Kalshi Econ Fed Link Trader

Cross-market strategy that uses CPI bin prices to estimate CPI level, then adjusts Fed rate market positions via a sensitivity model. High CPI means Fed less...

Security Scan

The skill's declared requirements match a trading strategy (SIMMER_API_KEY + SOLANA_PRIVATE_KEY) and its instructions are mostly coherent, but the provided source was truncated and there are small documentation mismatches — review the full code and the simmer-sdk dependency before supplying live credentials.

Purpose & Capability

The skill's name/description (cross-market trading using CPI bin prices to trade Fed markets) align with its declared requirements: a Simmer API key and a Solana private key for live execution. The pip dependency (simmer-sdk) is expected for a Simmer-integrated trader.

Instruction Scope

SKILL.md and trader.py describe CPI bin parsing, sensitivity modeling, and trade execution; instructions are scoped to trading. However, front-matter in SKILL.md lists only SIMMER_API_KEY while other sections and clawhub.json require both SIMMER_API_KEY and SOLANA_PRIVATE_KEY (minor inconsistency). The code attempts to import an optional 'tradejournal' from either an external package or a local skills.tradejournal module — importing a 'skills' package could access other skill modules if present, so confirm what that module does. Also, the runtime file provided in the prompt was truncated; I could not inspect the remainder of trader.py to verify there are no network calls or hidden endpoints beyond expected trading APIs.

Install Mechanism

There is no external arbitrary download/install spec. clawhub.json declares a pip dependency on 'simmer-sdk', which matches the SKILL.md and code import. This is a normal install pattern for a Python SDK; review the simmer-sdk package source before using live credentials.

Credentials

Requested environment variables (SIMMER_API_KEY and SOLANA_PRIVATE_KEY) are proportionate to a trading skill and are explicitly documented. Both are high-value credentials (especially the Solana private key). The skill also reads AUTOMATON_MAX optionally; no unrelated secrets or broad platform credentials are requested. The documentation inconsistency about required envs is worth fixing before install.

Persistence & Privilege

The skill is not marked 'always: true' and automaton.autostart is false. It declares an entrypoint for managed automaton execution but won't autostart on install. Autonomous invocation by the agent is allowed (platform default) but not elevated here.

Scan Findings in Context

[pre-scan-injection] expected: Static pre-scan reported no injection signals. That matches expectations for a trading skill but does not substitute for a full code audit.

What to consider before installing

What to consider before installing: - Do not provide your real SOLANA_PRIVATE_KEY or SIMMER_API_KEY until you audit the full code and the simmer-sdk dependency. The Solana private key is high-value and can transfer funds. - The prompt-provided trader.py was truncated; ensure you (or someone you trust) review the entire trader.py and any code in simmer-sdk for network endpoints, telemetry, or credential exfiltration before enabling live trading. - Use the default dry-run mode first (the skill states live trades only run with --live). Test thoroughly with paper/dry-run and small sandbox credentials or accounts. - If you decide to go live: limit financial exposure (set MAX_POSITION_USD small, low max trades, and conservative slippage), use a dedicated account with minimal funds, and rotate keys if you later remove the skill. - Confirm what optional integrations do (e.g., tradejournal or skills.tradejournal) — they may call external services or read local files; disable or inspect them if you don't need them. - Verify clawhub.json/ SKILL.md inconsistencies (SKILL.md front-matter omits SOLANA_PRIVATE_KEY) are resolved so required credentials are clear. If you want, I can: (a) scan the remainder of trader.py (provide full file), (b) fetch and summarize the simmer-sdk package source, or (c) identify exact code locations where credentials are read/used.

This is a template.
The default signal uses a static CPI-to-Fed sensitivity table -- remix it with live CME FedWatch data, yield curve signals, or Taylor Rule models.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

CPI data and Fed rate decisions are deeply linked. This skill reads CPI bin market prices to estimate the current CPI level, then uses a DATA_SENSITIVITY table to compute fair probabilities for Fed rate cut/hold/hike markets. When the Fed market price diverges from the model, trade the edge.

Key advantages:

• Cross-market signal -- uses information from one market type to trade another
• Fundamental economic linkage -- CPI-to-Fed is the most documented macro relationship
• DATA_SENSITIVITY table -- calibrated from historical Fed reaction function
• No external data needed -- all inputs come from Kalshi market prices

Signal Logic

CPI-to-Fed Linkage Model

1. Scan CPI bin markets and extract probability-weighted expected CPI level
2. Look up model Fed probabilities from DATA_SENSITIVITY table
3. Interpolate between table entries for precise CPI levels
4. Compare model probability to Fed market price
5. Trade when |model - market| >= entry_edge

DATA_SENSITIVITY Table

CPI Level	P(Cut)	P(Hold)	P(Hike)
1.0%	85%	12%	3%
2.0%	55%	38%	7%
2.5%	20%	65%	15%
3.0%	5%	55%	40%
4.0%	1%	24%	75%
5.0%	0%	10%	90%

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

Remix Ideas

• CME FedWatch: Compare model to CME futures-implied Fed probabilities
• Yield curve signal: Add 2y-10y spread as additional Fed predictor
• Taylor Rule: Replace sensitivity table with formal Taylor Rule computation
• Real-time CPI nowcast: Use Cleveland Fed nowcast instead of bin prices

Risk Parameters

Parameter	Default	Notes
Entry edge	10%	Min model-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-econ-fed-link-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

The automaton cron is set to null -- it does not run on a schedule until you configure it in the Simmer UI. autostart: false means it won't start automatically on install.

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)

All risk parameters are declared in clawhub.json as tunables and adjustable from the Simmer UI without code changes.

Variable	Default	Purpose
SIMMER_ECON_FEDLINK_ENTRY_EDGE	0.10	Min divergence between model and market to trigger trade
SIMMER_ECON_FEDLINK_EXIT_THRESHOLD	0.45	Sell position when price reaches this level
SIMMER_ECON_FEDLINK_MAX_POSITION_USD	5.00	Max USDC per trade
SIMMER_ECON_FEDLINK_MAX_TRADES_PER_RUN	3	Max trades per execution cycle
SIMMER_ECON_FEDLINK_SLIPPAGE_MAX	0.15	Max slippage before skipping (0.15 = 15%)
SIMMER_ECON_FEDLINK_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.