Kalshi Fed Speech Signal Trader

v1.0.2

Trades Fed rate markets on Kalshi based on hawkish/dovish sentiment signals from market question text. Scores net sentiment from keyword dictionaries and adj...

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by@diagnostikon

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Prompt PreviewInstall & Setup

Install the skill "Kalshi Fed Speech Signal Trader" (diagnostikon/kalshi-fed-speech-signal-trader) from ClawHub.
Skill page: https://clawhub.ai/diagnostikon/kalshi-fed-speech-signal-trader
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install kalshi-fed-speech-signal-trader

ClawHub CLI

Package manager switcher

npx clawhub@latest install kalshi-fed-speech-signal-trader

Security Scan

Capability signals

CryptoRequires wallet

These labels describe what authority the skill may exercise. They are separate from suspicious or malicious moderation verdicts.

VirusTotal

Benign

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OpenClaw

Benign

high confidence

✓

Purpose & Capability

Name/description (Kalshi Fed Speech Signal Trader) match the code and SKILL.md: the code discovers Kalshi markets (via the Simmer SDK), scores sentiment from market question text, and executes trades. The only declared external dependencies (simmer-sdk and trading keys) align with the trading purpose.

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Instruction Scope

SKILL.md and trader.py stay within trading-related actions (market discovery, sentiment scoring, trade execution). Minor inconsistencies: the SKILL.md top metadata lists only SIMMER_API_KEY while later sections and clawhub.json also require SOLANA_PRIVATE_KEY; trader.py prints errors and exits if SIMMER_API_KEY is missing. The code uses a direct internal client call (client._request) in addition to higher-level methods, which is an implementation detail but worth auditing.

✓

Install Mechanism

This is an instruction-only skill with a normal PyPI dependency (simmer-sdk). There are no downloads from arbitrary URLs, no archive extraction, and no unusual install steps in the files provided.

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Credentials

The skill requests only SIMMER_API_KEY and SOLANA_PRIVATE_KEY (and some optional behavior controlled by env tunables). Those credentials are directly relevant: SIMMER_API_KEY for the Simmer API and a Solana private key for live Solana/DFlow trades. Because SOLANA_PRIVATE_KEY is high-value, the SKILL.md appropriately warns 'treat as a high-value credential'—you should audit the code and the simmer-sdk before providing it.

✓

Persistence & Privilege

The skill is not forced always-on (always: false) and autostart/cron are not enabled by default. clawhub.json marks the automaton entrypoint as managed, but autostart is false. The skill does not request system-wide settings or other skills' credentials.

Assessment

This package appears to do what it says: discover Kalshi Fed rate markets, score sentiment from market text, and trade via the Simmer SDK. Before enabling live trading: (1) keep the skill in dry-run mode and confirm behavior/output; (2) review the simmer-sdk PyPI/GitHub source to ensure the SDK doesn't exfiltrate secrets; (3) do not supply your SOLANA_PRIVATE_KEY until you trust the SDK and code—use a low-value/test key or limited-fund account first; (4) note the small documentation inconsistency (SKILL.md top metadata vs later sections) and confirm how the private key is read/used in the remainder of trader.py; (5) monitor network traffic when running with a test key to catch unexpected endpoints.

Like a lobster shell, security has layers — review code before you run it.

latestvk977ycvqxdwz5z4ak6en9z9j3584an0r

100downloads

0stars

3versions

Updated 3w ago

v1.0.2

MIT-0

Kalshi Fed Speech Signal Trader

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

Scan all Fed rate market questions for hawkish/dovish keywords
Weight matches (some keywords stronger signals than others)
Compute net sentiment: dovish_total - hawkish_total
Adjust baseline cut probability by 5% per net unit
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

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: hello@simmer.markets

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

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