This repository implements a comprehensive backtesting and live-trading framework for prediction markets (specifically Polymarket and Kalshi), built on a heavily customized NautilusTrader engine. It provides historical replay of L2 order book data from multiple vendors (Telonex, PMXT, Polymarket native), realistic execution modeling (fill models, fee models with maker rebate inference), and a library of trading strategies (e.g., EMA crossover, microprice imbalance, late favorite hold). The framework supports parameter search optimization (random grid, TPE via optuna), joint-portfolio multi-market backtests, and generates detailed HTML/CSV/JSON reports with equity curves, drawdown, and Brier advantage analysis. A Rust native extension accelerates data conversion (book diffing, payload sorting), and a CLI TUI (main.py) discovers and runs backtest entrypoints. While primarily an application framework, it also includes numerous one-off research scripts and Jupyter notebooks (e.g., BTC snapshot model walkforward), giving it a hybrid character: a production-quality backtesting infrastructure with a heavy research emphasis. The codebase is well-tested (50+ test files), documented, and follows clean engineering practices (config validation, structured event logging, concurrency patterns). However, it is tightly coupled to NautilusTrader internals, relies on several environment variables, and contains large monolithic loader classes (e.g., Telonex loader at ~3600 lines) that limit reusability outside its specific domain. Maintenance appears active (last commit 2026-05-16) with low technical drift, though the heavy dependency on NautilusTrader and optional Rust build are potential friction points.
OptionSuite is an options trading strategy backtesting framework implemented in Python. The system uses an event-driven architecture to simulate trading strategies by processing historical options data from CSV files. It provides modular components for option types, data handling, strategy definition, portfolio management, and risk management. The main entry point is backTester.py which orchestrates the backtesting session by initializing data handlers, portfolio managers, and strategy managers. The framework supports various options strategies including naked puts, put verticals, and strangles, with configurable risk management rules for position exits.