Algo – algorithmic trading | Algorithmic trading

Mathematics takes investment decisions here.

Rule-based investing – fast, quantitative, reliable and inexpensive

Fund results (since 2013-01)
  • Returns 32.34%
  • Returns of the last 12 months 0.10%
  • Positive months 72.58%
  • Annual volatility 7.74%
  • Sharpe 0.72
Fund specifics
  • Target annual returns 15%
  • Fund stop loss -15%
  • High liquidity
  • Dynamic and extensive diversification
  • Excellent results during crisis (2008)
  • No correlation to other asset classes
Automatic trading
  • ∼ 300 algorithms portfolio (trading systems and instruments combination) trades live
  • ∼ 120 futures contracts per day (∼ 400 trades)
  • Multi–asset trading strategies trade ∼ 42 most liquid futures in largest futures exchanges
  • 11 different trading systems
  • More than 6000 algorithms library

Our team

The latest news

Algorithmic Trading Portfolio correlation with other funds equals to zero

It goes without saying that investors seek to make a positive return on their investment portfolios. Each investor forms investment portfolio very individually. Some rely more on investment rules, other less so. Either way, most investors rely on correlation as it plays a significant role in investment management. One of our potential customers asked to see how Algorithmic Trading Portfolio compares to other systematic – algorithmic funds based on returns correlation. We have decided to share a few graphs with you.

Graphs clearly show that large algorithmic funds are more likely to have correlated returns with each other than smaller ones. Typically, it is caused by liquidity problem. A smaller algorithmic fund is more flexible and can exploit wider range of trading strategies than a large one. Take for example Winton Capital Management algorithmic fund which manages 12.3 billion dollars of assets. It is hard to imagine how such a large fund would trade at night session when liquidity decreases considerably. It would also be difficult for a such fund to trade in higher frequencies. Therefore, large funds usually try to catch larger market trends and hold investments longer. This leads to similar and more correlated returns of large funds.

Meanwhile, small funds which manages from a few to tens of millions can trade in high frequency, at night time and trade less liquid financial instruments. Small funds are much less likely to correlate both with small and large funds. Thus, such funds are more suitable for the investment portfolio.

It is vital to perceive that financial markets are becoming more global than ever. They become highly dependent on each other, so if one falls, then usually so does the others. Consequently, it becomes increasingly difficult to find investments which would have a low correlation with other investments. Our advice to any investor is simple – make a wider diversification by mixing a variety of investments with as low correlation to each other as possible. Then the risk will be low.

Why do people stay away from investment?

I always observe conversations about investment. Lately, I have noticed that many people avoid investment. For example, the Danes, who have even had a negative deposit rate for a long time, still deposit lots of money into their bank accounts and do not invest. Recently, they have beaten their bank account deposits record. A similar situation is going on here in Lithuania. I have tried to analyse the reasons why. One of the main reasons is risk. It turns out that people tend to evaluate risk in terms of fear. Some fears are reasonable and widely analysed; others, unexpected.

The most popular fears are market distrust and fear of financial crisis. Both the local and worldwide media are full of negative information which increases doubts for investment – the financial crisis of 2007-2008, the Euro crisis, Brexit, China’s economic slowdown, mistrust in US President D. Trump, and many more. Most people see only losses in crisis. Fewer have better insight and see possibilities of earnings.

Another common fear arises from lack of knowledge. It is natural that a person be afraid of something that he or she does not understand. However, it is not a necessity to be an expert to invest somewhere. It is important to observe that there is a wide range of choices of investments and to choose the one which fits your priorities the best. Nowadays, investments are not limited to real estate, bonds, stocks, and gold. One could invest to fund of funds, hedge funds, funds which are based on algorithms, start-ups, technologies, or exotic investments; for example, wine, plants, art, etc. Investment choices meet every taste. None of the types of investment are best or worst – they all have their advantages. I believe it is important to ask yourself, ‘What is the most appropriate way for a person like me to invest?’ and then gain some understanding in that area. The key is to be consistent, not to change investment types after your first unfortunate moments, and to constantly expand your knowledge.

Another fear is the fear of being deceived by professional consultants. Therefore, some people tend to invest only on their own. If they fail, then they tend not to invest anymore in any field. After this, they sometimes blather on about how risky and unrewarding investments in general are and how it is best to avoid them. In doing so, they increase the fear even more. Stories of failure are usually more dramatic than success stories; hence, failure is widely covered in the media.

An interesting fear appears to be the fear of embarrassing yourself when a professional consultant or investor discovers that you lack knowledge in investment. In such cases I would ask why it is so scary to show that you do not understand something. However, this matter should perhaps be dealt with a therapist or psychologist.

There is also the fear of doing anything else other than one’s profession. Some people tend to avoid areas in which they do not work and in which they are not professionals. They choose to keep cash or deposit money in the bank because they feel more secure that way. However, as I have already mentioned above, it is not a necessity to be an expert. You do not have to be a professional investor to choose the right fund, or the right consultant, or the right investment area.

The fear of the liquidity and of the volatility is common in many investment areas. For example, people are discouraged to invest in real estate because of illiquid, long-term investment, especially if the investor is not sure if he or she might need the invested money in the near future. Contrarily, investments with very high liquidity, for example, the stock market, tend to be very volatile, so people are also repelled by the idea that they might lose a significant share of their investment in a few days.

For those who are still willing to or who have already invested, I suggest taking into account three key indicators before any investment: return, risk, and liquidity. It is essential to observe the interconnections between all these indicators rather than to focus on one of them. The Sharpe ratio (return divided from risk) shows the real benefit of investment. High risk is not bad if high return is expected, and, on the contrary, low return is not bad if it comes with very low risk. Liquidity is also a certain risk which must be considered while measuring the total risk of investment.

The money deposited in the bank account inevitably depreciates; therefore, one should overcome the fear of risk and take a closer look at the very wide range of investment possibilities. Evaluate the return and risk ratio and try to invest by yourself, trust an investment advisor/fund, or do both.

Confidence in algorithms grow as we begin to understand them

Before setting up an algorithmic investment fund I realized two things: one, it will take time to attract investors, and two, my role as a fund manager will have to be expanded to teaching. Nowadays algorithms are applied in various areas – human resource management, marketing, sales, education, customer service, finance, and many other. Everyone, who applies algorithms to make decisions or solutions raises a question – can I trust algorithm completely? My answer is that it depends on your understanding of what algorithms are capable of and where their ability ends.

The key is to understand and correctly identify questions and tasks that can be answered or solved by the algorithms as well as to comprehend what algorithms cannot do. Often algorithms are mistakenly regarded as human beings, users expect them to be exactly as human beings who do certain tasks. It is forgotten that algorithms do precisely what it was designed to do and finds solution only to exactly defined task. It does not interpret. It is not a human, who is influenced by many both internal and external factors.

When applying algorithms, a considerable threat is human desire to rely on their own judgment rather than algorithm at critical moment. Most often this happens when a person decides that the algorithm is not perfect. Of course it’s true. They are not flawless, however they perfectly and precisely do what they were designed to do. So when the task is specific and carefully specified they should be trusted without any interference to decision or solution.

What should be done? My suggestion is to have an interest in algorithms, to learn to know and trust them. Only it seems that trust is a difficult task.

In scientific study “Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them”, which was also presented at the Harvard Business Review magazine, the researchers conducted a study which examined three cases: when people ignore algorithms opportunities and rely solely on their own solutions, when people marginally adjust algorithms results based on their own knowledge, and then people only rely on algorithms. The results were the best, when there was no human intervention, slightly lower in case of marginal intervention and worst when algorithms were not used at all. However, people felt more satisfied and more secure when they had some influence on results. The study concluded that it is better to give people the opportunity to make some little contribution to final result or solution rather than use only algorithm, as it does not impair results drastically, but people feel better.

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