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#

MonthAdjReasonJan+0.10Housing OER annual resetFeb-0.05Post-holiday normalizationMar0.00Neutral transitionApr+0.05Spring demand, gasoline blend switchMay+0.05Summer driving beginsJun+0.10Peak summer energyJul+0.05Continued summer, moderatingAug0.00Back-to-school offsets energySep-0.05Summer demand fadeOct-0.05Autumn deflationNov0.00Pre-holiday neutralDec+0.05Holiday 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#

ParameterDefaultNotesEntry edge8%Min fair-vs-market divergence to tradeExit threshold45%Sell when position price reaches thisMax position size$5.00 USDCPer marketMax trades per run3Rate limitingMax slippage15%Skip if slippage exceedsMin liquidity$0Disabled by default

Installation & Setup#

bash

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.

ScenarioModeFinancial riskpython trader.pyDry runNoneCron / automatonDry runNonepython trader.py --liveLive (Kalshi via DFlow)Real USDC

Required Credentials#

VariableRequiredNotesSIMMER_API_KEYYesTrading authority. Treat as a high-value credential.SOLANA_PRIVATE_KEYYesBase58-encoded Solana private key for live trading.

Tunables (Risk Parameters)#

VariableDefaultPurposeSIMMER_ECON_SEAS_ENTRY_EDGE``0.08Min divergence to trigger tradeSIMMER_ECON_SEAS_EXIT_THRESHOLD``0.45Sell position when price reaches this levelSIMMER_ECON_SEAS_MAX_POSITION_USD``5.00Max USDC per tradeSIMMER_ECON_SEAS_MAX_TRADES_PER_RUN``3Max trades per execution cycleSIMMER_ECON_SEAS_SLIPPAGE_MAX``0.15Max slippage before skipping tradeSIMMER_ECON_SEAS_MIN_LIQUIDITY``0Min 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