Qbot is an open source quantitative research and trading platform that provides a full pipeline from data ingestion and strategy development to backtesting, simulation, and (optionally) live trading. It bundles a lightweight GUI client (built with wxPython) and a modular backend so researchers can iterate on strategies, run batch backtests, and validate ideas in a near-real simulated environment that models latency and slippage. The project places special emphasis on AI-driven strategies — including supervised learning, reinforcement learning and multi-factor models — and offers a “model zoo” and example strategies to help users get started. For evaluation and analysis, Qbot integrates reporting and visualization (tearsheets, metrics) so you can compare performance across runs and inspect trade-level behavior. It supports multiple strategy runtimes and backtesting engines, is organized for extensibility (strategies live in a dedicated folder).
Features
- GUI client (wxPython) for interactive strategy management and visualization
- Built-in simulation that models latency, slippage, and near-real execution conditions
- Support for AI/ML approaches (supervised models, reinforcement learning) with a model zoo
- End-to-end workflow
- Modular strategies directory and adapters to multiple backtest/trading engines
- Reporting and analysis tools (tearsheets, performance metrics, exportable results)
Project Samples
