An automated trading skill that executes Fed rate market trades on Kalshi by comparing current market prices to FOMC dot plot projections.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install kalshi-fed-dot-plot-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-dot-plot-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 Dot Plot Trader?
The Kalshi Fed Dot Plot Trader is a sophisticated financial automation tool designed for the Openclaw Skills ecosystem. It leverages the Federal Reserve's Summary of Economic Projections (SEP), specifically the dot plot median rate path, to establish a fair value for interest rate movements. By calculating the implied probability of rate cuts or hikes and comparing them against real-time market data from Kalshi, the skill identifies alpha-generating opportunities where market sentiment diverges from the Fed's own forecasts.
This skill serves as a robust template for developers looking to build mean-reversion strategies within prediction markets. It handles the heavy lifting of market discovery, execution plumbing, and risk management, allowing the user to focus on refining the signal logic. As part of the wider range of Openclaw Skills, it demonstrates how AI agents can interact with decentralized and centralized financial markets with precision.
Kalshi Fed Dot Plot Trader Use Cases
- Automating trades in Fed rate prediction markets when market prices disconnect from official FOMC projections.
- Developing mean-reversion financial agents that capitalize on quarterly SEP releases.
- Managing interest rate exposure through programmatic Kalshi order execution within the Openclaw Skills framework.
- Prototyping sophisticated trading alpha by remixing dot plot signals with live OIS-implied rates.
How Kalshi Fed Dot Plot Trader Works
- The skill loads the median rate path from the most recent FOMC dot plot release (SEP).
- It calculates the number of implied cuts or hikes from the current effective rate to each quarterly endpoint.
- A fair probability for the at least one cut scenario is generated for each specific meeting date based on the implied path.
- The agent fetches current market prices from Kalshi to identify any significant divergence from the calculated fair probability.
- If the edge exceeds the pre-defined entry threshold, the skill calculates position sizing based on conviction and executes the trade via the simmer-sdk.
Kalshi Fed Dot Plot Trader Setup
Install the skill using the following command:
clawhub install kalshi-fed-dot-plot-trader
To enable live trading, ensure your environment variables are configured in your shell or .env file:
export SIMMER_API_KEY="your_simmer_api_key"
export SOLANA_PRIVATE_KEY="your_solana_private_key"
By default, the skill runs in dry-run mode. To execute real trades on Kalshi, use the live flag:
python trader.py --live
Kalshi Fed Dot Plot Trader Data Schema & Taxonomy
| Component | Description | Metadata/Taxonomy |
|---|---|---|
| Signal Logic | FOMC Median vs Market | Probability comparison and edge calculation |
| Conviction Sizing | Dynamic position scaling | conviction = min( |
| Risk Parameters | Trade safeguards | Includes max slippage (15% default) and min liquidity |
| Environment | Credentials | Requires SIMMER_API_KEY and SOLANA_PRIVATE_KEY |
Kalshi Fed Dot Plot Trader Advanced Features
- Conviction-based position sizing that scales trade volume based on the strength of the statistical edge compared to the entry threshold.
- Built-in dry-run safety protocol that prevents unintended financial risk until explicitly overridden with the live flag.
- Customizable risk parameters including slippage caps, liquidity requirements, and maximum trades per execution cycle to prevent over-exposure.
- Seamless integration with the Simmer SDK for programmatic access to Kalshi via DFlow liquidity layers.
- Extensible signal architecture allowing users to integrate live SEP data, Fed funds futures, or OIS-implied rates into the Openclaw Skills environment.
SKILL.md
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