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I engineer institutional-grade algorithmic trading systems that combine quantitative finance, deep learning, and real-time market intelligence. WHAT I BUILD ⚡ End-to-End Trading Bots Full pipeline: signal generation → order execution → risk management Exchange integration: Kraken, Interactive Brokers, Binance, Bybit APIs Low-latency execution via REST & WebSocket connections Multi-asset support: crypto, equities, futures, forex 🧠 AI/ML-Driven Alpha Generation Deep Reinforcement Learning agents (PPO, A2C, SAC) for adaptive strategy optimization LSTM/Transformer models for price trajectory forecasting Ensemble methods: XGBoost, LightGBM, Random Forest for feature-rich prediction Online learning systems that adapt to regime changes 📰 Real-Time News & Sentiment Alpha Live news ingestion from Bloomberg, Reuters, Twitter/X, Reddit, Telegram NLP pipelines: FinBERT, GPT-based sentiment extraction Event-driven trading: earnings, FOMC, CPI, geopolitical triggers Alternative data integration: social sentiment scores, fear & greed indices, on-chain metrics 📊 Quantitative Research & Backtesting Walk-forward optimization with out-of-sample validation Monte Carlo simulations for strategy robustness Realistic market microstructure modeling (slippage, partial fills, latency) Statistical edge validation: Sharpe, Sortino, Calmar, max drawdown analysis TECHNICAL ARSENAL Languages: Python, R, SQL, MQL5 ML/DL: PyTorch, TensorFlow, scikit-learn, stable-baselines3 NLP: HuggingFace Transformers, spaCy, FinBERT, LangChain Data: pandas, NumPy, Polars, Apache Kafka, TimescaleDB Backtesting: Backtrader, VectorBT, QuantConnect, Zipline Execution: ccxt, ib_insync, Alpaca API Infra: Docker, AWS, GCP, Redis, PostgreSQL Viz: Power BI, Plotly, Streamlit dashboards MY DEVELOPMENT PROCESS Phase 1 → Strategy Discovery & Alpha Research Phase 2 → Feature Engineering & Model Development Phase 3 → Rigorous Backtesting & Stress Testing Phase 4 → Paper Trading Validation Phase 5 → Live Deployment with Kill Switches & Risk Controls Phase 6 → Continuous Monitoring & Model Retraining CREDENTIALS 🎓 PhD in Statistics & Data Science 🏦 10+ years in quantitative finance 🏛️ Former Team Lead — National Bank of Georgia 💼 Institutional experience: TBC Bank, IFC, hedge funds ($500M+ AUM) ⭐ Top Rated on Upwork | 5-Star Client Feedback IDEAL PROJECTS ✅ Custom trading bot development (crypto, stocks, futures) ✅ RL-based portfolio optimization agents ✅ Sentiment-driven trading systems ✅ Existing strategy automation & optimization ✅ Quant research & alpha discovery ✅ High-frequency data pipeline architecture

I build automated solutions that save you hours of manual work. With 4+ years of experience in Python development, I specialize in: → AI Integration & Automation — GPT/Claude API integration, RAG pipelines, AI-powered workflows → Web Scraping & Data Extraction — Selenium, Playwright, Scrapy, anti-bot bypass → Data Analysis & Visualization — pandas, NumPy, Streamlit dashboards → Backend Development — FastAPI, Django, PostgreSQL, Docker → Telegram & Discord Bots — Full-featured bots with payment systems, admin panels What sets me apart: • Fast delivery — I typically deliver 2-3x faster than the deadline • Clean, documented code — easy to maintain and extend • Clear communication — daily updates, no surprises I've built data pipelines processing 1M+ records, trading systems handling real-time market data, and automation tools that replaced 40+ hours of weekly manual work. Let's discuss your project — I'll give you an honest estimate and a clear plan of action.

I enjoy applying predictive modeling and machine learning to deliver value for my clients, assisting them with decision-making and automating their tasks intelligently. I was the winner of the USA Electricity Prices Prediction contest, held among four qualified Upwork data scientists. Through numerous experiments, I've developed expertise in often-overlooked ML techniques such as feature selection, ensembling, calibration, and hyperparameter tuning, where I’m developing novel approaches for my upcoming AutoML library, Diogenes. These days, my work primarily focuses on tabular machine learning tasks within the Python ecosystem [scikit-learn, gradient-boosted trees, PyTorch, Keras, TensorFlow]. I have experience with various software but currently prefer PostgreSQL for data storage, Plotly and Seaborn for data visualization, and Dash for dashboarding. For efficiency, I use Numba to accelerate my Python code and Dask to handle large computations across clusters when single-machine multiprocessing isn't enough. I prefer Polars as a dataframes engine. I prioritize code quality and continuously work to adopt best practices in my development process. While it's not perfect yet, I’m committed to improvement. In my free time, I contribute to open-source projects and explore ML applications in finance and trading. What I can do for you: ⭐️ generate ideas to optimize your business (pricing, customer engagement, ads and many more) ⭐️ build real-time decision support systems that take into consideration many factors and execute optimal actions ⭐️ train predictive models for your business needs (sales, churn, uplift, next-best-action, propensity) ⭐️ automate natural language processing (NLP) and Computer Vision (CV) tasks: improve products descriptions, analyze customers reviews, classify images ⭐️ build modern AI integrations (OpenAI's ChatGPT, Gemini, Claude etc) ⭐️ produce professional reports & dashboards, generate insights from your data ⭐️ create distributed applications, conduct high performance cloud computing (Azure/EC2/GCP) ⭐️ scrape, process, and structure data from APIs and web sources for analytics or reporting ⭐️ automate your daily routines (emails, documents, files, reports) ⭐️ develop and test trading and sports betting strategies and bots ⭐️ manage your Facebook or Google Ads programmatically (via Facebook Marketing API and AdWords API) ⭐️ move data to/from your eCommerce stores and CRM systems like Ebay, Amazon (MWS API, Product Advertising API, Ads API, Seller Central reports), Bigcommerce, Zoho, Shopify, SmugMug, Quickbooks, Salesforce, Airtable, etc.

🚀 Senior Software Engineer & ML Specialist With 7+ years building production systems, I deliver high-performance, scalable software and modern AI solutions. I work across enterprise-grade C#/.NET orchestration, Python-based ML/LLM pipelines, and JavaScript ecosystems—React, Next.js, and Node.js—for end-to-end applications. 🔧 Software Development & Architecture - C# & .NET – enterprise apps, microservices, distributed orchestration - React / Next.js / Node.js – full-stack web apps, API routes, serverless backends - System Orchestration – workflow management, job queues, task distribution - API Development – RESTful services, real-time communication, webhooks - High-Performance Computing – optimized algorithms, resource-efficient pipelines 🤖 Machine Learning & AI - LLMs – self-hosted models, OpenAI/Anthropic integrations, prompt engineering, RAG - Computer Vision – CNNs, object detection, image-processing pipelines - Deep Learning – PyTorch, TensorFlow, custom architectures - ML Pipelines – data ingestion, feature engineering, training, deployment automation 🛠 Technologies & Tools - Languages: C#, Python, JavaScript/TypeScript - Frameworks: FastAPI, Flask, Django, React, Next.js, Node.js, Vue 3, Express - Databases: PostgreSQL, MongoDB, Redis, vector stores (PGVector/FAISS) - Cloud & DevOps: Docker, Kubernetes, AWS, Netlify, Vercel - ML Stack: PyTorch, Pandas, NumPy, Scikit-learn, Transformers, llama.cpp 🤝 Communication & Collaboration - Clear documentation and code reviews - Mentorship for junior engineers, knowledge sharing - Client-focused delivery with regular updates and transparent timelines ⏰ Availability & Commitment - 35–40 hours per week, remote-first, flexible schedule - Quick turnaround on urgent issues - Open to long-term partnerships and ongoing support