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Co-founder of consulting firm Neurons Lab and advisor to AI products builders. On Medium, I write about proven strategies for achieving ML technology leadership

Ground principles of reinforcement learning in financial markets

Can you trade a cosine wave before the real markets?

How do reinforcement learning agents learn to trade like this? | Illustration by the author

There are many methodologies in algorithmic trading — from automated trade entry and close points based on technical and fundamental indicators to intelligent forecasts and decision making using complex maths and, of course, artificial intelligence. Reinforcement learning here stands out as a Holy Graal — no need to do intermediate forecasts or rule creation — you just have to define a target and the algorithm will learn the exact rules by itself!

There are a lot of amazing advanced tutorials that teach about modern learning algorithms (A3C, TRPO, PPO, TD3, and other scary acronyms) and deep learning architectures from CNNs…


Tips for educators and self-trained data scientists

How to learn in the age of commoditized knowledge and commercialized research

A photo from https://www.di.univr.it/?ent=bibliocr&id=236&tipobc=5&lang=en

Half a year ago I have agreed on a small adventure for myself. My university professor and thesis supervisor Luca di Persio offered me to join a teaching squad at a brand-new Data Science faculty at the University of Verona, my second alma mater. I was giving multiple lectures and tech talks for years before, but never as responsible for the long-term result of the student, so I have decided to accept the challenge.

By writing this article, I am targeting:

  • Educators, who are looking for more result-oriented alternatives to academic teaching approaches
  • Self-paced students, who aim to achieve results…


To learn new libraries or finally do what the clients want?

Strategies and tactics for independent experts

Illustration from Upslash

Remote work is a new normal today. The first months of “freedom” from the office were pretty fun but that job stays the job: dependent on a fixed salary / hourly rate, limited opportunities, risks of losing a warm place. Lots of us have tried to go solo, finding customers online and learning new in-demand skills. However you could notice, that new clients, that you have conquered so hardly, don’t pay you for Udemy certificates and Tensorflow skills — they just want “99% accurate solution in 2 weeks as cheap as possible. Don’t forget about that time tracking too”. As…


Don’t make the consultants fool you!

How to satisfy all stakeholders with a great product

Illustration from Upslash

Making buzzwords as “digitalization”, “innovation” and “big data” into the alive and profitable product is hard. And usually, it’s not the technology itself that fails, but the alignment between owners, managers, clients, employees, and sometimes the society. With AI products it’s even harder because this is a relatively new field where there are predominant:

  • either generalist consultants who can tell about bright future and economic impact without concrete numbers and next steps;
  • or deep experts, who know the potential and limitations of the technology, but they lack systematic business and social vision about it.

In this article, I want to…


Wave motion illustration http://animatedphysics.com/insights/modelling-photon-phase/

In the previous article, we have built experiments where we have learned how to approximate physical laws models with machine learning algorithms, which was a preamble for a “real” data generation process. In this article, we won’t simply approximate the dependency between a time step and the exact position of the object but will generate whole trajectories as objects coming from a data distribution and will try to control this process and the variables as we do in classic mathematical models.

This article concludes the idea of the evolution of mathematical modeling from human-designed first to data-driven first and I…


Wave motion illustration http://animatedphysics.com/insights/modelling-photon-phase/

GANs and other generative machine learning algorithms are still hyped and work fantastically with images, texts, and sounds. They’re able not only to generate data for fun but solve important theoretical issues and boost production ML pipelines. Unfortunately, typical today’s practical use case is limited to “fine-tune pre-trained StyleGAN2 for zombies generation”. And what’s worse, almost no one cares to explain why do we need generative modeling in the real world and where the roots of such need are coming from.

The following two articles aim to bridge the gap between modern cool stuff and slightly forgotten old-school mathematical modeling…


SIMULATIONS, RISKS, AND METRICS

Understanding strategy risk and the probability of overfitting: small numbers that change everything

Illustration from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3544431

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

This is the third and the last article in a short series about “how to believe the backtests”. We have started with an overview of classic metrics for risk and reward and expanded them with new ones that tell more not only about the PnL curve itself but about underlying data, models, fat-tails, and…


SIMULATIONS, RISKS, AND METRICS

Combinatorial and scenario-based backtesting from historical data and simulations

Illustration from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3544431

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

Let’s disassemble backtests and make them great again :) In the previous part, we have reviewed the main dangers of the classic backtesting routine with the historical data and standard metrics related to the strategy performance. …


SIMULATIONS, RISKS, AND METRICS

On the dangers of walk-forward backtesting, how to measure them and not feel right, but to be right

Illustration form https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3544431

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details.

Quantitative research is a process with many intermediate steps, each of which has to be carefully and thoroughly validated. Asset selection, data collection, feature extraction, modeling — all these phases take time and are delivered and tested by different teams. But what at the end the investor wants to see? That “flawless” backtest on…


Coding or selling the code?

Disassembling careers of the technology rockstars and young entrepreneurs all over the world

https://www.scmp.com/magazines/style/tech-design/article/3023666/9-mind-blowing-tech-predictions-steve-jobs-bill-gates

There are two topics that are covered in a veil of secrecy, myths, and misunderstandings. They keep busy the minds of most people not depending on the circumstances. We are making life-changing decisions just to get these two things in one or another way. These things are sex and money. I hope that with the first one you’re happy and satisfied, so in this article, I’d like to focus on the latter, in particular, the main way of accumulating wealth: building a successful career through improving skills, promotions, publicity, and other moves. My thoughts will be based on biographies of…

Alexandr Honchar

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