Polybot and Qbot replicate traders

Antonio Stano’s Polybot and UFund-Me’s Qbot are two open-source projects aimed at a similar bottleneck: turning trading ideas into repeatable systems. Polybot is built around Polymarket, the crypto prediction market, and packages ingestion, analytics, monitoring and execution into a microservices stack. Qbot is broader. It presents itself as an AI-oriented quantitative investment research platform that spans data handling, modeling, backtesting, simulation and trading. The overlap is less about asset class than workflow. Both repositories package the plumbing that usually slows research teams down — data collection, strategy testing, observability and deployment — into reusable templates. That makes them notable less as one-off bots than as examples of how small teams can assemble research and execution pipelines from open components. ### What exactly does Polybot ship? Polybot’s GitHub page describes it as an “open-source Polymarket trading infrastructure and strategy reverse-engineering toolkit.” The repository says the system handles automated execution in paper and live modes, strategy runtime and market making, market and user trade ingestion into ClickHouse, and “quantitative analysis and replication scoring.” Antonio Stano’s repository also lays out the architecture in concrete terms. (github.com) The project uses Java 21 microservices, a ClickHouse and Redpanda event pipeline, and a monitoring stack built with Grafana, Prometheus and Alertmanager, according to the README. The repo also points to a future layer called AWARE, which it describes as covering trader intelligence, fund mirroring and API and UI features. That design matters because prediction-market trading is not only about a signal. (github.com) A user trying to mimic successful wallets needs ingestion, storage, scoring, dashboards and a way to test before risking capital. Polybot’s paper and live modes are meant to cover that gap, based on the repository description. ### How is Qbot different from a single trading bot? UFund-Me’s Qbot positions itself as a broader quant research platform rather than a narrow strategy copier. (github.com) The GitHub page describes it as an “AI-powered Quantitative Investment Research Platform” and says it supports supervised learning, market-dynamics modeling and reinforcement learning. The repository’s structure points in the same direction. Qbot includes modules labeled for funds, futures and trading, alongside scripts, tests, monitoring and web components, and its public docs describe a workflow from data acquisition through strategy development, backtesting, simulation and optional live trading. (github.com) A secondary documentation source summarizing the repo says Qbot integrates frameworks including backtrader, vnpy, easyquant and quantstats. (github.com) The scale is also different. Qbot’s GitHub page showed about 17,300 stars and roughly 2,500 forks when fetched on May 24, 2026, versus about 651 stars and 130 forks for Polybot. That does not make the projects interchangeable, but it suggests Qbot has drawn a larger general developer audience. ### Why are people grouping these two repositories together? The common thread is reproducibility. Polybot packages a live-market replication engine around Polymarket data. (github.com) Qbot packages a conventional quant-research loop around machine-learning models and backtests. In both cases, the user is not starting from a blank notebook. The repository already includes the data path, the testing environment and at least some route toward deployment. That makes the projects useful as templates for prototyping. (github.com) A developer studying prediction markets can inspect Polybot’s ingestion and scoring stack. A researcher building factor models can start from Qbot’s modules for data, strategy and simulation. The codebases target different corners of trading, but both try to shorten the distance between an idea and a working system. ### What should a reader look at first in each repo? Polybot’s README points readers to its service layout, ClickHouse pipeline, monitoring stack and `research/` toolkit for snapshots, analysis and replication metrics. (github.com) Those are the files that show how the project moves from wallet activity on Polymarket to a scored strategy and then to paper or live execution. Qbot’s GitHub page points readers toward its online documentation, strategy modules and trading components. (github.com) The repository was updated about two months before May 24, 2026, and the docs remain the clearest starting point for understanding which parts are research tools, which are simulation layers and which are intended for real or simulated trading. (github.com)