Practical overview
When it fits
Use Homerun when prediction-market work requires code-level strategy control, custom signal ingestion and a path from historical testing or shadow simulation to paper or live execution.
Who it is for
- Python developers building prediction-market strategies
- Researchers testing strategies against historical order-book data
- Traders evaluating paper or shadow execution before live trading
- Teams connecting external signals to a self-hosted trading dashboard
Documented uses
- Write custom prediction-market strategies in Python
- Connect RSS, REST, WebSocket or script-based signals
- Replay historical level-two order-book data for backtesting
- Simulate fills in shadow mode
- Promote strategies to paper or live execution on Polymarket and Kalshi
Requirements & limits
The repository is public and identifies the project as open source under the AGPL-3.0 license. It documents Python 3.10+ and deployment options including web, desktop operating systems, Docker and self-hosted server environments.
- Full asynchronous Python classes are used for strategy logic rather than a restricted expression or YAML format.
- Historical testing includes level-two order-book replay, parameter sweeps and walk-forward analysis.
- Signal inputs can be built from RSS, REST APIs, WebSockets or custom Python sources.
- Polymarket and Kalshi are presented through a unified execution pipeline.
- Shadow mode uses a microstructure-aware fill simulator, but simulated results are not live-performance guarantees.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Connect a custom signal to a Homerun strategy
Use an RSS feed, REST API or script as an input to a prediction-market strategy.
Before you start
- A Homerun strategy written in Python.
- A signal source such as RSS, a REST API or a custom script.
Steps
Create or use a Python data source for the selected signal.
Connect the source to a strategy through an event-driven data-source update subscription or a direct strategy SDK read.
Use the signal as an input to the strategy's detection or evaluation logic.
Expected outcome
The signal is available to the strategy for prediction-market decision-making.
Guide limitations
- The repository identifies supported signal types and connection patterns but does not document configuration details for every integration.
Evaluate a strategy in shadow mode
Assess simulated strategy execution with Homerun's fill-simulation workflow before live trading.
Before you start
- A Python strategy configured for Homerun.
- Historical or live market data available to the application.
Steps
Run the strategy with shadow mode enabled.
Use the documented microstructure-aware fill simulator to model execution against market conditions.
Review the simulated behavior before considering the live execution mode.
Expected outcome
The strategy produces shadow-mode execution results for evaluation against simulated fills.
Guide limitations
- Shadow results are simulations and do not provide performance guarantees or prove live-market compatibility.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 2 October 2026
- Original website title
- braedonsaunders/homerun · Official homepage title · retained in its source language