Tamara Makarova, Backtesting Trading Strategies Developer in Prague, Czech Republic
Tamara Makarova

Backtesting Trading Strategies Developer in Prague, Czech Republic

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.
Tamara is now available for hire


  • TickUp
    Python 3, Data Science, Quantitative Research, Time Series Analysis, Pandas...
  • Quoine
    Git, Bash, PostgreSQL, SQL, IPython, Bokeh, Keras, Scikit-learn, SciPy...
  • Soft Retail
    Git, IPython, PostgreSQL, Scikit-learn, Bokeh, Pandas, Python


  • Data Science 9 years
  • SQL 9 years
  • Backtesting Trading Strategies 8 years
  • StatsModels 4 years
  • Scikit-learn 4 years
  • Python 4 years


Prague, Czech Republic



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.


  • Senior Data Scientist (Independent Full-time Consultant)

    2020 - 2021
    • Led the alpha research project focused on getting trading signals based on daily credit card usage data in the US, starting 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 data 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
  • Data Scientist

    2018 - 2019
    • 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.


  • Languages

    SQL, Python, Bash, Java, Fortran, Regex, Python 3
  • Libraries/APIs

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

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

    Data Science, Quantitative Research
  • Storage

    PostgreSQL, MongoDB, Databases, ClickHouse
  • Other

    Machine Learning, Statistics, Regression Modeling, Decision Trees, Financial Markets, Backtesting Trading Strategies, Model Validation, Data Analysis, Data Cleaning, rege, Bokeh, Trading, Natural Language Processing (NLP), LSTM Networks, Data Analytics, Data Reporting, Data Visualization, Time Series Analysis
  • Platforms

  • Industry Expertise

    High-frequency Trading (HFT)


  • Master's Degree in Applied Mathematics
    2007 - 2009
    Chelyabinsk State University - Chelyabinsk, Russia
  • Bachelor's Degree in Applied Mathematics
    2003 - 2007
    Chelyabinsk State University - Chelyabinsk, Russia


  • Deep Learning Specialization
    Deeplearning.ai via Coursera
  • Data Analyst Nanodegree

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