Tag: artificial intelligence

  • HFT Strategies – The Tips and Secrets

    HFT Strategies – The Tips and Secrets

    3 min read

    HFT strategies - the tips and secrets
    HFT uses practically basic and simple strategies. High-frequency trading is not about implementing the strategy, it is all about speed of execution and flexibility.

    Well, the main strategy of HFT is to run faster than others. Of course, the principles of high-frequency trading (HFT) firms are secrecy, strategy, and speed.

    Algo trading is linked with the execution of trade orders. But HFT refers to the implementation of proprietary trading strategies.

    High-frequency trading consists of a variety of AT.

    Yes, both enable traders and investors to speed up the response on market data.

    The society of market participants using HFT is extremely mixed.

    There is a crowd of various organizations with various business forms that use HFT and there are many hybrid models.

    For example, some brokers and exchanges are utilizing HFT systems. So, in the estimation of HFT, it is essential to consider a practical perspective.

    It doesn’t matter if HFT is just an add-on technology to realize trading strategies.

    Liquidity providing is one of the HFT strategies.

    HFT strategies - the tips and secrets 1
    Well, the most frequent HFT strategies are to serve as a liquidity provider.

    How does HTF provide it?

    HFT liquidity providers have two primary reservoirs: when they provide markets with the liquidity they pocket the spread between the bid and ask limits. Also, there is a trading income by granting discounts or lowered transaction fees. The aim is to increase market quality and attractiveness.

    HFT firms will never discover their ways of acting. The significant experts linked with HFT are undercover. Well, this is not quite true. Maybe we could say they want to be in front of the public eyes less than others.

    Those firms operate with various strategies to trade and earn money. The strategies are often many kinds of arbitrage. For example, volatility arbitrage, or index arbitrage.

    HFT employs software that is incredibly fast. They have access to all market data and can make connections with minimum latency.

    HFT firms regularly use own money, own technology and a number of special strategies to produce profits.
    There are numerous strategies applied by traders to earn money for their firms.

    Even the controversial strategies.

    For example, HFT firms may trade from both parties.

    Hence, they can place orders to sell using a limit order above the market price. Also, they can place the buy order a little bit below the market price.

    And, voila! There is a profit for them. The difference between the two prices. They are market makers. All these transactions are very fast, in a millisecond by using algorithms and robust computers.

    Spread capturing as HFT strategy

    HFT strategies - the tips and secrets 2
    HFT firms are liquidity providers. They profit from the spread between the bid and ask prices.

    How?

    They are buying and selling securities all the time.

    With each trade, they receive the spread between the price at which shareholders buy contracts and the other at which they can sell contracts.

    Rebate driven strategies

    The liquidity provision strategies are developed on particular stimulus systems.

    In order to encourage liquidity providers, some trading venues use unsymmetric pricing. They charge a lower fee or give a rebate for market makers or passive trading.

    Why?

    Such traders bring liquidity to the market.

    On the other side, for more aggressive tradings they charge a higher fee. Why? Such traders remove liquidity from the market.

    An unsymmetric fee arrangement aims to boost liquidity provision.

    Point is: traders supplying liquidity earn their profits from the market spread. Fee discounts or rebates stimulate a market‘s liquidity.  

    On this way, those markets look promising comparing to their rivals.

    Arbitrage

    Chances to perform arbitrage strategies generally survive only for fractions of a second.

    But computers mission is to examine the markets in a millisecond. That feature causes the arbitrage to become the main strategy employed by HFTs.

    To conduct arbitrage HFT use the same method as traditional traders. But they use an algorithm to profit from short-lived differences between securities. The other types of arbitrage are not restricted to HFT and such, they are not the subject of this post.

    Latency arbitrage

    The latency arbitrage is the ability of HFTs to recognize new market information before other market participants even get it.

    The latency arbitrage uses direct data feeds and co-located servers to short the reaction time. Latency arbitrageurs profit from speed power. Such market participants can reduce the prices at which other traders are able to trade. That’s why you can find them under the name of predatory.

    Liquidity detection

    HFTs try to recognize the patterns other traders leave and adjust their actions accord to them. The focus of liquidity detectors is large orders.

    Liquidity detectors are getting information about algorithmic traders is usually called sniffing out the other algos.

    The bottom line

    HFT is not a trading strategy. It is the usage of advanced technology that performs traditional trading strategies. The individual trading strategies need to be assessed rather than HFT as such.

    HFT should never be banned. It would be contrary to market efficiency. High-frequency trading contributes to market liquidity and to the ability of the price creation.

    However, any strategies that have a contradictory influence on market integrity or enable market abuse, has to be are completely reviewed.

    This is particularly important for HFT. If anyone believes this technology promotes the implementation of abusing strategies, moreover, makes them more profitable and creates unfair circumstances on the market, should check the other participants too.

    Our confidence in technology is huge, but we are very cautious when it comes to the people.  

    Technology by itself is without morality. The people are those who can add it to high-tech.   

    Fortunately, we, ordinary people, don’t have any access to HFT.

    Don’t waste your money!

    risk disclosure

  • Investment prediction for 2019 – Traders Paradise prediction

    Investment prediction for 2019 – Traders Paradise prediction

    Investment predictions for 2019 - Traders Paradise prediction 1The image is taken from depositphotos.com

    By Guy Avtalyon

    Investment prediction for 2019. is in front of you, so let’s start.

    Investment prediction is a really tricky job. This year has been full of market volatility, climate disasters, personal data frauds, economic insecurity.

    And now, in the end, we are waiting for a fresh start? Things never go in that direction. It looks that 2019 promises to be in a mess. That’s why Traders Paradise is trying to predict what will be real in the next year. And we find this, some other guys may find something different:

    Bear market – is here

    Nearly half the stocks in the S&P 500 index are in a bear market at the end of this year. They are down 20% from their highs.

    The second-largest stock exchange in the world by market capitalization, NASDAQ, is officially in bear territory. If you don’t know yet how to trade- here’s our full guide.

    All signs are pointing to more damage to the stocks.

    Equity markets in more than 20 countries are in bear territory. Investors are worried about how bad it will be and how long it will last.

    Bears are necessary and unavoidable cycles in markets and have been for centuries. But they are cruel. This will be a great theme in 2019. That is our investment prediction.

    And each investor should be prepared and to diversify the portfolio. 

    Artificial Intelligence (AI)

    One investment prediction, more.

    Japanese tech company Groove X introduced a robot whose task is to make people happy. The “Lovot” uses artificial intelligence. It can mimic human empathy.

    This cute robot represents the revolution of artificial intelligence. “Robot” can feel emotions and communicate with people. It is 3kg tall and 43cm tall, the optical camera helps it move. And can be our new friend for $5,300. Some cost us even more.

    There’s a vertiginous line of AI applications on the table right now. We expect this term will be very popular in 2019 and the list will become larger.

    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 READ HERE

    Socially Responsible Investing – Impact investing

    Socially responsible, or ESG investing accounts for environmental, social, and governance factors. But does not necessarily result in worse performance. There are those that think ESG investing can outperform the markets, and there are those who strongly believe the contrary. There are specific examples that will back up both sides of the argument.

    People are often asking us what is a social enterprise, and we are usually answering by asking “what is social investing?”. Sometimes the phrase is social impact investing; sometimes it just impacts investing.

    Impact investing carries risk, that’s true. But also it generates great returns and impact. It is smart and moreover, profitable to invest in companies that actively have positive social or environmental influences. It is a step further than divesting from negative impacts. For example, allocate your investment portfolio away from fossil fuels. Instead, use your money to consciously tackle society’s challenges. And to make a financial return, of course.

    Investors’ concerns

    Investors sometimes ask how much return they will have to trade-off in order to make impact investments.

    Firstly, there is no “impact see-saw”. Just because a business is creating a more positive impact, that does not mean they are creating a less financial return. Indeed, in many cases, because the impact is at the heart of the business model, the more impact they create, the more profit they make, and vice versa. Some research even suggested that impact-focused businesses are more sustainable and profitable in the long-term.

    In any investment, there are different levels of risk and return and there are also different levels of impact. An impact investment may be riskier. It has high returns and high impact. Or, it could be less risky since it brings market-rate returns and significant social or environmental impact.

    As with any investment, it depends on the business or the fund.

    The statistic shows that 89% of investors making impact investments find these are meeting their return expectations, and 54% of investors are targeting market-rate or above market-rate returns.

    There are many ways to get involved in impact investing. Crowdfunding has even helped retail investors, who have less risk capital, to get involved in this space.

    Generally, our investment prediction that this kind of investment will be more popular in the next year.

    Blockchain

    Traders Paradise’s investment prediction is this will be one of the most popular terms in 2019.

    Blockchain technology provides a way to make transactions and transfers online without the use of an intermediary. Instead of trusting a third party to keep the transaction history safe and accurate, blockchain technology lets you seal “pages” of transactions with a key code for security.

    One of the most relevant reasons that many companies are adopting blockchain technology is efficiency. We can all realize how exchanges can become quicker. And simpler too, when they don’t have to go through a third party. It’s also beginning to move document authentication toward obsolescence, removing a step in the translational process.

    How To Make Money With Blockchain Technology READ THIS TOO: 

    Blockchain technology can also make companies feel like their information is safer and more secure. In an age where hacking banks cannot always resist off attempts to attack people’s financial privacy. Therefore, blockchain technology is a way to feel a greater sense of control over transactions.

    Short Selling

    Many experienced investors think that short selling has an important part in the markets. It improves price discovery and rational capital allocation. At the same time,  prevents financial bubbles and finding fraud.
    Shorting is a trading strategy where traders are selling a borrowed stock with a belief that it will drop in value. So, they can buy it back later at a lower price. Academic research has shown the stocks of companies that complain about short-sellers tend to falter.

    Investment prediction can be an ungrateful job

    This term is already hot.  Let’s show how much on the example of TESLA.

    It is a stressful time to be an investor in Tesla, of course. On September 29th shares in the electric-car manufacturer soared by 17% after its boss, Elon Musk, settled fraud charges with America’s Securities and Exchange Commission (SEC). Just days later, on October 4th, a series of belligerent tweets by the firm ’s founder sent shares tumbling by more than 7%.

    You might be interested Apple is charging its batteries with Tesla’s employees 

    The tweets in question were targeted at short-sellers, who aim to make money by selling borrowed shares and buying them back later at a lower price. With a quarter of its publicly traded shares lent out to facilitate short-sellers’ bets, Tesla is one of the most heavily shorted companies in America. Elon Musk has publicly feuded with short-sellers for years, calling them “haters”, “jerks” and “not super smart”. Research suggests that such insults are undeserved. Short-sellers are savvy investors who help to keep the market’s exuberance in check.

    So, Traders Paradise believes that short-selling may continue in the next year. The bear market just started.

    So, think about this investment prediction.

    Unlike Amazon stock – which we truly believe will rise and get to new highs.

    Our investment predictions are based on personal research and act as an observation about what we all can expect in the coming year. But we have to admit, nothing good. We hope we are wrong.

    Anyway, we wish you a healthy, happy, and fruitful new year! You can have it!

  • 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