Ivan Vinnik
Private message
Offer work
Kyiv,
Ukraine
available for hire
Rating
CV
Hello!
I am a Quantitative Software Engineer with a background in high-energy physics
I specialize in developing infrastructure for data collection, algorithmic trading, and Web3. From deep R&D (Research & Development) of markets and strategy backtesting to writing fast execution routers - I help transform a raw idea into a systematic, profitable algorithm.
How exactly I can help you (My services):
R&D, On-chain Analytics & Niche Discovery: Have an idea (e.g., an arbitrage algorithm or a MEV bot) but aren't sure where to deploy it best? I conduct deep quantitative market research: analyzing blockchains, exchanges, liquidity, and competition. I will help you find the optimal network and niche specifically for your strategy and capital size.
Backtesting & Strategy Validation (Quant Research): Testing your trading logic on historical data with rigorous statistical analysis. I will build a test environment (Paper Trading / Backtester) and mathematically prove whether the strategy works or not, completely eliminating the risk of historical overfitting.
Microstructural Data Collection (Data Engineering): Setting up continuous collection of Order Books (L2), ticks, or historical data from crypto exchanges, Polymarket, or DeFi protocols. Packing data into convenient formats (Parquet, DBs) for further analytics with zero data leakage.
Trading Bots & Execution Systems Development: Creating scripts and routers for automated trading. I develop logic that accounts for real market conditions: network latency (latency tax), spread widening, slippage, and protection against toxic order flow.
System Development & DeFi Infrastructure: Designing a fault-tolerant backend for blockchain interaction. From reliable routing via RPC nodes and smart contract integration to deploying scalable server architecture. I ensure stable system performance with no bottlenecks.
My Technical Stack:
Languages & Backend: Python (FastAPI, Pandas, statistical analysis), Rust (high-performance modules, system logic), TypeScript.
Web3 & Crypto:
Smart Contracts & EVM: Solidity (reading, development, and auditing of economic logic), deep understanding of EVM architecture, gas optimization, and state management.
Network Interaction: Ethers.js, Web3.py, low-level RPC request optimization (WebSockets, Multicall, transaction batching) to minimize latency.
DeFi & On-chain Mechanics: Mempool analysis, understanding of DEX architecture, lending protocols, prediction markets (Polymarket), and MEV activity vectors.
Data Engineering: Parquet Data Lakes, PostgreSQL, optimization of time-series data storage.
Infrastructure: Docker, Linux, setup of cloud or dedicated servers (AWS, GCP, Bare Metal) depending on the project's latency requirements.
Working Format:
I can work either as an independent R&D researcher (audit, analytics, consulting) or as a hands-on engineer writing production code.
Thank you for your attention! 😼
Skills and abilities
Programming
Services
Administration
Portfolio
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113 USD High-performance engine of semantic search
AI & Machine Learning
RustySearch is a computational core written in Rust that transforms any database into an intelligent response system. Instead of classical keyword matching, the system understands the semantics of the text, combining machine learning models with the speed of a low-level programming language.
Architecture: from text to vector
The search process is built on a hybrid approach and consists of two stages:
… 1. AI vectorization: A neural network (based on Transformer architecture) analyzes the input query and converts it into a multidimensional vector (embedding), capturing the meaning and context.
2. Rust core: The search algorithm instantly calculates the distance between vectors and finds the most relevant results in large data arrays.
Technical innovation under the hood
To avoid slow linear search, the system implements an Inverted File Index (IVF). Using the K-means clustering algorithm, the vector space is divided into Voronoi cells. As a result, the engine does not check each record in the database but directly accesses the required cluster, drastically speeding up the output.
Key advantages of the system
Performance: The search time among hundreds of thousands of records is less than 2 ms — this is 30–50 times faster than similar scripts in Python.
Versatility: The core works with any data sources, from local files (JSON/CSV) to industrial databases (SQL/NoSQL).
Flexibility of settings: The Rust architecture allows easy adaptation of the system to specific business tasks, changing similarity metrics, or integrating it into complex distributed networks.
Autonomy: Performance at the level of cloud vector databases (e.g., Pinecone), but with full control over your own data and without monthly subscriptions.
Areas of application
RAG systems (Retrieval-Augmented Generation): Creating intelligent assistants that answer questions based on internal documentation.
E-commerce: Accurate recommendation systems that suggest products based on descriptive or unconventional user queries.
Big Data analytics: Searching for similar patterns, duplicates, or anomalies in large datasets.
Efficiency in numbers
Algorithmic complexity: Reduced from linear O(N) to sublinear O(√N).
Accuracy (Recall): 90–98% while maintaining high processing speed.
Response time: Average search query latency — 1.4 ms.
#AI #MachineLearning #SemanticSearch #nlp #RAG #highload #LowLatency #PerformanceOptimization #Algorithms #SystemProgramming #Backend #Rust
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180 USD High-Frequency Trading Bot for Polymarket (Web3 / Python)
Cryptocurrency & BlockchainDeveloped a high-frequency trading (HFT) backend for the Polymarket decentralized prediction market (Polygon network).
The system analyzes streaming data from Binance in real-time, computes a multi-factor scoring model, and automatically executes trades via smart contracts.
… Key Engineering Highlights:
• Sub-300ms Pipeline: The entire journey from signal generation to a confirmed Web3 order takes under 300 milliseconds.
• Zero-Latency Discovery: Implemented a deterministic algorithm to predict future contracts without API pagination, providing a critical speed advantage at the window open.
• Smart Order Routing: Advanced liquidity-bypass system (Limit -> Cancel -> FOK) to minimize slippage in thin order books.
• Advanced Risk Management: Hard-coded execution limits for daily loss, maximum drawdown, and maximum spread, alongside an emergency Kill-Switch.
• Async Web3 Integration: Highly optimized EIP-712 cryptographic signing (~45ms signature latency) and automated on-chain position redemption via Polygon RPC providers.
Tech Stack: Python 3.11, WebSocket, Web3.py, asyncio, EIP-712, Telegram Bot API.
#python #web3 #tradingbot #polymarket #crypto #backend #bot #blockchain #api
Reviews and compliments on completed projects 1
14 April
30 USD
Analysis of the bot's structure
Everything went successfully. I recommend for collaboration.
Activity
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Finding and Deploying the Best Projects for 4 Mac Mini M4 Machines
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Bot for Bybit: tracking liquidity withdrawal and FVG
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Compilation of btk core 0.4.0
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