Jun 2024 – Present · 1 yr 11 mos · Remote

• Engineered a high-performance, multi-exchange Order Book module for seamless real-time data integration (WebSocket/FIX/REST) from leading crypto exchanges (e.g., Binance, Bybit, Kraken, OKX, Deribit), enabling ultra-low-latency (ULL) market-making and arbitrage strategies.
• Developed a proprietary, high-frequency Matching Engine with sub-millisecond internal processing for order handling and matching, leveraging lock-free data structures and advanced C++ parallelism to optimize fill rates for large volumes.
• Designed and implemented real-time fraud detection systems targeting manipulative behaviors (spoofing, layering, quote stuffing, wash trading) using advanced pattern recognition and ML models to ensure regulatory compliance and protect fund capital.
• Built a predictive Order Toxicity Detection module employing advanced statistical analysis, feature engineering, and ML ensembles (XGBoost, LightGBM, Random Forests) to proactively filter toxic order flow, minimizing adverse selection.
• Developed real-time analytics and monitoring tools for multi-exchange liquidity dynamics, order flow imbalances, and market microstructure, providing actionable insights for strategy enhancement and risk management.
• Achievements:
• Boosted order execution efficiency by over 40%, significantly minimizing slippage and increasing market-making profitability.
• Successfully detected and mitigated fraudulent activities, safeguarding fund capital and maintaining market integrity.
• Contributed to a scalable, fault-tolerant trading architecture supporting multi-asset crypto portfolios and new exchange integrations, enabling future fund expansion.
• Architected and implemented a robust multi-venue arbitrage engine that seamlessly synchronizes order books across 4 crypto exchanges, leveraging adaptive latency compensation algorithms and lock-free data structures to minimize execution risk and slippage during arbitrage trades.

Apr 2024 – Jul 2024 · 3 mos · Remote

• Developed a robust C++ library for real-time calculation of USD/AED exchange rates, ensuring high precision and efficiency in a fast-paced OTC Forex trading environment.
• Integrated the library with trading systems to support live quoting, hedging strategies, and automated trade execution across multiple counterparties.
• Engineered algorithms for currency rate smoothing using Kalman filters and adaptive models to reduce noise and enhance prediction accuracy.
• Conducted in-depth backtesting and Monte Carlo simulations to validate pricing strategies, achieving a significant reduction in spreads and slippage.
• Optimized the library’s performance to handle high-frequency data streams, achieving up to a 40% reduction in computational latency.
• Achievements:
• Successfully reduced pricing errors by 15% through innovative algorithmic enhancements.
• Improved the speed and reliability of the pricing engine, leading to a 20% increase in the firm’s execution efficiency for OTC trades.
• Designed scalable architecture for integration with future trading pairs and expanding asset classes.

Mar 2023 – Apr 2024 · 1 yr 1 mo · Remote

• Spearheaded the development of sophisticated trading algorithms using C# for major cryptocurrency exchanges like Deribit and Binance, and traditional platforms such as the Moscow Exchange and Interactive Brokers.
• Designed and optimized trading strategies on the WealthLab platform to maximize profitability and mitigate risk.
• Performed real-time market trend analysis to adapt strategies swiftly, maintaining a competitive edge in dynamic trading environments.
• Collaborated with developers and analysts to refine trading tactics and share crucial insights.
• Ensured compliance with various financial market regulatory frameworks, enhancing operational integrity.
• Managed a diverse portfolio of assets, including cryptocurrencies, stocks, options, and futures, leveraging in-depth market knowledge to drive strategic trading decisions.

Jul 2022 – Dec 2022 · 5 mos

• Implemented the Black-Litterman model integrated with Recurrent Neural Network (RNN) techniques for optimized cryptocurrency portfolio allocations.
• Developed and executed advanced hedging strategies to minimize risks due to market volatility.
• Utilized SQL, WebSocket, Python, Java, and TensorFlow to develop sophisticated trading algorithms, focusing on Ethereum derivatives.
• Enhanced trading systems using Keras, optimizing them specifically for cryptocurrency scenarios.
• Managed algorithmic execution and data on Amazon Web Services (AWS), ensuring scalable and secure operations.
• Developed robust trading infrastructure using Go, focusing on scalability and maintainability.
• Implemented server-side components for real-time data processing and trading logic using Go, ensuring robust and fault-tolerant systems essential for algorithmic trading.
• Leveraged Go’s concurrency model to handle high-throughput data streams and trading signals, improving the efficiency and reliability of trading operations.

Jan 2021 – May 2022 · 1 yr 4 mos · Moscow, Moscow City, Russia · Remote

• Designing architecture of crypto-trading bot using C#-Framework (Lean-QuantConnect)
• Development automated trading system for crypto-derivatives trading in Deribit Exchange (Option, Futures)
• Using machine-learning algorithms (LSTM, RNN, Gradient Boosting, Random Forests) to predict on-chain metrics
• Time-series forecasting using ARIMA, SARIMA, GARCH, Prophet models, and various stochastic models for volatility prediction
• Writing option-pricing models for crypto-assets (ETH, BTC, XRP) and development option trading strategies (Covered Put, Covered Call, Long Straddle)
• Development of arbitrage strategies (Spatial Arbitrage, Statistical Arbitrage, Triangular Arbitrage)
• Developed a trading engine in Java for executing advanced crypto strategies, including momentum trading, mean reversion, and market-making strategies

May 2019 – Dec 2020 · 1 yr 7 mos · Moscow, Moscow City, Russia · Hybrid

• Portfolio optimization and strategy modeling in Equity and Bond Markets
• Development Machine Learning models using LSTM-architecture to predict asset prices (Equity, Bonds, ETF, Index)
• Researching and analyzing markets data
• Using Monte-Carlo and Walk-Forward optimization for back-testing quantitative trading strategies
• Conducting in-depth statistical analysis of financial data to identify patterns and trends
• Developing and implementing risk management techniques for portfolios
• Conducting regular performance analysis of trading strategies and making recommendations for improvement
• Achievements:
• Achieved a 25% increase in portfolio returns by implementing optimization strategies in Equity and Bond Markets.
• Developed Machine Learning models using LSTM-architecture that accurately predicted asset prices (Equity, Bonds, ETF, Index) with an average accuracy rate of 62%.
• Conducted in-depth statistical analysis of financial data to identify patterns and trends that resulted in a 20% improvement in portfolio performance.
• Successfully back-tested quantitative trading strategies using Monte-Carlo and Walk-Forward optimization, resulting in a 5% increase in profitability.
• Developed and implemented risk management techniques that reduced portfolio risk by 30%.
• Conducted regular performance analysis of trading strategies, identified areas for improvement, and made recommendations that resulted in a 10% increase in profitability.
• Researching and analyzing markets data
• Using Monte-Carlo and Walk-Forward optimization for back-testing quantitative trading strategies
• Conducting in-depth statistical analysis of financial data to identify patterns and trends
• Developed and implemented risk management techniques such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) that reduced portfolio risk by 30%
• Conducting regular performance analysis of trading strategies and making recommendations for improvement

Apr 2017 – Jan 2019 · 1 yr 9 mos · Moscow City, Russia · Remote

• Developed and tested Forex trading strategies using MQL4/5, focusing on optimizing profitability in volatile market conditions.
• Applied econometric models, including ARIMA and GARCH, for currency rate forecasting based on time-series analysis.
• Built risk assessment and capital management models, incorporating metrics such as Sharpe ratio and maximum drawdown.
• Automated trading processes, including scripts for order execution and position management.
• Conducted A/B testing of strategies using historical data and evaluated the impact of model parameters on overall profitability.
• Achievements:
• Successfully implemented arbitrage strategies (spot arbitrage, statistical arbitrage), leading to a 12% portfolio yield increase.
• Developed Python scripts to create heatmaps for currency pair correlations, enhancing portfolio diversification.
• Optimized computational performance of indicators, reducing processing load by 30% and enabling real-time trading adaptability.

Sep 2016 – Apr 2017 · 7 mos · Vladimir, Vladimir, Russia · Hybrid

• Conducted Forex market data analysis using SQL and Python to identify patterns and optimal entry/exit points.
• Developed trading algorithms and custom indicators, including RSI, ATR, and oscillators, aimed at improving prediction accuracy and reducing transaction costs.
• Researched and tested hedging strategies to minimize risks when trading currency pairs.
• Created dashboards for real-time visualization of trading signals and risk monitoring.
• Performed scenario analysis using Monte Carlo simulations to evaluate the robustness of strategies under varying market conditions.
• Achievements:
• Reduced execution error risk by 25% by implementing automated controls for order parameters.
• Designed a volatility forecasting model that increased the win ratio by 18%.
• Built a library of templates for technical indicator evaluation, accelerating strategy development by 40%.