One of the key trends in software infrastructure in 2019 is Observability. https://hub.packtpub.com/key-trends-in-software-infrastructure-in-2019/
It has gained a lot of attention recently.
This year, a theatre production series called Tale of the Century was launched in Estonia. Throughout the year, 22 local theatres presented their interpretations of the past hundred years of Estonian history to the audiences. In the draw, the Russian Theatre was assigned the topic of the future of Estonia.
Each one of us has their own ideas about what the future might look like — what we’re afraid of and what we dream of. However, we didn’t want to create a play that would just tell the audience how the narrow circle of people at the theatre sees…
To paraphrase H.S. Thompson’s Fear and Loathing in Las Vegas, ’We had two virtual machines, seventy-five sites, thousands of metrics and machines to monitor, a bunch of Python scripts, one database and one message queue, InfluxDB, and a whole galaxy of multi-colored libraries… and also pandas, NumPy, Dash, Flask, SQLAlchemy. Not that we needed all that for creating a monitoring system, but once you get locked into serious component collection, the tendency is to push it as far as you can.’
Failing twice is bad enough, but failing at failure detection is even worse.
The monitoring of distributed systems is…
This is short article about understanding time series and main characteristics behind that.
We have time-series data with daily and weekly regularity. We want to ﬁnd the way how to model this data in an optimal way.
One of the important characteristics of time series is stationarity.
In mathematics and statistics, a stationary process (a.k.a. a strict(ly) stationary process or strong(ly) stationary process) is a stochastic process whose joint probability distribution does not change when shifted in time.
Consequently, parameters such as mean and variance, if they are present, also do not change over time. Since stationarity is an assumption…
This is the second part of the article about investment strategies applied to the market of crypto assets.
With the breakthrough of Deep Neural Networks and Reinforcement Learning we can deeply explore many entrenched problems at the financial markets which haven’t been reachable till now.
The investors’ interest in topic is growing rapidly and here are some intriguing opinions about using Deep Learning on financial markets:
There are existing a lot of Deep Learning approaches to the financial market trading. However many of them try to predict price movements or trends (Heaton et al., 2016; Niaki and Hoseinzade, 2013; Freitas…
Portfolio selection, aiming to optimize the allocation of wealth across a set of assets, is a fundamental research problem in computational finance and a practical engineering task in financial engineering.
There are two major schools for investigating this problem, that is, the
Mean Variance Theory [Markowitz 1952; Markowitz 1959; Markowitz et al. 2000] mainly from the finance community
Capital Growth Theory [Kelly 1956; Hakansson and Ziemba 1995] primarily originated from information theory.
The Mean Variance Theory, widely known in asset management industry, focuses on a single-period (batch) portfolio selection to trade off a portfolio’s expected return (mean) and risk (variance)…
My name is Aleksandr Tavgen and I work as a Software Architect in Playtech. I have always loved playing. When I was younger, I mostly played with LEGOs, but these days, my toys are slightly more complex. For example, recently I have been playing around with Recurrent Neural Network models.
Last year, I proposed a collaboration to a friend of mine, Aleksandr Zedeljov (http://faershtein.com/), who is a composer, musician, and musical director at the Russian Theatre of Estonia. …