Topic: GRAYLL [IEO] Simple Automated Investment App Driven by AI & ML - page 14. (Read 7992 times)

Really appreciate your desire of "search and destroy" scams. Scalability, market manipulation, or governement's decisions are minor issues if compared to scammers - the worst nightmare of this industry-. I am also concerned about this and in fact most of my merits achieved in forum is just because of being a scammer hunter.
example:
https://bitcointalksearch.org/topic/massive-cheat-of-related-accounts-red-trust-tag-from-dt-users-needed-5105258
https://bitcointalksearch.org/topic/how-should-this-be-adressed-want-to-know-communitys-opinion-5125186
https://bitcointalksearch.org/topic/help-needed-found-scam-gang-mafia-5110956
https://bitcointalksearch.org/topic/another-translation-scam-and-probably-the-same-guy-with-an-alt-account-5122389
https://bitcointalksearch.org/topic/would-like-to-know-the-opinion-of-community-about-the-reputation-of-this-user-5117944


So, I also tried to do a proper research of the company before being involved here. And yes, also took a look long time ago at the thread you quoted. Below, there are parts that seem to be blatantly copy pasted, these that you quoted. But now, to the point.. is that not just about statistics? Are the statistics of certain period subjected to changes in any way? The answer for me, was obviously NO, so I joined Grayll, and in fact right now is my TOP1 project. For a long time in continuous contact with CEO, and I can promise he got a mastermind.. Product seems to be working fine and ROI is insanely high. Soon, time will proove if I was right or wrong.
 

We will should be able to unlock the User App this month of December 2019 to our investors so that they can finally experience the some of the features and familiarize themselves with the layout and options. Their feedback will be used to make any required improvements as we keep developing. The public Beta release date cannot be disclosed, the System is still being tested to support 100,000's of users opening multiple algorithmic positions each.

The problems that are required to be solved on a daily basis for everything to work well, securely and efficiently on the front and back-end, there are no out of the box tools or code that we can use, so we just keep working at it everyday.

We have some retail and professional investors that were "burned" in the past, maybe because they were too greedy, unaware of the full risks, unprepared to invest like a Venture Capital firm, lack of due diligence capabilities and underestimated their risk tolerance. These same traders/investors now often fear to be early adopters, so they often are wait for the Beta versions of an App being released, before they make invest, which does mean they could miss the highest ROI % achievable on their investment.

Clearer images facial images on the website? What an odd request these days when many excellent tech firms use their creative expression of their designers to be more original.However if this is an important concern for you could also click through the related URLs/hyperlinks on the website to review the executive team's LinkedIn profiles. This will give you a much better idea of the potential capabilities of the team based on their education, work history and relevant experiences based on their employment and ventures.

Also you may consult the team's profiles on ICObench which also has hyperlinks to LinkedIn: https://icobench.com/ico/grayll

@ChiNgadOr if you are probably promoting this project or if your are part of there team can you please address the never to be worked on concerns here about GRAYLL.

https://bitcointalksearch.org/topic/grayllio-plagiarized-whitepaper-5133185

How many phases of MVP for you to finally release the final version of your App?
And can you provide us a clearer photos of your team members if it is not too much to ask from your end?

Algorithmic Trading in Practice
Suppose a trader follows these simple trade criteria:

• Buy 50 shares of a stock when its 50-day moving average goes above the 200-day moving average. (A moving average is an average of past data points that smooths out day-to-day price fluctuations and thereby identifies trends.)  
• Sell shares of the stock when its 50-day moving average goes below the 200-day moving average.

Using these two simple instructions, a computer program will automatically monitor the stock price (and the moving average indicators) and place the buy and sell orders when the defined conditions are met. The trader no longer needs to monitor live prices and graphs or put in the orders manually. The algorithmic trading system does this automatically by correctly identifying the trading opportunity.

source: https://www.investopedia.com/articles/active-trading/101014/basics-algorithmic-trading-concepts-and-examples.asp

We’ve all heard the term “patience is a virtue”, and yet we all seem to underrate it. Do you consider yourself a patient person? Many of us are a teensy tad out of practice when it comes to this powerful behavioral trait!


We live in a world of instant access, with thrills and spills unfolding behind the tiny screens in our pockets. Fast food, fast shopping, and even faster download speeds! In the moments when we do find ourselves stuck in a queue or a traffic jam, the veins in our temples are inclined to bulge. Does this sound familiar?

If you have been swept along with the current mind set of allowing voids to grow where patience aught to reside, it’s time to train yourself otherwise and flex that patience muscle! It turns out, patience is a virtue that comes bundled up with a host of health and happiness benefits. So, let’s explore what this trait is — and crucially what it isn’t — and how to build it into our own personal behavioral tool-kits.

What Defines Patience?


As we become more aware of our patience — or cringe-worthy lack there of — we can observe three distinct types that each play their own role in our daily lives. The first is interpersonal patience. This is the patience that allows us to be there for a friend who just can’t seem to get over a dastardly ex, even though months have passed, or to be kind to a sales representative who is making calamitous mistakes at the end of a double shift that — lets face it — they probably won’t be well paid for.

The second kind of patience is the one we engage as we go through difficult periods in life. This is the resource we call on when we’ve been made redundant and have to juggle financially while going to endless job interviews in the hope of the next stable position. It is the one we tap into when our child is sick and treatment after treatment don’t seem to be working.

The third and final form that patience takes is the kind that carries us through daily moments of frustration as we find ourselves stuck in traffic or realising our train has been cancelled when we have an important meeting ahead. It’s the kind that carries us through when our car, or our computer, don’t work as they should, or the ultimate dread: when the internet goes down!

What Patience Isn’t


Culturally, we have a misinformed inclination to view patience as synonymous with weakness. Surely impatience or even anger are the more powerful states in which to land? The problem with this appraisal is that when we indulge in impatience, we lose touch with our instincts. Getting tightly wound over our frustrations pulls out our capacity for emotional freedom like a magician sharply yanking away a sequined tablecloth! We lose sight of everything but our dissatisfaction, leaving us humourless, inclined to procrastinate, and likely to set everyone around us on the back foot.

Patience doesn’t mean refusing to acknowledge the inconvenience upon us, but it does mean not letting our blood rise to the boil, and staying in a place of zen equanimity. Rather than being a pushover, patience allows us to make a cool and calm assessment and navigate each situation without disturbing our inner tranquility.

In the US, a writer for Forbes expressed concern that the media was chattering over a looming economic recession, despite economic experts predicting no such calamity. Could rumour of recession be enough to tip an economy into an imaginary abyss? Across the pond in the UK, the Brexit saga continues to unfold, and we have to wonder what level of self-fulfilling prophecies are driving that particular run-away train. Political scientist Nicholas Aylott observed rumblings of a general election before the planned October Brexit date, and anticipated what would come to pass: “Once speculation has begun, it feels like a self-fulfilling prophecy.“ Here we are, a couple of months later, and party campaigns are ramping up for an election that may, or may not, nudge the divided nation towards some kind of closure.

The Potential Of Prophecy

Self-fulfilling prophecies can be harnessed towards the most extraordinary forward leaps, given the right circumstances. For example, many of our modern technologies were once nothing more than the fanciful dreams of science fiction writers — from the internet to electric cars — and the world would no doubt look very different today without imaginations unfettered by current practical limitations. Indeed, companies from Ford to Pepsi have been known to hire science fiction writers to help them conceptualise what the future might hold. Industry calls this “corporate visioning”, although one might also consider it taking self-fulfilling prophecies by the reigns.


The cause-and-effect of this phenomena can have damaging repercussions too, when the perception in question is negative in nature. Research from the American Psychological Association revealed a self-fulfilling prophecy unfolding within the US police force, as officers appeared to react to increasingly negative opinions of the force in terms of racism and unreasonable force by becoming more inclined to those exact behaviours. The reality that any negative stereotypes we allow to permeate our thoughts can be self-fulfilling, creating self perpetuating cultural divides, is something that should send us all into some deep-delving introspection!

Avoiding Creating Our Own Negative Prophecies

When we recognise that allowing expectations to manifest in our minds can bring those very expectations into reality, there’s something to be said for cultivating some speedy self-awareness. It is important to tease apart our emotional responses — you can feel anxious about an approaching interview — and our prophesying — your nervousness and a bad interview are not mutually exclusive! When we buy into negative expectations that we place on ourselves, we alter our outwards behaviour, and then experience those expectations mirrored right back at us. By being mindful of the narratives that we allow to run in our minds, and correcting them positively, we can make good work of creating self-fulfilling prophecies for ourselves that we want to manifest. From relationships to finances, what power could you unleash if your own prophecies became a force for your own elevation?

Creating Prosperity With GRAYLL



GRAYLL is an App that aims to make digital investment available to everyone, in a world that finds conventional financial systems coming up short. We are fascinated by the untapped potential that we all possess for living the lives we dream of. Discover more about GRAYLL, and begin building prophecies that steer you towards your own health, wealth, and happiness today!

Do you ever get the feeling that some fateful hand of destiny is reaching in and steering the course of things? Without speculating on the concepts of divine or fantastical intervention, there is a lot of evidence to suggest that we unwittingly slide into the rails of self-fulfilling prophecies all the time. You might well ask, is that a good thing or a bad thing?

Well, it can be either, depending on the prophecy in question! American industrialist and business magnate Henry Ford once said “Whether you think you can or you think you can’t, you’re right.” Things start to get really complicated is when we see that self-fulfilling prophecies are generated at a societal level. This phenomena is not something that we can overcome alone, but understanding the impact of our pre-conceptions can be a powerful tool to help us navigate this weird and wonderful world towards greater prosperity.

Prophecy Of Our Own Creation


Imagine a couple, in which one partner can’t stop imagining that their partner is cheating. After endless anxious probing, the accused begins to feel that they might as well be unfaithful, because their guilt is perceived whether it’s based in fact or not. Elsewhere, a struggling athlete who can’t seem to pull their performance up to standard switches from one team to another, and becomes an overnight success. Perhaps you can recall your own past self-prophesying. Perhaps an interview or business meeting that you didn’t feel confident about, that was just as awful as you thought it would be — or another, which you thought you would nail, and low and behold, it went like absolute clockwork.

What can be traced is that when we anticipate a certain outcome, we tend to shift our behaviour in such a way that propels us towards what we anticipated. Truth or illusion, and conscious or sub-conscious, our pre-conceptions steer our course more than we know. This holds a huge power for reinforcement. When we expect a negative outcome, and that prophecy indeed self fulfils, we can wind up getting stuck with a negative narrative: “I was right to believe it will always go wrong.” The negative cycle that follows is damaging if we don’t catch on to our own prophetic powers.

Prophecies On A Grander Scale


Back in 2014, a very bizarre turn of events was triggered at the New York Times. A tongue-in-cheek, and utterly self-parodying trend piece was published, announcing that the monocle was coming back into fashion. With some astonishment, the fashion industry, and indeed anyone who noted the trend, were dumb-struck as monocle sales made a massive leap. Simply imagining that others had confidence in the monocle actually led people to desire them!

The scale of the self-fulfilling prophecy knows no bounds. We can trace it to research into the Chinese stock exchange, and results indicating that this highly speculative market is powerfully influenced by investors’ expectations. The publication in question referenced British statistician Francis Galton, who identified that when a consulted crowd is large enough — even without professional understanding — the aggregate amateur wisdom of that crowd will closely predict end results in just about any area. Galton dubbed this phenomenon “wisdom of the crowd”. So, which crowds are you a part of?

good related content to share:  






Even the above described framework is generally correct, a lot of important parts are still missing, and they are actually crucial for profitable trading. I won’t talk about commission, shorting issues, liquidity, bet sizing or anything what is related actually to the strategy (I’ll touch some backtesting issues, but it’s needed for machine learning part). I will concentrate on problems in machine learning, dataset formation, evaluation of a model etc. It’s unbelievable coincidence, but most of the solutions to the problems (at least theoretical ones) I’ve found in the following book:

Advances in Financial Machine Learning
https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089?source=post_page-----a41f94048b8c----------------------

and I highly recommend it as well as second really useful source:
https://www.quantopian.com/lectures?source=post_page-----a41f94048b8c----------------------


Bars data == weak data

Most of people use well known HOLC (high, open, low, close) prices for some time period and trades volume. This information reflects not enough information about the market and what its participants are doing. We really need bids and asks from the order book to trade well — it will give us “raw” information and allows to build better features like dollar volume, bid/ask spread and others.




You still can build candle bars from this, but you will also get access to queues and their length, detailed information on how many people want to buy or sell particular asset (not just plain volume), tick imbalances and a lot of other interesting features, that have much more signal than just averaged noise in the bars.

Concentration on a single asset

This mistake is really bad. Most of my articles concentrate on taking some single asset, learning to forecast it for some fixed horizon and backtest the long-short equity strategy. Maybe some individual traders with $10k in the pocket really do this — they build some indicator-based strategy for some currency pair and trade it. But if we think about it for a while, it looks a lot like overfitting for this particular asset! What’s the point of having a strategy that is overfitted for a single time series (and we even aren’t sure that it will perform same good in the past). Hedge funds never do this. They do trade in so called universe of assets (possible with the same strategy). Portfolio is balanced to short or long these assets, or if some strategy is used to trade them it’s expected to have good performance on all of them.
Moreover, when you compare your strategy performance to some benchmark (for example in case of crypto trading this benchmark can be HODL strategy) you’re interested in calculation of alpha (outperformance of a benchmark) and beta (strategy risk exposure).

Fixed sized horizon forecasting

When I was preparing a dataset to train a model, each pair {x_i, y_i} was a window of N past days and price change (or direction of price movement) in some time after last date in the historical window. Let’s think about it again. Some time after. Fixed time. Well, the word “fixed” in financial world is ridiculous. We can’t even be sure that in some time there will be bids or asks to perform the trade! This is very serious issue, that actually ruins all our forecasting framework. To be honest, I haven’t found any easy fix for this problem, just two, but they’re drastic. First solution is stop forecasting and start executing trades, which leads us to control theory and reinforcement learning immediately. It will help us to deal with any fixed time horizon (at least to some extent), but it’s a bit off the topic for now. Second option I found in a book and it’s pretty interesting.





Basically on the picture above you can see “non-fixed-time” creation of labels for the dataset. It’s called “triple barrier” and works like following: we build three barriers — one on the top, that will mean taking the profit, the bottom one as a stop loss and the last, vertical one, will mean some kind of expiration period. This labelling method allows to build much more flexible and realistic strategies based on predictions.


source: https://medium.com/@alexrachnog/ai-for-algorithmic-trading-7-mistakes-that-could-make-me-broke-a41f94048b8c

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Hi everyone again! It’s been a long since my last post about machine learning for algorithmic trading and I had some reasons for it. After I could show some rather successful results in forecasting assets prices using neural networks I received interesting offers from different institutions: starting from banks and ending family funds and individual traders that were expecting to have some fruitful earnings using AI. I already had some strategies properly backtested (as I thought), so I was happy to help them. Meanwhile I also talked to several experts in the area and studied some literature and things appeared to be not that easy as someone can think. Today I want to share with you lessons I’ve learnt from professionals, some literature and, the most important, own experience, that can potentially save you a lot of money if you’re trying to incorporate AI in your trading process.

Current framework
As you can see, my whole blog is concentrated on solving the following machine learning problem: having a window of past N observations predict N+t one, where N can be 30 days of bars (or other variables as news headlines) and t is forecasting horizon (can vary depending on your strategy). What we have done correctly here?

Feature preparation and normalization
Indeed, we have prepared our windows correctly: no look-ahead bias, normalization done for each window separately (not to mix different regimes etc)
Different task selection
We also have played with different forecasting objectives: on the high level they were both classification (binary movement “up” or “down”) and regression — returns forecasting, volatility forecasting etc.
Backtesting
Training neural nets is cool, but it’s not the end of the process. We performed simple, but informative backtesting to show that if we would use these predictions to long/short assets we could make some profits (what’s important, better than some benchmark)
Avoided classical overfitting
The main challenge in “normal” machine learning is to generalize our model to perform well on unseen data. To do this we use train/dev/test set splits of our initial data and do some cross validation to estimate how model behaves with changes of data distribution. We have shown, that neural networks can perform well on out of sample data (but wait for tricky details). On the image below is something what I call “classical” overfitting for financial time series, which looks as perfect forecast, but basically it just predicts the last observation



source: https://medium.com/@alexrachnog/ai-for-algorithmic-trading-7-mistakes-that-could-make-me-broke-a41f94048b8c

Read the story about Renaissance here:
https://qz.com/1741907/renaissance-technologies-jim-simons-and-the-birth-of-quant-trading/




More than three decades ago, Jim Simons embraced a radical investing approach by crunching data and creating predictive algorithms—years before these tactics were embraced in Silicon Valley and elsewhere.

A former mathematics professor, Simons is worth $23 billion today. He subsidizes the salaries of thousands of public-school math and science teachers, is working to cure autism, and expanding our understanding of the origins of life.

He and his colleagues at Renaissance Technologies have racked up trading profits of more than $100 billion since 1988. Their flagship hedge fund, Medallion, boasts average annual returns of 66%. Warren Buffett, George Soros, Peter Lynch, Steve Cohen, and Ray Dalio all fall short.

Today, hedge funds and other quantitative, or quant, investors are the market’s largest players, controlling 31% of all stock trading, inspired by the success of Simons and his colleagues. But it took Simons more than a decade to find a formula to beat the market.

It took even longer for Simons, one of the greatest geometers of the last century, to fully trust predictive algorithms and computer models.



For much of the 1980s, Simons traded bonds, currencies and other investments using crude computer-trading models, along with his own instincts. He suffered deep losses, and clients called with complaints. Fellow mathematicians couldn’t figure out why Simons had discarded a thriving career to sit in a makeshift office in a dreary Long Island strip mall, next to a women’s clothing boutique and two doors down from a pizza joint, losing money.

He had to find a different approach.

“I don’t want to have to worry about the market every minute. I want models that will make money while I sleep,” Simons told a colleague. “A pure system without humans interfering.”

Suspecting he’d need reams of historic data so his computers could search for price patterns across a large swath of time, he had a staffer travel to lower Manhattan to visit the Federal Reserve office to painstakingly record interest-rate histories and other information not yet available electronically.

For more recent pricing data, Simons tasked his office manager, Carole Alberghine, with recording the closing prices of major currencies. Each morning, Alberghine would go through the Wall Street Journal and then climb on sofas and chairs in the firm’s library to update various figures on graph paper hanging from the ceiling and taped to the walls. (The arrangement worked until Alberghine toppled from her perch, pinching a nerve and suffering permanent injury.)

For the most part, Simons tested trading strategies based on his mathematical insights and intuitions.

If a currency went down three days in a row, what were the odds of it going down a fourth day?

Do gold prices lead silver prices?

“There’s a pattern here; there has to be a pattern,” Simons insisted.


By 1985, Simons was working with James Ax, another prize-winning mathematician. On the heels of a painful divorce, Ax moved the firm to Huntington Beach, California to the top floor of a two-story office park owned by a subsidiary of oil giant Chevron. Oil wells pumped away in the parking lot, and the smell of crude oil permeated the neighborhood. It was about the last place one would expect to find a cutting-edge technology firm.
“I want models that will make money while I sleep. A pure system without humans interfering.”

By 1986, the firm was trading 21 different futures contracts, including British pounds, Swiss francs, and various commodities. Mathematical formulas generated the firm’s moves, as did Ax’s judgment calls. Sometimes the results were impressive; often, they left the team frustrated. Simons and his colleagues couldn’t unearth new ways to make money.

He considered the possible influence of sunspots and lunar phases on markets, but few reliable patterns resulted. A staffer had a cousin who worked at AccuWeather, so he made a deal to review Brazilian weather history to see if it could predict coffee prices, another waste of their time.

Ax searched for fresh algorithms, but he was also playing racquetball, learning how to windsurf, and attending to an emerging midlife crisis.

Simons flew to California to discuss potential new trading approaches but his visits produced more misery than breakthroughs.


To improve their predictive models, Ax decided to bring in someone with experience developing stochastic equations, which model dynamic processes that evolve over time and can involve a high level of uncertainty.

René Carmona, a professor at nearby University of California, Irvine, got a call from a friend.

“There’s a group of mathematicians doing stochastic differential equations who are looking for help,” the friend said. “How well do you know that stuff?”

A 41-year-old native of France, Carmona didn’t know much about investing, but stochastic differential equations were his specialty. These equations can make predictions using data that appears random; weather-forecasting models use stochastic equations to generate reasonably accurate estimates. Simons and his team viewed investing through a math prism and understood financial markets to be complicated and evolving, with behavior that is difficult to predict, at least over long stretches—just like a stochastic process.

Carmona liked the challenge of improving their investment models, as well as the idea of picking up extra cash working for Simons’s firm a few days a week.

“The goal was to invent a mathematical model and use it as a framework to infer some consequences and conclusions,” Carmona said. “The name of the game is not to always be right, but to be right often enough.”

Carmona wasn’t certain the approach would work, or even that it was much better than the investment strategies embraced by most others.

“If I had a better understanding of psychology or traders on the floor of the exchange, maybe we would do that,” Carmona said.

By 1987, Carmona was plagued by guilt. His pay came from a portion of Ax’s bonus, yet Carmona was contributing next to nothing. He decided to spend that summer working full-time at the firm, hoping to find success. He made little headway.

“I was taking money from them and nothing was really working,” he said.

One day, Carmona had an idea. Simons and his colleagues used simple linear regressions, a basic forecasting tool relied that analyzes the relationships between two sets of data or variables under the assumption those relationships will remain linear.

Market prices are sometimes all over the place, though. A model dependent on running simple linear regressions through data points generally does a poor job predicting future prices in complex, volatile markets marked by freak snowstorms, panic selling, and turbulent geopolitical events—all of which can play havoc with prices.

Simons’s team had collected dozens of data sets with closing prices of commodities from various historical periods. Carmona decided they needed regressions that might capture non-linear relationships in market data.

Carmona’s idea was to have computers search for relationships in all the data Simons had amassed. Perhaps if they could find instances in the remote past of similar trading environments, they could examine how prices reacted, and develop a forecasting model capable of detecting hidden patterns.

For this approach to work, the team needed even more data than Simons had collected. They began to model data. To deal with gaps in the historical data, they used computer models to make educated guesses as to what was missing.

Staffers didn’t have extensive cotton pricing data from the 1940s, for example, but maybe creating the data would suffice. Just as one can infer what a missing jigsaw puzzle piece might look like by observing pieces already in place, Simons’s team made deductions about the missing information and inputted it into its database.

Carmona suggested letting the model run the show by digesting all the various pieces of data and spitting out buy-and-sell decisions.

He was proposing an early machine-learning system. The model would generate predictions for various commodity prices based on complex patterns, clusters, and correlations that Carmona and the others didn’t understand themselves and couldn’t detect with the naked eye.

The idea was to use sophisticated algorithms to give a framework to identify patterns in current prices that seemed similar to those in the past.

When he shared the approach with Simons, however, he blanched. The linear equations they had been relying on generated trade ideas Simons could understand. But it wasn’t clear why Carmona’s program produced its results. Carmona’s results came from running a program for hours, letting computers dig through patterns and then generate trades.

It just didn’t feel right.

“I can’t get comfortable with what this is telling me,” Simons told the team one day.

Later, Simons became more exasperated.

“It’s a black box!” he said with frustration.

Carmona persisted.

“Just follow the data, Jim,” he said. “It’s not me, it’s the data.”

Ax became a believer in the approach, defending it to Simons.

“It works, Jim,” Ax said to Simons. “And it makes rational sense . . . humans can’t forecast prices.”

Let computers do it, Ax urged. It was exactly what Simons originally had hoped to do.

They were on their way to a historic breakthrough.

Gregory Zuckerman’s book The Man Who Solved the Market: How Jim Simons launched the quant revolution is available as of Nov. 5, 2019 from Penguin Random House. This article is adapted from the book and used with permission of the publisher, Penguin Random House. Copyright ©2019 Gregory Zuckerman. All rights reserved.

Some more previews of the GRAYLL App - Sign Up process.

Have you ever stopped to think about why you perceive the value of things in the way that you do? From luxury goods to fine wines, our perception of value finds it’s roots in psychology, more than we may be inclined to credit. So, is the marketing magic that goes into manipulating our perceptions a showy blast of smoke and mirrors? How do we navigate this as a consumer, once we’ve taken a peek behind the curtain?

Here, we will explore some of the most masterful marketing campaigns in human history, in which perception of value was entirely transformed, and then delve into what makes us so susceptible to such razzle-dazzle!

Lobsters In Train Carriages


Let’s play an association word game: If I shout “lobster!”, what will you shout back at me? I’m willing to bet it wont be “prison”, or “cat food!”, but they would both be bang-on answers. You see, lobsters were not always perceived as the fine-dining luxury that we assume them to be today. If you hop into a time machine and head back to the 17th Century, you will find an era in which lobsters were considered fit only for prison inmates and lowly servants. It wasn’t until the 19th Century that the lobster made the lofty ascent from cat food status to fine dining delicacy.

This particular magic trick took enterprising minds who thought to make lobster the dish du-jour in luxurious train dining carriages on the early railroads. These highly fashionable mobile eateries crossed inland, where lobster was far more scarce and didn’t hold such negative associations. The early 20th Century cemented the elevated reputation of the lobster, as it was served to WWII soldiers — introducing it to palettes far and wide — and new conservation laws generated greater scarcity that would put upper class diners into a frenzy for the dish now deemed decadent!

Torches Of Freedom


Back in the 1920s, after WWI, tobacco sales were booming. Despite his soaring sales, George Washington Hill — president of American Tobacco Company — was hungry for more. He saw that opportunity was knocking! At that time, a cigarette was a masculine status symbol, while a woman who smoked was considered nothing short of scandalous. A lady who lit up would be labelled as immoral, and may even have faced a prison sentence. Despite these strong social biases, following the active role that women had played in the war, the feminist movement was picking up steam, and creating space for changes of opinion.
Hill hired a PR mastermind by the name of Edward Bernays, the cousin of Sigmund Freud, saying “If I can crack that market, it will be like opening new gold mine right in our front yard!” And so began a marketing campaign that would make the Lucky Strike brand synonymous with gender equality, reinventing cigarettes as “torches of freedom” for women who wanted just that. To kick things off, on 31st March 1929, a polished young socialite by the name of Bertha Hunt stepped out into the Fifth Avenue Easter Parade, while lighting up a Lucky Strike. The media were tipped off and on site with cameras in hand, ready to capture the shocking moment, and so the transformation of perception was set underway.

A Diamond Is Forever


Long before Marilyn Munroe sang that “diamonds are a girl’s best friend” in the 1953 cinema classic, Gentlemen Prefer Blondes, a monumental marketing strategy made diamonds the status symbol that we see them as today. Thanks to the 2006 Leonardo Dicaprio epic, Blood Diamond, you are probably aware of the shady history of diamond mining, in terms of environmental devastation, and human atrocities. What you may not know is that at the beginning of the last Century, they simply weren’t that desirable.

Back in 1870, huge deposits of diamonds were discovered in South Africa. British imperialist, Cecil Rhodes, jumped on the discovery, and began buying up every slip of diamond land he could find, including a farm owned by the de Beer brothers. Between the various mine owners of the region, the de Beers Consolidated Mines consortium was born, with Rhodes at the helm. What came next was to transform the branding of de Beers name from consortium to cartel, as they quickly took control of the majority of diamond sales and distribution the world over.

In order to generate huge profit from this relatively invaluable mineral, New York ad agency N.W. Ayer was set to the task. A copywriter named Mary Frances Gerety captured emotional value perception, in the absence of literal value, quite perfectly, when she created the infamous slogan: “A diamond is forever”. The reign of the De Beers name has faded into history — marred by scandals and lawsuits — but to this day, 75% of brides wear a diamond, for which their husbands likely paid hefty prices.

The Hierarchy Of Needs


Abraham Maslow, an American psychologist, defined a hierarchy of needs back in 1943 that can help us shed a little light on what makes psychology so powerful when it comes to perceived value, and effective marketing. Maslow defined our needs as layers of a pyramid, starting with the physiological fundamentals at the bottom: air, food, water, shelter, sex and so on; followed by that which gives us security: safety, resources, employment, freedom and health; followed upward by love and belonging: friendship, family, connection and so forth; before progression to our self-esteem needs: respect, recognition and status. Right at the top of the pyramid sits self-actualisation: the capacity to seek out our best selves — to become the most that we can be.

Maslow argued that from the base of the pyramid upwards, as each level of needs is more or less met, we will begin to seek the next. From a marketing perspective, these human needs are powerful to identify, because they are not all purely practical. A useful object will not necessarily boost our sense of self-esteem, but a powerful brand identity attached to that object might create a perception that it does. With perfectly delivered PR, we can come to add huge value to ideas, brands, and the things that they sell us.

The Real Power of PR & Marketing


So strong is our desire to meet the complex psychological needs that appear as we move up Maslow’s pyramid, that our judgement can be compromised as our perception plays tricks on us. A group of wine drinkers demonstrated just that when they were given three glasses each to taste-test. In reality, each glass contained the exact same wine, but the participants expressed favour towards — and inclination to pay top-dollar for — the wine that they were told had the most exotically unpronounceable name!

More layers of complexity enter the playing field of marketing today, as consumers are becoming more morally driven, and more hungry for authenticity. Brands that demonstrate responsibility are perceived as more valuable by the modern shoppers and, no doubt thanks to the immense access we all enjoy to information, consumers are more scrupulous when it comes to sniffing out a flawed campaign. We still, however, love to invest in ideas, and the experience that association with such ideas bring. The music industry has seen this impact, as streaming and sharing platforms bottomed out the CD market, but sales of tickets to live events, and the romanticised record, with it’s large sleeve of carefully crafted artwork are seeing surging sales.


Of course, any given desirable object has to be relatively affordable. At the end of the day, we make a trade of the wealth we hold for the value we perceive. Whether that makes us meticulous investors in our very human needs, or whimsical wallies falling for useless possessions is pure perspective. This is a choice we each have to make for ourselves.

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Still lots of work to complete, many tests and reviews, but the GRAYLL PWA (Progressive Web App) should be accessible to our investors (not Stellar DEX traders) to familiarize themselves with the GRAYLL App functions. A PWA is for mobile & desktop. In fact Twitter, Uber, Starbucks are all PWAs...

We want to share today an article which seems great to us
source: http://factmyth.com/what-is-guy-fawkes-day-about/




Understanding Guy Fawkes Day

We explain Guy Fawkes, Guy Fawkes Day, and the symbolism behind the the Guy Fawkes mask, the fifth of November, and the Gunpowder, Treason, and Plot.

“Remember, remember, the fifth of November, Gunpowder Treason and Plot. I know of no reason, why gunpowder treason, should ever be forgot.” (one of many versions of the Gay Fawkes Day rhyme). – A very ironic rhyme considering most have forgotten the point behind remembering Gunpowder Treason."



Who was Guy Fawkes?

Guy Fawkes, a pseudonym of Guido Fawkes, was a member of a group of radical English Catholics who planned the failed Gunpowder Plot of 1605. Although he was only one of 13 conspirators, Fawkes is today the individual most associated with the failed plot.

FACT: Robert Catesby was the actual leader of the Gunpowder Plot. Guy Fawkes was romanticized by William Harrison Ainsworth‘s 1841 historical romance Guy Fawkes; or, The Gunpowder Treason which portrays Fawkes in a generally sympathetic light (prior to this Fawkes and the other plotters were seen as villains).

What was the Gunpowder Plot of 1605?

The Gunpowder Plot of 1605 was a plan to assassinate King James I and restore a Catholic monarch to the throne (not to bring liberty to the people or anything like that).

James I / James IV in Scotland was a Scottish pro-Protestant philosopher king of sorts, he was arguably the first to argue for the Divine Right of Kings before Robert Filmer… and thus his name largely stands next to Locke and Hobbes on a list of important philosophers; hardly the perfect villain (see James and Buchanan).

Fawkes didn’t like the Scottish nobles at the time and was disappointed with their failure to extended greater religious tolerance to English Catholics.

The plan was to blow up the House of Lords during the State Opening of England’s Parliament on 5 November 1605, as the prelude to a popular revolt in the Midlands during which James’s nine-year-old daughter, Princess Elizabeth, was to be installed as the Catholic head of state.


TIP: This means that by any reasonable definition Guy Fawkes and company were planning “a violent act of terror” due to their religious views. This doesn’t excuse James’ policy toward Catholics (anti-Catholic legislation was introduced soon after the plot’s discovery, but loyal Catholics retained high office during King James I’s reign), but it doesn’t exactly paint Fawkes in the best of lights (especially for those who know the history of James, Buchanan, liberalism, and Protestantism in Scotland / England / Wales).

What is the Symbolism of Guy Fawkes and the Guy Fawkes Mask?

When we see the Guy Fawkes today it means two things:

    1. Today it means: We should celebrate “a little rebellion now and then“. In a free society, we have the right to non-violent protest and must remind “the powers that be” of our liberties and rights.
    2. Originally it meant, and still means: Gunpowder, Treason, and Plot is not tolerated in civil society, the Fifth of November is meant to remember that we are glad the King survived the assassination plot by the terrorist Guy Fawkes. In civil society we have courts, discourse, and laws. Before you try to blow up the House of Lords, make sure you have exhausted your legal options first. If you do think you have to take measures outside the law, you better have some consensus and a philosophically correct Declaration of Independence level document backing you… Or you just may find yourself paying a visit to the gallows.

Why the Guy Fawkes Day is an Odd Symbol for Activists

At first one may think, given V for Vendetta and Anonymous (who both helped popularized the Fawkes mask and modern social radical activism), that Guy Fawkes day is all about the joys and wonders of radical liberalism and standing up to “the establishment”, where the masked rebels are heroes of the highest order… but it is actually the opposite of that in many ways.

Essentially Fawkes was planning to blow up a bunch of officials sitting in parliament who represented both sides of the isle and a slow progression toward our modern age. The plotters were well intentioned in their own minds, but in practice they were about to blow up a rather good and fair King (historically speaking).

Not only this, but the plotters were doing it because of anti-Scottish, anti-Establishment, and Pro-Catholic ideology (thus they had their own divisive ideology, they weren’t exactly standing up for all).

It isn’t like they sent a letter or tried to pass a law or rally political support first. They didn’t lobby the king with legal means, they went straight to the gunpowder when early attempts to secure greater equality for Catholics seemed to fade.

None of our philosophical founders have ever said “gunpowder, treason, and plot are good, go for it”, sure Buchanan and Locke give justification for regicide (when no legal recourse is possible, as a last resort), but even our radical founder Thomas Jefferson was willing to put down those rebels who “unwisely” took up arms rather than using legal means (see Shays’ rebellion).

THE ORIGIN OF GUY FAWKES DAY: On 5 November 1605 Londoners were encouraged to celebrate the King’s escape from assassination by lighting bonfires, “always provided that ‘this testemonye of joy be carefull done without any danger or disorder'”. An Act of Parliament designated each 5 November as a day of thanksgiving for “the joyful day of deliverance”, and remained in force until 1859. The way the day is celebrated changes over time and location, but today it less about thanksgiving, Catholicism, or its original meaning, and more about celebrating via a night of bonfires and fireworks.

FACT: Settlers exported Guy Fawkes Night to overseas colonies, including some in North America, where it was known as Pope Day. Although the tradition of celebrated died out during the revolution. Learn more about Guy Fawkes Day/Night.

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What makes us tick, as human beings, has certainly undergone some changes! Way back when, for our ancestors, the need for the basics — things like animal hides for making clothing, and wheat for turning into bread — dominated daily life. This was an Agrarian Economy, focused on commodities and staying alive. This status quo rolled on for millennia, until the industrial revolution kicked things up a notch.

Suddenly, we could turn commodities into shiny new products, like fancy clothing and delicious pre-made dishes, for which we were happy and able to pay more. The shift soon delivered the ushering in of the Service Economy. We went from buying flour (a commodity), to buying bread (a product), to having bread baskets with our meal in a restaurant (the service experience), in a relatively short space of time.

The Rolling Out Of Experience Economy


Before the era of the busy and highly competitive digital marketplace, in which we buy so much today, the service economy was built upon product quality and value for money. Then something strange began to happen. As the internet realm delivered accessibility to the world, we began investing not only in stuff, but in the experience that came with it. Products began to have a sense of identity associated with them, and the customer experience became adventurous and exploratory.

Not only that, but the millennial generation started to view experience as more important than possessed things. So much so that 72% of them would rather spend money on a life event than new stuff. Welcome to the Experience Economy! So, what does this mean today, and how will it shape tomorrow?

The Delicate Dance Between IRL And The Instagram Effect


When we talk about modern living, the narrative is dominated by criticism of our dopamine addicted, screen obsessed selves, ravaged by social media induced anxiety. Ouch! While concerns over digital social solitude do warrant attention and debate, it is important to recognise that our technology laden lives are still evolving, and it might be worth seeing where the chips fall. With a little deeper delving, it becomes evident that the changes afoot are clearly complicated, and still in metamorphosis.

Consumers juggling the existential anxiety that comes with perpetual inter-connectedness are turning to IRL (in real life) experiences as a meaningful escape. From taking up adventure sports, to seeking out unusual opportunities, authentic moments are valued highly. There is, however, a new element to the modern experience, as the potential to create our autobiographical narratives is prioritised simultaneously. Memorable moments are deemed more meaningful when they present photo opportunities, and a story that can be shared! Self defining through experience-capture is the next-gen way to seek social acceptance.

Investing In Alternative Experiences


So much do we crave valuable experience, that millions of people have been spending their money on virtual fantasy lifestyles. Within huge online platforms such as Second Life, users can create, live out, and fundamentally be things that are potentially unattainable in the real world. From becoming an otherworldly creature, to designing objects and spaces that IRL constraints would never allow, virtual worlds facilitate the experiences people dream of, and the space for their creativity to flourish. From the virtual arts, to virtual time travel; from virtual education to virtual romance: in Second Life, anything is possible!

Following the success of Second Life, many more virtual realms began to attract millions of users, such as Smeet, Kaneva and Mabinogi. Fascinatingly, not only are users happy to spend their hard earned money on living out an alternative fantasy lifestyle, but entirely enclosed economies, with their own virtual currencies, are now a reality. You can earn virtual currency as an entrepreneur in Second Life, and cash out fiat currency in the real world. For a mind boggling stat, the market value of Second Life for 2015 was a staggering 500 million US dollars, placing the GDP of this virtual realm above some small countries.

What Does This All Mean For Businesses?



Knowing that consumers hold experience as the highest measure of desirability, businesses need to pay attention to their customer experience, or risk sliding into irrelevance. Among all of the online noise, businesses that want to stand out need to hone their offering — either providing a seamless user experience, or creating true novelty — and the only way to do this is by opening innovative paths for dialogue with their audience.

Thanks to the FOMO effect (fear of missing out), businesses that don’t focus on the user experience leave their audience as easy pickings for direct competition. What’s more, as consumers sculpt their own narratives, they also become far more selective in terms of the standard of authenticity. They seek out brands that match their sense of individuality, their aspirations and ultimately their moral standards. Experience is a very personal thing, and a positively charged experience is becoming a benchmark expectation. We each interpret uniquely, and so the complexity of the modern consumer is not to be underestimated!

Evolution That Is Yet To Come

As the current generation wrestle with digital dualism — the juxtaposition of IRL and URL — it is hard to say exactly how this evolution will progress, except in recognising that positive adaptation will probably be fed by the increasing capacity for us to create genuine connection, in both realms. We can hope that our URL lives will begin to serve us in terms of achieving greater prosperity and abundance, rather than simply feeding our vulnerabilities. At a point in which we find ourselves possessing more data than ever before on the behaviours of modern consumers, perhaps it’s time to utilise our resourcefulness not to reduce people to mere statistics, but to humanise them in greater technicolour than ever before.

Using The Latest Technology To Cultivate Health, Wealth, And Happiness



Modern lifestyles often serve to highlight the value, and sparsity, of our precious time. This makes the quality of our experiences all the more vital! With these values in mind, the developers behind GRAYLL envisioned creating an App that would allow their users to reduce the sacrifices of time and experience made for generating income. Discover a new world of digital finance, in which you can harness cutting-edge Artificial Intelligence to grow large or small investments, and create wealth that will allow you to enjoy unique experiences in the time that you have.

Discover the GRAYLL App, and become part of it’s community today.

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We do our best to create an environment where someone with little money and that can limit their greed in which they can make excellent returns.
But if you have little money and live in an expensive city or country, then it is just unrealistic that $50 will set you free in 1 year...
What we do provide is more time for you to focus on things that are important to you, so whilst that $50 is hard at work within GRAYLL,
you could be studying or creating a new business idea for example...

We have a GitHub which is private and also use other repositories, also private.
maybe ask Goldman Sachs, Credit Suisse or JP Morgan or some algo trading hedge funds whether they can share their GitHub repo with you...
We prefer to keep the secret sauce a secret.