Python backtesting engine built on NautilusTrader for end-to-end quant research: market data ingestion/validation, microstructure calibration, single-asset and stat-arb strategy creation, walk-forward optimization, analytics, portfolio-of-strategies backtesting, and portfolio weight optimization.

Research framework for evaluating systematic trading strategies using walk-forward optimization, statistical validation, and robustness testing in Python.

Automatic crypto futures trading bot using Bollinger Band Squeeze strategy. Includes backtesting, optimization, and live trading modules.

Institutional-grade quantitative trading framework for Binance, OKX, and BitMEX. Features a low-latency execution engine (CCXT-free), multi-strategy concurrency (Trend/Scalp/HV), and a rigorous Walk-Forward Optimization (WFO) pipeline.

This is my local algorithmic trading system

Can 2x Claude 4.6 Opus generate infinite money?

Intraday short-selling system for parabolic micro-cap reversals. Polars + Numba rules engine with SAC reinforcement learning position sizing.

Showcase a scalable, low-latency algorithmic trading framework for multi-exchange quantitative copy-trading with robust backtesting tools.

Analyzes how well walk-forward-optimized trading strategies maintain profitability when applied to new, unseen market data.

It's a Trading bot with my exclusive experience and Reinforcement Learning