Viewing profile — asavinov

You could check out a tool for trade signal generation based on machine learning and feature engineering: https://github.com/asavinov/intelligent-trading-bot It trains ML models ba…

The sequence FROM Table AS t WHERE t.Condition SELECT t.col1, t.col2, ... might be more natural than the traditional SELECT t.col1, t.col2, ... FROM Table AS t WHERE t.Condition If…

From my experience with crypto currencies and the intelligent trading bot [0] I would say that transformers will not provide significant benefits when applied to the traditional st…

I agree that the conventional (numeric) forecasting can hardly benefit from the newest approaches like transformers and LLMs. I made such a conclusion while working on the intellig…

Does it work synchronously or asynchronously? For example, if you integrate WebSockets then you could act as soon as you get new data. If it works synchronously then you regularly …

When developing an automatic trading system the following aspects are important: - Data feeds and data ingestion. It can be a fairly independent component which collects data from …

> What gives you advantage is trading algo, which is always hard to find. At the end it is necessary to make a decision whether to buy or sell (and how much), which will compete wi…

For any such tool, two questions are of primary importance: - How connections between multiple tables are represented and managed - How derived data is described (queries, workflow…

> I have always kept in mind is that feature engineering is almost always the key difference between success and failure I also developed an ML-powered service heavily relying on f…

> Joins are what makes relational modeling interesting! It is the central part of RM which is difficult to model using other methods and which requires high expertise in non-trivia…

One idea is to use columns instead of cells. Each column has a definition in terms of other columns which might also be defined in terms of other columns. If you change value(s) in…

Looks interesting although I miss a short introduction or how-to guide. I found that one can "Create and train machine learning models to predict market values." In this context, a…

Having Python expressions within a declarative language is a really good idea because we can combine low level logic of computations of values with high level logic of set processi…

Similar products: - https://rows.com/ - https://www.rowshare.com/ - https://www.airtable.com/ - fieldbook - Power BI

Most of the self-service or no-code BI, ETL, data wrangling tools are am aware of (like airtable, fieldbook, rowshare, Power BI etc.) were thought of as a replacement for Excel: wo…

Hamilton is more similar to the Prosto data processing toolkit which also relies on column operations defined via Python functions: https://github.com/asavinov/prosto However, Pros…

> I think SQL is irritatingly non-composable, many operations require gymnastics to express One approach to radically simplify operations with data is to use mathematical functions…

One alternative to SQL (type of thinking) is Column-SQL [1] which is based on a new data model. This model is relies on two equal constructs: sets (tables) and functions (columns).…

Here is another project based on the same idea of processing data using functions : https://github.com/asavinov/lambdo - Feature engineering and machine learning: together at last!…

The main motivation is that the conventional approaches to data processing are based on manipulating mathematical sets for all kinds of use cases: we produce a new set if we want t…

> I always felt like there was some super deep & fundamental link between these mathematical concepts and relational modeling ideas. The relational model is relies on set theory (m…