Kalshi Fed Data Reaction Trader

v1.0.3

Trades Fed rate markets on Kalshi based on macro data releases (CPI, jobs). Scans CPI bin markets for implied CPI, adjusts rate cut probabilities using data...

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by@diagnostikon

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for diagnostikon/kalshi-fed-data-reaction-trader.

Previewing Install & Setup.

Prompt PreviewInstall & Setup

Install the skill "Kalshi Fed Data Reaction Trader" (diagnostikon/kalshi-fed-data-reaction-trader) from ClawHub.
Skill page: https://clawhub.ai/diagnostikon/kalshi-fed-data-reaction-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-data-reaction-trader

ClawHub CLI

Package manager switcher

npx clawhub@latest install kalshi-fed-data-reaction-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

View report →

OpenClaw

Benign

high confidence

✓

Purpose & Capability

Name/description (trade Kalshi rate markets based on CPI/jobs) match the actual code and declared requirements: the skill uses simmer-sdk to discover/execute markets and requires SIMMER_API_KEY and SOLANA_PRIVATE_KEY for live trading. Requested dependencies (simmer-sdk) are proportionate to the purpose.

ℹ

Instruction Scope

SKILL.md and trader.py focus on market discovery, computing implied CPI, and trading logic. One small inconsistency: frontmatter lists only SIMMER_API_KEY but the Installation & Setup and clawhub.json also require SOLANA_PRIVATE_KEY. The code may read/write its own skill config via simmer_sdk skill helpers (load_config/update_config/get_config_path) which is expected for tunables and state.

✓

Install Mechanism

No arbitrary downloads or extract operations. Dependencies are pulled from PyPI (simmer-sdk) as declared in clawhub.json and SKILL.md; this is a standard packaging mechanism. The SKILL.md even advises auditing the PyPI package before supplying live credentials.

ℹ

Credentials

The skill requires two high-sensitivity values: SIMMER_API_KEY (API authority for Simmer) and SOLANA_PRIVATE_KEY (base58 private key used to sign live trades). Both are justified by the trading use case, but SOLANA_PRIVATE_KEY is a high-risk secret — providing it to a skill gives it full signing power for on-chain actions. The skill also reads optional envs like TRADING_VENUE and AUTOMATON_MAX not listed in frontmatter.

✓

Persistence & Privilege

always is false and autostart is false. clawhub.json marks automaton.managed with entrypoint trader.py (so the platform can run it when enabled), which is expected for a managed trading skill. The skill does not request elevated system-wide privileges or modify other skills' configs.

Assessment

This skill appears to be what it claims: a Simmer/Kalshi trading bot. Before enabling live mode, do the following: - Audit the simmer-sdk PyPI package (and its GitHub source) to confirm there are no backdoors or unexpected network endpoints. - Treat SOLANA_PRIVATE_KEY as highly sensitive: prefer a restricted signing key or a custody/workflow that limits fund exposure; never paste your main wallet key into third-party code unless you fully trust and audited it. - Start in dry-run mode (default) and monitor actions, logs, and any external network calls. Confirm the bot only contacts simmer-markets/Kalshi endpoints and expected services. - Be aware of the minor metadata inconsistency (SKILL.md frontmatter omits SOLANA_PRIVATE_KEY while other places require it); verify environment variable requirements before providing secrets. - Consider running the skill in an isolated environment (separate machine or container) and rotate keys after testing.

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

latestvk975sdyvbr0ms2wfpkwvdtdg9h84ay69

98downloads

0stars

4versions

Updated 3w ago

v1.0.3

MIT-0

Kalshi Fed Data Reaction Trader

This is a template. The default signal uses static data sensitivity coefficients -- remix it with live BLS data feeds, real-time CPI nowcasts, or Fed funds futures reactions. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

After CPI/jobs data releases, Fed rate probabilities adjust predictably. This skill scans Kalshi CPI bin markets to compute the market-implied CPI, classifies the data regime (high CPI, low CPI, neutral), and adjusts the fair probability of a rate cut accordingly. When the adjustment creates a gap vs. rate cut market prices, it trades.

Key advantages:

Data-driven -- uses market-implied CPI from Kalshi's own CPI bin markets
Predictable reaction function -- high CPI is hawkish, low CPI is dovish
Cross-market information -- extracts signal from CPI markets to trade rate markets

Signal Logic

Data Sensitivity Model

Scan CPI bin markets to compute probability-weighted implied CPI
Classify regime: high_cpi (>3.5%), low_cpi (<2.5%), or neutral
Apply sensitivity shift to baseline cut probability (50%)
Compare adjusted fair probability to rate cut market prices
Trade when |fair - market| >= entry_edge

Sensitivity Coefficients

Regime	Cut Probability Shift
High CPI	-15% (hawkish)
Low CPI	+10% (dovish)
Strong jobs	-10% (hawkish)
Weak jobs	+15% (dovish)

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-data-reaction-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_DATA_ENTRY_EDGE`	`0.10`	Min divergence to trigger trade
`SIMMER_FED_DATA_EXIT_THRESHOLD`	`0.45`	Sell position when price reaches this level
`SIMMER_FED_DATA_MAX_POSITION_USD`	`5.00`	Max USDC per trade
`SIMMER_FED_DATA_MAX_TRADES_PER_RUN`	`3`	Max trades per execution cycle
`SIMMER_FED_DATA_SLIPPAGE_MAX`	`0.15`	Max slippage before skipping trade
`SIMMER_FED_DATA_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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