Learning to Rank algorithms applications in Financial Machine Learning and Algorithmic trading. Motivation, Strategies, and Examples

Aleksandr Proskurin
July 8

from 07:00 p.m. till 09:00 p.m.

Offline and Online
Facebook

On July 8, Aleksandr Proskurin will hold a meetup “Learning to Rank algorithms applications in Financial Machine Learning and Algorithmic trading. Motivation, Strategies and Examples

Format — offline and online.

We offer both options:

  • Offline — for those who miss live meetings and networking. We will be waiting for you in our favorite Creative Quarter office in BC Gulliver (Everything will be held in compliance with quarantine regulations)
  • Online — for those who still prefer to study from anywhere in the world. 

The success of a variety of algorithmic trading strategies depends critically on accurately ranking assets before portfolio construction. Contemporary techniques perform this ranking step either with simple heuristics or by sorting outputs from standard regression or classification models, which have been demonstrated to be suboptimal for ranking. To address this deficiency, learning-to-rank algorithms, can solve the problem of optimal asset ranking.

On the meetup:

🔹 we will discuss the initial motivation why this approach is better compared to naive probability sorting.

🔹 will get a high-level overview of learning-to-rank algorithms.

🔹 discuss strategies that may utilize the approach.

Meetup is useful for:

🔹 data scientists, data analysts, developers, algorithmic traders, and investment professionals.

🔹 anyone connected with financial markets.

 

Prices: 

Offline
Till June 30 — 400 UAH.
Starts from July 1 — 500 UAH.

Online — 200 UAH. 

Tickets

Online
200
Registration
Offline early bird
400
Registration

Discounts

5% — from 2 tickets
7% — from 3 tickets
10%— from 5 tickets