Trading · AI · Automation
Trader & Researcher Dmitry Maltsev
Trader-researcher and Python developer building analytical systems for COT positioning, options, volatility, natural gas and AI-powered market research.
Hands-on trading experience across options and digital assets. Research into market cycles and structure, disciplined risk management, and systematic strategy development and refinement.
Development of Telegram bots with aiogram, JSON and SQLite data pipelines, Docker deployment, API integrations, logging systems and reliable automation for data-intensive workflows.
Deployment of AI agents on VPS infrastructure, integration into business processes and workflow automation. Consulting, implementation and operational support for applied AI solutions.
Three horizons, one discipline. Weeks: institutional positioning. Days: options structure. Minutes: volume flow. A signal emerges where the layers align.
In one minute, the bot completes hours of manual research: it parses raw CFTC reports and turns them into a structured view of positioning. The data becomes clear market context: who is driving the move and where funds and hedgers stand.
The algorithm identifies the volatility curve in real time: contango, backwardation, IV crush, the current market regime and the matching playbook. GARCH models complete the volatility triangle and frame the regime ahead.
A professional modular options aggregator and visualizer with advanced analytics: Delta metrics, UOA, Premium Flow, Rolling ΔOI Timeline, Risk Reversal, Max Pain, IV/HV and Net GEX, with historical data stored in SQLite.
An automated signal system for natural gas markets (Henry Hub, EIA, CME), built around ensemble uncertainty analysis of ECMWF weather forecasts.
A minute-level layer tracking volume spikes versus baseline, flow in liquid instruments (NG, SILV, COCOA) and ETFs (BOIL/KOLD), calculated TAS windows and related events. The move is captured as it forms, not after it appears in a candle.
Advanced technical analysis with decision logic for key instruments: liquidity maps, levels with test history, VWAP/ATR context, stops and a ready if-then plan instead of a directional opinion.
Crowd sentiment from international platforms as a contrarian lens: bear/bull balance, volume z-scores and panic extremes. When the crowd becomes unanimous, it is time to examine the other side.
An assistant inside the Terminal that explains module signals and helps users learn and navigate the research library. It does not tell users where to enter; it reveals why the system reads the market the way it does.