Kalshi Crypto Cycle Model Trader#

This is a template.

The default signal uses Bitcoin's 4-year halving cycle with diminishing returns to project fair year-end price probabilities -- remix it with on-chain metrics, options-implied vol, or macro regime models.

The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview#

Bitcoin year-end price bin markets on Kalshi price outcomes independently. This skill prices them using the well-documented 4-year halving cycle pattern, where each post-halving cycle delivers diminishing but still substantial returns.

Key advantages:

• Halving cycle is public and verifiable -- historical pattern of 100x/30x/8x/3x post-halving returns
• Lognormal model -- converts expected price and volatility into bin probabilities
• Cycle position awareness -- April 2024 halving means we are in year 2 of cycle 4, approaching the historically strongest phase

Signal Logic#

Halving Cycle Model#

1. Determine current position in the 4-year halving cycle (year 2 of cycle 4)
2. Project year-end price from cycle ROI pattern with diminishing returns
3. Use lognormal distribution to compute probability for each price bin
4. Compare model probability to Kalshi market price
5. Trade when |model - market| >= entry_edge

Historical Cycle Returns#

CycleHalving DatePre-Halving PricePeakROI1Nov 2012$12$1,200100x2Jul 2016$650$20,00030x3May 2020$8,500$69,0008x4Apr 2024$64,000~$192,000 (proj)~3x

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#

• On-chain metrics: Hash rate, active addresses, MVRV ratio for cycle confirmation
• Options-implied distribution: Compare model to Deribit options implied vol
• Macro regime overlay: Adjust volatility/expected return based on Fed policy, DXY
• Multi-cycle ensemble: Weight multiple cycle models for more robust estimates

Risk Parameters#

ParameterDefaultNotesEntry edge10%Min model-vs-market divergence to tradeExit threshold45%Sell when position price reaches thisMax position size$5.00 USDCPer marketMax trades per run5Rate limitingMax slippage15%Skip if slippage exceedsMin liquidity$0Disabled by default

Installation & Setup#

bash

clawhub install kalshi-crypto-cycle-model-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

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#

VariableRequiredNotesSIMMER_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.

VariableDefaultPurposeSIMMER_CRYPTO_CYCLE_ENTRY_EDGE``0.10Min divergence between model and market to trigger tradeSIMMER_CRYPTO_CYCLE_EXIT_THRESHOLD``0.45Sell position when price reaches this levelSIMMER_CRYPTO_CYCLE_MAX_POSITION_USD``5.00Max USDC per tradeSIMMER_CRYPTO_CYCLE_MAX_TRADES_PER_RUN``5Max trades per execution cycleSIMMER_CRYPTO_CYCLE_SLIPPAGE_MAX``0.15Max slippage before skipping (0.15 = 15%)SIMMER_CRYPTO_CYCLE_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

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