Skill flagged — review recommended

ClawHub Security found sensitive or high-impact capabilities. Review the scan results before using.

Kalshi Econ Nowcast Trader

v1.0.1

Trades CPI bin markets on Kalshi using the Cleveland Fed CPI Nowcast to compute fair bin probabilities via a normal distribution model. Requires SIMMER_API_K...

0· 87· 2 versions· 0 current· 0 all-time· Updated 23h ago· MIT-0

Install

openclaw skills install kalshi-econ-nowcast-trader

Kalshi Economic Nowcast Trader

This is a template.
The default signal uses the Cleveland Fed CPI Nowcast point estimate and standard deviation to price CPI bins -- remix it with real-time nowcast scraping, multiple nowcast sources, or Bayesian updating as data arrives.
The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Kalshi lists CPI bin markets ("Will CPI be between 0.2% and 0.3%?"). This skill prices each bin using the Cleveland Fed's CPI Nowcast as the mean of a normal distribution, then trades when the market price diverges from the model probability.

Key advantages:

  • Cleveland Fed Nowcast is free and public -- updated regularly with high accuracy
  • Normal distribution is well-calibrated for CPI -- historically fits actual outcomes
  • Multiple bins per release -- diversified signal across the distribution

Signal Logic

Nowcast-to-Bin Model

  1. Load Cleveland Fed CPI Nowcast estimate and standard deviation
  2. Compute P(CPI in [low, high]) = Phi(z_high) - Phi(z_low) for each bin
  3. Compare model probability to Kalshi market price
  4. Trade when |model - market| >= entry_edge

Example (with defaults: estimate=0.3%, stddev=0.15%)

BinModel PMarket PEdgeAction
0.1%-0.2%9.2%15%-5.8%Hold
0.2%-0.3%34.1%20%+14.1%BUY YES
0.4%-0.5%9.2%22%-12.8%BUY NO

Remix Ideas

  • Live nowcast scraper: Auto-refresh from Cleveland Fed website
  • Multi-source ensemble: Average Cleveland Fed, NY Fed, and Atlanta Fed nowcasts
  • Bayesian updates: Incorporate PPI, import prices, and other leading indicators
  • Volatility scaling: Widen stddev near data release dates

Risk Parameters

ParameterDefaultNotes
Entry edge10%Min model-vs-market divergence to trade
Exit threshold45%Sell when position price reaches this
Max position size$5.00 USDCPer market
Max trades per run3Rate limiting
Max slippage15%Skip if slippage exceeds
Min liquidity$0Disabled by default

Installation & Setup

clawhub install kalshi-econ-nowcast-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.

ScenarioModeFinancial risk
python trader.pyDry runNone
Cron / automatonDry runNone
python trader.py --liveLive (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

VariableRequiredNotes
SIMMER_API_KEYYesTrading authority. Treat as a high-value credential.
SOLANA_PRIVATE_KEYYesBase58-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.

VariableDefaultPurpose
SIMMER_ECON_NOW_ENTRY_EDGE0.10Min divergence between nowcast model and market to trigger trade
SIMMER_ECON_NOW_EXIT_THRESHOLD0.45Sell position when price reaches this level
SIMMER_ECON_NOW_MAX_POSITION_USD5.00Max USDC per trade
SIMMER_ECON_NOW_MAX_TRADES_PER_RUN3Max trades per execution cycle
SIMMER_ECON_NOW_SLIPPAGE_MAX0.15Max slippage before skipping (0.15 = 15%)
SIMMER_ECON_NOW_MIN_LIQUIDITY0Min market liquidity USD (0 = disabled)

Dependency

simmer-sdk is published on PyPI by Simmer Markets.

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

Version tags

latestvk97b0avyr9qfed6djyc59k2d25847ab5