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We employ a disciplined research process which follows scientific method. Trading and systematic experience guide our research objectives which are validated by the unbiased judgment of the research results. The developed algorithms include many unique and innovative features taking ideas from different branches of science and engineering. All elements of the trading strategies are backed by rigorous and extensive testing that shows consistency over many time frames and environments.

Tesoro Management is the team of passionate geeks that performs innovative research in AI and deep machine learning by means of neural networks, Bayesian models, decision tree methods, application of algorithmic information theory, and some other ways of quantitative analysis with the main focus on financial research and algorithmic trading.

Our investment and trading process based on appropriate systematic algorithmic approach with fully automated execution which relies on the ultimate knowledge and long-term quantitative research. Market volatility creates an opportunity to use systematic long/short strategies to potentially generate superior performance by capturing both bull and bear markets. Our adaptable trading system is expected to achieve much better performance compared to a buy and hold strategy.

Our risk management framework constantly monitor various types of risks as managing risk and preserving capital are of primary importance to us. Besides our trading systems do not use leverage as the way to boost performance. And although there is no absolute guarantee that it will prevent losses to occur we aim to deliver superior risk-adjusted returns.

Our team

Oleg
Anufriiev
CEO

Oleg is finance professional and tech entrepreneur who launched multiple successful businesses. He possesses 15+ years experience as financial advisor and trader, holds MS in mathematics and financial law. Oleg obtained Certified Project Management associate degree from IPMA.

Roman
Rizvanov
CTO

Mr. Rizvanov is the extraordinary AI/ML specialist who invented and implemented face recognition system in Viewdle (acquired by Google) and managed the team which created face-tracking technology of Snap lenses. Roman won Bronze medal at the International Olympiad in Informatics and ACM International Programming contest. He holds Masters degree in Applied Mathematics from Kiev National University.

Yurii
Monastyrshyn
Investor, Board member

Yurii is a serial entrepreneur and investor. Co-founder of Looksery Inc that was acquired by Snap for 150M$. Ex Sr. Director of Engineering at Snap where he was leading the development of Snap AR platform from its inception. Yurii got Applied Mathematics degree at Odessa National University and is a prize winner of multiple programming contests.

Roman
Bilyi
ML Researcher

Roman Bilyi is the well-recognized specialist in ML and AI, who worked as a key researcher in several AI startups. Roman won Bronze medal in ACM ICPC World Finals. Also he reached the 11th place at Facebook hackers Cup finals, 2018 among 8216 participants. Roman held Legendary Grandmaster title at Codeforces (7th rank among 56000). He holds Masters degree in Applied Mathematics from Lviv National University.

Rinat
Rizvanov
ML Engineer

Rinat Rizvanov is a leading Machine Learning Engineer who specializes in trading model’s optimization and opinion mining of social networks, e.g. Twitter. He was the Participant of III stage of Ukrainian Olympiad in Informatics and programming contest held by Minor Academy of Sciences of Ukraine. Rinat holds Bachelor’s degree in Reprography, NTUU “KPI".

Anton
Orlov
Software Engineer

Anton Orlov is a software engineer with a wide experience. He worked as System Administrator, Software Engineer, Team Leader and taught his own course about Software Engineering. As a student he has authored multiple scientific papers in Software Engineering and Information Retrieval sphere. Anton holds Masters degree in Applied Mathematics from Saint-Petersburg State University. He also set a Guinness Book record as a "strongest boy" in his childhood.

Research Timeline

2022
June

Testing of live parameter optimization bot

May

Updated version of Trading algo which normalize trading volume is deployed

April

Testing of models ensemble to fine tune the results of Trading bot

2021
November

Advanced statistics tracking liquidity issues is implemented

August

Launch of Private Fund

July

New version of trading algo is deployed

March

Parallel backtesting which traces algo performance in real-time is implemented

2020
December

Advanced market making algorithm is developed

October

Launch of cutting-edge new AI long/short algo on cyryptomarkets

July

Algo backtesting and bug fixing

April

Advanced Backtester with Algo parameters optimization feature is developed

January

Based on internal research pivot to cryptomarkets is done due to much higher risk/reward ratio

2019
December

New advanced neural network architectures are implemented

August

Liquidity issues fixed

2018
November

Proprietary algo ranking system is developed

June

Series of practical experiments were hold in Tesoro (more details in Research section)

January

Testing of VIX trading algorithm using VX futures combos

2017
December

1st working neural network architecture implemented

May

Testing of different approaches to trading on futures and stock markets

February

Start of the development of brand new AI trading algo

January

Top AI/ML researchers joined Tesoro Management

2016
September

Extended deep research of VIX and VIX term structure

February

VIX research using statistical methods

Contact us

for experienced and professional investors only

The materials on this website are for illustration and discussion purposes only and do not constitute an offering. An offering may be made only by delivery of a confidential private placement memorandum to appropriate professional investors.

If you are a professional investor who is looking for more information on the fund and its historical performance, please fill in the contact form below and we will reach you out to arrange a call.