Kalshi Crypto Cycle Model Trader

Trades Bitcoin year-end price markets on Kalshi using the 4-year halving cycle pattern to compute fair price probabilities. Requires SIMMER_API_KEY and simmer-sdk. Use when you want to capture alpha from halving-cycle mispricing on BTC price bin markets.

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

Cycle	Halving Date	Pre-Halving Price	Peak	ROI
1	Nov 2012	$12	$1,200	100x
2	Jul 2016	$650	$20,000	30x
3	May 2020	$8,500	$69,000	8x
4	Apr 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

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	5	Rate limiting
Max slippage	15%	Skip if slippage exceeds
Min liquidity	$0	Disabled by default

Installation & Setup

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.

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_CRYPTO_CYCLE_ENTRY_EDGE	0.10	Min divergence between model and market to trigger trade
SIMMER_CRYPTO_CYCLE_EXIT_THRESHOLD	0.45	Sell position when price reaches this level
SIMMER_CRYPTO_CYCLE_MAX_POSITION_USD	5.00	Max USDC per trade
SIMMER_CRYPTO_CYCLE_MAX_TRADES_PER_RUN	5	Max trades per execution cycle
SIMMER_CRYPTO_CYCLE_SLIPPAGE_MAX	0.15	Max slippage before skipping (0.15 = 15%)
SIMMER_CRYPTO_CYCLE_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.