TrendLib: 325 technical-analysis indicators and patterns. Rust core, Python API, with batch and streaming results that agree bitwise.
LSTM-based cryptocurrency price trend predictor. Features: custom LSTM with attention, technical indicators, data preprocessing, configurable pipeline, and classification metrics. Forecasts short-term crypto trends (up/down/neutral) using historical data.
Автономный торговый агент для MOEX: сигналы на индикаторах, ML-фильтр на LightGBM, независимый риск-слой, LLM вне горячего пути
A modular quantitative finance research framework for factor-based alpha modeling, return prediction, portfolio optimization, and backtesting in Python.
Professional self-hosted Bybit signal trading bot for automated Spot and Futures trading with TradingView webhooks, PyDev Signal Engine, Telegram control, risk management and advanced SQLite trading analytics.
A two-stage ML framework combining return prediction and macro-driven gating to improve trading decisions under noisy financial signals.
A comparative analysis of MLP and CNN (GAF) models for time series forecasting on AAPL stock, exploring stationarity, log returns, and fractional differencing.
A hybrid Deep Learning framework (LSTM + FinBERT) that predicts S&P 500 intraday volatility by fusing high-frequency market data with real-time financial news sentiment.
Quantitative trading infrastructure for Polymarket: market discovery, microstructure signals, risk-gated execution, and a read-only monitoring dashboard. Paper trading only until validated.
Official Python client for the FXNewsBias API: sentiment scores for the 8 major currencies, 15 forex pairs, gold, silver, oil, crypto and US indices, as JSON.
A real-time monitoring dashboard for MetaTrader 5 algorithmic trading. Built with Python and Streamlit to visualize account equity, manage risk, and monitor 3-bar reversal patterns across 14+ currency and commodity markets.
AI-powered candlestick pattern trading model for NIFTY 500 stocks using XGBoost + LightGBM
A Deep Learning framework using LSTM-GANs to generate realistic, synthetic financial market data (S&P 500) for quantitative analysis, algorithmic trading, and risk management stress testing.
QuantForge: Options Intelligence AI
Deterministic event-driven backtesting engine in C++23 with reproducible market-data tooling and explicit domain-event architecture.
A Master Project implementing a Deep Reinforcement Learning (DDPG) agent for transaction-cost-aware option hedging. Features Behavioral Cloning for a "warm start" and is backtested on real-world SPY ETF data.
Institutional-grade event-driven quantitative trading framework for research, backtesting, optimization, analytics, live market infrastructure, and broker integration. Built with immutable architecture, deterministic execution, and strict typing.
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