Kalshi Fed Speech Signal Trader

Trades Fed rate markets on Kalshi based on hawkish/dovish sentiment signals from market question text. Scores net sentiment from keyword dictionaries and adjusts rate cut probabilities. Requires SIMMER_API_KEY and simmer-sdk.

This is a template. The default signal uses static keyword dictionaries -- remix it with NLP sentiment models, live Fed speech transcripts via FRED API, or real-time news feeds. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Fed speeches contain hawkish and dovish signals that predict rate decisions. This skill scores net sentiment from keyword matching on market question text, then adjusts the fair probability of a rate cut. When the adjustment creates a gap vs. rate cut market prices, it trades.

Key advantages:

• No external data needed -- extracts signal from market question text itself
• Extensible -- add new keywords, adjust weights, or plug in NLP models
• Cross-signal aggregation -- pools sentiment across all Fed-related markets

Signal Logic

Sentiment Scoring

1. Scan all Fed rate market questions for hawkish/dovish keywords
2. Weight matches (some keywords stronger signals than others)
3. Compute net sentiment: dovish_total - hawkish_total
4. Adjust baseline cut probability by 5% per net unit
5. Trade rate cut markets when |fair - market| >= entry_edge

Keyword Dictionaries

Hawkish (reduce cut probability): "inflation persistent", "tightening", "restrictive", "price stability", "higher for longer", etc.

Dovish (increase cut probability): "data dependent", "labor softening", "gradual", "balanced", "appropriate to reduce", etc.

Conviction-Based Sizing

• conviction = min(|edge| / entry_edge, 2.0) / 2.0
• size = max($1.00, conviction * MAX_POSITION_USD)
• Larger edge = larger position, capped at MAX_POSITION_USD

Risk Parameters

Parameter	Default	Notes
Entry edge	10%	Min fair-vs-market divergence to trade
Exit threshold	45%	Sell when position price reaches this
Max position size	$5.00 USDC	Per market
Max trades per run	3	Rate limiting
Max slippage	15%	Skip if slippage exceeds
Min liquidity	$0	Disabled by default

Installation & Setup

bash

clawhub install kalshi-fed-speech-signal-trader

Requires: SIMMER_API_KEY and SOLANA_PRIVATE_KEY environment variables.

Cron Schedule

Cron is set to null -- the skill does not run on a schedule until you configure it in the Simmer UI.

Safety & Execution Mode

The skill defaults to dry-run mode. Real trades only execute when --live is passed explicitly.

Scenario	Mode	Financial risk
python trader.py	Dry run	None
Cron / automaton	Dry run	None
python trader.py --live	Live (Kalshi via DFlow)	Real USDC

Required Credentials

Variable	Required	Notes
SIMMER_API_KEY	Yes	Trading authority. Treat as a high-value credential.
SOLANA_PRIVATE_KEY	Yes	Base58-encoded Solana private key for live trading.

Tunables (Risk Parameters)

Variable	Default	Purpose
SIMMER_FED_SPEECH_ENTRY_EDGE	0.10	Min divergence to trigger trade
SIMMER_FED_SPEECH_EXIT_THRESHOLD	0.45	Sell position when price reaches this level
SIMMER_FED_SPEECH_MAX_POSITION_USD	5.00	Max USDC per trade
SIMMER_FED_SPEECH_MAX_TRADES_PER_RUN	3	Max trades per execution cycle
SIMMER_FED_SPEECH_SLIPPAGE_MAX	0.15	Max slippage before skipping trade
SIMMER_FED_SPEECH_MIN_LIQUIDITY	0	Min market liquidity USD (0 = disabled)

Dependency

simmer-sdk is published on PyPI by Simmer Markets.

• PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk
• Publisher: [e-mail]

Review the source before providing live credentials if you require full auditability.