Prediction Market Terminal Dashboard: Bloomberg-Style Monitoring for Kalshi Traders

# Prediction Market Terminal Dashboard: Bloomberg-Style Monitoring for Kalshi Traders Professional equity traders don't watch prices in a browser tab. They run terminals. Multi-pane displays with live order books, position Greeks, news feeds, and alert triggers — all updating in real time, all keyboard-navigable. If you're trading Kalshi seriously, you need the same infrastructure mindset applied to prediction markets. This post covers how to build and operate a Bloomberg-style monitoring setup for Kalshi, including what to watch, how to structure your terminal layout, and which tooling actually holds up under active trading conditions. --- ## Why a Terminal Dashboard Beats a Browser UI for Prediction Markets Kalshi's web interface is fine for casual browsing. It's not fine for: - **Scanning across 50+ markets simultaneously** to catch mispriced contracts before they correct - **Running thesis-based logic** (e.g., "if CPI prints above 3.5%, fade the Fed rate cut markets") - **Position monitoring with P&L attribution** across correlated contracts - **Automated alerting** when a market moves more than X basis points from your entry Browser UIs are stateless, click-heavy, and don't compose with the rest of your toolchain. A terminal dashboard is a first-class part of your workflow — it pipes into shell scripts, feeds into notebooks, and sits alongside your data sources without context-switching overhead. --- ## The Core Panes of a Prediction Market Terminal A well-structured trading terminal for Kalshi typically has four functional zones: ### 1. Market Scanner A continuously refreshing view of markets you care about, sorted by criteria like volume, spread, or distance from your fair-value estimate. You want to see ticker, current yes/no price, 24h volume, and time to resolution. ### 2. Position Monitor Open positions with entry price, current price, unrealized P&L, and any active orders. For a macro trader running 10–20 positions, this needs to be compact — one line per position, color-coded by direction. ### 3. Thesis Feed This is what separates prediction market trading from dumb price-following. You need a pane that surfaces the reasoning behind each position and flags when external data (Fed statement, economic print, news headline) invalidates that thesis. ### 4. Alert Log Triggered alerts with timestamps: price thresholds crossed, volume spikes, resolution events, and fill confirmations. --- ## Setting Up the CLI Layer with SimpleFunctions [SimpleFunctions](https://simplefunctions.dev) is an agentic runtime built specifically for prediction market trading. It provides a CLI that maps cleanly onto the four panes above, plus an MCP server and REST API for more complex integrations. Install the CLI globally: npm install -g @spfunctions/cli Authenticate with your Kalshi credentials: sf auth login From here, you have four primary commands that anchor the terminal setup: `sf scan`, `sf agent`, `sf positions`, and `sf dashboard`. --- ## Building the Market Scanner Pane `sf scan` is your market discovery and monitoring command. It queries Kalshi markets and applies filters you define — by series, category, volume floor, or spread threshold. Scan all active Fed decision markets: sf scan --series KXFEDDECISION --sort volume --limit 20 Scan energy markets with a minimum daily volume of 10,000 contracts: sf scan --series KXWTIMAX --min-volume 10000 Scan NBER recession markets and output to JSON for piping into other tools: sf scan --series KXRECSSNBER --format json | jq '.markets[] | {ticker, yes_price, volume}' For a live-refreshing scanner pane in a tmux split, wrap it in a watch loop: watch -n 5 'sf scan --series KXFEDDECISION --sort spread' This gives you a 5-second refresh rate on Fed rate decision markets — enough granularity to catch fast-moving repricing without hammering the API. **Practical example**: KXFEDDECISION-25DEC-T2.00 (December 2025, target rate at 2.00%) might sit at 12 cents yes when your macro model puts the probability at 8%. That 4-cent discrepancy is what the scanner is designed to surface before the market corrects it. --- ## Position Monitoring with `sf positions` The positions pane is straightforward but critical. `sf positions` pulls your current open contracts from Kalshi and displays them with live mark-to-market pricing. sf positions --format table Example output: TICKER SIDE QTY ENTRY CURRENT PNL KXFEDDECISION-25DEC-T2.00 YES 500 0.12 0.09 -15.00 KXWTIMAX-25JUL-B75 NO 200 0.61 0.58 6.00 KXRECSSNBER-25Q3 YES 300 0.31 0.35 +12.00 For traders running correlated positions — say, short KXFEDDECISION rate cuts AND long KXRECSSNBER recession probability — you want to see these grouped by macro thesis, not just sorted alphabetically. The `--group-by` flag handles this: sf positions --group-by tag --tags fed,recession,energy You apply tags when you open positions through the agent, which we'll cover next. For continuous position monitoring in a terminal pane: watch -n 10 'sf positions --format table --show-pnl' Combine this with a tmux layout and you have the equity equivalent of a live blotter. --- ## Thesis-Driven Agents: The Real Edge This is where SimpleFunctions diverges from simple API wrappers. `sf agent` lets you run natural language thesis statements that the agent translates into market monitoring logic and, optionally, trade execution. A thesis-driven agent for Fed policy: sf agent run --thesis "Fed holds rates through Q3 2025 given sticky services inflation; fade any KXFEDDECISION markets pricing >20% probability of cuts before September" --markets KXFEDDECISION --mode monitor In monitor mode, the agent watches matching markets and fires alerts when the thesis is challenged — either by market price movement or by external data you pipe in. Switch to execute mode to enable auto-trading within position size limits you set: sf agent run \ --thesis "WTI crude stays below $80 through Q3 as demand slowdown offsets OPEC cuts" \ --markets KXWTIMAX \ --mode execute \ --max-position 1000 \ --confidence-threshold 0.65 The agent evaluates KXWTIMAX contracts against your thesis and executes when its confidence that the thesis applies to a specific contract exceeds 65%. It tags all resulting positions with the thesis ID, which feeds back into your grouped position view. The full agent documentation is at [simplefunctions.dev/docs](https://simplefunctions.dev/docs) — particularly worth reading is the section on confidence calibration and thesis invalidation triggers. --- ## The `sf dashboard` Command: Single-Pane View For traders who want everything in one terminal window rather than a tmux layout, `sf dashboard` renders a multi-section TUI (terminal UI) with scanner, positions, alerts, and agent status in a single interactive view. sf dashboard --watchlist KXFEDDECISION,KXWTIMAX,KXRECSSNBER --refresh 10 Keyboard shortcuts: - `s` — jump to scanner pane - `p` — jump to positions pane - `a` — view alert log - `t` — view active theses - `q` — quit The dashboard pulls data from the same underlying API as the individual commands, so there's no latency difference — it's purely a rendering convenience. For servers or headless environments, the individual commands with JSON output are more composable. --- ## tmux Layout for a Professional Kalshi Terminal Here's a practical tmux configuration for a four-pane setup: #!/bin/bash # kalshi-terminal.sh — launch a Bloomberg-style Kalshi trading terminal tmux new-session -d -s kalshi -x 220 -y 50 # Pane 1 (top-left): Market Scanner tmux send-keys -t kalshi "watch -n 5 'sf scan --series KXFEDDECISION,KXWTIMAX,KXRECSSNBER --sort volume --limit 30'" Enter # Pane 2 (top-right): Positions tmux split-window -h -t kalshi tmux send-keys -t kalshi "watch -n 10 'sf positions --format table --show-pnl --group-by tag'" Enter # Pane 3 (bottom-left): Agent Status tmux split-window -v -t kalshi:0.0 tmux send-keys -t kalshi "sf agent status --follow" Enter # Pane 4 (bottom-right): Alert Log tmux split-window -v -t kalshi:0.2 tmux send-keys -t kalshi "sf alerts --follow --format compact" Enter tmux attach-session -t kalshi Save as `kalshi-terminal.sh`, `chmod +x` it, and run it at session start. This gives you the equivalent of four Bloomberg panes, each auto-refreshing independently. --- ## Integrating External Data Feeds Prediction market alpha often comes from information asymmetry — you have access to data or analytical frameworks that the crowd doesn't. The terminal setup needs to accommodate external data inputs. **Piping Fed data into the agent:** # Pull latest Fed funds futures implied rate from CME (example using a local scraper) python3 get_ff_futures.py | sf agent inject --thesis-id fed-q3-hold When you inject external data into an active thesis, the agent re-evaluates its confidence scores and may trigger new alerts or rebalance recommendations. **Webhook alerts via Telegram:** SimpleFunctions includes a Telegram bot integration for mobile alerts when you're away from the terminal: sf alerts configure --telegram --channel @your_trading_bot sf alerts set-trigger --market KXWTIMAX --threshold 0.05 --direction both This pushes a Telegram message if any KXWTIMAX contract moves more than 5 cents in either direction — useful for overnight monitoring without keeping the terminal open. --- ## Key Markets to Watch on Kalshi For traders building a macro-focused watchlist, these series are the highest-liquidity, most institutionally-interesting contracts on Kalshi: **Monetary Policy** - `KXFEDDECISION` — Fed funds target rate decisions, with granular strike prices per FOMC meeting - Multiple expiries let you build a full implied Fed path **Macroeconomic Events** - `KXRECSSNBER` — NBER recession call probability by quarter; one of the few liquid instruments for recession hedging outside of fixed income - Slow-moving but meaningful for portfolio-level risk management **Commodities** - `KXWTIMAX` — WTI crude oil monthly maximum price; useful for expressing range-bound views on oil without futures margin - Correlates with broader energy equity risk For the complete list of active series and their resolution rules, see the [SimpleFunctions market guide](https://simplefunctions.dev/docs/guide) which maintains an updated reference alongside the API documentation. --- ## Latency and Rate Limit Considerations A few practical notes on running a high-refresh terminal setup: - Kalshi's API has rate limits that vary by endpoint. Scanner endpoints are more permissive than order endpoints. The SF CLI handles backoff automatically, but if you're running multiple watch loops simultaneously, stagger their refresh intervals. - `sf scan` with `--format json` and `--cache 30` will serve cached results for 30 seconds, reducing API calls while maintaining reasonable freshness for scanner panes. - For true real-time data (sub-second), use the WebSocket feed via the MCP server rather than polling CLI commands. # Start the MCP server for WebSocket-based real-time feeds sf mcp start --markets KXFEDDECISION,KXWTIMAX --port 3001 Connect your own tooling to `ws://localhost:3001/stream` for tick-level market data. --- ## Putting It Together: A Day in the Kalshi Terminal A realistic workflow for an active macro trader using this setup: 1. **Morning**: `sf dashboard` for market overview. Check overnight volume, resolution events, any large price moves. 2. **Pre-market**: Run `sf scan --series KXFEDDECISION --sort spread` to identify markets with unusually wide spreads that may indicate uncertainty around a coming data release. 3. **Data release (e.g., CPI)**: Agent is running in monitor mode against your Fed thesis. Post-print, you inject the data, agent reassesses, surfaces specific contracts to trade. 4. **Execution**: Switch agent to execute mode for specific tickers, with position limits set. 5. **Throughout day**: Position pane auto-refreshes every 10 seconds. Telegram alerts fire if anything moves significantly. 6. **EOD**: `sf positions --export csv` for reconciliation and record-keeping. This is the operational pattern that makes prediction market trading feel less like gambling on a website and more like running a systematic macro book. --- ## Conclusion The infrastructure gap between equities trading and prediction market trading is real, but it's closeable. The primitives — scanners, position monitors, thesis agents, alert systems — all translate directly. The missing piece has been tooling that treats prediction markets as a first-class trading venue rather than an afterthought. The CLI-first approach outlined here composes naturally with the rest of a developer-trader's workflow: shell scripts, Python notebooks, CI pipelines, and monitoring infrastructure. Whether you run the full tmux dashboard or just drop `sf positions` into an existing terminal workflow, the goal is the same: reduce cognitive overhead and increase the signal-to-noise ratio of information you're acting on. For the complete CLI reference and agent configuration options, the [SimpleFunctions documentation](https://simplefunctions.dev/docs) covers all available flags, authentication methods, and integration patterns in detail.