Tamara Makarova, Developer in Prague, Czech Republic
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Tamara Makarova

Verified Expert  in Engineering

Backtesting Trading Strategies Developer

Location
Prague, Czech Republic
Toptal Member Since
January 13, 2020

Tamara's recent professional experience includes analyzing financial and transaction data and dealing with various supervised and unsupervised data problems. Along with having strong analytical skills and math background, Tamara is also a proactive and highly motivated person focused on extending and leveraging her expertise in data analysis, machine learning, and data visualization.

Portfolio

Second Foundation
Python, Data Science, Data Modeling, Optimization...
TickUp
Python 3, Data Science, Quantitative Research, Time Series Analysis, Pandas...
Liquid Crypto Exchange
Git, Bash, PostgreSQL, SQL, IPython, Bokeh, Keras, Scikit-learn, SciPy, Pandas...

Experience

Availability

Full-time

Preferred Environment

PyCharm, IPython, MacOS

The most amazing...

...thing I have developed is a high frequency market making strategy that helped boost liquidity for Liquid, a crypto currency exchange.

Work Experience

Senior Data Scientist

2022 - 2024
Second Foundation
  • Developed a model of spot electricity prices in European geography, including 16 countries. Combined linear optimization and machine learning approaches.
  • Created seasonality and smoothing models for electricity and gas forward curves based on traded futures price data.
  • Developed various metrics and indicators for electricity trading on long-term markets, backtesting, and live trading.
Technologies: Python, Data Science, Data Modeling, Optimization, Backtesting Trading Strategies, Time Series Analysis, Machine Learning, Energy, Quantitative Analysis

Senior Data Scientist

2020 - 2021
TickUp
  • Led the alpha research project focused on getting trading signals based on daily credit card usage data in the US, from communication with the vendors and data evaluation to model building, backtesting, and running the production pipeline.
  • Built prediction models of the company's revenue. Assembled the linear models, decision tree methods, ensembles, revenue surprise, and market reactions based on the analysis of consumer spending trends for more than 250 public companies.
  • Developed the time series prediction models of daily consumer spending for more than 400 companies, including Prophet, SARIMAX, and state space models.
  • Gained experience with data providers, including Bloomberg, Refinitiv, Estimize, ConsumerEdge, Algoseek, Ravenpack, Apptopia, and Quandl.
  • Coordinated the data acquisition and usage to provide consistency and point in the timeliness of backtesting procedures.
Technologies: Python 3, Data Science, Quantitative Research, Time Series Analysis, Pandas, Scikit-learn, StatsModels, PostgreSQL, ClickHouse, Quantitative Analysis, Quantitative Finance

Data Scientist

2018 - 2019
Liquid Crypto Exchange
  • Built an adaptive market-making strategy for liquid crypto markets, which included risk control, fair price estimation and prediction of bid-ask spread related market metrics.
  • Adjusted a Kalman filter model for FX rates in multi-market executions.
  • Designed and developed a flexible backtesting framework for testing and optimization of high-frequency trading strategies; developed effective visual reports of strategy performance.
  • Optimized an ETL pipeline and reporting system for analysis of trading activity on different external exchanges, redesigned daily and monthly auto-generated P&L reports.
  • Performed research and prototyping for improving predictions of various market metrics (Linear models, decision tree methods, GARCH, recurrent neural networks).
  • Led the project to support internal and external audit requests: ad-hoc analysis, reporting and data investigations, data issues backtracking and coordination of required fixes.
Technologies: Git, Bash, PostgreSQL, SQL, IPython, Bokeh, Keras, Scikit-learn, SciPy, Pandas, Python

Data Analyst (Contractor)

2017 - 2017
Soft Retail
  • Identified fraud cases in transaction and customer data using unsupervised anomaly detection algorithms (local outlier factor, and isolation forest).
  • Designed a database scheme and implemented a regular transfer of client data from local CRM to relational DB (PostgreSQL).
  • Developed a set of effective indices and indicators for analysis and visualization of profit trends, customer segments, and customer behavior trends.
  • Implemented and deployed an interactive online dashboard to track main performance indices.
Technologies: Git, IPython, PostgreSQL, Scikit-learn, Bokeh, Pandas, Python

Quantitative Analyst

2009 - 2015
Applied Technologies
  • Developed a set of metrics for investor competence rankings based on SEC13F filings.
  • Applied data mining techniques (visualization, decision trees, and clustering) to identify a group of stocks and main trading patterns useful for the generation of buy/sell signals.
  • Developed and backtested a set of index strategies; each strategy aimed to cover a specific decision logic, for example, build a balanced portfolio based on top competent investors and so on.
Technologies: MATLAB, Statistics, Microsoft Excel, SQL, Java

Lecturer Assistant

2007 - 2010
Chelyabinsk State University
  • Organized practical and laboratory classes (including econometrics, probability theory and statistics, and Monte-Carlo techniques).
  • Contributed to a few research projects devoted to statistics and mathematical modeling.
  • Developed study materials and home projects for the course "Applied Probability Theory and Monte-Carlo."
Technologies: MATLAB, Fortran, Java

High-frequency Trading Strategy for Crypto Markets

I developed an adaptive market making strategy for liquid crypto markets, which includes risk control, fair price estimation and prediction of bid-ask spread related market metrics.

Tasks Accomplished:
• Worked with matching engine event logs and order book snapshots.
• Performed a thorough analysis of the data structure and developed a set of metrics and features important for trading decision process.
• Built strong prediction models for key metrics and incorporated them into the strategy.
• Developed a flexible backtesting platform to run various strategies on historical data.

Retention Analysis for Online Business

Based on data about user transactions and user online activity, I conducted an analysis of user retention and retention dynamics.
Tasks Accomplished:
• Analyzed user activity statistics and proposed retention metrics suitable for the business.
• Performed cohort analysis and analyzed retention dynamics.
• Identified user segments that retain differently from others.
• Formulated a few recommendations that had the potential to improve retention.
2007 - 2009

Master's Degree in Applied Mathematics

Chelyabinsk State University - Chelyabinsk, Russia

2003 - 2007

Bachelor's Degree in Applied Mathematics

Chelyabinsk State University - Chelyabinsk, Russia

OCTOBER 2019 - PRESENT

Deep Learning Specialization

Deeplearning.ai via Coursera

JANUARY 2017 - PRESENT

Data Analyst Nanodegree

Udacity

Libraries/APIs

Pandas, Keras, SciPy, Scikit-learn, NumPy, Matplotlib, Plotly.js, D3.js

Tools

IPython Notebook, IPython, PyCharm, Git, Microsoft Excel, StatsModels, MATLAB, Jupyter

Languages

SQL, Python, Bash, Java, Fortran, Regex, Python 3

Paradigms

Data Science, Quantitative Research

Storage

PostgreSQL, MongoDB, Databases, ClickHouse

Industry Expertise

High-frequency Trading (HFT)

Platforms

MacOS

Other

Machine Learning, Statistics, Regression Modeling, Decision Trees, Financial Markets, Backtesting Trading Strategies, Model Validation, Data Analysis, Data Cleaning, Bokeh, Trading, Natural Language Processing (NLP), LSTM Networks, Data Analytics, Data Reporting, Data Visualization, Time Series Analysis, Generative Pre-trained Transformers (GPT), Data Modeling, Optimization, Energy, Quantitative Analysis, Quantitative Finance

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