Prediction Assistant
Vision
AI-powered trading assistant for Kalshi prediction markets. Uses automated strategies (watchdog scanning, portfolio stacking with Option D hedging) to generate consistent returns while bounding worst-case loss. The system deploys a 4-bot architecture — Pregame Stacker, Late-Game Lock, Bulk Sweep, and Edge Learner — driven by a watchdog fan-out pattern that scans markets and dispatches opportunities to specialized bots. Users connect their Kalshi API credentials through an iOS app at prediction-assistant.com and the system trades on their behalf — they watch results live in the official Kalshi app.
User Stories
| Key | Story Note | Role | Success Metric |
|---|---|---|---|
| portfolio-builder | <a href="story-prediction-assistant-portfolio-builder">AI Portfolio Builder</a> | Trader (Lucas) | Positive EV across 100+ trades, worst-case loss under 6% |
| watchdog-trading | <a href="story-prediction-assistant-watchdog-trading">Watchdog Trading</a> | Trader (Lucas) | 85%+ win rate, $0.10–$0.15 avg profit per contract |
| app-experience | <a href="story-prediction-assistant-app-experience">App Experience</a> | Consumer | First automated trade within 5 min of connecting credentials |
| credential-onboarding | <a href="story-prediction-assistant-credential-onboarding">Credential Onboarding</a> | Consumer | 90% of users complete setup without support |
| platform-setup | <a href="story-prediction-assistant-platform-setup">Platform Setup</a> | Developer (Lucas) | CI/CD push-to-deploy in under 10 minutes |
| landing-page | <a href="story-prediction-assistant-landing-page">Landing Page & Registration</a> | Consumer | 80% of visitors who start registration complete it |
| bot-marketplace | <a href="story-prediction-assistant-bot-marketplace">Bot Marketplace</a> | Consumer | User activates first bot within 2 minutes of login |
Architecture
- Domain Model — User, Credential, Strategy, Portfolio, Trade, Market entities
- Data Flow — watchdog scan loop, position monitoring, user auth flows
- Deployment — k3s + Tailscale Funnel + Hetzner edge + Caddy for prediction-assistant.com
- Rails — Rails 8 application architecture
- API — API layer design
- App — Application structure
- Postgres — Database schema and design
- Frontend — Frontend architecture
- Auth — Authentication and authorization
- Keycloak — Keycloak integration for prediction-assistant
Key decisions:
- Rails 8 with Solid Queue (no Sidekiq/Redis)
- Kalshi API auth: RSA-PSS SHA-256 per-request signatures
- Strategy polymorphism via strategy_type + jsonb parameters
- Three-repo model: pal-e-platform, pal-e-services, pal-e-deployments
Board
Status
Project setup complete. All 16 tickets reviewed and in todo across 4 sprints. Bot architecture established (4 bots on watchdog fan-out). Strategy docs, API integration, and bot specs documented. PR #19 merged with bot specs and user stories. PR #20 merged with Late-Game Lock math fix and Edge Learner synthetic parlay clarification. Rails scaffold and infrastructure provisioning are next (Sprint 1).
Milestones
- 2026-07-02 — Strategy research and API exploration complete
- 2026-07-03 — Bot architecture established, all tickets reviewed and moved to todo, PR #19 and #20 merged
Repos
| Repo | Platform | Role | Status |
|---|---|---|---|
| ldraney/kalshi-assistant | Forgejo | Rails app + docs | Active (docs only, Rails scaffold pending) |
| ldraney/kalshi-assistant-ios | Forgejo | Turbo Native iOS shell | Not yet created |