How to Build a Prediction Market Trading Bot with SimpleFunctions CLI

## What We're Building A trading bot that: 1. Maintains a macro thesis (e.g., "US recession by end of 2026") 2. Continuously scans Kalshi for contracts where market price diverges from thesis-implied value 3. Alerts you (or trades automatically) when edges appear 4. Tracks P&L and adjusts based on new information This isn't a latency-sensitive HFT bot. Prediction markets reward information advantage, not speed. The bot's job is to systematically find and act on mispriced contracts. ## Prerequisites npm install -g @spfunctions/cli sf setup You'll need: - **SF API key** (free at [simplefunctions.dev/dashboard](https://simplefunctions.dev/dashboard)) - **Kalshi API key** with read+write permissions - **OpenRouter API key** (for the agent's LLM — used for evaluation, not trading logic) `sf setup` walks you through all of these with real-time validation. ## Step 1: Create Your Thesis sf create "US enters a recession by end of 2026 — tariffs drive supply chain costs, inflation stays sticky, Fed can't cut fast enough" This takes ~30 seconds. The engine: - Decomposes your thesis into a **causal tree** of testable sub-claims - Scans Kalshi and Polymarket for contracts matching each node - Calculates **edges** — where market price ≠ thesis-implied price - Sets initial confidence ## Step 2: Monitor with the Heartbeat The heartbeat engine runs server-side every 15 minutes: # Check what the heartbeat found sf context # See top edges across all theses sf edges # Detailed evaluation history sf feed --hours 24 You don't need to build a cron job. The server handles news scanning, price refreshing, and milestone detection automatically. ## Step 3: The Trading Loop For manual trading with bot-like efficiency: # See what's actionable sf edges --json | jq '.[] | select(.executableEdge > 10)' # Check liquidity before entering sf liquidity --topic oil # Place an order sf buy KXRECSSNBER-26 100 --price 32 For agent-assisted trading: sf agent > analyze my top edges and suggest trades > buy 100 contracts of the recession market at 32 cents The agent has 21+ tools including `place_order` and `cancel_order`. Every trade shows a preview and requires confirmation. ## Step 4: Scenario Analysis Before committing capital, test scenarios: # What if the war ends? sf whatif --set "n1=0.1" --set "n3=0.2" # What if oil hits ? sf whatif --set "n3.3=0.95" This is pure computation — zero LLM cost, instant results. It shows you how your edges change under different scenarios. ## Step 5: Monitor from Anywhere # Terminal dashboard (Bloomberg-style) sf dashboard # Telegram bot (runs in background) sf telegram --token YOUR_BOT_TOKEN --daemon # Track performance over time sf performance The Telegram bot pushes alerts when confidence changes >2% and responds to natural language queries. ## Architecture Your thesis ↓ Causal tree decomposition (LLM, one-time) ↓ Market scan: Kalshi + Polymarket ↓ Edge detection: thesis price vs market price ↓ Heartbeat (every 15 min): - News scan (Tavily) - Price refresh (Kalshi orderbook) - Milestone check (economic calendar) - LLM evaluation → confidence update ↓ You: read context → decide → trade The bot isn't making trading decisions — it's maintaining an up-to-date view of where markets disagree with your thesis. You decide when to act. ## Cost - **SF API**: Free tier covers 15M tokens/month - **OpenRouter**: ~/bin/zsh.05 per evaluation (Claude Sonnet) - **Tavily news**: Smart scheduling keeps this under /thesis/month - **Kalshi**: Standard exchange fees Total running cost for one active thesis: roughly -30/month. For the complete API reference, see [simplefunctions.dev/docs](https://simplefunctions.dev/docs).