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senior
Регистрация: 04.11.2022

Aleksandr Vedeneev

Специализация: Quantitative Developer / Financial Data-Scientist / Python
I started my career as a trader back in the 2007, and since then I’ve been developing quantitative trading models, automated trading systems, for myself and various clients over the world. I'm specialized in trading strategy backtesting, infrastructure development, and time-series analysis. I have deep knowledge in financial market microstructure, sentiment analysis and derivatives trading. MY SPECIALIZATION - Trading strategy backtesting, quantitative risk modeling, Monte-Carlo simulations, derivatives pricing - Financial time-series analysis / statistical modelling - Python development in financial markets domain - Machine-learning / data-science in application to financial series - Developing of trading infrastructure: quotes DB, algorithmic order management, portfolio management and execution - Probabilistic modeling and stochastic processes TECH STACK Python, Numpy, Pandas, Cython/Numba, MongoDB, Scikit-Learn, Dash, asyncio, Jupyter Lab, MatplotLib / Plotly, RabbitMQ / ZeroMQ, FIX Protocol API, REST API
I started my career as a trader back in the 2007, and since then I’ve been developing quantitative trading models, automated trading systems, for myself and various clients over the world. I'm specialized in trading strategy backtesting, infrastructure development, and time-series analysis. I have deep knowledge in financial market microstructure, sentiment analysis and derivatives trading. MY SPECIALIZATION - Trading strategy backtesting, quantitative risk modeling, Monte-Carlo simulations, derivatives pricing - Financial time-series analysis / statistical modelling - Python development in financial markets domain - Machine-learning / data-science in application to financial series - Developing of trading infrastructure: quotes DB, algorithmic order management, portfolio management and execution - Probabilistic modeling and stochastic processes TECH STACK Python, Numpy, Pandas, Cython/Numba, MongoDB, Scikit-Learn, Dash, asyncio, Jupyter Lab, MatplotLib / Plotly, RabbitMQ / ZeroMQ, FIX Protocol API, REST API

Портфолио

Yet Another Universal Backtesting Engine Release (YAUBER) – Executor

A boilerplate project for building asyncio Python distributed infrastructure. - Fully asynchronous - Uses RabbitMQ and MongoDB - Supports message communication between scripts and RPC calls - Sample infrastructure example

Yet Another Universal Backtesting Engine Release (YAUBER) – Backtester

yauber-backtester is a bare-bone portfolio backtesting engine that: - supports various portfolio management techniques: asset ranking, basket trading, portfolio rebalancing, etc. - intended to work on large asset universes (like 2000-3000 US Stocks EOD), or small intraday asset universes (like futures or forex, 1h timeframe). - supports meta-strategies, building and managing a portfolio of other trading strategies - allows simulating capital allocations, costs, margin trading, etc.

Yet Another Universal Backtesting Engine Release (YAUBER) – Algo Lib

Collection of standalone algorithms for financial time series analysis. Highlights: - It heavily uses Numba for improving performance of the code. It’s generally faster than comparable Pandas algorithms. - It’s build based on strict principles: future reference free, consistent when starting point of history changes, NaN friendly, built-in data validation checks - It’s stable and well tested, this means that logic of algorithms won’t change in the future and any algorithm in this package is 100% covered by unit tests.

Скиллы

Python
Algorithmic Trading
Machine Learning
Trading Strategies
Trading Infrastructure

Опыт работы

Freelancer
08.2016 - 07.2022 |upwork.com
Python, AsyncIO, RabbitMQ, Flask (as backend API), Jupyter Notebook
I’ve become a top-rated plus (top 3%) contractor on this platform in the quantitative modelling, data science, python programming field. I was targeting of long-lasting projects, typically 3+ months, in the field of financial market, data science, quantitative modeling.
Quantitative Developer
04.2016 - 07.2022 |TMQR EXO (TMQR Technologies)
Python, MongoDB, Pandas, Numpy, FIX protocols, Plotly Dash
I designed and created the core trading framework that TMQR used for executing strategies, backtesting, maintaining multi-account algorithmic fund, co-managing over $300m AUM.
Proprietary derivatives trader
01.2010 - 01.2016 |on my behalf
Python, custom trading frameworks
Implementing my own frameworks for creating, testing, and running a diversified portfolio of trading systems.
Head of algorithmic operations
09.2006 - 01.2010 |Proprietary trading firm
SQL, C#, C++
I started my career as a junior trader in a small asset management desk, but over a couple of years progressed to middle derivative trader, and later to the head of the desk of algorithmic trading and arbitrage operations.

Образование

Qualified Financial Specialist in brokerage / dealing
2007
NAUFOR
Qualified Financial Specialist in asset management and fund management
2007
NAUFOR
Finance and Credit / Banking
2003 - 2009
Saratov State Social-Economic University

Языки

АнглийскийСвободно владеюРусскийРодной