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Nothing happening here… just quantum teleportation…

Teleportation is a building block for a wide range of technologies. “Long-distance teleportation has been recognized as a fundamental element in protocols such as large-scale quantum networks and distributed quantum computation,” says the Chinese team.

In theory, there should be no maximum distance over which this can be done. But entanglement is a fragile thing because photons interact with matter in the atmosphere or inside optical fibers, causing the entanglement to be lost.

As a result, the distance over which scientists have measured entanglement or performed teleportation is severely limited. “Previous teleportation experiments between distant locations were limited to a distance on the order of 100 kilometers, due to photon loss in optical fibers or terrestrial free-space channels,” says the team.

But Micius changes all that because it orbits at an altitude of 500 kilometers, and for most of this distance, any photons making the journey travel through a vacuum. To minimize the amount of atmosphere in the way, the Chinese team set up its ground station in Ngari in Tibet at an altitude of over 4,000 meters. So the distance from the ground to the satellite varies from 1,400 kilometers when it is near the horizon to 500 kilometers when it is overhead.

Related Story

To perform the experiment, the Chinese team created entangled pairs of photons on the ground at a rate of about 4,000 per second. They then beamed one of these photons to the satellite, which passed overhead every day at midnight. They kept the other photon on the ground.

Finally, they measured the photons on the ground and in orbit to confirm  that entanglement was taking place, and that they were able to teleport photons in this way. Over 32 days, they sent millions of photons and found positive results in 911 cases. “We report the first quantum teleportation of independent single-photon qubits from a ground observatory to a low Earth orbit satellite—through an up-link channel— with a distance up to 1400 km,” says the Chinese team.

This is the first time that any object has been teleported from Earth to orbit, and it smashes the record for the longest distance for entanglement.

That’s impressive work that sets the stage for much more ambitious goals in the future. “This work establishes the first ground-to-satellite up-link for faithful and ultra-long-distance quantum teleportation, an essential step toward global-scale quantum internet,” says the team.

It also shows China’s obvious dominance and lead in a field that, until recently, was led by Europe and the U.S.—Micius would surely have been impressed. But an important question now is how the West will respond.

Ref: arxiv.org/abs/1707.00934: Ground-to-satellite quantum teleportation

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Breakthrough tool predicts properties of theoretical materials

as I have mentioned before, new tools excite me. Because it is out of new tools that our range of possibilities broaden…

Scientists at the University of North Carolina at Chapel Hill and Duke University have created the first general-purpose method for using machine learning to predict the properties of new metals, ceramics and other crystalline materials and to find new uses for existing materials, a discovery that could save countless hours wasted in the trial-and-error process of creating new and better materials.

Researchers led by Olexandr Isayev, Ph.D., and Alexander Tropsha, Ph.D., at the UNC Eshelman School of Pharmacy used data on approximately 60,000 unique materials from the National Institute of Standards and Technology’s Inorganic Crystal Structure Database to create a new methodology they call Properties Labeled Materials Fragments.
Using machine learning to analyze and model existing crystal structures, the PLMF method is able to predict the properties of new materials proposed by scientists and engineers. The tool was even able to fill in missing values for properties of materials in the NIST database that had never been tested experimentally.
“Technology is often driven by the discovery of new materials, but the process of discovering these materials has always been rather haphazard,” Tropsha said. “Out new tool applies the data- and knowledge-driven approach we use in the pharmaceutical sciences to design drugs. Because creating new materials takes an incredible amount of time and effort that often ends in disappointment, our PLMF tool allows materials scientists to test a new idea before they even lift a finger to synthesize it.”

https://phys.org/news/2017-07-breakthrough-tool-properties-theoretical-materials.html

The PLMF method works by creating “fingerprints” from the structure of the crystals that comprise the smallest units of inorganic materials like ceramics, metals and metal alloys. Combining the fingerprints with machine learning allowed the creation of universal models capable of accurately predicting eight critical electronic and thermomechanical properties of virtually any inorganic crystalline material. The properties include conductivity, stiffness and compressibility, heat transfer and response to temperature change, and the team plans to incorporate more properties as they collect more data, Isayev said.
“In many practical projects, people know the range of values they want for a particular property,” Isayev said. “We can leverage what we know about these materials and savvy machine learning to rapidly screen potential materials for the right property. Researchers can quickly narrow candidate materials and avoid many extraneous and complex calculations. This saves money, time and computational resources.”
In the first practical application for the machine learning, the team worked with Assistant Professor Jim Cahoon, Ph.D., in the UNC Department of Chemistry to design a new electrode material for a type of low-cost solar cells. The currently used nickel oxide, is not very efficient, toxic and requires organic solvents to work in the cell.
Scientists virtually screened 50,000 known inorganic compounds and identified lead titanate as the most promising material and subsequent testing confirmed it. The devices using lead titanate exhibited the best performance in aqueous solution, allowing a switch away from solvents to a water-based solution that could help drive down costs while being more environmentally friendly.
“Lead titanate likely would not have been the first choice of most materials scientists because its structure is so dissimilar to nickel oxide,” Isayev said. “Materials derived from iron, cobalt or copper would be more likely to be considered because they are more chemically similar to nickel. The PLMF and machine learning found a simple and novel solutions that saved untold hours of trial-and-error searching.”

 

Read more at: https://phys.org/news/2017-07-breakthrough-tool-properties-theoretical-materials.html#jCp

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DARPA Wants Brain Implants That Record From 1 Million Neurons

 

DARPA is known for issuing big challenges. Still, the mission statement for its new Neural Engineering Systems Design program is a doozy: Make neural implants that can record high-fidelity signals from 1 million neurons.

Today’s best brain implants, like the experimental system that a paralyzed man used to control a robotic arm, record from just a few hundred neurons. Recording from 1 million neurons would provide a much richer signal that could be used to better control external devices such as wheelchairs, robots, and computer cursors.

What’s more, the DARPA program calls for the tech to be bidirectional; the implants must be able to not only record signals, but also to transmit computer-generated signals to the neurons. That feature would allow for neural prosthetics that provide blind people with visual information or deaf people with auditory info.

Today the agency announced the six research groups that have been awarded grants under the NESD program. In a press release, DARPA says that even the 1-million-neuron goal is just a starting point. “A million neurons represents a miniscule percentage of the 86 billion neurons in the human brain. Its deeper complexities are going to remain a mystery for some time to come,” says Phillip Alvelda, who launched the program in January. “But if we’re successful in delivering rich sensory signals directly to the brain, NESD will lay a broad foundation for new neurological therapies.”

One of the teams taking on the challenge is the Silicon Valley startup Paradromics. Company CEO Matt Angle says his company is developing a device called the Neural Input-Output Bus (NIOB) that will use bundles of microwire electrodes to interface with neurons. With four bundles containing a total of 200,000 microwires, he says, the NIOB could record from or stimulate 1 million neurons.

“Microwire electrodes have been used since the 1950s, but traditionally they’re un-scaleable,” Angle tells IEEE Spectrum in an interview. With existing systems “you need to wire up one microwire to one amplifier—so if you want to use 100,000 microwires, that’s a lot of soldering work for a grad student,” he says.

Paradromics gets around this problem by polishing the end of a microwire bundle to make it very flat, and then bonding the whole bundle to a chip containing an array of CMOS amplifiers. “We make sure the probability of a single wire coming down and touching the pad on the CMOS is very, very high,” says Angle, “but if you have a few spots that don’t get wires, that doesn’t matter much.”

Each microwire in the bundle has a diameter of less than 20 micrometers.
As always, DARPA emphasizes the practical application of technology. By the end of the four-year NESD program, the teams are expected to have working prototypes that can be used in therapies for sensory restoration.

Paradromics’ goal is a speech prosthetic. The NIOB device’s microwires will record signals from the superior temporal gyrus, a brain area involved in audio processing that decodes speech at the level of sound units called phonemes (other areas of the brain deal with higher-level semantics).

The company drew inspiration from neuroscientist Robert Knight at University of California Berkeley, who has shown that when people read aloud or read silently to themselves the neural signal in the superior temporal gyrus can be used to reconstruct the words. This finding suggests that a user could just imagine speaking a phrase, and a neural implant could record the signal and send the information to a speech synthesizer.

While Paradromics has chosen this speech prosthetic as its DARPA-funded goal, its hardware could be used for any number of neural applications. The differences would come from changing the location of the implant and from the software that decodes the signal.

The challenges ahead of Paradromics are significant. Angle imagines a series of implanted chips, each bonded to 50,000 microwires, that send their data to one central transmitter that sits on the surface of the skull, beneath the skin of the scalp. To deal efficiently with all that data, the implanted system will have to do some processing: “You need to make some decisions inside the body about what you want to send out,” Angle says, “because you can’t have it digitizing and transmitting 50 GB per second.” The central transmitter must then wirelessly send data to a receiver patch worn on the scalp, and must also wirelessly receive power from it.

The other five teams that won NESD grants are research groups investigating vision, speech, and the sense of touch. The group from Brown University, led by neural engineer Arto Nurmikko, is working on a speech prosthetic using tens of thousands of independent “neurograins,” each about the size of a grain of table salt. Those grains will interface with individual neurons, and send their data to one electronics patch that will either be worn on the scalp or implanted under the skin.

In an email, Nurmikko writes that his team is working on such challenges as how to implant the neurograins, how to ensure that they’re hermetically sealed and safe, and how to handle the vast amount of data that they’ll generate. And the biggest challenge of all may be networking 10,000 or 100,000 neurograins together to make one coherent telecommunications system that provides meaningful data.

“Even with a hundred thousand such grains, we would still not reach every neuron—and that’s not the point,” Nurmikko writes. “You want to listen to a sufficiently large number of neurons to understand how, say, the auditory cortex computes ‘the Star Spangled Banner’ for us to have a clear perception of both the music and the words.”

http://spectrum.ieee.org/the-human-os/biomedical/devices/darpa-wants-brain-implants-that-record-from-1-million-neurons

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the crazy pace of crispr…

Rewriting the code of life has never been so easy. In 2012 scientists demonstrated a new DNA-editing technique called Crispr. Five years later it is being used to cure mice with HIV and hemophilia. Geneticists are engineering pigs to make them suitable as human organ donors. Bill Gates is spending $75 million to endow a few Anopheles mosquitoes, which spread malaria, with a sort of genetic time bomb that could wipe out the species. A team at Harvard plans to edit 1.5 million letters of elephant DNA to resurrect the woolly mammoth.

“I frankly have been flabbergasted at the pace of the field,” says Jennifer Doudna, a Crispr pioneer who runs a lab at the University of California, Berkeley. “We’re barely five years out, and it’s already in early clinical trials for cancer. It’s unbelievable.”

The thing to understand about Crispr isn’t its acronym—for the record, it stands for Clustered Regularly Interspaced Short Palindromic Repeats—but that it makes editing DNA easy, cheap and precise. Scientists have fiddled with genes for decades, but in clumsy ways. They zapped plants with radiation to flip letters of DNA at random, then looked for useful mutations. They hijacked the infection mechanisms of viruses and bacteria to deliver beneficial payloads. They shot cells with “gene guns,” which are pretty much what they sound like. The first one, invented in the 1980s, was an air pistol modified to fire particles coated with genetic material.

http://The Gene Editors Are Only Getting Started – School Information System https://apple.news/AgMnx20ynPYqkAuDX-kqR4A

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Market thoughts and position update 7/8/17

Greetings.

It has been another busy week. And this short review will reveal my lack of time. There is a chance that the NDX has finished its decline and now will trade sideways, but I would not bet on it. I think we still need a puke-day. However, it must be said that with market rotations you are never given real climax-selling, just sideways action.

My medium-term algo still has me in the market. Although it may do some contract-switching from NDX exposure to SPX exposure this coming week. I will keep you updated.

Above you can see the SPX chart. Note how Gaap earnings are finally over $100 per share. Unemployment is drifting sideways. But the Fed rate is only 25 basis points from the 2 year rate. Inversions will signal a recession-scare within a year.

Note above that the total option ratio is signalling a sell on SPX. Green line is below the red.

As is the NDX also. Not enough for me to sell. But worrying.

Below is the Momentum models we follow.

We are currently long NDX, no hedges, naked as a newborn. Hoping the headwinds will turn. Luckily, the fund is up strongly this year. So the draw-down isn’t too painful. Oh, you wanted to sleep at night? Psshhh… white-belt…

Have a great weekend.

 

 

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30 years of lessons… with John Boorman

I love this teaching article. It’s worth your time…

Today marks 30 years since a confident young man walked into the back office of Schroder Investment Management in London, to start his first day on the job, the first in his career. Ask me a question back then and I would have answered assuredly and quickly. Today I’d be more likely to say ‘I don’t know’ with just as much confidence.

Now older, wiser, but with just as much hair, I have over the years seen many people come and go. Clients, colleagues, bosses, company mergers, bankruptcies (thankfully not my own), through bull and bear markets, booms, crashes, and have seen my own fortunes fluctuate too before setting out on my own a few years ago.

Thirty years is a long time. The good news is it was all worth it.

The first thing to point out is I don’t have all the answers. That’s not what this post is about. I’m always learning. But I have benefited enormously from people sharing their time and expertise, so if I can help others in the same way, I’m happy to share what I’ve learnt also.

These are 30 observations, guiding principles, or simply things that work for me. Some of you who have followed me for a while will recognize many of them. These aren’t universal truths, they’re my truths, my beliefs, shaped by my experience.

And that’s probably a good place to start.

“The more you believe something to be true, the more you will have accumulated evidence to support it.”

That’s a quote from trading coach Van Tharp, and I’ve applied it to so many areas as a simple way of explaining people’s expression of their beliefs, my own, and the realization of how powerful confirmation bias is. Van believes we don’t trade the markets, we trade our beliefs in the market. A trading system therefore is simply a set of beliefs, and I think he’s right.

Buy high, sell higher.

Buying a stock at x+1 can be a lower risk trade than buying it today at x. Forget buy low, sell high. When something is falling, it’s more likely to keep falling than it is to reverse, and vice versa. It’s called momentum, and along with value, it’s one of the most empirically proven anomalies to academic theory that the Nobel Prize winners wish would go away. Note to self: Look into buying value stocks that show upward momentum.

Trade small to win big.

All traders and investors need trend and time to profit. Even if you don’t consider yourself a trend-follower, no matter what your timeframe, to make money you need something to trend, even if it’s just a couple of ticks higher, you need price movement.

If you are a long-term trader, time is also your friend. Time allows trends to develop, persist, and time in big trends allows you to trade in smaller size. If you are a daytrader, time is your enemy. The clock is ticking, there’s only x minutes left in the session. You need greater frequency of trades, or you have to trade in greater size and take greater risk.

It amazes me that newcomers to trading choose to start with an area that instantly requires them to either trade more frequently, or in greater size through leverage or margin. It should be the other way around. Only after years of experience and having amassed a fortune should someone attempt such a thing, but of course they don’t. A successful trader or investor will continue to do what made them that money in the first place, and it won’t have been daytrading. 99% of daytraders (a conservative estimate) are under-capitalized and would do better to build up their savings instead of daytrading them.

Limit orders limit performance.

I once worked for a PM who always put on limit orders. It was like chasing a bar of soap around the bathtub. Sometimes weeks or months later the order would still be on our desk, but the stock would now be way way higher. You either want to own it or you don’t. Is a penny here or there really the difference between whether you want to own it or not? Because your limit order is potentially making it exactly that.

I’ve held stocks for over a year and looked back at when I bought it. I could have bought it the next day, the next week, open, close, whatever. It wouldn’t have made a whole lot of difference. Unless you’re trading Cliff Asness/AQR size, for goodness sake, quit playing games with the HFT pikers. Just buy it and move on.

I have never found a way to consistently make money shorting stocks.

If you’re starting out, put this one in the ‘too difficult’ pile until you have the time, energy, or intellectual curiosity to tackle it. Just know that even amongst CTAs, even though they are long/short many different futures markets, the short side of what they do rarely makes much money overall, it merely helps them perform well during ‘crisis alpha’ periods of non-correlation, and smooths the equity curve longer-term, but the lion’s share of performance comes from the long side. That’s futures. Stocks are even harder.

The best strategy is one you’ll stick with.

Or more correctly, the best strategy is the one that you’ll stick with and meets your objectives. There is no one way of investing that is suitable for everyone. There is only what’s right for you. Lots of things work. Buy and hold works. Value works. Momentum works. There are others too. Start with the evidence-based empirically-proven stuff. Find which one, or which combination works for you, in accordance with your timeframe, objectives, and investment horizon.

Buy and hold giving you 7% is fine, but if you can’t tolerate 50-60% drawdowns or trust yourself to not bail precisely when you should be adding any spare cash you have to it, then it’s not for you. Pick a strategy that delivers an acceptable return that won’t have you reaching for the sick bag when turbulence hits.

When to add.

Whether trading or investing, the simplest way to know how and when you should add to a position is to imagine you don’t already have a position. What would it take to get you in? That way you’ll be doing it for the right reasons, the same as your initial entry rationale, rather than reacting emotionally.

The best movie about trading is “Wall Street”, then “Trading Places”, then something else.

The vast majority of arguments on social media could be avoided if both sides simply declared at the outset what their timeframe is. You mean we could have diametrically opposed views and yet both make money? Yes, that’s right.

No amount of reading or paper trading will prepare you for how it truly feels in the heat of battle.

There is a great scene in ‘Bridge On The River Kwai’ where Jack Hawkins brings a young soldier in and hands him a knife, asking him if he thinks he could use it in cold blood. The boy doesn’t know. “Well, at least he’s honest.” The fact is, none of us know until we face that enemy whether you can thrust that blade home or pull the trigger on your order.

Don’t blithely tell me your backtest says you would have taken that trade in ’87, or 2008/09. You don’t even know what the market liquidity would have been, whether you could trust the prices you’re seeing, or if you could even see any prices. You’ll know in your walk-forward.

I know, because I’ve been there and done it. Traded like an idiot with my own money in the ’87 crash, and have since safely navigated in various trading roles the LTCM collapse, the Asian crisis, the Dotcom crash, 9/11, the Global Financial Crisis, and most recently for myself and clients through a couple of flash crashes. I consider it an edge, one of the few that can never be taken from me. You can’t buy experience like that.

I can’t predict markets, and neither can you.

No, seriously, you can’t. No. You can’t.

Entries, exits, position size.

Watch any trading software ad and you’ll likely hear lots about getting entry signals. The perception is it’s more important than the others, but it’s not. I think exits are more important. A good exit signal doesn’t just get you out when needed, a really good exit signal keeps you in, staying just below the action and not triggering until the trend is over.

Look back at the entry of a successful position you’ve held for many months. How important was it to enter at that precise time, that day? It’s likely what followed was more important. What allowed you to tolerate the volatility and ride it higher to where it is now, making it the big winner it is. That’s all exits and position size, not entry.

Sure, without an entry there’s no trade, but it’s only the exit signal that determines whether in relation to that entry the trade is a winner or loser. Even more important, the position size will determine by how much. Entries merely determine the frequency of trades, or how many signals you have.

The longer your investment horizon, the higher your equity allocation should be to passive strategies.

Yes, I’m an active manager, but hear me out. If I have a 20-something come to see me as a prospect, I’m going to tell him to just put it in an index fund for 15bps and come back and see me when he’s over 40. Come on, the guy’s got 5 decades ahead of him. Go live your life, save, invest, have an emergency fund, put more cash to work every time the market plunges 25%-30%.

By the time he’s 50 and thinking about retirement however, those 30% plunges on that tidy sum he’s built up won’t look like the opportunities they once were. The percentages will be the same, but the nominal amounts will make it way scarier, seeing his hard-earned go up in smoke.

The closer you are to needing your money, or put another way, the less of an investment horizon you have remaining in which to recover losses, the higher your allocation to active strategies should be. By the time you are nearing retirement, your equity allocation should be 100% active, zero passive.

People tend to think in simple terms that passive = safe, and active = risky. The opposite is true. A truly passive strategy exposes you to 100% of the market’s drawdown. With passive you get what you pay for – zero risk management. Active management is risk management. That’s what you pay for. Risk management.

If you want to own oil, buy oil, don’t buy oil stocks.

If you want to own tech, buy a tech ETF, don’t buy Apple. Having a top-down macro view and then trying to apply it to a micro level is one of the hardest things to do. I did it once, and made a lot of money, but now realize it was mostly dumb luck. I have seen people make brilliant calls that were completely right but they lost money executing it horribly. Buy what it is you got your signal on, not where or how you think it might play out a second or third degree. One is quantitative, the other is a guess.

Hedging a position often increases risk instead of reducing it.

I’ve seen traders take on a position and then immediately look for something to hedge it with. Why? Just reduce your initial position. Or sometimes the exposure becomes too great. How can I hedge it? Why not just reduce it down to a more comfortable level? Size it correctly and it won’t need to be hedged, and you’ll also have more capital available.

I once had a boss on the prop desk who insisted on every position being hedged with the equivalent size in index futures. Absolutely insane. Now I’ve got one position I wanted and a whole load of futures I didn’t. He was a big Buffett fan. Insisted the only true measure of our performance was whether we beat the index or not. Weren’t we here just to make money for the firm? Apparently not. When I bought a utility that went up 5% but the index went up 10% over the same period (and I didn’t hedge) he said it was a bad trade.

I was a bit gung-ho and I let him get to me. When I left the desk I thanked him for making me a better trader. The look on his face! But I was serious, he challenged all my beliefs and as maddening as it was, it made me re-evaluate what it was I believed in and why. You should want to be challenged on everything you believe and be calm and comfortable in explaining it, and in fact, welcome any new information that disproves your existing position, so that you can immediately correct it.

The best book on trading is “Reminiscences Of A Stock Operator.”

It’s an obvious, popular, and cliched choice, but for good reason. Yes, its main protagonist committed suicide, and it’s written in archaic language, but it’s because the stories are from a hundred years ago, and that’s precisely why it appeals. The lessons stand the test of time. The stocks, companies, and players change, but human nature never changes. We’re all human, even millennials.

“If it’s so good, why would they sell it?”

This is one of the most egregious fallacies in the finance periphery. Why would they sell it? Why do you think? Do the math. Let’s take an example of an area where this is most commonly targeted; newsletter writers or subscription services. Imagine for a moment a trader has a $1m portfolio. He makes on average 10% a year, or $100k. That’s his trading income. If he also runs a subscription service that sells for a $1000 a year, he can get an additional $100k a year with 100 subs. That’s very nice passive income.

Now I used $1m in my example. In reality most traders are capitalized at $100k or less. They would only need 10 subscribers to get the same return. If they had 100 subs, it would match their entire portfolio value! The question then becomes not “If it’s so good why would they sell it?” but instead “If it’s so good, why wouldn’t they sell it?”

And it’s also grossly unfair to limit this logic to newsletter/sub services. If hedge fund managers are so good, why do they need clients? We know why. The fees. They can make way more from managing other people’s money than just their own. It’s the exact same principle.

I’ve seen many people get tarred with this brush unfairly, especially in the area of technical research, and yet fundamental research with its dire record gets a pass. I’ve seen it firsthand too. If you give something away for free people think it can’t be worth anything. If you charge for it “If it’s so good, why would you sell it?”

Broker research is mostly redundant.

There are many excellent analysts that no doubt create value for others, but the ratings systems are useless and as analysts they are being assessed incorrectly. Buy/Sell/Hold means nothing. There are so few Sell ratings. They are terrified of not getting corporate business. Broker X upgrades XYZ from Sell to Hold. How do I hold it after you recommended I sell it? Shouldn’t you move to a Buy rating first? Neutral/Outperform/Underperform. Overweight. Yes I am.

The only way it would make sense is if you asked the analyst to rank all his buy ratings. So you cover the tech sector and you have 50 names with a buy rating. That doesn’t help me. How about you rank them 1-50 for me? Now we’re talking. That could be useful. Buy the top one, short the worst, let’s see if he’s any good.

Price targets are also mostly redundant.

Under the guise of assigning their fair value to a company, price targets are simply a way for an analyst to stay in front of clients in a name and reiterate or update their research periodically without necessarily changing their rating. It’s a useful tool for them, but unless you’re also a value investor where a specific value would cause you to act, for the rest of us it’s just another unwelcome noise item that anchors you to a price in the market, and tempts you to act when you should instead just follow whatever your existing plan or strategy is.

If you want to own the strongest stocks, buy the strongest stocks. Buy something that’s already doing what you want it to. Going up.

The closing price is the most important price.

Let me qualify that. I have likely said before that it’s all that matters but that’s not true. The close is the most significant, simply because so many other investors or traders act off it for end of day signals.

I like to think of the trading day as a jury deciding what a stock is worth that day. The opening statements are heard, and the intraday prices from the high to low reflect the arguments being made throughout the session. The close is the verdict. That’s what stock XYZ is worth today. Record the verdict. Price the mutual funds. Put it in the paper.

I’ve heard people place more emphasis on intraday extremes, but why? The high and low are likely the two lowest volume prints of the entire session, and therefore arguably the two least important. You could argue they provide support/resistance levels, but again by volume I would think the closing price is a better reflection of where most people are gathered or potentially anchored so it has more significance.

And let’s clear something else up. I’ve heard people say amateurs open the market, pros close it. OK, let’s assume for a minute that’s true. Which price would you rather take your trading signal from, and who would you rather trade against? Amateurs or pros? I take my signals from the close and trade at the next day’s open.

For high net worth individuals there is no need for a specific allocation to bonds.

I’m biased. I’ve been an equities guy for 30 years, but seriously, if you don’t need the income/interest, why allocate to bonds or treasuries at all? You can get exposure via a managed futures strategy. If there’s a meaningful sustained trend, up or down, you’ll catch it, and in 30yr, 10, 5, 2, and even German, Japanese too. You could allocate 50% to Managed Futures, 50% to Equities, and allocate that equity portion to passive/active strategies depending on your age, or maybe a combination of value and momentum. 50% Equities, 50% Futures, covering Trend Following, Momentum, and Value. You don’t need bonds.

If you want to perform differently to the index, you have to invest differently to the index.

When I worked as an assistant to a Portfolio Manager at Schroders we had client portfolios that had something like 60 stocks or more in Japan alone, and that might only be 25% of the entire portfolio. I’d see a stock do really well and it barely made a blip of difference to the portfolio. After a while I would understand there are many playing this game of marginal differences in portfolio structure, overweight this, underweight that. The market goes down 20%, your fund is down 19%. Yay, you beat your benchmark and get a bonus. The incentives are all wrong. Relative returns is a game I know I have no interest in playing.

In my days at Kemper/Zurich/Scudder they had more concentrated portfolios where the stock selection mattered more, and then I got to do that to an even greater extreme as a prop trader at Lehman where you may only have two or three positions, whatever it is you want. It’s not even considered a portfolio. I typically held 8-10 and often do the same now. Through a combination of all these factors, reading material like Van Tharp’s position sizing strategies, and Howard Marks’ letters, I’ve become very comfortable with a highly-concentrated portfolio and all the parameters and performance distribution that entails.

Stocks don’t follow economic theory. They follow socionomic theory.

This is why when a stock goes up people will want to buy more of it. And when it goes down people will sell. That’s not how it works with traditional economic laws of supply and demand. When the price of shoes go up, people don’t rush out to buy more. And when they go on sale people don’t run out of the store. In a supermarket consumers act rationally and logically, but there are no consumers or producers of stocks, there are only investors, and investors herd and are emotional and irrational.

Price is sentiment.

There are some variants to this. Price is truth. Only price pays. I think the way I would phrase it is that price accurately reflects prevailing sentiment. Some think it’s supply and demand, I think it’s Socionomics/social mood, but regardless, whether you believe it’s wrong that it’s trading up at $100 when your fair value is $50, it’s irrelevant. If you want to trade it, the price is $100. You may think it’s wrong, but that is the price. In terms of reflecting current sentiment, price is always right.

I’m ready for what’s next. I have no idea what the market will do tomorrow, what the next payrolls number will be, or when the Fed will next raise rates, and frankly I don’t care. News is noise. All I know is I will follow my plan. It took me 25 years to work that out. You’re welcome.

I am responsible for everything that happens to me.

Everything. Good and bad, but this mostly comes into play for something bad. You won’t find me blaming the Fed, QE, HFT, or any conspiracy nonsense if my portfolio performs badly. The outcome is a result of my decisions. That bad trade was my stock selection, my execution, my choice of broker, all my decisions that led to that outcome. If it’s something I enacted it always comes back to me. If it’s something that happened to me, it’s because I put myself in that situation. If it’s something my child did, it’s something I allowed them to be doing. Everything is a risk. Getting up, going out, crossing the road, but ultimately I am responsible for everything that happens to me.

People really appreciate honesty.

It might sound obvious, but from the reaction I get it suggests there’s not enough of it around. I’ve made a conscious effort to say “I don’t know” when I don’t know. It can be quite empowering. When I’ve talked about positions or trades on social media, I’ve made a point of following up when things haven’t gone so well. It’s one of the hardest things for me to do. But it’s only right. You can’t just sing when you’re winning. The losing periods are when I least feel like writing something, but when I most need to, because it’s also when anyone who’s been paying attention to anything I say will most need to hear it too.

“All cruelty springs from weakness.”

Social media is a tough arena. I slip up sometimes and get sucked into some troll’s orbit, and on the occasions it’s happened, even when I’ve sent someone packing with their tail between their legs, the short-term satisfaction soon gives way to wishing I hadn’t responded.

When I’m driving and I’m getting frustrated with someone in front of me I imagine I know the person. It’s amazing how it changes how you react. In a similar vein, on twitter now I try to talk to people as you would if having a conversation face to face. Be nice. We all have off days. You never know what’s going on in people’s lives. Everyone’s going through something.

When you’re young, you have so much time but never enough money. When you’re old you have money but never enough time.

How you perceive and value time and money will change many times throughout your life, but at the end there’s only one you’ll want more of, would give anything for, but it won’t be available at any price. Cherish it while you can.

Thanks for being a part of my journey. Here’s to the next 30 years.

John Boorman CMT

http://www.broadswordcapital.com/things-ive-learned-last-30-years/

 

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AI marches into the hospital

A team of researchers at Stanford University, led by Andrew Ng, a prominent AI researcher and an adjunct professor there, has shown that a machine-learning model can identify heart arrhythmias from an electrocardiogram (ECG) better than an expert.

The automated approach could prove important to everyday medical treatment by making the diagnosis of potentially deadly heartbeat irregularities more reliable. It could also make quality care more readily available in areas where resources are scarce.

The work is also just the latest sign of how machine learning seems likely to revolutionize medicine. In recent years, researchers have shown that machine-learning techniques can be used to spot all sorts of ailments, including, for example, breast cancer, skin cancer, and eye disease from medical images.

“I’ve been encouraged by how quickly people are accepting the idea that deep learning can diagnose at an accuracy superior to doctors in select verticals,” Ng said via e-mail. He adds that it’s encouraging to see researchers looking beyond imaging to other forms of data such as ECG.

Until recently, Ng was the chief scientist at the Chinese tech giant Baidu, where he helped found an institute dedicated to applying deep learning to different business problems.

The Stanford team trained a deep-learning algorithm to identify different types of irregular heartbeats in ECG data. Some irregularities can lead to serious health complications including sudden cardiac death, but the signal can be difficult to detect, so patients are often asked to wear an ECG sensor for several weeks. Even then it can be difficult for a doctor to distinguish between irregularities that may be benign and ones that could require treatment.

https://www.technologyreview.com/s/608234/the-machines-are-getting-ready-to-play-doctor/

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Thoughts about AI revolution

This is interesting. I agree up to a point. The AI revolution is about everything, health, entertainment, transportation, robotics, etc. Just ONE OF THE FACETS of this revolution is the nanny-care like experience that technology will begin to provide for us. 

The coming revolution is about an AI understanding the human brain — our preferences, our choices, or desires. That will require a Herculean effort. For one thing, my preferences change. Today I’m thinking about biking apparel, tomorrow I’m thinking about going to the beach. An AI will have to adapt, respond, adjust, and customize a thousand times per day. It will need to work like the human brain, constantly making micro-adjustments based on changing variables. A true AI is one that serves us and knows us; we no longer have to know or serve it. We speak and it hears us. We don’t need to learn its parameters, it will learn our parameters.

We’re not there yet, of course. Most of us are still tethered to a smartphone all day. By 2030 or so, bots will become adaptive assistants that learn about our behaviors and fit smoothly into our daily routine. We’ll stop being enamored by tech. Tech will be enamored by us.

https://venturebeat.com/2017/07/05/the-fourth-industrial-age-will-be-about-ai-understanding-us-not-the-other-way-around/

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Cancer immunological progress…

With swift shots to the arm, doctors safely and effectively prime our immune systems to fight off deadly infectious diseases. Now, with tightly crossed fingers, they plan to do the same for cancers.In two early clinical trials involving 19 patients with skin cancer, personalized vaccines appeared safe and effective at spurring immune responses to attack and destroy tumors. The vaccines worked by coaching killer immune cells—T cells—to destroy tumors by seeking out uniquely mutated proteins on each patients’ one-of-a-kind cancer cells, while leaving healthy cells unharmed.

The results of the two trials, both published this week in Nature, follow years of basic research and animal studies on this strategy. Researchers are optimistic, but there are big hurdles ahead of these small trials, including bigger trials with more patients and controls. If those go well, researchers will likely have to figure out how to streamline creating vaccines for individual patients, which is currently tedious and expensive.

“The two studies confirm the potential of this type of approach,” Cornelis Melief, a cancer immunologist at Leiden University Medical Centre in the Netherlands, wrote in an accompanying commentary. “Controlled, randomized phase II clinical trials with more participants are now needed to establish the efficacy of these vaccines in patients with any type of cancer.”

More here