Interview With AlgotradeSoft ($OVL)

10 January 2019
The Market Owl

We’ve had the pleasure to interview Alexander Pshenichniy, Founder of AlgoTradeSoft, the company behind DARWIN OVL, among other DARWINs.

$OVL is currently one of the 20 DARWINs with the  highest number of investors on the Darwin Exchange. It’s also included in the Good Scores, Trending, Under the radar, and Return > 50% predefined filters.


Alexander, tell us a little about your group of traders. How did you get started and how long have you been trading for separately and together?

I’ve been trading for the past 10 years. Most team members have more than 5 years of experience on financial markets. Not only trading experience in forex, but also in options, futures and the stocks market, and also in brokerage companies.

As a team, we exist since 2015. I considered the idea of ​​creating a company for several years, and it turned into an ambitious project, which is now known to everyone as AlgoTradeSoft.

It all started with an innovative idea and 2 people: a programmer and an analyst. And as the ambitions, resources and inspiration grew, it turned into a team of 15 people, which, by the way, is not so easy to manage =).

Key participants of the team are Alexander Pshenichniy, Founder, Sergey Medvid, CEO, Maksym Berdnyk, COO and Andriy Vasyliev, Head Of R&D.


OVL.4.17


About your trading strategy: what pairs do you usually trade? Do you always trade the same timeframes?

We use several strategies and each of them uses different currency pairs.

The development of strategies is a dynamic process, so it’s more correct to speak about which currency pairs we are trading now. These are EURUSD, GBPUSD, USDJPY, GBPCAD, GBPCHF, EURAUD, EURGBP, EURCAD.

Regarding timeframes, we usually use data from different time intervals. This largely depends on the trading strategy, for some strategies the basis for calculation is tick data.

In addition, we conduct research in the field of statistical arbitrage, where the basic intervals for calculations are tick data and non-standard timeframes, for example, 1 second, and the range of instruments covers almost the entire forex market. It is worth avoiding unnecessary affection, because the timeframe and trading instruments cannot be chosen in advance, it is just a consequence of the research.


As a group of traders, what would you say is your greatest strength?

The group’s biggest advantage is that, as in the portfolio of strategies, each of the participants has a different set of knowledge and skills that are automatically distributed to other team members, launching a powerful synergistic process.

Thus, starting a new project or improving the current one – it just needs to ask who has the necessary knowledge for it. This largely saves resources on analysis and the search for suitable ideas, because the entire team is involved in all discussions.


And your biggest weakness?

Quite often, each of the participants goes into really deep research of the details of a separate strategy and does not notice frankly simple and effective strategies.

A team can lead a large number of simultaneous projects, wasting efforts on separate tasks and temporarily postponing really important strategic tasks.


Our algos tell that your DARWIN OVL is very good in 10 out of 12 attributes. Any ideas for other traders who need to improve on those attributes?

In fact, the main and determining factor in an effective system is not so much the focus on individual indicators as on the overall positive result.

Let’s say the mean reversion strategies (which are the largest part of our portfolio) in most cases have more attractive indicators (percentage of profitable deals, recovery factor) than, for example, trend following strategies. However, this doesn’t mean that they are much more efficient and reliable. The most important indicator, as I see it, is the ratio of potential return to risk, which can be seen by calculating the Sharpe and Sortino coefficients, as well as the deep diversification of the portfolio, which allows you to decrease the risks of each individual system and portfolio as a whole.

In the basic version, all strategies can be divided into trend following, mean reversion and momentum, therefore, it would be more correct to compare only strategies from one group to each other.

I would advise traders to focus on the research of the basic attributes of their strategy and the development of its most important components, because a good trading strategy will attract investors’ attention even without the extremely high Darwinex score.


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What is the most important lesson you’ve learnt in your trading career?

The most important lesson in my trading career was the realization that risk control is a fundamental factor and it’s impossible to build an effective asset management system without it.

That’s why the most important and primary goal of our team is the safety of both our funds and our investors’ funds, and only then the capital increase.

Perhaps this conclusion is not so obvious at the beginning of a career, but one way or another, over time, all professional traders come to it.


Any books you like to recommend to fellow traders?

Any sources of information (blogs, books, articles from journals) about algorithmic strategies in particular are a very valuable source of information, so you can extract a lot of useful and practical tips from almost any book about algorithmic trading.

Of course, this does not exclude the need of practical skills development for creating strategies and real trading, but it should be said that only through fundamental knowledge one can turn a just good trading strategy into an excellent one.

I can recommend such books as:

  • The Evaluation and Optimization of Trading Strategies, by Robert Pardo
  • Algorithmic Trading and DMA: An introduction to direct access trading strategies, by Barry Johnson
  • Trading Systems That Work, by Thomas Stridsman
  • Black Swan, by Nassim Taleb

Finally, any comment you’d like to share with the fellow traders who read this?

What would I like to share? Focus on knowledge, in the end, that’s what determines your efficiency and prospects in this over-competitive trading environment.

And not only on a fundamental knowledge: statistical analysis, programming, machine learning, but also knowledge about the market, its participants, new profitable trading methods.

The most profitable strategies are usually based on fairly simple principles. Explore, practice, earn.


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Any specific questions you would like to ask Alexander? Leave them as comments and we’ll try to get you the answer!


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