Crypto Trading Signals – How They Work (Updated Guide 2019)

Cryptocurrency Trading Signal: What Are Crypto Trading Signals?

Cryptocurrency Trading Signal: What Are Crypto Trading Signals? submitted by henrykene to doronize [link] [comments]

##What are the best Bitcoin Binance Crypto Trading BOT & Signals on Telegram

##What are the best Bitcoin Binance Crypto Trading BOT & Signals on Telegram submitted by freecryptosignalsapp to CryptoIndia [link] [comments]

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(16) Sana Shaikh's answer to What are the best Telegram channels to provide a Bitmex Binance crypto trading signal? - Quora

(16) Sana Shaikh's answer to What are the best Telegram channels to provide a Bitmex Binance crypto trading signal? - Quora submitted by BitcoinTraderfx to CryptoCurrencyTrading [link] [comments]

The 4th way of algorithmic trading (Signal Processing)

Algorithmic trading types classified based on development perspectives:
1) Technical Analysis
2) Statistics and Probability
3) Machine Learning
I took a different path which is not discussed widely in this subreddit.
4) Signal Processing
I'm not a good storyteller, but this is my journey and advices for the beginners
First, my background:
- Electrical and Electronic engineer,
- Software developer (20+ years)
- Trader (5+ years)
- Algorithmic trader (3+ years)

How I Found The Alpha:

Before algorithmic trading, I was somehow profitable tradeinvestor. Like most of you, when I began to algorithmic trading, I tried to find magic combination of technical indicators and parameters. Also I threw OHLCV and indicators data into the RNN for prediction.
I saw that, even very simple strategies like famous moving average crossover is profitable under right market conditions with correct parameters. But you must watch it carefully and if you fell it is not working anymore, you must shut it down. It means you must be experienced trader to take care of your algorithm.
I am a fulltime software developer, algorithmic trading was my side project also it became my hobby. I tried to learn everything about this industry. I watched and listened hundreds of hours of podcasts and videos in all my free time like commuting from home to work.
These are the most useful to me:
- Chat with traders: https://www.youtube.com/channel/UCdnzT5Tl6pAkATOiDsPhqcg
- Top traders unplugged: https://www.youtube.com/usetoptraderslive
- Ukspreadbetting: https://www.youtube.com/channel/UCnKPQUoCRb1Vu-qWwWituGQ
Also I read plenty of academic papers, blog posts and this subreddit for inspiration.
Inspiration came from my field, electronics. I will not give you much detail about it but I have developed a novel signal processing technique. It is a fast and natural technique which I couldn’t find any article or paper which mention this method. It can transform any interval price data into meaningful, tradable form. The best part is, it doesn't require any parameter and it adapts to changing market conditions intrinsically.
These are the concepts that inspire me:
- Information Theory: https://en.wikipedia.org/wiki/Information_theory
- Signal Processing: https://en.wikipedia.org/wiki/Signal_processing
- ADC: https://en.wikipedia.org/wiki/Analog-to-digital_converter

What a Coincidence:

While googling to improve my algorithm, I found out that, Signal Processing is used by Jim Simon's Renaissance Technologies according to various sources including wikipedia: https://en.wikipedia.org/wiki/Financial_signal_processing

Proverbs Integration:

Output of the process can be used to develop endless type of profitable strategies. I made some money with different momentum based strategies while thinking about how I can use this technique more efficiently.
I like to combine different fields. I think trading and life itself have many things in common. So beside general trading concepts, I think that I can try to implement concepts of the life. Also because of the parameterless design, it's more like a decision making process than an optimization problem.
I searched proverbs and advices for better decision making. I handled them one by one and thought how I could implement them in a unified strategy while preserving the parameterless design. In time, this process was significantly improved stability and reliability while it was evolving from momentum to mean reversion.
These are some proverbs which I use them at various aspects of the algorithm:

- “The bamboo that bends is stronger than the oak that resists.” (Japanese proverb)
- "When the rainwater rises and descends down to where you want to cross, wait until it settles." (Sun-Tzu)
- "If you do not expect the unexpected you will not find it, for it is not to be reached by search or trail" (Heraclitus)
If you wonder how I implement them in the code, think about the last one; how do you define the unexpected, how to wait for it and how to prepare your algorithm to generate profit.
By the way, I strongly recommend: The Art of War (Sun-Tzu)

Result:

I have plenty of ideas waiting to be tested and problems that need to be solved. Nevertheless these are the some of the backtest results, for the time being:
Crypto:
- Market fee and spread are considered, slippage is not.
- For multiple assets testing; Survivorship bias was attempted to be eliminated using historical market rank of the assets. Data is acquired from coinmarketcap.com weekly report.

ETH / BTC
BNB / BTC
Binance Historical Top 100 / BTC
Other Markets:
My main focus is crypto trading. But all the improvements are cross checked in different markets and intervals and validated empirically and logically. It can’t beat every asset and every interval but it tends to work profitably across them.

https://preview.redd.it/l865fw6mjfd51.png?width=900&format=png&auto=webp&s=ff217d35637b41e26db8d7cfc3df14c3fb7ec14e
Live:
The algorithm is running live for over 1.5 years with evolving strategies I mention before. The last one is running for months.

Warnings and Advices:

- Bugs: A few months ago, before bedtime, I released new version for fixing small cosmetic bug and gone to sleep. When I woke up, I saw that nearly 40% of my account wiped out in a few hours. Instead of live settings, I published test settings. It was very painful. I have been coding since childhood, so everyone must be careful. I recommend, implement hard limit for stopping the algorithm.
- Fully Automatic Strategy: Finding an edge is not enough. If you need fully automated trading system, you need a portfolio manager (a lot of research is going on at this field) and especially an asset selector mechanism which is maybe more important than the edge itself. If your algorithm is not be able to select which assets to trade, you must select manually. It's not an easy task and it's prone to error. I was very lucky with that: A mechanism already contained in the algorithm was used to rank and select the assets based on their momentums.
- Fee-Spread: Because of the market fee and spread, trading is a negative sum game. Do not ignore it when backtesting your algorithm.
- Slippage: It's really a problem for low volume assets like penny stocks and lower market cap crypto currencies. Stay away from them or play with small capital or find a way to determine how much money you can use.
- Latency: Don’t think it's a HFT only problem. If your algorithm synchronize multiple assets data from the market and run calculations before sending order back to the market, you lose significant amount of time. This usually causes losses that you have not considered before, especially in a volatile environment. Also if you want to develop realtime strategy, you must seriously consider what you will do in downtime.
- Datasource: This is the most important part for preparation before developing you strategy. If you don’t have good, reliable data; you cannot develop a good strategy. For free data for various market; I suggest investing.com, but you should consider that volume data is not provided. For crypto, all of the exchanges provide their real data for any asset and any interval, you can use them freely. Also you can buy data , especially if you want intraday data, but I can't suggest any because I never tested them.
- Biases: Before developing algorithm, please take a look at and understand the common biases like: Survivorship bias, Look-ahead bias, Time period bias. Or you can be sure that you will face them when you go live.
- Live trading: When you think your algorithm can make money, don’t wait till perfection. Go live as soon as possible with small capital to wake up from your dreams and face with the facts early.
- Psychology: If your education is based on STEM and you don’t have trading experience, it’s not easy in the real world to swallow all those ups and downs that you see in minutes during backtest. It can affect your mood and your life much more than you think. I suggest, work with a professional trader or only invest what you can really afford to lose.

Last Words:

After over 3 years of journey, I have a profitable algorithm that I trust. I was supposed to lie on the beach and drink beer while my algorithm printing money. But I am consistently checking it’s health and I have always things to do like all software development projects.
I posted some of the backtest results, but I don’t know are they considered P/L Porn or not. If so, I can remove it.
Sorry about mysterious parts of this post. I removed some parts unwillingly before posting, but there is really a thin line between giving away your edge freely (also it means loosing it) and inspiring people to find their own way.

“Non est ad astra mollis e terris via" - Seneca

EDIT:


For those engineers and EE students who are bombing my inbox for guessing what I did; I can not write all of you in private, also I want to explain it publicly.
I must say, you are on the wrong way. If I open sourced the signal processing part, probably it doesnt mean anything to you and you can not turn it into a profitable algorithm.
I have to clarify that; before I developed the technique, I knew what I am looking for exactly. Signal processing is not magically trading the market, I am trading the market. it's just a tool to do what is in my mind near perfectly.
Also proverbs are the way of thinking. I read them and think if it means anything for trading.

Lastly watch the Kung Fu Panda :)
https://www.youtube.com/watch?v=rHvCQEr_ETk

submitted by if-not-null to algotrading [link] [comments]

I bought $1k of the Top Ten Cryptos on January 1st, 2018. Result? -74%

I bought $1k of the Top Ten Cryptos on January 1st, 2018. Result? -74%

EXPERIMENT - Tracking Top 10 Cryptos of 2018 - Month 31 -74%
See the full blog post with all the tables here.
tl;dr: purchased $100 of Top Ten Cryptos in Jan. 2018, haven't sold or traded, repeated in 2019 and 2020, update y'all monthly. July was very strong for crypto. For 2018 Top Ten: ADA finished the month on top. ETH and XRP also very strong. Overall, BTC still waaaay in the lead and is approaching break even point. Three cryptos (IOTA,NEM, DASH) have lost over 90% of value. Over three years, cryptos outperforming S&P if I'd taken a similar approach.

Month Thirty One – Down 74%

Summary after 31 months
Crypto came roaring back in July after an almost all-red June. Each crypto in the 2018 Top Ten finished July at a significantly higher value, led by ADA which ended the month +57%.

Question of the month:

Which member of all three Top Ten Crypto Index Fund Experiments turned 5 years old in July?

A) Bitcoin B) Ethereum C) Bitcoin Cash D) XRP
Scroll down for the answer.

Ranking and July Winners and Losers

Not a ton of movement for the 2018 Top Ten group this month. Cardano and XRP both climbed one position while NEM gained two, clawing itself back into the Top Thirty. Dash headed in the other direction, dropping two places in the rankings.
Considering all that has changed in the world of crypto since the beginning of 2018, it’s interesting to note that only four out of the ten cryptos that started 2018 in the Top Ten have dropped out. NEM, Dash, IOTA, and Stellar have been replaced by Binance Coin, Tether, BSV, and newcomer CRO.
July Winners – It was a very strong month: all cryptos made significant gains in July. But for the third month in a row ADA outperformed the field, gaining +57% in July. ETH finished a close second, up +55% followed by XRP which gained +52%.
July Losers – Even during a good month, NEM can’t catch a break. Its +23% gain made it the worst performer of the 2018 Top Ten.
How has your favorite crypto fared over the first 31 months of the 2018 Top Ten Crypto Index Fund Experiment?
Bitcoin still has the most monthly wins (7) but look at this: thanks to its strong 2020 including three straight monthly wins, Cardano is now right behind BTC with 6 monthly wins. Which project has the most monthly losses? NEM stands alone with 6. Every crypto has at least one monthly win and Bitcoin is unique as the only cryptocurrency that hasn’t lost a month. It came close this month, gaining “only” +26%.

Overall update – BTC approaching break even point, second place ETH in the lonely middle, NEM still worst performing.

Although it wasn’t able to keep pace with its peers in July, BTC continues to slowly but surely approach its break even point. It is down about $1,500 (-12%) since my purchase in January 2018. My initial investment of $100 thirty-one months ago is now worth about $88.
Even though Ethereum has lost half of its value since the experiment began, it is all alone in second place: no other crypto is close.
NEM seems comfortable in its usual place, down at the bottom. It has lost -94% over the life of the experiment. That initial $100 investment in NEM is now worth $5.78. Dash and IOTA join NEM as the only three cryptos in the Top Ten that have lost at least -90% of their value since January 2018.

Total Market Cap for the entire cryptocurrency sector:


Total market cap since Jan 2018
The crypto market added about $82B in July, making up a ton of ground. The last time we saw a similar level in terms of overall crypto market cap was way back in the fifth month of the 2018 Top Ten Experiment: May 2018.

Bitcoin dominance:

Le Bitdom since January 2018
Since Bitcoin receives much of the attention in the press, it may surprise the casual observer to learn that Bitcoin Dominance dropped quite a bit in July, especially considering BitDom had been stuck at roughly the same level for most of 2020. This signals an interest in altcoins and a willingness to buy into riskier cryptos.
Some context: since the beginning of the experiment, the range of Bitcoin dominance has been quite wide: we saw a high of 70% BitDom in September 2019 and a low of 33% BitDom in February 2018.

Overall return on investment since January 1st, 2018:

The 2018 Top Ten Portfolio gained over $70 in July 2020. If I cashed out today, my $1000 initial investment would return about $260, down -74% from January 2018.
This sounds horrible but don’t hang yourself with a celibate rope: the 2018 return on investment is back where it was about a year ago. Take a look at the ROI over the life of the experiment, month by month, for some context:
Yes, you may notice that the 2018 Top Ten portfolio has finished over half of the first thirty one months down at least -80%, but it’s nice to see the low -70s for a change.
So the Top Ten Cryptos of 2018 are down -74%. What about the 2019 and 2020 Top Tens? Let’s take a look:
So overall? Taking the three portfolios together, here’s the bottom bottom bottom line:
After a $3000 investment in the 2018, 2019, and 2020 Top Ten Cryptocurrencies, my combined portfolios are worth $3,6965 ($260+ $1,722 +$1,713).
That’s up about +23% for the three combined portfolios, compared to -10% last month. It also marks the highest ROI of the three combined portfolios since I added this metric this year. The previous high was +13% back in January 2020.
Having trouble visualizing? Don’t worry, I got what you need:
Combined ROI
So, a +23% gain by dropping $1k on whichever cryptos were in the Top Ten on January 1st for three straight years, fine. But what if I’d done the same with just one crypto? Bitcoin always wins, right? Thanks to Reddit user u/sebikun for the idea for a new metric and let’s take a look:
3-year club ROI
As you can see, only five cryptos have remained in the Top Ten for all three years: BTC, ETH, XRP, BCH, and LTC. Best one to have gone all in on at this point in the Experiment? Ethereum, which would have nearly doubled. Worst choice? If I went with XRP, I would have been down -23%.

Comparison to S&P 500:

I’m also tracking the S&P 500 as part of the experiment to have a comparison point with other popular investments options. The US economy continued to recover in July: the S&P 500 is back up to pre-COVID levels. The initial $1k investment into crypto on January 1st, 2018 would have been worth about $220 had it been redirected to the S&P.
But what if I took the same invest-$1,000-on-January-1st-of-each-year approach with the S&P 500 that I’ve been documenting through the Top Ten Crypto Experiments? Here are the numbers:
  • $1000 investment in S&P 500 on January 1st, 2018: +$220
  • $1000 investment in S&P 500 on January 1st, 2019: +$310
  • $1000 investment in S&P 500 on January 1st, 2020: +$10
Taken together, here’s the bottom bottom bottom line for a similar approach with the S&P:
After three $1,000 investments into an S&P 500 index fund in January 2018, 2019, and 2020, my portfolio would be worth $3,540.
That is up over+18% since January 2018, compared to a +23% gain of the combined Top Ten Crypto Experiment Portfolios.
That’s a 5% swing in favor of the Top Ten Crypto Portfolios! As you’ll see in the table below, this is the first time since I started recording this metric that crypto has outperformed the S&P had I taken a similar investment approach. This is a big turnaround from the 22% difference in favor of the S&P just last month.

3 x $1k crypto vs. S&P

Implications/Observations:

The 2018 Top Ten Cryptos have consistently under-performed when compared to the overall crypto market. This month, for example, the total market cap is down -29% from January 2018 compared to the -74% loss for the cryptos that began 2018 in the Top Ten. At no point in the first 31 months of the Experiment has this investment strategy been successful: the 2018 Top Ten as a group have under-performed the overall market every single month.
This of course suggests that I would have done a bit better if I’d picked every crypto, or different cryptos: throwing that $1k on January 1st, 2018 to Bitcoin, for example, would have lost me -12% instead of -74%.
On the other hand, this bit of diversification has served me well compared to going all in on NEM, Dash, or IOTA, all of which are down at least -90%.
The follow-on Top Ten experiments in 2019 and 2020 have seen similar, but not identical, results. There have been a few examples of the Top Ten approach outperforming the overall market in the first 19 months of the parallel 2019 Top Ten Crypto Experiment. And up until the last few months of the most recent 2020 Top Ten Index Fund group of cryptocurrencies, this approach had outperformed the overall market 100% of the time.

Conclusion:

Crypto had an undoubtedly strong month in July, green across the board. Was this just a happy blip, are we in for some consolidation, or are we on the way up? Stay tuned.
Final words: take care of each other, wear your mask, wash your hands.
Thanks for reading and for supporting the experiment. I hope you’ve found it helpful. I continue to be committed to seeing this process through and reporting along the way. Feel free to reach out with any questions and stay tuned for progress reports. Keep an eye out for my parallel projects where I repeat the experiment twice, purchasing another $1000 ($100 each) of two new sets of Top Ten cryptos as of January 1st, 2019 then again on January 1st, 2020.

And the Answer is…

B) Ethereum
Ethereum celebrated its 5 year anniversary on July 30th, 2020.
submitted by Joe-M-4 to CryptoCurrency [link] [comments]

TRADE OF THE DAY: NVIDIA ($NVDA) Vertical Spread 415/417.5 CALL 28AUG [07/28/20]

Begun, The Dollarpocalypse Has . . .

Pre-Market Summary
Two headlines grabbed my attention yesterday:

Gold price hits record high on new fears for the economy

Dollar index slides to 2-year low

I can't help but suspect this is a zeitgeist moment; will these serve as a weathervane for what's to come?
S&P futures and European stocks slumped as market optimism faded during the busiest week of earnings season while Gold was hammered moments after the December future hit an all time high of $2000 following a record-breaking rally, with spot gold tanking more than $30 in minutes and silver dropping as much as 2% before regaining composure.
Gold hit a record high on Tuesday before the sheer scale of its gains drew a burst of profit-taking, with the volatility prompting the Shanghai Gold Exchange to issue a notice on risk prevention and express a willingness to take action if required. The dump in gold also helped the dollar rebound from two-year lows.
After rising almost $40 higher at one point to reach $1,980 an ounce, gold was hit by a wave of selling which it pushed back to $1,915 in volatile trade. Gold is still up over $125 in little more than a week as investors bet the Federal Reserve will reaffirm its accommodating policies at its meeting this week, and perhaps signal a tolerance for higher inflation in the long run.
"Fed officials have made clear that they will be making their forward guidance more dovish and outcome-based soon," wrote analysts at TD Securities. “The chairman is likely to continue the process of prepping markets for changes when he speaks at his press conference.” One shift could be to average inflation targeting, which would see the Fed aim to push inflation above its 2% target to make up for years of under-shooting.

Top Overnight News

Trade Of The Day
The game of musical chairs continues with large-cap tech stocks; eventually the music will stop playing and no seats will be found before prices drop, but until that event occurs, most of the market gains are coming from this sector.
Today's Trade of the Day comes via NVDA who does not report earnings until August 13th. It's set up in a flag formation with a lot of potential energy, ready to break. And as we've seen lately, stocks are breaking BEFORE their earnings date.
Here are two potential plays:
  1. What makes trading NVDA a challenge, like every other big-cap tech stock, is the price of $415/share. If you don't define a stop loss, then buying just ONE share at $415 represents the maximum 2% account risk for a $21k account! This is where fractional shares can come to the rescue for small accounts. I can define a fixed dollar amount in Robinhood (or other brokers supporting fractional shares) and they will create a fractional share position equivalent to my investment. For example, if I wanted to create a $40 NVDA position, then Robinhood would grant me .096 shares of NVDA.
  2. Alternatively, I could still leverage the gains/losses of a 100-share NVDA position by trading a vertical options spread using the 28AUG option series, going long the $415 call and simultaneously selling the $417.5 call as "insurance." This trade allows me to cap my risk at $125/contract, which would represent a max 2% risk to a $6250 account.
Disclaimer — This is a trade idea meant to help anyone learning options or looking for outside opinion, not an instruction manual for what to do with your money. You are the only one responsible for your portfolio.
Check out /tradeoftheday to submit your own daily trades & talk more about stocks / options / crypto / spacs / everything else trading! See you there!
Cheers and beers,
ReadySetTrade
submitted by ready-set-trade to options [link] [comments]

Deep Dive on the first Reddit Points, $DONUT Token 🍩 🍩 🍩Very Attractive, Low-Cap Opportunity 💎

DONUT TOKEN 🍩 🍩 🍩

TL;DR:


Fun fact, @cslarson (head moderator of ethtrader and founder of DONUTS as far as I can tell) was actually hacking on SourceCred before DONUTS happened. He, along with @lkngtn and @jvluso had recently coded up credao at a hackathon, a project that mints ERC-20 tokens in an Aragon DAO according to Cred scores, when he got the call from Reddit offering support for prototyping DONUTS on ethtrader. Can’t blame him:)
... 👇👇👇

MAIN POST:

Funnily enough, this is actually an alpha: right now you can ‘farm DONUT’ by contributing to ethtrader through high-quality memes, discussions, comments etc. Just by being an active member of the community, you can earn DONUT 🍩 tokens which you can sell for real $ETH. I’ll explain later why people would want to buy DONUT. Or, you can HODL them, which is highly recommended. Based on the last rewards distribution (https://www.reddit.com/ethtradecomments/i48u9g/new_donuts_distribution/) if you earned a mere 100 or so Karma points in the sub, you would have received 10,000 DONUT tokens which you can then sell for ETH on a growing list of exchanges, namely Uniswap (which has growing liquidity).
This is an example of what DeFi and Ethereum are all about and is one of the more significant community-focused projects. You have all sorts of crummy community tokens out there but none have the ecosystem to back them up. Don’t get me wrong, I’m not saying DONUT is a $LEND, $COMP, etc but it ticks all the boxes to be considered a moon-shot: meme-worthy, existing network effect, undeniable utility, Reddit-backing, AragonDAO support, and more.
...
The Ethtrader Group is a 100% community-run subreddit-collective where the governance token DONUT 🍩is used to vote on proposals regarding tokenomics. Slashing supply, changing tokenomics and other decisions can be made in their AragonDAO with the more DONUT 🍩 you hold resulting in higher voting power. It makes sense that Aragon was used seeing as the lead developer Carlson was working on Credao (a similar concept) using Aragon before he was approached by Reddit to work on the very first iteration of their Community Points system before rolling it out across the entire platform. Source. Any member of the community can propose changes by first gauging sentiment through polls in the subreddit (something you need DONUT for by the way), following up with proposals in AragonDAO which require voting (again voting power is tied to DONUT holdings).
...
Growth over time: DONUT 🍩 has thus far followed similar growth trajectories of projects that start out organic, community and product-focused and over time attract real interest, real activity and real growth. This is in opposition to projects that market first and deliver later. DONUT hasn’t marketed anything as the community has focussed internally during the bear market and the ecosystem is relatively new, which is why it isn’t already worth more. I have been trading crypto since 2011 and ALTs since 2014 and I’ve learned to spot these nuanced differences between projects, and the all-important signals. The DONUT token launched in its current state in Jan 2019 with a volume of $30 and a price of $0.0019. But I am going to focus on December 2019 as the start date for a number of reasons: first, due to some teething pains with the direction of Ethtrader & $DONUT some of the team split off, the Token also underwent a shift and you can see on the chart this early phase does not reflect any organic price action. So, starting from the latter date, looking at the chart, you can see an organic price development typical of many promising projects. Slow, steady accumulation, followed by sharper ups and downs with the bottoms rising upwards. I saw this same pattern on pretty much every organic-driven ALT I’ve invested in with success. In the last 2 weeks, ATHs have been broken across the board.
...
Similar successes: Let’s face it. In our funky community, tokens of all kinds can grow astronomically. Even those without a single use-case can grow simply because they are meme-worthy. Think $DogeCoin or $Garlicoin. More recently you have $TEND which is growing in popularity and is currently worth $1 (when I first started writing this post, it was at $0.50. DONUT was at $0.005 and is now touching $0.01).
DONUT 🍩 is unique in that it has potential to be a significant Ethereum meme token on par with these examples but more importantly, it also has tangible use-cases which will ensure the project remains active over a longer course of time, with accessibility open to anyone with spare time to meaningfully contribute to the community. But that isn’t the clincher. The Ethtrader group is large and getting larger with almost a quarter of a million members at the time of writing. That is a valuable audience of highly relevant people interested in cryptocurrency, especially Ethereum. DONUT 🍩is used in a Harbinger Tax style system (whereby someone would use DONUT to buy ad space from the current owner for a price set by that owner. This person would then set a new price — this will be the cost someone who wants it back will need to pay — and then based on this new price there will be a 100% daily tax for as long as you choose to hold the banner for). This adversarial system will ensure you have projects (typically with deep pockets) buying up lots of DONUT 🍩 to ensure they can control the banner, spending those DONUT tokens on getting the banner, and the process will continue over and over. If we enter a new bull market for DeFi, this will be a significant value and liquidity driver as let’s face it, that is prime real estate for brand exposure. I'll draw your attention back to the feedback loop I mentioned in the TL;DR.

Tokenomics:

📸IMAGE: https://imgur.com/a/CnFpfQr
*note, this is just a quick thing I slapped together and shows just one process and one use-case and is not a comprehensive diagram. Hopefully, it is useful anyway.
Deflationary or inflationary?
The DONUT used to buy the banner is always burnt, currently, 3 Million DONUT is burned per month. While there is monthly issuance (the source of contribution rewards), there is also frequent burn events. Currently, the banner is burning 100,000 DONUT per day compared to the 4,000,000 issued per month.
This daily burn can increase or decrease depending on the cost of the banner which can increase or decrease based on what the owner sets it as. This means when demand increases (exchanges, dapps, projects bid for the banner space), the burn rate will exceed the issuance rate, resulting in deflationary tokenomics. Conversely, if the cost of the banner decreases and is below the threshold (as it currently is, only slightly) then technically it will be inflationary.
The deflationary dominance has already proven to be effective seeing as the token started out with 100m units and now on around 90m. Furthermore, the issuance rate can at anytime be slashed if put to a community vote which anyone in the community can initiate, so long as they own DONUT. So, DONUT is also used here as a governance token, the more you have the stronger your vote on such decisions. To use DONUT to vote on community initiatives or a change in the tokenomics, you’ll need to visit their integrated AragonDAO and learn more about the process. This can be found here: https://mainnet.aragon.org/#/0x57EBE61f5f8303AD944136b293C1836B3803b4c0 and is also where DONUT is claimed from.

Takeaways:

Resources:

In the news:

I hope this was in-depth and useful. I have tried to add as much as possible but I have no doubt missed some stuff as well. As always, DYOR and make an informed decision. For me, at this price, it's a no brainer.
submitted by defi-chad to CryptoMoonShots [link] [comments]

The Turkey City Lexicon - annotated for 40K by Matt Farrer circa 2004 - and Farrer's analysis of Abnett's eye-ball kicks

I wrote a suggestion on how to create a Space Marine OC (the whole thread is a good reading for aspiring fan authors so I'll link it), and it got me thinking about writing within the 40K setting. Back in the day when Black Library still had their own forum, I saved Matt Farrer's annotation of the Turkey City Lexicon (the original, pre-internet version of TV Tropes). I searched the subreddit for it earlier with no results, so I'll share it again here.

Please note: The Turkey City Lexicon is specifically, explicitly non-copyright and is encouraged to be shared/reposted/expanded. Posting it here in its entirety violates no copyright legislation in any country - in fact, Matt Farrer himself asked us to share it with our fellow writers. Hat off to you, Mr Farrer, for your contributions to the 40K lore from a longtime fan.

[Originally posted to Black Library Online, November 2004, by user Matt Farrer]
The Turkey City Lexicon (Annotated with some Games Workshop observations)
The Turkey City Lexicon is a terminology guide that’s been floating around in one form or another since the late eighties (Google will turn up plenty of hits if you want to see one of the original copies; I got this one from the SFWA website). The Lexicon is deliberately not copyrighted and is intended to be copied at will and passed on to other writers (note that you shouldn’t try this with anything else on the SFWA site, if you go there – there are some great articles but most of them are copyrighted).
There’s a tendency for people to look at the Lexicon as a list of “common mistakes” or “things not to do”, which is not entirely correct as I understand its purpose. Certainly seeing a common problem set down pithily can help crystallise that particular example of bad technique, but a couple of the terms in here are complimentary and many others aren’t necessarily fatal problems. As in “you might want to watch out for funny-hat characterisation on page four, although with the narrative voice you use it works well”. What it is meant to be is a useful resource for critiquers, giving you a quick and easy shorthand for a known quantity you’ve observed in writing. In the above example, you don’t need to spend half a paragraph describing a shaky spot in the characterisation, you have a quick term to cover it and save space and time for both of you.
The early, simple version of the lexicon by Lewis Shiner was expanded and added to by Bruce Sterling, not, in my opinion, always for the better. There are no real differences in actual content between the two, so for this version I’ve picked whichever version of an entry I thought was better phrased. The GW-specific notes are my own – I’ll add more as I think of them, if I have the time. Discussion of any or all of the entries is of course welcome - it's what I'm posting this for.
Anyway, let’s get on with it.
The meta-rule:
Cherryh's Law
No rule should be followed over a cliff. (C.J. Cherryh)
MF - There are times when the literary or dramatic effect of breaking any supposed "rule" about writing is going to be worth it, and that includes any and all of the points about writing offered in the Lexicon. Such principles are based on experience that shows that certain approaches work better than others, but getting carried away with imposing a set of rules as though they were holy writ simply turns into an attempt to stamp out creativity and have every writer write exactly alike. Know the principles, understand why they work as they do, but don't wear them like shackles.
Part One: Words and Sentences
Brenda Starr dialogue
Long sections of talk with no physical background or description of the characters. Such dialogue, detached from the story's setting, tends to echo hollowly, as if suspended in mid-air. Named for the American comic-strip in which dialogue balloons were often seen emerging from the Manhattan skyline.
"Burly Detective" Syndrome
This useful term is taken from SF's cousin-genre, the detective-pulp. The hack writers of the Mike Shayne series showed an odd reluctance to use Shayne's proper name, preferring euphemisms like "the burly detective" or "the red-headed sleuth." This comes from a wrong-headed conviction that the same word should not be used twice in close succession. This is only true of particularly strong and visible words, such as "vertiginous." Better to re-use a simple tag or phrase than to contrive cumbersome methods of avoiding it.
Brand Name Fever
Use of brand name alone, without accompanying visual detail, to create false verisimilitude. You can stock a future with Hondas and Sonys and IBM's and still have no idea with it looks like.
"Call a Rabbit a Smeerp"
A cheap technique for false exoticism, in which common elements of the real world are re-named for a fantastic milieu without any real alteration in their basic nature or behavior. "Smeerps" are especially common in fantasy worlds, where people often ride exotic steeds that look and act just like horses. (Attributed to James Blish.)
Gingerbread
Useless ornament in prose, such as fancy sesquipedalian Latinate words where short clear English ones will do. Novice authors sometimes use "gingerbread" in the hope of disguising faults and conveying an air of refinement. (Attr. Damon Knight)
Not Simultaneous
The mis-use of the present participle is a common structural sentence-fault for beginning writers. "Putting his key in the door, he leapt up the stairs and got his revolver out of the bureau." Alas, our hero couldn't do this even if his arms were forty feet long. This fault shades into "Ing Disease," the tendency to pepper sentences with words ending in "-ing," a grammatical construction which tends to confuse the proper sequence of events. (Attr. Damon Knight)
Pushbutton Words
Bogus lyricism like "star," "dance," "dream," "song," "tears" and "poet". Used to evoke a cheap emotional response without engaging the intellect or critical faculties, getting us misty-eyed and tender-hearted without us quite knowing why. Most often found in titles.
Roget's Disease
The ludicrous overuse of far-fetched adjectives, piled into a festering, fungal, tenebrous, troglodytic, ichorous, leprous, synonymic heap. (Attr. John W. Campbell)
"Said" Bookism
An artificial verb used to avoid the word "said." "Said" is one of the few invisible words in the English language and is almost impossible to overuse. It is much less distracting than "he retorted," "she inquired," "he ejaculated," and other oddities. The term "said-book" comes from certain pamphlets, containing hundreds of purple-prose synonyms for the word "said," which were sold to aspiring authors from tiny ads in American magazines of the pre-WWII era.
Tom Swifty
An unseemly compulsion to follow the word "said" with a colourful adverb: "'We'd better hurry,' Tom said swiftly." This was a standard mannerism of the old Tom Swift adventure dime-novels. Good dialogue can stand on its own without a clutter of adverbial props.
Part Two: Paragraphs and Prose Structure
Bathos
A sudden, alarming change in the level of diction. "There will be bloody riots and savage insurrections leading to a violent popular uprising unless the regime starts being lots nicer about stuff."
Countersinking
Expositional redundancy. "'Let's get out of here,' he said, urging her to leave."
Dischism
The unwitting intrusion of the author's physical surroundings or mental state into the text of the story. Authors who smoke or drink while writing often drown or choke their characters with an endless supply of booze and cigs. In subtler forms of the Dischism, the characters complain of their confusion and indecision -- when this is actually the author's condition at the moment of writing, not theirs within the story. "Dischism" is named after the critic who diagnosed this syndrome. (Attr. Thomas M. Disch)
False Humanity
An ailment endemic to genre writing, in which soap-opera elements of purported human interest are stuffed into the story willy-nilly, whether or not they advance the plot or contribute to the point of the story. The actions of such characters convey an itchy sense of irrelevance, for the author has invented their problems out of whole cloth, so as to have something to emote about.
False Interiorisation
A cheap labour-saving technique in which the author, too lazy to describe the surroundings, afflicts the viewpoint-character with a blindfold, an attack of space-sickness, the urge to play marathon whist-games in the smoking-room, etc.
Fuzz
An element of motivation the author was too lazy to supply. The word "somehow" is a useful tip-off to fuzzy areas of a story. "Somehow she had forgotten to bring her gun."
Hand Waving
An attempt to distract the reader with dazzling prose or other verbal fireworks, so as to divert attention from a severe logical flaw. (Attr. Stewart Brand)
Laughtrack
Characters grandstand and tug the reader's sleeve in an effort to force a specific emotional reaction. They laugh wildly at their own jokes, cry loudly at their own pain, and rob the reader of any real chance of attaining genuine emotion.
Show, Don’t Tell
A cardinal principle of effective writing. The reader should be allowed to react naturally to the evidence presented in the story, not instructed in how to react by the author. Specific incidents and carefully observed details will render auctorial lectures unnecessary. For instance, instead of telling the reader "She had a bad childhood, an unhappy childhood," a specific incident -- involving, say, a locked closet and two jars of honey -- should be shown.
Rigid adherence to show-don't-tell can become absurd. Minor matters are sometimes best gotten out of the way in a swift, straightforward fashion.
Signal from Fred
A comic form of the "Dischism" in which the author's subconscious, alarmed by the poor quality of the work, makes unwitting critical comments: "This doesn't make sense." "This is really boring." "This sounds like a bad movie." (Attr. Damon Knight)
Squid in the Mouth
The failure of an author to realize that his/her own weird assumptions and personal in-jokes are simply not shared by the world-at-large. Instead of applauding the wit or insight of the author's remarks, the world-at-large will stare in vague shock and alarm at such a writer, as if he or she had a live squid in the mouth.
Since SF writers as a breed are generally quite loony, and in fact make this a stock in trade, "squid in the mouth" doubles as a term of grudging praise, describing the essential, irreducible, divinely unpredictable lunacy of the true SF writer. (Attr. James P Blaylock)
Squid on the Mantelpiece
Chekhov said that if there are dueling pistols over the mantelpiece in the first act, they should be fired in the third. In other words, a plot element should be deployed in a timely fashion and with proper dramatic emphasis. However, in SF plotting the MacGuffins are often so overwhelming that they cause conventional plot structures to collapse. It's hard to properly dramatize, say, the domestic effects of Dad's bank overdraft when a giant writhing kraken is levelling the city. This mismatch between the conventional dramatic proprieties and SF's extreme, grotesque, or visionary thematics is known as the "squid on the mantelpiece."
MF – I’ve heard several versions of the supposed “Chekhov’s Gun” principle, no two of them meaning exactly the same thing. For example, the version I first heard is “If a character produces a gun, then it should be used to shoot someone, or threaten someone, or go off by accident, or fail to fire when it’s needed, and so on. If it does none of these things, then it is superfluous and should be taken out altogether.” That’s a point about narrative tidiness rather than timely deployment of plot elements.
White Room Syndrome
A clear and common sign of the failure of the author's imagination, most often seen at the beginning of a story, before the setting, background, or characters have gelled. "She awoke in a white room." The 'white room' is a featureless set for which details have yet to be invented -- a failure of invention by the author. The character 'wakes' in order to begin a fresh train of thought -- again, just like the author. This 'white room' opening is generally followed by much earnest pondering of circumstances and useless exposition; all of which can be cut, painlessly.
It remains to be seen whether the "white room" cliche' will fade from use now that most authors confront glowing screens rather than blank white paper.
Wiring Diagram Fiction
A genre ailment related to "False Humanity," "Wiring Diagram Fiction" involves "characters" who show no convincing emotional reactions at all, since they are overwhelmed by the author's fascination with gadgetry or didactic lectures.
MF – A trap hard SF often falls into, in my experience. I suppose the related ailment in GW fiction would be “fluff-diagram fiction” (sorry Gav), in which the story is sidelined by the author’s desire to lay out in detail some aspect of his take on the game-universe.
You Can't Fire Me, I Quit
An attempt to diffuse the reader's incredulity with a pre-emptive strike -- as if by anticipating the reader's objections, the author had somehow answered them. "I would never have believed it, if I hadn't seen it myself!" "It was one of those amazing coincidences that can only take place in real life!" "It's a one-in-a-million chance, but it's so crazy it just might work!" Surprisingly common, especially in SF. (Attr. John Kessel)
Part Three: Common Workshop Story Types
Adam and Eve Story
Nauseatingly common subset of the "Shaggy God Story" in which a terrible apocalypse, spaceship crash, etc., leaves two survivors, man and woman, who turn out to be Adam and Eve, parents of the human race!
MF – Not an issue for GW writing for obvious reasons. See Alfred Bester’s “Adam With No Eve” in the brilliant anthology Starburst for a rather good twist on the idea.
The Cosy Catastrophe
Story in which horrific events are overwhelming the entirety of human civilization, but the action concentrates on a small group of tidy, middle-class, white Anglo-Saxon protagonists. The essence of the cosy catastrophe is despite the supposed devastation the hero actually has a pretty good time (a girl, free suites at the Savoy, fancy cars for the taking) while everyone is dying off. (Attr. Brian Aldiss)
Dennis Hopper Syndrome
A story based on some arcane bit of science or folklore, which noodles around producing random weirdness. Then a loony character-actor (usually best played by Dennis Hopper) barges into the story and baldly tells the protagonist what's going on by explaining the underlying mystery in a long bug-eyed rant. (Attr. Howard Waldrop)
MF - Not unrelated to Roger Ebert's remarks about the Talking Killer device, aka "Before I kill you, Mister Bond..." The killer gets the protagonist at his mercy and then decides to put off killing him so that he can fill the hero in on exactly what's been going on, and bring the reader up to speed at the same time. You know, like I did at the end of Crossfire. Although this is a plot device rather than an actual story type.
Deus ex Machina or "God in the Box"
Story featuring a miraculous solution to the story's conflict, which comes out of nowhere and renders the struggles of the characters irrelevant. Oh look, the Martians all caught cold and died.
The Grubby Apartment Story
Writing a little too much about what you know. The penniless writer living in a grubby apartment writes a story about a penniless writer living in a grubby apartment. Stars all his friends.
The Jar of Tang
"For you see, we are all living in a jar of Tang!" "For you see, I am a dog!" Mainstay of the old Twilight Zone TV show. An entire pointless story contrived so the author can jump out at the end and cry "Fooled you!" For instance, the story takes place in a desert of coarse orange sand surrounded by an impenetrable vitrine barrier; surprise! our heroes are microbes in a jar of Tang powdered orange drink.
This is a classic case of the difference between a conceit and an idea. "What if we all lived in a jar of Tang?" is an example of the former; "What if the revolutionaries from the sixties had been allowed to set up their own society?" is an example of the latter. Good SF requires ideas, not conceits. (Attr. Stephen P. Brown)
When done with serious intent rather than as a passing conceit, this type of story can be dignified by the term "Concealed Environment." (Attr. Christopher Priest)
Just-Like Fallacy
SF story which thinly adapts the trappings of a standard pulp adventure setting. The spaceship is "just like" an Atlantic steamer, down to the Scottish engineer in the hold. A colony planet is "just like" Arizona except for two moons in the sky. "Space Westerns" and futuristic hard-boiled detective stories have been especially common versions.
MF – Then again, one of the fun things about the GW settings – the 40Kverse more than the Warhammer world, it seems to me – is the way you can rip all kinds of stuff off and stuff it in there to do a 41st-millennium tribute to it. Not necessarily a bad thing, providing you don’t end up in Bat Durston territory (more about him another time).
[From another post:] In case you are not familiar with the term, a Bat Durston refers derogatorily to a science fiction story which is little more than a traditional western using sf settings and icons. Taking the comparison to alternate history, the better stories in this genre should create the story’s world for some reason other than merely creating a nice setting for an adventure.
The Kitchen-Sink Story
A story overwhelmed by the inclusion of any and every new idea that occurs to the author in the process of writing it. (Attr. Damon Knight)
The Motherhood Statement
SF story which posits some profoundly unsettling threat to the human condition, explores the implications briefly, then hastily retreats to affirm the conventional social and humanistic pieties, ie apple pie and motherhood. Greg Egan once stated that the secret of truly effective SF was to deliberately "burn the motherhood statement." (Attr. Greg Egan)
MF - He wasn’t kidding, either. Greg Egan writes some of the most powerful and disturbing hard SF I’ve read, precisely because he’s not afraid to back away from the full implications of the science and technology he writes about.
I think that 40K writing is vulnerable to this to a certain degree: I’ve seen quite a few stories that dip a toe into the grim, violent, insane world of the 41st Millennium, stay there for a moment but quickly falls back into “but the Imperium is actually an OK place and lots of people there are nice and happy just like us”.
Discussion on this welcome.
The "Poor Me" Story
Autobiographical piece in which the male viewpoint character complains that he is ugly and can't get laid. (Attr. Kate Wilhelm)
Re-Inventing the Wheel
A novice author goes to enormous lengths to create a situation already tiresomely familiar to the experienced reader. Reinventing the Wheel was traditionally typical of mainstream writers venturing into SF without actually reading any of the existing stuff first (because it's all obviously crap anyway). Thus you get endless explanations of, say, how an atomic war might get started by accident, and so on. It is now often seen in writers who lack experience in genre history because they were attracted to written SF via movies, television, role-playing games, comics or computer gaming.
MF – Not that coming into the genre that way is a bad thing per se, but when a writer hasn’t had much exposure to written specfic in this way it usually shows, and not in a good way. To quote Terry Pratchett, you should be importing, not recycling.
The Rembrandt Comic Book
A story in which incredible craftsmanship has been lavished on a theme or idea which is basically trivial or subliterary, and which simply cannot bear the weight.
The Shaggy God Story
A piece which mechanically adopts a Biblical or other mythological tale and provides flat science-fictional "explanations" for the theological events. (Attr. Michael Moorcock)
MF – Although he wrote them himself: arguably his finest and most powerful story, called “Behold The Man”, does this for the life of Jesus. I remember it disturbed me when I read it, and I’m not even religious.
The Slipstream Story
Non-SF story which is so ontologically distorted or related in such a bizarrely non-realist fashion that it cannot pass muster as commercial mainstream fiction and therefore seeks shelter in the SF or fantasy genre. Postmodern critique and technique are particularly fruitful in creating slipstream stories.
The Steam-Grommet Factory
Didactic SF story which consists entirely of a guided tour of a large and elaborate gimmick. A common technique of SF utopias and dystopias. (Attr. Gardner Dozois)
MF – See the opening of Huxley’s Brave New World for an example of this done effectively.
The Tabloid Weird
Story produced by a confusion of SF and Fantasy tropes -- or rather, by a confusion of basic world-views. Tabloid Weird is usually produced by the author's own inability to distinguish between a rational, Newtonian-Einsteinian, cause-and- effect universe and an irrational, supernatural, fantastic universe. Either the FBI is hunting the escaped mutant from the genetics lab, or the drill-bit has bored straight into Hell -- but not both at once in the very same piece of fiction. Even fantasy worlds need an internal consistency of sorts, so that a Sasquatch Deal-with-the-Devil story is also "Tabloid Weird." Sasquatch crypto-zoology and Christian folk superstition simply don't mix well, even for comic effect. (Attr. Howard Waldrop)
MF – I’m not as convinced as the Lexicon that these two genres are utterly incompatible. Well, obviously not, since I work in a setting which combines them without hesitation. Which isn’t to say that the combination doesn’t need to be handled delicately, since those aforementioned different mindsets lead to different storytelling conventions as well as different world views.
The Whistling Dog
A story related in such an elaborate, arcane, or convoluted manner that it impresses by its sheer narrative ingenuity, but which, as a story, is basically not worth the candle. Like the whistling dog, it's astonishing that the thing can whistle -- but it doesn't actually whistle very well. (Attr. Harlan Ellison)
Part Four: Plots
Abbess Phone Home
Takes its name from a mainstream story about a medieval cloister which was sold as SF because of the serendipitous arrival of a UFO at the end. By extension, any mainstream story with a gratuitous SF or fantasy element tacked on so it could be sold.
And plot
Picaresque plot in which this happens, and then that happens, and then something else happens, and it all adds up to nothing in particular.
Bogus Alternatives
List of actions a character could have taken, but didn't. Frequently includes all the reasons why, as the author stops the action dead to work out complicated plot problems at the reader's expense. "If I'd gone along with the cops they would have found the gun in my purse. And anyway, I didn't want to spend the night in jail. I suppose I could have just run instead of stealing their car, but then..." etc. Best dispensed with entirely.
Card Tricks in the Dark
Elaborately contrived plot which arrives at (a) the punchline of a private joke nobody else will get, or (b) the display of some bit of learned trivia only the author is interested in. This stunt may be intensely ingenious, and very gratifying to the author, but it serves no visible fictional purpose. (Attr. Tim Powers)
Idiot Plot
A plot which functions only because all the characters involved are idiots. They behave in a way that suits the author's convenience, rather than through any rational motivation of their own. (Attr. James Blish)
Kudzu plot
Plot which weaves and curls and writhes in weedy organic profusion, smothering everything in its path.
Plot Coupons
The basic building blocks of the quest-type fantasy plot. The "hero" collects sufficient plot coupons (magic sword, magic book, magic cat) to send off to the author for the ending. Note that "the author" can be substituted for "the Gods" in such a work: "The Gods decreed he would pursue this quest." Right, mate. The author decreed he would pursue this quest until sufficient pages were filled to procure an advance. (Dave Langford)
MF - Nick Lowe expands on the idea in an excellent article at www.ansible.co.uk/Ansible/plotdev.html . Cheers to Bill King for the link.
Second-order Idiot Plot
A plot involving an entire invented SF society which functions only because every single person in it is necessarily an idiot. (Attr. Damon Knight)
MF – The assertion that this applies to the 40K Imperium is not a new one. Floor’s open…
Part Five: Background
"As You Know Bob"
A pernicious form of info-dump through dialogue, in which characters tell each other things they already know, for the sake of getting the reader up-to-speed. This very common technique is also known as "Rod and Don dialogue" (attr. Damon Knight) or "maid and butler dialogue" (attr Algis Budrys).
The Edges of Ideas
The solution to the "Info-Dump" problem (how to fill in the background). The theory is that, for example, the mechanics of an interstellar drive (the centre of the idea) are not important. What matters is the impact on your characters: they can get to other planets in a few months, and, oh yeah, it gives them hallucinations about past lives. Or, more radically: the physics of TV transmission is the center of an idea; on the edges of it we find people turning into couch potatoes because they no longer have to leave home for entertainment. Or, more bluntly: we don't need info dump at all. We just need a clear picture of how people's lives have been affected by their background.
Eyeball Kick
That perfect, telling detail that creates an instant visual image. The ideal of certain postmodern schools of SF is to achieve a "crammed prose" full of "eyeball kicks." (Rudy Rucker)
MF - See the other thread.
Frontloading
Piling too much exposition into the beginning of the story, so that it becomes so dense and dry that it is almost impossible to read. (Attr. Connie Willis)
Infodump
Large chunk of indigestible expository matter intended to explain the background situation. Info-dumps can be covert, as in fake newspaper or "Encyclopedia Galactica" articles, or overt, in which all action stops as the author assumes center stage and lectures. Info-dumps are also known as "expository lumps." The use of brief, deft, inoffensive info-dumps is known as "kuttnering," after Henry Kuttner. When information is worked unobtrusively into the story's basic structure, this is known as "heinleining."
"I've suffered for my Art" (and now it's your turn)
A form of info-dump in which the author inflicts upon the reader hard-won, but irrelevant bits of data acquired while researching the story. As Algis Budrys once pointed out, homework exists to make the difficult look easy.
Nowhere Nowhen Story
Putting too little exposition into the story's beginning, so that the story, while physically readable, seems to take place in a vacuum and fails to engage any readerly interest. (Attr. L. Sprague de Camp)
Ontological riff
Passage in an SF story which suggests that our deepest and most basic convictions about the nature of reality, space-time, or consciousness have been violated, technologically transformed, or at least rendered thoroughly dubious. The works of H. P. Lovecraft, Barrington Bayley, and Philip K Dick abound in "ontological riffs."
Space Western
The most pernicious suite of "Used Furniture". The grizzled space captain swaggering into the spacer bar and slugging down a Jovian brandy.
Stapledon
Name assigned to the voice which takes centre stage to lecture. Actually a common noun, as: "You have a Stapledon come on to answer this problem instead of showing the characters resolve it."
Used Furniture
Use of a background out of Central Casting. Rather than invent a background and have to explain it, or risk re-inventing the wheel, let's just steal one. We'll set it in the Star Trek Universe, only we'll call it the Empire instead of the Federation.
Part Six: Character and Viewpoint
Funny-hat characterization
A character distinguished by a single identifying tag, such as odd headgear, a limp, a lisp, a parrot on his shoulder, etc.
MF – This can work if done deftly and with minor characters. Stephen King excels at it, and Ed McBain is pretty good too.
Mary Sue
A ridiculously perfect and idealised character, moving through a story which serves no other purpose than demonstrating how ridiculously perfect and idealised Mary Sue is. None of the other characters have anything to do other than rave about Mary Sue's wonderfulness; challenges and obstacles exist only for Mary Sue to solve effortlessly to admiring gasps from everyone else.
Also known as "avatars" or "self-insertion", since the most common Mary Sues are thinly-disguised versions of the author and are more about wish-fulfiment fantasies than conventional storytelling. Endemic to fanfic; the term apparently originates from an early and infamous example in an old Star Trek fanzine.
MF - There are lots of definitions and examples of Mary Sue, although the term as it's used here isn't really attributable to one author any more. The definition supplied here owes much to Teresa Nielsen Hayden's rather good one at http://nielsenhayden.com/makinglight/archives/004188.html .
GW fanfics and homebrew backgrounds aren't immune either - you can find them pretty easily once you know the signs. The twist is that the Mary Sue is often a Guard regiment, Space Marine Chapter, Eldar Craftworld or an entire galactic state.
Common warning signs: "The Mary Sue Regiment fought so ferociously in the Battle of Sueville that even the [famous Space Marine Chapter] were awe-struck that unaugmented humans could fight so hard, and their Chapter Master officially declared the Mary Sue regiment the equals of Space Marines". "Inquisitor Mary Sue has demonstrated such amazing ability that the High Lords have personally ordered that nobody is allowed to stand in her way or question her actions". "Now that it has declared independence from the Imperium the Mary Sue Republic has become a haven of enlightenment and progress, where technology is being developed at an exponential rate with no aura of superstitious mysticism, painless and fully-effective techniques to protect psykers from daemonic attack have been developed, alien races of all kinds are putting aside their differences and living contentedly side by side, and where every Imperial who sees what's going on immediately defects once they see how wonderful and free life among the Mary Sues is".
I've since found out that even the original "Ensign Mary Sue" in that old seventies fanfic was a satire on the trope, so clearly it was already a fiction cliche by then.
Mrs. Brown
The small, downtrodden, eminently common, everyday little person who nevertheless encapsulates something vital and important about the human condition. "Mrs. Brown" is a rare personage in the SF genre, being generally overshadowed by swaggering submyth types made of the finest gold-plated cardboard. In a famous essay, "Science Fiction and Mrs. Brown," Ursula K. Le Guin decried Mrs. Brown's absence from the SF field. (Attr: Virginia Woolf)
...stamped on their forehead
The story lets a character get away with something illogical or impossible because they have "hero" (or "villain", "sidekick", disposable underling", or whatever) stamped on their foreheads. There's nothing wrong with heroes triumphing against the odds or villains being brought low through their own flaws, but those consequences need to come about because of the characters and their actions rather than despite them.
Adapted from Aaron Allston's roleplayers' glossary from a few years ago, which included "He's got 'PC' [player character] stamped on his forehead" as an all-purpose excuse for why characters unquestioningly accepted or trusted one anothers' actions while treating non-player characters differently. (Aaron Allston.)
MF - This was partly prompted by the "script immunity" and "Hollywood Shield" ideas in the discussion thread, although the scene I had in mind for it was actually in Walking Tall, where the main character is manifestly guilty of all manner of assaults and property destruction but is acquitted in court when he makes a sentimental speech about down-home values. It doesn't even resemble making a legal case for his innocence, but he gets let off because he's got "hero" stamped on his forehead.
Submyth
Classic character-types in SF which aspire to the condition of archetype but don't quite make it, such as the mad scientist, the crazed supercomputer, the emotionless super-rational alien, the vindictive mutant child, etc. (Attr. Ursula K. Le Guin)
MF – You can pick the GWverse submyths for yourselves, I’m sure.
Viewpoint glitch
The author loses track of point-of-view, switches point-of-view for no good reason, or relates something that the viewpoint character could not possibly know.
Part Seven: Miscellaneous
AM/FM
Engineer's term distinguishing the inevitable clunky real-world faultiness of "Actual Machines" from the power-fantasy techno-dreams of "Fething Magic."
MF – Except the original Lexicon didn’t say “fething”. :grinning_emoticon: Well worth remembering for 40K and Necromunda fiction, which deliberately shies away from the sleek, clean, super-reliable dream-tech of settings like Star Trek.
Consensus Reality
Useful term for the purported world in which the majority of modern sane people generally agree that they live -- as opposed to the worlds of, say, Forteans, semioticians or quantum physicists.
Intellectual sexiness
The intoxicating glamor of a novel scientific idea, as distinguished from any actual intellectual merit that it may someday prove to possess.
The Ol' Baloney Factory
"Science Fiction" as a publishing and promotional entity in the world of commerce.

Additional suggestions from other forum members:
User Chiron: Script Immunity
The tendency of lynchpin characters to be blatantly immune to harm, despite the fact that they consistently place themselves in situations that they cannot reasonably be expected to survive.
User Vortemir: Hollywood Shield / Imperial Stormtrooper Syndrome
Bad Guys will never be able to hit essential characters no matter what they're armed with or how hard they try.

[Originally posted to Black Library Online, October 2004, by user Matt Farrer]
A term from the Turkey City Lexicon that might be useful here is the "eyeball kick", Rudy Rucker's term for that perfectly-turned descriptive phrase that creates an instant, telling visual image for the reader. An example that springs to mind from the opening of Necropolis:
After a minute or so, raid-sirens in the central district also began keening. The pattern was picked up by manufactory hooters and mill whistles all through the lower hive, and in the mill whistles and outer habs across the river too. Even the great ceremonial horns on the top of the Ecclesiarchy Basilica started to sound.Vervunhive was screaming with every one of its voices.
That last line provides the eyeball kick.
Some other examples that spring to mind: "[he] screamed out two mouthfuls of silent spun glass" (Stephen King); "the sky above Chiba City was the colour of a television tuned to a blank band" (William Gibson); "a great moist loaf of a body... features as bunched as kissed fingertips" (E. Annie Proulx); "[after walking through snow] my feet, in wet socks, slowly turned to marble and fell off" (Donald Westlake).
I don't know if there's a way you can break down an eyeball kick to pick apart the technique, since its whole impact comes from lateral thinking and the effect of an incongruous image that nevertheless fits exactly with what you're describing. It's an imagination thing rather than a technique thing. However, the paragraph from Necropolis that I used above is also a very good example of how to maximise the effect of a good piece of description, and worth having a closer look at.
Firstly, the rest of the paragraph has been describing the machinery that makes the sound, and doing so in fairly neutral, inorganic terms: "keening", at the start of the para, is about as close as we get to an emotive word. The rest is a pretty calm description about how a series of klaxons and horns are going off. That increases the wrench when we suddenly switch gears into words that you'd use to describe a living being in agony: "screaming with every one of its voices", which gives weight to the sense of foreboding that dominates the early pages. This is reinforced further by the way that the previous sentences tend to be longer, with more connecting commas and lots of adjectives to slow their rhythm and give a more discursive feel, while the last sentence is a simple, flat declarative. Using the rhythm of words and sentences for a setup and payoff like that is a very good way of driving home a piece of exposition or description, and it's something that Dan uses quite a bit.
Secondly, look at the way that the passage, which at first blush is about the sounds of the sirens, actually helps build a visual image as well. We've been going through all the various parts and districts of Vervunhive, watching as different kinds of buildings in different areas go off. Look at how the mental "camera" moves down the lower hive, then down the river, then up to the top of the Basilica. Then in the last sentence we get an eyeball kick that describes the whole of Vervunhive as a single entity: the effect is like pulling back sharply from an individual scene or building and seeing the whole Hive at once. And that concludes the main piece of visual scene-setting at the opening: notice that in the next line Dan can start in on conversations between individual characters around the Hive because the major scene has been laid out.
The broad point to take away from this is that each piece of text should work on as many levels as possible, and even a short passage like that one can be far more than the sum of its parts. I suspect that the reason a lot of bad fiction (including, I am sorry to say, a lot of fanfic I've seen) seems so flat and plodding is that each sentence is put down to do one thing: make a statement, provide a description or what have you. But there's no depth to the prose, no interaction between them to create any rhythm, or momentum, or startling switch in imagery. It's like a song from your favourite band, with each element (vocals, percussion, each instrument) separated and played end to end. It sounds so much better when they're all working together.

That's it. Got any suggestions for new 40K-specific tropes to add?
submitted by Medicaean to 40kLore [link] [comments]

Become a Verasity Influencer

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NVidia – Know What You Own

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:
  1. GPUs used in workstations for things such as CAD graphical processing (Quadro Line)
  2. GPUs used in workstations for computational workloads such as running engineering simulations (Quadro Line)
  3. GPUs used in workstations for machine learning applications (Quadro line.. but can use gaming cards as well for this)
  4. GPUs used by enterprise customers for high performance computing (such as modelling oil wells) (Tesla Line)
  5. GPUs used by enterprise customers for machine learning projects (Tesla Line)
  6. 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!
submitted by HyperInflation2020 to stocks [link] [comments]

S&P 1700 within 6 Months


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

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 bullish pricing.

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?
Are retail investors buying in greater volumes?
Are retail investors dumb money?

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.

Talk to me about 2008!

Thanks to u/mister_woody for asking for more data.

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.

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...

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.

Disclosure


Spreads
Equities
Currency

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.

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...

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.

TL:DR



submitted by DongusMcLongus to StockMarket [link] [comments]

Compound Price Prediction 2020

Compound Price Prediction 2020
Compound is a Decentralized Finance (DeFi) protocol based on the Ethereum technology. The project was created in 2018 by the company Compound Labs. Today Compound is one of the industry-leading lending platforms that allow users to earn interest or borrow assets against collateral. The platform supports such popular cryptocurrencies as DAI, ETH, USDC, USDR, and others.
by StealthEX
COMP is an ERC-20 token that allows the community to manage the Compound protocol. COMP token holders discuss, propose, and vote on all protocol changes.
Nowadays Compound is among TOP-50 cryptocurrencies by market capitalization.

Compound Statistics

Source: CoinMarketCap, Data was taken on 27 August 2020 by StealthEX
Current Price $176.18
ROI since launch >73.26%
Market Cap $451,253,430
Market Rank #37
Circulating Supply 2,561,279 COMP
Total Supply 10,000,000 COMP

Compound achievements and future plans

In 2019 the project has the following main updates and news:
• Compound’s Brand was updated. The team unveiled its new brand and homepage.
• The compound protocol was upgraded to version 2.2.
• The developers announced a Compound lending proxy, for developers building stake-to-play, stake-to-buy, and stake-to-X dapps.
• Compound ROI was announced to surface the highest yielding opportunities on the Compound platform.
• The developers launched the project called Open Price Feed.
• Argent integrated Compound into their smart contract wallet.
• Huobi Wallet added Compound and started supporting cTokens.
• Lumina announced Compound support.
• The Compound Interface has been upgraded with WalletLink.
• The community voted and selected Maker (MKR) and Tether (USDT) as the next adding Compound assets.
• Set Protocol has announced the integration of Compound tokens and the launch of the first cToken enabled Set, the ETH RSI 60/40 Yield Set.
• Dozens of interfaces and applications have integrated the protocol, and many more are building on Compound.

What to expect in the future?

The project has no official roadmap. The main goal of the Compound Team is to create unstoppable, upgradable financial infrastructure. So the developers will continue working on full decentralization of the platform.

Compound Technical Analysis

Source: Tradingview, Data was taken on 27 August 2020 by StealthEX

Compound Price Prediction 2020

TradingBeasts COMP price prediction

The Compound price is expected to reach $155.54 by the beginning of September 2020 (-11.72%). According to TradingBeasts opinion by the end of 2020, the COMP coin price may reach its maximum price of $190.414 per coin (+8.08%). While its average price will be around $152.331 (-13.54%).

Wallet investor COMP price prediction

Wallet investor.com thinks that Compound is not a good option for a long-term investment as a current investment may be devalued in the future. By the end of December 2020, the COMP price may drop to $82.735 per coin (-53.04%), while its average price is expected to stay around $127.957 (-27.37%). Wallet investor’s analytics are sure that the Compound project will not replace Bitcoin in the near future.

Crypto-Rating COMP coin price prediction

Crypto-Rating predicts that Compound price action will remain dull over the coming month, but this particular market bears the promise of substantial gains, so traders should look diligently for confirmed reversal signals.

DigitalCoinPrice Compound coin price prediction

DigitalCoinPrice thinks that COMP is a good investment option. The COMP price may reach $288.57 per coin by the end of December 2020 (+63.79%).

Buy Compound coin at StealthEX

Compound COMP is available for exchange on StealthEX with a low fee. Follow these easy steps:
✔ Choose the pair and the amount for your exchange. For example, BTC to COMP.
✔ Press the “Start exchange” button.
✔ Provide the recipient address to which the coins will be transferred.
✔ Move your cryptocurrency for the exchange.
✔ Receive your coins!
Follow us on Medium, Twitter, Facebook, and Reddit to get StealthEX.io updates and the latest news about the crypto world. For all requests message us via [email protected]
The views and opinions expressed here are solely those of the author. Every investment and trading move involves risk. You should conduct your own research when making a decision.
Original article was posted on https://stealthex.io/blog/2020/08/27/compound-price-prediction-2020/
submitted by Stealthex_io to StealthEX [link] [comments]

Winning Crypto Signals - YouTube INS Crypto trading signals with 284% profit Crypto Trading Signals Perfect for Beginners - YouTube Crypto Trading Signals That Work - YouTube The Best Crypto Trading Signals 2020

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