Tag: AI

  • The Most Valuable AI Startups In Europe

    The Most Valuable AI Startups In Europe

    2 min read

    AI startups in Europe

    The field of AI and MA is one of the most interesting businesses for European venture capital. From the beginning of this year, there were 323 VC contracts in Europe in these businesses. That brought almost $2.1 billion in total.

    Much more than the last year.

    The reason behind is that there are so many fields to apply AI technology.

    This variety is seen in the top five most important European AI startups. Let’s take a look at them.

    Graphcore (Valuation: $1.7 billion) 

    This is one of the most valued AI startups in Europe. As a difference from the other companies, Graphcore is essentially involved in the hardware behind AI tech. This England-based unicorn develops a new-generation computer processor to hasten machine intelligence learning. Graphcore carries the highest valuation of AI startups in Europe. It reached $200 million Series D co-led by Atomico and Sofina.

    Darktrace (Valuation: $1.65 billion) 

    Based in the UK this cybersecurity startup utilizes AI and machine learning to examine and recognize security vulnerabilities and malicious traffic. Darktrace reached its current valuation when it raised a $50 million Series E led by Vitruvian Partners last September KKR and TenEleven Ventures also funded this startup

    Meero (Valuation: $1 billion)

    The France-based developer of an on-demand photography platform is one of two French startups on the list. It uses AI to quickly process digital images for customers in over 100 countries. Meero funded $230 million in a Series C co-led by Eurazeo and Prime Ventures. 

    Iov42 (Valuation: €520 million)

    This startup rides AI and Blockchain. Iov42 develops an AI-powered blockchain-operating platform that gives service to cryptocurrencies. Its latest investment was a €20 million Series C3. 

    ContentSquare (Valuation: €276.8 million)

    ContentSquare develops a platform that assists businesses to understand how and why clients are associating with their app, mobile and web sites. This Paris-based company applies a mixture of behavioral data, AI and big data to give automatic recommendations, measure content performance and understand visitor intentions. In January, the company raised a €52.55 million Series C led by Eurazeo. Highland Europe and Canaan Partners also funded it.

    Bottom line

    Artificial intelligence (AI) is an important sector for Europe and it can be a hack for economic growth. For some people, it still represents destructive robot troops that will wipe out humans.

    As you can see it is worth business.

    Technology, throughout history, has demonstrated to increase the productivity of countries creating new jobs. But instead, this technology is connected to the deadly robot troops.

    We need a new tech generation. Yes, it is very expensive and not truly strong enough to make a notable impact on economies. But it absolutely will be

     

  • Automated Trading Systems Can Increase Your Trading Profits

    Automated Trading Systems Can Increase Your Trading Profits

    Automated Trading Systems Can Increase Your Trading Profits 1This is not something you can do over the weekend and let run happily ever after.

    By Guy Avtalyon

    People maybe get automated trading wrong. I’ll explain this. But first comes first.

    Wouldn’t it be great to have a robot trade on your behalf and earn guaranteed profits? It’s everyone’s dream is to find the perfect computerized trading system for automated trading. The one which guarantees profits and requires little input from the trader themselves.

    There are many automated trading systems available. But there are still a few burning questions that need to be answered.

    First of all, what is the automated trading?

    They are computer programs designed by expert developers to follow a given market algorithm, every minute of the day.

    You should consider automation if you want to participate in the futures market but lack the time to monitor, formulate, and implement your own trading plan.

    Automated Systems are programmed to look for trends, analyze market data, and apply specific mathematical/technical formulas which in turn generates signals: buy and sell orders – to go long or short.

    The performance – whether hypothetical or live- is tracked in real-time and you can subscribe, activate, and deactivate any system at any time.

    Automated Trading is comfortable

    They are programs that place orders on behalf of the trader. A trader sets the essential condition for order placement based on technical analysis principles.

    The system will place orders automatically based on the necessary conditions.

    The automated trading system facilitates backtesting on a demo account which gives a fair idea of the efficiency of the strategy.
    Nice!!!

    But you need more about how an automated system can increase your trading profit.

    In other words, can trading strategies that are automatically executed in financial markets be profitable?

    The investment is a process, so automation is a logical conclusion. To be honest, auto-trade is like a driverless car. It can go fast or slow.

    It all depends on the robustness of your design. People maybe get automated trading wrong. This is not something you can do over the weekend and let run happily ever after.

    If you believe that manual trading is highly competitive and intense, then imagine sitting in a fast car without a steering wheel, accelerator or brake pedals.

    So, why people fail?

    The way to design a profitable trading system is counter-intuitive. It must be built to withstand erosion. People think of the best possible outcome when they design a strategy.

    Wrong.

    You can’t design and test drive snow tires in Sahara. There are some reasons for that, don’t you think?

    You need to design automated strategies with failure in your mind. Always.

    Take care of the bad scenarios and the good ones will take care of themselves.

    That is not sexy,  but you wouldn’t take some luxury car for a spin if you knew that brakes are porous?

    When people think about failures, they think about stop losses or blow-ups. This is not the main issue, really.

    The problem is transforming near misses into near losses.

    Like you know, the market flip-flops all the time and does not care about your feelings.

    The whole game is about:

    1. a) moving the peak of profitable trades from small losses into small profits. In order to achieve a compounding system. That can only be achieved by having a solid exit policy
    2. b) elongating the right tail: ride your winners and cut your losers short. Profits look big as long as losses look small

    But as always, simple is not easy.

    The privilege of simplicity is that it imposes itself, even to those who do not understand its sophistication.
    Automated trading is a great tool to have in your trading toolkit.

    We would say the hardest thing about it is codifying your strategy. Converting your strategy into code that a computer can interpret can be very difficult, but totally doable if you put your mind to it.

    So if market conditions cause it to enter losing positions, it’s gonna enter losing positions, and quickly!

    These losses can mount up so have proper risk management systems in place:

    – Set a max drawdown limit that’ll kill the bot if it’s triggered
    – Use stop losses on every trade

    The strange thing is that it works and frankly speaking, no one would never be able to achieve the same results manually. You do not want to sit in front of a monitor and contemplate all the time.

    Computer trades are like a psychopath.

    It has no emotion and can do many more markets.

    The algo makes better decisions quantified as a higher gain expectancy/trading edge than you would.

    There is no question about it. It takes trades to makes money.

    The mindset of an autotrader is different than a manual trader. You have to trust the system.

    Once you auto trade, you have to let the machine take the trade even if your guts scream NO.

    The good news is you can monitor multiple markets on your own time.

    The machine keeps trading away and that is an incomparable feeling of freedom.

    You can spend that time with your friends.

    Isn’t it amazing?

     

  • Artificial intelligence and machine learning we can apply on the financial markets

    Artificial intelligence and machine learning we can apply on the financial markets

    What is artificial intelligence and machine learning and can we apply it on the financial markets?How can we apply artificial intelligence to the financial markets

    By Guy Avtalyon

    What is artificial intelligence and machine learning and can we apply it to the financial markets?
    It took us 3 and a half years of research and development until we finally reached a point we can trust our software.

    Obviously you can find all sort of information on the internet about machine learning and AI, like these articles on Wikipedia for example, but the concept is quite simple: You run an algorithm (there are many) on the set of data, and once the algorithm is finished, the software will know how to run by itself on new sets of data, even if it’s never been seen.

    There are 2 types of algorithm methods –

    1.       Supervised – Similar to training a dog: if it does good you pet them, if it does wrong you scold at them. After a while, they will learn how to behave
    2.       Unsupervised – This is the most interesting algorithm out there. This means you give the algorithm a set of data but you DO NOT tell it what is wrong and what is good. It does it by itself.

    So, can you apply those algorithms in the financial markets?

    First, let’s start by learning a bit about how ML (Machine Learning) and AI (Artificial Intelligence) work and its purposes.

    To create simple computer software, we need to insert some scenarios we want it to handle, we add the way we’d like the software to act, and let it run.

    A “stupid” software will ONLY KNOW HOW TO WORK according to the scenarios we entered and taught it.

    An AI software will take the same scenarios we entered and ways to behave we told it to, and will be able to do it NOT only on the ones we told it to but also on SIMILAR scenarios.

    This is basically why AI and ML are the future in any way you can imagine – Because it’s not limited to what the programmer writes in the code, but also it can adjust and act to things that aren’t inside its code and also, over time, will be smarter in handling situations only by itself.

    OK let’s go back a bit

    Scenarios? Ways to behave? WHAT??

    Say we got a lifetime doctor’s records of some people. They are anonymous, of course, because we don’t care who they are. We only care about their DATA.

    Now we want to find something, like, maybe, can we find cancer disease BEFORE the person knows it’s happening – or in other words – Can we predict cancer?

    We can check – are they the cigarette smokers? If yes, how many had cancer?

    This has been the way until now.

    You probably can already guess why it’s not merely enough.

    If they don’t smoke – does that mean they won’t have cancer? We already know it’s not true.

    And sadly there’s a variety of cancers to almost every organ in the human body (cancer is when some cells of our own body stop dying unlike the other cells and the body starts to attack them. Basically, nature makes our body suicide from inside).
    So what can we do if we want to predict cancer?

    It’s simple – We take into consideration as many parameters we can. Like:

    Age, gender, place of living, place of working, family history, doctors’ appointments, and medical record, food and drink habits, etc.

    Those are the objective data.

    We need also subjective data such as happiness in life, the scale of pressure, type of person, etc.

    Once we have ALL this data for every person, we need to do 3 things:

    1.       Check which one of the parameters can, in fact, be some kind of prediction to cancer
    2.       Run a statistics machine learning algorithm (like Naïve Base)
    3.       Use the results to solve a worldwide problem  

    We wish, right?

    Now we get on to the problems of artificial intelligence (AI) and ML:

    1.  Data

    Data is extremely difficult to collect, and then to manipulate. In our example to get these data, we need to cooperate with medical services to get their clients’ data, create a questioner, and send it to all the clients and analyze the data. Though there is such cooperation around the world, it’s still not easy to also get subjective data.

    1. Analyzing big data

    Big data has become a known word around the world.

    There was a time companies said they work with big data and clients threw the money at them.

    But it’s not that simple. Every data you add for the algorithm to learn from – increases exponentially the time for the software to analyze…

     

    Inefficient software may take a very LONG period of time to run.

    Funny personal anecdote, our first AI software we developed to learn how to predict price changes in the stock market looked so genius at first, but after we started running that artificial intelligence and measuring the time it will take to finish, we saw it will take no less than 27,000,000,000,000,000 years from now(!!) Obviously, we couldn’t wait, and in future articles, I will explain how we lowered it to only a few hours running time.

    Let me give you an example of the difference between Big Data and just simple data with a game:

    I chose a number between 1-1000. You have to guess which one is it. But there’s a catch – you need to find the number in as little time possible. How would you do it?

    Think about it for a second.

    Got a solution?

    If you guessed that you should ask me “Is it higher than 500?” and then according to my answer (If I chose the number “990”), the answer is yes. Then your next question will be “Is it higher than 750″… You get the point.

     

    That’s easy, right?

    What if you got a number with 80 digits? Then it might take a long long time until we break this number, maybe even months. And that’s only one running time. What if we need it to create strategies for trading and investing and we need it to go over millions of possible strategies?

    It will take a lot of time.

    As humans, we can’t really comprehend really big (or small) numbers. Like these two questions, I like to ask people once I talk about large numbers.

    1.       If 1 million seconds is 12 days, how much time is 1 billion seconds?
    2.       And, if your salary is $100,000 each month, how long will it take until you reach 1 billion dollars (say you can save all of it each month)?

    You can easily calculate it, but it’s an intuition question, not a math one. Think for yourself, what’s your intuition answers are? The answers will be later on in this article.

    So we’ve talked about what’s machine learning algorithm and a bit on big data problems.

    Now, can we apply artificial intelligence to the financial markets?

    In short, yes.

    But it’s easier said than done.

    It took us 3 and a half years of research and development until we finally reached a point we can trust our software.

    Because other than the ML and big data problems, we face a whole different problem in the field of financial markets, since they act like in a chaotic environment it makes predicting a lot harder.

    And, (and it’s the most important and) because of the spread whenever you enter a position you face an average of 56% against you.

    That’s probably the time to say there are two kinds of players in the financial markets:

    1.       Investors – They invest their money for years ahead and they gain the average rate the market makes (around 8% a year). By the way, according to decades of studies, there’s one stock that if you’re an investor you should put all your money on, and that’s the S&P500 stock (Symbol SPY). In another post, I’ll prove this fact.
    2.       Traders – They usually use time limit (options) or profit/loss lines (if it reaches +X get out with a profit and if it reaches -Y get out in a loss)

    We are on the traders’ side.

    We want to gain more money, faster, and more chances of getting out in time.

    But unlike investors who buy now and then forget about it, as traders we must beat not only the commissions our broker offers us but also the spread (the difference between the lowest price a seller is willing to sell and the highest price a buyer is willing to buy). The spread is usually set by the broker and it’s one of the best ways for a broker to gain profits.

    So, we also know that like in gambling the house always wins, so as in the financial markets – the broker’s always gaining profits.

    Back to our financial algorithm – we found a broker service that lets us collect the financial data, and we’re saving it. Now, we need to analyze it to find patterns. But how?

    In an everyday changing environment, how can we rely on anything? 

    We solved that problem by relying on our algorithm on behavior analysis. We figure that even though the market can change, the forces that control it (the investors and traders) will stay the same (Obviously, they change too, but way slower).

    So we’re talking about collecting on average millions of data and parameters a day for each stock. Once we try to collect 1000 stocks for a few years time you can imagine how much data is inside, so it’s just a matter of creating a super-fast unsupervised machine learning algorithm with only one rule: The most money you can make is the better – and let it run and find the best way to trade by itself.

    Creating artificial intelligence

    In conclusion, it is possible to create an automatic software or some artificial intelligence to trade for you in the financial markets, but it’s EXTREMELY difficult. You need to overcome many problems in serval fields in order to do it. And after you do it, it’s unlikely that you will let anyone use it.

    But we’re different. We will let our subscribers use our algorithm for free, just to have a sense of how it works.

    Subscribe now to get more information about artificial intelligence in the financial markets and to get informed once our algorithm is ready for outside users.

    Our software will let you choose which assets you want to buy, and when – and it will tell you when to get out. Simple, yet important.

    By the way, the answers to the question before are:

    1.       One billion seconds are 32 years
    2.       It will take 830 years to gain one billion dollars if your salary is 100K per month

    Was that your intuition?

    Sign up below to our newsletter for a free test drive on our trading algorithm! Find more about artificial intelligence.

    Top Image Credit: Photo : iStock/MF3d



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