may tidy it up and add more references and charts tomorrow.Update 2:Have expanded the post to answer as many comments and requests for references wherever possible and tagged in the requestors.
Intro: Are we in a recession?
If you believe so, or that we are heading into a recession then there are four things needed to support a genuine rally out of a recession
Improving economic health indicators
Accurate pricing reflecting the end of the recession and tempered optimism
We are missing 2 out of those 4 criteria; the overwhelming monetary and fiscal policy (world-records) are compensating for lack of positive indicators and volatile and bullishpricing.
What do you mean by pricing?
It can be argued that the current price of stocks is not discounting for the acute and likely chronic harm to consumer sentiment and spending power. For example; the UK clothing retailer Next Group closed their bricks and mortar stores (share price increased 4%) then they cancelled all online shopping (share price increased 3%) and finally they cancelled all orders with their supply chain (shares leapt 12.8% during the rally.) There is the massive amount of second, third and fourth order effects that this one company does to the UK economy (and Turkish factories). Suppliers, shipping, design, marketing etc all cancelled and the staff furloughed. This is one example but the indexes are currently full of similar examples and some analysts are ringing the alarm bells.
Lazard Asset Management are concerned that the pandemic “will persist longer than many investors suspect and that the economic damage will be deeper and potentially longer-lasting”.
Reddit is quick to mention that stonks only go up but there is some truth to that sentiment at present since any negative factors are dismissed as being priced in and all positive factors are heralded as a cause for stocks to rally. If priced in was accurate then we would not see record-beating market rallies back to back. 10% volatility swings over 48 hours is the very definition of not priced in. There is evidence to suggest that, well, the bullish sentiment is wrong and mainly because it is retail investors being taken for a ride whilst funds re-balance and offload. Retail traders "buying the dips" is normally a contrarian signal, meaning that it's time to sell. This section is for u/lntoIerant in response to a comment.
Edit to answer some comments about this portion thus far.
Do retail investors move the market?
No, they act as a sentiment indicator that the market is reaching a peak absurdity. Similar sentiments have preceded major recessions in the past. When you hear a layman offering stock tips or googling how to buy stocks then we are reaching the precipice of a depression. new market entrants are not the same as traditional retail investors.
Are retail investors buying in greater volumes?
That is hard to say because the majority of retail trades are done off-book. The trades are mixed in with portfolio moves or using the retail service which is a dark pool.
Are retail investors dumb money?
Well, no. Kind of. It depends. This white paper indicates that retail investors are more knowledgeable, more profitable and better informed than previously thought. However, a lot of their trades, as mentioned above, are done off-book as part of a larger portfolio and they simply lose a fraction of a basis point because market timing is not that critical.
What does this have to do with the S&P dividend and the EPS?
Major indexes are comprised of stocks that pay handsome dividends; normally 2% yield a year. The companies have reached their limit of growth (HSBC haven't discovered 5 million new customers and Shell are not finding new fossil fuels) so investors hold the stock for income-seeking reasons. The FTSE 100 was priced in to generate £89 billion in dividends for 2019 and £90 billion+ in 2020. That has largely collapsed. The only companies that pay dividends are those taking on debt to do so like Shell. And they have; a 10Bn credit line to maintain dividends. The Bank of Englandhad to slap 5 UK banks from issuing dividends at this time. That means that their primary valuations as income-generating stocks are questionable... ...especially since the dividends are not expected to return to the 2020 levels for another 10 years now. Edit to add: This portion is taken from the market report by BNY Mellon. You can see the chart here. The analyst is John Velis of BNY. Thanks to u/flash_aaaah_ahhhhh for prompting me.
“By 2021, the market expects dividends per share for the S&P 500 to be down to under $38 per share (a staggering 41 per cent drop from recent highs of approximately $63 per share) and then to start slowly rising again. Going out 10 years to 2030, the expectation is that dividends will just about recover to pre-Covid-19 levels.”
Main body: Onto the S&P
In 2021 the market expects the dividends per share for the S&P to be reduced to $38 per share. That is priced in and common knowledge. That is a 41% drop from the recent highs of $63 a share and seems alarming for income seeking investors since we are not expected to recover to those prices for 8-10 years. Source. But DataTrek have noted that we are still currently trading at 21X the trailing 10 year earnings of $122 a share. Dividends per share normally don't fall as far as earnings per share. But they are inverted at present. For the S&P to be trading at 2,650 level (or even higher) it means the market does not believe the pandemic or recession will have any long-term damage. That puts us squarely at odds with items 3 and 4 in our list of factors needed to exit a bear market.
In other recessions, including 2008, the dividend price per share drops approximately 12-15% but the earnings per share drop by considerably more; as much as 85%. That means that in 2008 financial crisis and subsequent bear market; the dividends per share dropped by a lower percentage amount than the total index value drop. You can see that in this chart here.
The market drop was approximately 56% and the Dividend drop was 14%
The market drop was 56% and the earnings drop was 85%
Right now, we have the reverse. Dividend share drop in this market is 41% (which is chilling) and market drop was approximately only 30% and rallying heavily back to the mid-20's only. That makes no financial sense unless the assets were being propped up by buyers...
S&P ATH: 3386 to 2488 on April 4th (26.5% drop)
S&P ATH Dividend: From $63 expected to $38 (a 41% drop)
S&P ATH EPS:
If the S&P follows the same playbook at 2008-9, then we would expect to see levels of around 1400 at the bottom but that seems extremely bearish expecting that this crisis is worse than 2008. If previous indications hold true, then we would expect the S&P to drop by approximately 50-60%ish at the true bottom to reflect the 41% decrease in expected shares plus additional discounts and negative market sentiment. In reality, we are probably likely to pull back to between 13X and 15X trailing average which puts the S&P between 1600 (low side) and 1800 (high side).
You are putting a lot of faith in a re-run of the 2008 crisis
I am. No doubt about it. After October 2008, stocks fell for another four months, piling up 40% of losses before the recently ended bull market began in March 2009.
New market indicators
Since I wrote this post, the DJIA was up over 4% and closed down on the day. Thank you to theTwitter feed of Jim Bianco for this: Since 1925 (95 yrs!), up more than 4% and closing down on the day has happened only one other time ... Oct 14, 2008 (Tsy Sec Hank Paulson forced the banks to take TARP money). The S&P 500 was up 3.5% at the high and closed down on the day. Since April 1982 (daily H,L,C began) has happened three other times...Oct 3, 08, Oct 14, 08, and Oct 17, 08. This mkt continues to trade like Oct 08. It was six months and another 25% down before the low. Bezinga are also playing up the 2008 similarities.
Why is bullish sentiment so wrong?
The negative reports are so wildly negative that the almost defy belief. We are dealing with insane numbers way beyond our traditional frame of reasoning. This is topped only by the insanity of the scale of quantitative easing. Less than a year ago, a small movement in the non-farm payrolls would lead to a 2-3% move in the markets; now we are hitting 700K jobs lost, a truly ugly number and the market rallies hugely. Future economic students will study this to try and understand what was happening. In the space of weeks the majority of the Western economies have swung to being effectively state-sponsored, centralised economies and no one really knows how to unwind these positions. It is impossible to reconcile being a bull with a centralised state economy and blue-chip stocks that refuse to pay dividends but the share price remains at the same levels as when they paid a 2% yield. The UK forecast is for the deepest contraction since 1900. Business surveys have shown activity crashing faster in March than during the financial crisis. The Office for National Statistics has published experimental research on the impact of Covid-19 on the economy.
With entire swaths of the economy having shut down “traditional forecasting methods become irrelevant”, warned Chiara Zangarelli, economist at investment bank Nomura.
Michelle Girard, economist at NatWest, said that while there was huge uncertainty about the precise magnitude of the contraction in gross domestic product in the second quarter, “there is little doubt that it will be off the scale” That is not a bullish sentiment. It means markets are acting irrationally since fundamentals are being dismissed as priced-in. In reality; nothing is priced in.
I am long VIX to 78 (expected by end of Apri but ideally by 24/4)
I am short India to 7800 (expected by 15/05)
I am short S&P to 2200 (expected by mid-late of May)and will be to 1810-50
I am short Dow to 19000 (expected by mid-late May)and will be again to 17000
I am short FTSE to 5200 and will be again to 4800 (expected by mid-late May)
No current active hedges / all spreads due to being tax free profits in the UK
Further spread betting the swings to the upside where I can to scalp
I am holding a portfolio of streaming services and gaming companies
I am holding Microsoft and Disney
I own a very small quantity of crypto, primarily XRP
Edit to add: So, your entire thesis is totally destroyed if companies keep paying dividends?
Yes. In a nutshell. But something else will be destroyed; the western taxpayer and future growth.
If companies are using 0% interest rates to take out loans and then transferring those loans a small 1% of the populace via dividends; that bill will come due to the citizen taxpayer and/or shareholder of the future
If companies are taking federal or governmental aid to furlough workers but still paying dividends to shareholders? That bill will come due to the citizen taxpayer and effectively is an even more extreme form of socialising market losses; it means that we truly can never have a correction since the top 1% will lose. Not lose the investment itself, which can rebound, but will simply lose the yield on an investment and only for a short period of time. If we have reached a point where that is considered unacceptable then we truly are living in a new socialist, centrally planned world.
Here is Tesco defending their decision today of £635m in dividends...despite receiving considerable amounts of VAT, Rates and Rental relief from the UK Government (£585m)...they have done an admirable job and are profitable but this market signal and their stated reasons for doing so are alarming.
CEO said 'every pound we receive [in rates relief] will be invested in ensuring Tesco is able to support British shoppers...' That is tax payers paying a subsidy to a free-market company for the ability to shop...and also... Mr Lewis said that the needs of savers and pension funds also needed to be considered in the debate around dividends. “We’ve thought long and hard about our responsibilities here . . . we are in a strong position to pay out for the benefit of those people
Edit to add: What about the FED and stimulus
u/tauriel81 and u/aliveintucson325 and u/100PERCENTYOLO_VEQT OK - to truly test my own assumptions; here is my argument AGAINST my position. The Fed have not quite printed money as Reddit loves to meme. They have issued liquidity and central banks worldwide have allowed banks to relax their requirement to hold reserves of cash. That injects money into the business world by allowing lending and borrowing to continue. It also reduces theoretical risk since the models are back within tolerance. When the time comes they will remove the credits gradually without causing hyperinflation. They do this by paying banks not to lend back into the system by holding a % of their assets at the Federal Reserve. So they pay the banks but the banks keep the deposit at the Fed and don't pass on the liquidity to potential borrowers..gradually and sustainably. https://www.aier.org/article/powells-new-monetary-regime/ That means the borrower of the future (home purchasers, entreprenuers etc) will have very few credit facilities available so RIP to the long-term economic growth. We also have unprecedented government support for citizens. The largest social security welfare plan since WW2, especially in Europe. If you believe that the Western economies can weather this storm using the bridging devices by central banks then it pays to dollar cost average into the market and keep buying the dips as a retail investor. Lots of buoyant news from European nations and China about the slowing pandemic is overwhelming the negative leading and lagging economic indicators about economic data. If you believe the economy can return to normal within 36 months, then it pay to be bullish and invest. If you are day-trading, swing-trading or short-term options trading then the overwhelming market moves are likely to crush people as the system flexes under lots of volatility. You are also likely prioritising the negative news and technical analysis in your filter bubble and de-prioritising the positive news particularly when that news is fiscal or monetary policy since those things are dry, boring and incomprehensible half the time. So you miss Fed backstops critical bankingi and instead hear UK Prime Minister in intensive care. If you want to know what is going on...
Look at the short term fundamentals
Zoom out. Re-look.
Zoom out to an even longer timeline. Re-look.
Zoom out to an even even longer timeline. Re-look.
Zoom out to an even even even longer timeline. Re-look.
Decide where you making a prediction. Plan your trade, trade your plan. How do the FED take money back out of the economy? They FED purchase the security initially to then sell it back to the asset-holder later. So the balance of credit-deficit merely swaps but by paying a small premium on the excesses that they hold, they can cushion the inflation or deflation of the currency. So, they effectively give the bank liquidity and then remove that liquidity later by passing the asset back...but also provide a small premium to cushion the blow; 50% of the premium is then held on Federal Reserve books so that the market is not flooded with new money. The FED previously reduced their balance sheet from $4.4 trillion to $3.7 trillion but it remains to be seen if they can unwind a position of this size.
2 out of the 4 necessities for exiting a recession are not present
S&P currently trading at 21X the trailing 10 year average dividend
In previous recessions a 50% drop in the market was accompanied by a 15% drop in dividends
Market analysts expecting for a 41% drop in dividends but only trading a 26% drop in the market. At present the S&P dividend per share drop is 41% but the S&P is rallying back to less than 20% drop...whilst dividends are not expected to return to 2019 levels of income for 8-10 years
In previous recessions the dividend per share drop is much less than the overall index drop
S&P highly overvalued, completely inverted when compared with dividend expectation and market dividend pricing
S&P pull back to 1600-1800 over short-medium time frame (1 month-6 months).
If market history is to be believed then 1400 is not unfeasible based on percentages but you have to be hoping for a total economic destruction for this to happen.; expect a total Governmental response if this happens.
If S&P continues to rise then it indicates companies are taking on debt or other instruments to pay dividends rather than innovate, upgrade or consolidate their business position which some are (Shell etc).
Economic data will eventually overpower the stimulus and the Coronavirus is not priced in; hardly anything is priced in and analysts are now saying so publicly.
How Not To Pressurize Your Mind While Trading Crypto?
As a crypto trader, you must realize that crypto trading is time consuming. It’s hard not to be checking prices and price charts every second when you know your money is on the line. People get so obsessed with social media and crypto trends, and it seeps into their dreams. The worst part is it never ends, and it never takes a break. The market is always open, and trades occur at all times; people stare at the screen all day trying to cash in on an opportunity. The volatility of the crypto market is why it is easy to get stress out; your mind is continually thinking of new ways to enhance trades, and a time might come when the pressure becomes overwhelming. This will affect your ability to make the right decisions, and your health will be at risk. Some signs that your mind is pressured and experiencing trading stress include being hyper-alert, short, and shallow breaths every time a trade goes awry, experiencing sick feelings and other negative feelings.
It’s pretty clear that detaching from crypto isn’t easy in no way; it’s a skill that takes time to cultivate. However, it would be best if you detached to shield your mental health during crypto trading or investments. Below are tips that help keep the pressure at bay:
Fix Trading Hours
Even though exchanges are available at all hours of the day, you don’t have to be also. Your body is not a machine and needs some time off. You can’t always cash in on all trade opportunities even if you’re up at all times, so take a break. Fix hours that you will trade and stick to those hours, no matter the temptation.
Losses should be treated as a Learning Opportunity
When a trade goes awry, there’s usually a lesson to learn from it. Don’t let it be an avenue for despair or feeling anger, instead learn from the situation to make better decisions next time. Sometimes, it is not your fault, and you were just unlucky; focus on the longterm plan and don’t get overwhelmed. Analysis always helps to minimize emotional outbursts.
Put the Emotions Aside
When you trade with your emotions, you’re bound to have lots of issues. The pressure on your mind could be overwhelming. That’s why it is essential to tame the feelings; cold logic works best in crypto trading. If you don’t control your emotions, crypto trading will drive you insane. Don’t feel too good and don’t feel too bad about wins and losses; endeavor to strike a balance.
Make a plan and don’t deviate
What’s your plan going into trading? Once you have an idea about how you intend to trade instead of simply willing the charts to go in a particular direction, trading becomes more natural. To minimize the pressure of trading, plan out your trade in advance and set your point of entry and exits. Don’t change your plans in the middle of a trade because you feel a particular way; don’t deviate from your plan.
Have a second source of income
It’s hard to keep the pressure off if it is your source of livelihood; rational thinking goes out the winder when your income source is on the line. That’s why you need to have another source of income; it’s easy to keep calm when you don’t depend on crypto trading to survive.
At the beginning of trading, keeping a positive mindset is easy. But then the first trade goes wrong, and the pressure kicks in, the need to always make the right choice and never miss any opportunity. However, if you practice these tips and stick to them, you will find a way to stop your mind from being pressured in crypto trading. Getting to this point is crucial; it will make you a better trader. If you like it, never hesitate to give us thumbs-up
What do you think of my lack of a trading strategy?
I've dabbled in trading for years study charts and indicators, writing complex algorithms and doing a bunch of analysis. Yet, I never found any success. Somehow I would always lose more than I've won but the following non-strategy has been consistently profitable for the last 2 years. I should also mention that I don't use this strategy to day-trade per-se - I typically hold a position for a day or two, sometimes more - sometimes alot more - but it can certainly be scaled to trade on any time chart. Here's the strategy (if you can all that): First, I only trade securities that I don't mind holding for a while. Typically, these are stocks whos companies I just like (TSLA) or technologies that I believe in (crypto). Then I only go long on a decent downturn. The idea is to be a buyer when a healthy amount of sellers are out of the market. 4% drop? I'm in! I open the initial trade with some small portion of my account (1-2%), sometimes more if I'm feeling confident or if there's a significant drop with high volatility (aka fear aka overeaction). I don't use any stop loss or anything like that. If it moves in my favor I try to ride out the profit until it finds resistance or if it just kinda moves laterally, Ill try to exit on a small profit or loss. But if it moves against me, I don't sell. If the move is relatively small (up to a few percent) I just hold and hope it rebounds. But if it moves even more against me, I buy more! At this point, I'm upside down in a position but because it's only a small portion of the account, I don't really care also because it's a company I like, I kinda just think of myself as an investor with some holdings in my long term portfolio - with that sort of horizon, it's not really important that you're down a 4-5%. So I just sit on it. If it moves up, because I purchased more and lowered my average price, Ill be profitable at a lower price than my initial position needed to be. So if it moves into a profitable zone, I kinda feel out the momentum to try and let profits run but really my initial position was a mistake so I just try to take 1-2% (or less if it's really struggling) and call it a day (trade). Now if the security moves against me a third time and/or the position REALLY tanks I buy even MORE! So now I'm in with like 10% of my account in a security that is eating it. But it's one I like so at least there's that. Again, I think of myself as an investor and hold, maybe collect some dividends at this point if I really get stuck. I still have 90% of my account to go trade something else in the meantime while this trade sits. Obviously, you can buy your way into a security that will lose 90% of its value and you'll be loading up on more and more of it on the way down. But again, your average price will trail along the more you buy at a lower price and as more and more sellers are cleared out of the market, buyers have to come along eventually and provide some support. And again I trade only stocks I like and have some intrinsic value (APPL, AMZN etc). Worst case I just end up holding shares in these companies. If I have enough shares I can trade options against then. But so far, I've managed to get in and out of positions taking very few losses. If anything, I'd say the downside is that the profits are a bit slow with this strategy. It's probably 95% wins with most wins at about 1-5% but they come only 1-2 times a week. Sometimes more frequently, sometimes less. YTD I'm up about 60% on my account which I guess isn't that much for you guys but it sure beats the S&P
Have you ever traded with statistical edge?Our Allen trade talks about backing up the trading network and leveraging it from excellent newspapering. This is a stage that is undermined by many traders but fairly, it can be a crucial factor in boosting your trust and believing in your system. For those interested in this sort of research, you can check out the FTMOStatistical Application. Trading with a Statistical edge Although many traders back-test and record their trades to verify the trading system 's feasibility, monitoring and using the data to maximize both your stop loss and profit goal is a tremendous advantage. Two of the most critical pieces of data that I record when reporting trades is the drawdown and the benefit potential. The drawdown, to be sure, is how far a trade goes against my place before it goes in my favour. Whereas the benefit potential is the maximum distance from my entry which the trade moves in my favor. It isn't important and it's uncommon, in general, that I actually exit the trade. Yet definitely coming out at or as close as can be. Firstly, I record my trades in two ways, using screenshots of the charts themselves where I annotate my entry, date, type of trade and all other relevant details related to my methodology, such as strength and weakness analysis , multiple time frame analysis and correlation. I also note on the map the drawdown and benefit potential of the trade. Then I go through my Excel spreadsheet with main details. See "excel" below. Excel spreadsheet with main details. This includes the date, day, session, pair, time, route, entry price, closing price, type of setup, type of entry, type of exit, drawdown, potential for benefit and outcome. I then let excel do all the heavy lifting for myself as I can sort my trades numerous ways, by day, by session, by pair, by route, by type of set-up etc. But where the really cool stuff is under the "Mind-blowing stats" tab where I have some of the above filterable statistics that will help me to optimize both my stop loss and my benefit goal. Here is a summary of the specification. When you use a risk percentage account to calculate your position size (as you should), so the lower the pause, the larger a position size you will trade in. The stop must, therefore, have a high likelihood of remaining. The vast majority of trading books, guides, videos, etc., advise that after a recent high / low swing, the stop will be many pips. But my trade documents helped me to come up with a statistical advantage for my stoppage placement. As can be seen in the "Drawdown" tag, Trading my Type 1 BO (breakout out) on GBPAUD, 79.55 percent of the time my drawdown was less than 25 pips, although it was just 81.82 percent at 30 pips and 84.09 percent at 35 pips. Statistical Edge Trading So when using a larger pause, an extra loss or 2, the advantage of having a greater size of the place and thereby netting more money makes the extra loss(s) inconsequential. Furthermore, the income goal can also be optimized. Looking at the "Profit Potential" connection and remaining on GBPAUD again for my Type 1 BO trades, we can easily see that almost 80 percent of the time, those trades get between 20 and 30 pips. Statistical Edge Trading (b) It is a perfect place to take off 1/2 of the spot and push the stop to flat. So we can let the rest of the half run to about 50 pips where 59.09 percent of the trades touch. Obviously market conditions aren't always the same, so if you can recognise when they are, i.e. linked moves or strengthening or weakening other classes (commodity pairs or safe haven pairs), then you can make educated decisions about how far a trade will go. Statistical Edge Trading (meme) I hope this information 's helpful to you. Eva " Forex " Canares . Cheers and Profitable Trading to All. About FTMO - They fund forex traders. Just Pass their risk management rules and begin trading for their company. They'll provide you capital up to $300k USD for trading the financial markets. 70% of profits you keep and losses are covered by them. How does it work? How to Become a Funded Forex ,Stocks or CryptoCurrency Trader?
[Free] The Complete Day Trading Course - YouTube Playlist (New 2020)
Day Trading & Technical Analysis System For Intraday Trading Stocks, Forex, Crypto, Options Trading & Financial Trading What you'll learn
Learn All The Charting Tools, Trading Strategies And Profitable Hacks For Day Trading With Real World Examples! Dedicated Support from the Course Instructors and the Learning Community. 100% Questions Answered Within 24 Hours! How to Build a Solid Strong Foundation For Day Trading How to Use TradingView For Chart Analysis & Paper Trading How to Choose The Best Chart Time Frames For Day Trading How to Use Different Day Trading Order Types How to Short Sell & Deal With Short Squeezes How to Avoid Blowing Up Your Account How to Use Support & Resistance How to Trade Profitable Technical Indicators & Overlays That Work Well For Day Trading How to Identify Market Directions Using EMA How to Identify Market Directions Using MACD How to Identify Overbought and Oversold Conditions Using RSI How to Use Bollinger Bands to Buy Low Sell High How to Trade Profitable Chart Patterns That Work Well For Day Trading How to Trade Broadening Tops and Bottoms How to Trade Wedges and Triangles How to Trade Flags and Pennants How to Trade Gaps How to Trade Double Tops and Bottoms How to Trade Rounding Tops and Bottoms How to Trade Diamond Tops and Bottoms How to Trade Cup and Handle How to Trade Head and Shoulders How to Trade Dead-Cat Bounces And a lot more...
Tip #1: Reputable Cryptocurrencies Invest only in reputable and already established projects with a good development team. Projects like IOTA, Ethereum, Ripple, or Cardano have proven in the past that they are able to enter into partnerships with companies outside the crypto world. Tip #2: Diversify! Don’t count on just one crypto. Any successful portfolio has some degree of diversification. If a sudden event destroys the future of a coin, your other coins can still make up for the loss. Tip #3: Fundamental Analysis Just monitor not only the chart development but also keep an open eye on the technical developments and partnerships of your coins. Charts can be created by large investors and day traders using short squeezes or similar methods of manipulation. It’s extremely important to follow the fundamental development of your cryptocurrencies, especially for a long investment horizon. Tip #4: Rational Decisions Avoid FOMO and FUD. FOMO (Fear of missing out) and FUD (Fear, uncertainty, and doubt) are two terms that were derived from emotions in human psychology. The point is that you should always try to make rational decisions when investing. Emotions have no place in trading! Tip #5: Strategy Planning It’s necessary to have a strategy. Whether you want to buy cryptocurrencies via day trading or invest long-term. Just plan in advance how you will behave if your goals are achieved. Switchere Blog
Coinviva Bitcoin Market Weekly Report - Week of 10/08/2020
Coinviva BTC-USD Hourly Chart The Bitcoin price has been trading within a range last week. The short-term trend has become more bullish with higher lows established in the past couple of days. If the momentum starts to build up in the next day, it is possible that the price would break above the current resistance at $12,000. The target for the next 2 weeks is $13,000, while the support level is at $11,000. Disclaimer: The above market commentary is based on technical analysis using historical pricing data, and is for reference only. It does not serve as investment or trading advice. Review of the week: Crypto asset manager Grayscale Investments has publicly filed with the United States Securities and Exchange Commission (SEC) on behalf of its Ethereum Trust to become a company reporting to the commission. If this application is successful, Ethereum Trust will become the second platform to achieve the status of a digital currency investment vehicle reporting to the SEC after Grayscale Bitcoin Trust earlier in January. Grayscale hopes to attract more institutional investors into the crypto space with this filing as most of these investors are wary of making investments in instruments that are not registered with the commission. Grayscale said that over one-third of U.S investors have now shown interest in investing in Bitcoin and other crypto assets and this might be a wakeup call for financial advisors to catch up on crypto assets and facilitate the transition of new adopters in the crypto market.
Pattern recognition plays an important role in trading. Traders look for unique patterns on charts in order to find good opportunities. Often the biggest problem is you can draw an endless number of patterns on a chart, like in the above chart ^ You will get an information overload. Maybe this would be nice as a… Read More » Bitcoin Trading Guide for Intermediate Crypto Traders This bitcoin chart analysis guide is built to be your one-stop-shop tutorial for intermediate crypto trading. Crypto trading seems complicated at first glance. Fortunately, it’s not nearly as perplexing as you think. Once you learn how to read charts and perform basic technical analysis, it all starts to... We’re also going to outline our favorite cryptocurrency analysis tools and resources for trading Bitcoin and altcoins. Crypto Candlestick Charts. There are a couple of different other ways to look at the charts, but our favorite crypto price chart is the candlesticks chart. As with candlestick patterns, chart patterns should be used in confluence with other methods, such as indicators or trend analysis, for better results. Top 8 Chart Patterns for Crypto Trading. There are too many chart patterns to list them all here, so we will just be picking some that have a relatively high success rate. A series on cryptocurrency trading basics, focusing on breaking down crypto technical analysis at a beginner's level for everyone to understand.. When it comes to analyzing cryptocurrencies (or any kinds of investments for that matter), there are 2 main ways that you can perform your analysis; namely fundamental analysis and technical analysis.
For Daily Trading support, trading ideas, trading education, access to my personal trades and much more consider joining the Jim of All Trades Telegram. To do so, just become a Patreon supporter ... Free account for charts and technical analysis Tradingview http://bit.ly/trading_view Coinigy Crypto Currency Chart Program 30 Days free with signup Then we look at how the tenets of Dow Theory can be applied to the crypto space to get a better understanding of how and why markets move. For more videos, courses, and guides, visit us over at ... 12:00 Hedge funds n crypto 13:00 Tether 14:20 BSV Chart 15:00 BSV growth and projects 16:20 Kava 16:50 Atom chart 17:00 Algo chart 17:20 OMG chart 18:30 Eidoo chart 19:00 Tezos 20:20 Chainlink ... Daily streams covering the biggest news, movers, themes, and trades in crypto. Hit subscribe so you don't miss an episode & drop a like if you enjoy the updates! Remember to turn POST ...