Resources & Documentation
Everything you need to understand and use OpenSignal effectively
Getting Started Guide
Step-by-step guide to install, authenticate, and start analyzing charts.
API Documentation
Complete API reference for the FastAPI backend and analysis pipeline.
Model Documentation
Details on our ONNX models, training data, and accuracy metrics.
Source Code
Open-source repository with full access to training and inference code.
Frequently Asked Questions
Get answers to common questions
What models are used for chart analysis?
We use custom YOLOv5 and ResNet models for candlestick detection, liquidity sweep identification, and structure recognition. All models are trained on synthetic datasets and exported to ONNX format for efficient inference.
Is my data safe with OpenSignal?
Yes! OpenSignal is decentralized and Nostr-native. Your private key (nsec) never leaves your device. Screenshots are processed locally, and signals are published directly to Nostr relays.
How accurate are the AI predictions?
Our models achieve 90%+ accuracy on synthetic test sets. Real-world performance depends on chart quality, timeframe, and market conditions. We recommend using signals alongside your own analysis.
Can I use this on desktop?
Yes! OpenSignal is built with Kotlin Multiplatform, so we provide both Android and Desktop versions. The desktop app has the same features and UI.
Is there an API for integrations?
The FastAPI backend can be deployed separately. See our API documentation for endpoints, authentication, and example requests.
How do I publish signals to Nostr?
After analyzing a chart, tap "Publish Signal" to generate a Nostr event and send it to your configured relays. You need a Nostr account (public key) to publish.
Technical Stack
Built on proven, modern technologies
Frontend
- Kotlin Multiplatform
- Jetpack Compose
- Android + Desktop
Backend
- FastAPI (Python)
- ONNX Runtime
- OpenCV
Protocols
- Nostr Protocol
- NIP-96 (Blossom)
- NIP-07 (Auth)