An automated trading agent that executes Kalshi Fed rate market positions by analyzing market-implied CPI signals and macro data sensitivity.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install kalshi-fed-data-reaction-trader
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install kalshi-fed-data-reaction-trader using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Kalshi Fed Data Reaction Trader?
The Kalshi Fed Data Reaction Trader is a sophisticated Openclaw Skills implementation designed to capture alpha during high-impact macro economic events. By scanning Kalshi CPI bin markets, the skill extracts market-implied inflation expectations and compares them against current Fed rate cut probabilities. This cross-market analysis allows the agent to identify pricing gaps and execute trades based on predictable hawkish or dovish reactions to CPI and employment data.
This skill acts as a robust technical framework, handling the complex plumbing of market discovery, risk safeguards, and trade execution. Built on the simmer-sdk, it provides developers with a modular template that can be remixed with real-time BLS data feeds or Fed funds futures to create a highly customized macro trading bot.
Kalshi Fed Data Reaction Trader Use Cases
- Automating trades in Kalshi Fed rate markets immediately following Bureau of Labor Statistics (BLS) data releases.
- Hedging macro economic risk by utilizing market-implied CPI signals from bin markets.
- Executing conviction-based position sizing based on the divergence between fair probability and market price.
- Arbitraging information gaps between inflation markets and interest rate markets.
How Kalshi Fed Data Reaction Trader Works
- The agent scans Kalshi CPI bin markets to calculate a probability-weighted implied CPI value.
- It classifies the current economic regime into high CPI, low CPI, or neutral categories based on predefined thresholds.
- A data sensitivity model applies probability shifts (e.g., -15% for high CPI) to a baseline 50% rate cut probability.
- The skill compares this newly calculated fair probability against the live market prices on Kalshi.
- If the divergence (edge) exceeds the user-defined threshold, the agent calculates conviction-based sizing.
- The trade is executed via the simmer-sdk, adhering to strict slippage and liquidity safeguards.
Kalshi Fed Data Reaction Trader Setup
To install and configure the Kalshi Fed Data Reaction Trader, follow these steps using Openclaw Skills:
# Install the skill from the hub
clawhub install kalshi-fed-data-reaction-trader
# Set required environment variables
export SIMMER_API_KEY="your_api_key_here"
export SOLANA_PRIVATE_KEY="your_base58_private_key"
# Run in dry-run mode to verify logic
python trader.py
# Execute live trades on Kalshi
python trader.py --live
Kalshi Fed Data Reaction Trader Data Schema & Taxonomy
The skill organizes trading logic and risk parameters through a structured environment variable taxonomy and internal sensitivity tables:
| Data Component | Format | Description |
|---|---|---|
| Regime Classification | String | Categorizes data as high_cpi, low_cpi, or neutral. |
| Sensitivity Coefficients | Table | Mapping of macro regimes to probability shifts (e.g., strong jobs = -10%). |
| Conviction Metric | Float | Calculated as `min( |
| Execution Logs | Metadata | Tracks max trades per run, slippage, and liquidity filters. |
Kalshi Fed Data Reaction Trader Advanced Features
- Multi-market signal extraction using Kalshi bin markets for real-time inflation nowcasting.
- Conviction-based position sizing that scales trade volume based on the statistical edge identified.
- Native support for DFlow execution via Solana for low-latency live trading.
- Customizable risk parameters including entry edge, exit thresholds, and max slippage per execution cycle.
- Modular architecture allowing for the integration of external BLS data feeds or real-time macro-economic APIs.
SKILL.md
Loading
Related Openclaw Skills
Kalshi Fed Speech Signal Trader
An automated sentiment analysis and execution engine for trading Federal Reserve interest rate prediction markets on Kalshi.
Kalshi Fed Dot Plot Trader
An automated trading skill that executes Fed rate market trades on Kalshi by comparing current market prices to FOMC dot plot projections.
Fudan University
A specialized AI agent skill providing deep knowledge about Fudan University academics, history, and research capabilities.
MeetMatch Sales Coach
An AI-driven sales coaching integration that provides personalized morning briefings and predictive performance insights for sales teams.
Kalshi Fed Futures Arb Trader
An automated trading skill that captures alpha by identifying pricing gaps between institutional CME FedWatch futures and Kalshi prediction markets.
Documentation Organization
A systematic framework for restructuring project documentation to optimize AI context usage and prevent information drift.
Featured*
