Kalshi Econ Seasonal Trader

Trades CPI/inflation markets on Kalshi using documented seasonal patterns in CPI data. Energy costs spike summer, housing adjustments January. Requires SIMME...

Kalshi CPI Seasonal Trader

This is a template. The default signal uses static seasonal adjustment factors for CPI bins -- remix it with real-time BLS data feeds, energy futures curves, or housing indices for live seasonal calibration. The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

CPI has well-documented seasonal patterns that retail traders ignore. Energy costs spike in summer (June peak), housing OER resets in January, and holiday demand lifts December. This skill biases CPI bin probabilities based on the current month's historical seasonal adjustment, then trades when Kalshi market prices diverge from the seasonally-adjusted fair value.

Key advantages:

• Seasonal patterns are persistent -- decades of BLS data confirm monthly CPI biases
• Energy dominance -- summer energy spikes are the strongest and most predictable signal
• January housing reset -- OER annual adjustment creates a reliable January hot-print bias
• Bin structure exploitable -- Kalshi CPI bins create discrete mispricings when seasonal effects shift probability mass

Signal Logic

Seasonal Adjustment Model

1. Look up current month's seasonal adjustment factor (+/- percentage points)
2. Classify each CPI market into a bin category (low, low_mid, mid, high_mid, high)
3. Shift probability mass: positive adj -> higher bins more likely, negative -> lower bins
4. Compare adjusted fair value to Kalshi market price
5. Trade when |fair_value - market| >= entry_edge

Monthly Adjustments

Month	Adj	Reason
Jan	+0.10	Housing OER annual reset
Feb	-0.05	Post-holiday normalization
Mar	0.00	Neutral transition
Apr	+0.05	Spring demand, gasoline blend switch
May	+0.05	Summer driving begins
Jun	+0.10	Peak summer energy
Jul	+0.05	Continued summer, moderating
Aug	0.00	Back-to-school offsets energy
Sep	-0.05	Summer demand fade
Oct	-0.05	Autumn deflation
Nov	0.00	Pre-holiday neutral
Dec	+0.05	Holiday demand

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	8%	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-econ-seasonal-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_ECON_SEAS_ENTRY_EDGE	0.08	Min divergence to trigger trade
SIMMER_ECON_SEAS_EXIT_THRESHOLD	0.45	Sell position when price reaches this level
SIMMER_ECON_SEAS_MAX_POSITION_USD	5.00	Max USDC per trade
SIMMER_ECON_SEAS_MAX_TRADES_PER_RUN	3	Max trades per execution cycle
SIMMER_ECON_SEAS_SLIPPAGE_MAX	0.15	Max slippage before skipping trade
SIMMER_ECON_SEAS_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]