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Kalshi Crypto Cycle Model Trader

作者 diagnostikon · GitHub ↗ · v1.0.1 · MIT-0
cross-platform ✓ 安全检测通过
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版本数
在 OpenClaw 中安装
/install kalshi-crypto-cycle-model-trader
功能描述
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 simme...
使用说明 (SKILL.md)

\r \r

Kalshi Crypto Cycle Model Trader\r

\r

This is a template. \r 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. \r The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.\r \r

Strategy Overview\r

\r 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.\r \r Key advantages:\r

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

Signal Logic\r

\r

Halving Cycle Model\r

\r

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

Historical Cycle Returns\r

\r | Cycle | Halving Date | Pre-Halving Price | Peak | ROI |\r |-------|-------------|-------------------|------|-----|\r | 1 | Nov 2012 | $12 | $1,200 | 100x |\r | 2 | Jul 2016 | $650 | $20,000 | 30x |\r | 3 | May 2020 | $8,500 | $69,000 | 8x |\r | 4 | Apr 2024 | $64,000 | ~$192,000 (proj) | ~3x |\r \r

Conviction-Based Sizing\r

\r

  • conviction = min(|edge| / entry_edge, 2.0) / 2.0\r
  • size = max($1.00, conviction * MAX_POSITION_USD)\r
  • Larger edge = larger position, capped at MAX_POSITION_USD\r \r

Remix Ideas\r

\r

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

Risk Parameters\r

\r | Parameter | Default | Notes |\r |-----------|---------|-------|\r | Entry edge | 10% | Min model-vs-market divergence to trade |\r | Exit threshold | 45% | Sell when position price reaches this |\r | Max position size | $5.00 USDC | Per market |\r | Max trades per run | 5 | Rate limiting |\r | Max slippage | 15% | Skip if slippage exceeds |\r | Min liquidity | $0 | Disabled by default |\r \r

Installation & Setup\r

\r

clawhub install kalshi-crypto-cycle-model-trader\r
```\r
\r
Requires: `SIMMER_API_KEY` and `SOLANA_PRIVATE_KEY` environment variables.\r
\r
## Cron Schedule\r
\r
Cron is set to `null` -- the skill does not run on a schedule until you configure it in the Simmer UI.\r
\r
## Safety & Execution Mode\r
\r
**The skill defaults to dry-run mode. Real trades only execute when `--live` is passed explicitly.**\r
\r
| Scenario | Mode | Financial risk |\r
|----------|------|----------------|\r
| `python trader.py` | Dry run | None |\r
| Cron / automaton | Dry run | None |\r
| `python trader.py --live` | Live (Kalshi via DFlow) | Real USDC |\r
\r
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.\r
\r
## Required Credentials\r
\r
| Variable | Required | Notes |\r
|----------|----------|-------|\r
| `SIMMER_API_KEY` | Yes | Trading authority. Treat as a high-value credential. |\r
| `SOLANA_PRIVATE_KEY` | Yes | Base58-encoded Solana private key for live trading. |\r
\r
## Tunables (Risk Parameters)\r
\r
All risk parameters are declared in `clawhub.json` as `tunables` and adjustable from the Simmer UI without code changes.\r
\r
| Variable | Default | Purpose |\r
|----------|---------|---------|\r
| `SIMMER_CRYPTO_CYCLE_ENTRY_EDGE` | `0.10` | Min divergence between model and market to trigger trade |\r
| `SIMMER_CRYPTO_CYCLE_EXIT_THRESHOLD` | `0.45` | Sell position when price reaches this level |\r
| `SIMMER_CRYPTO_CYCLE_MAX_POSITION_USD` | `5.00` | Max USDC per trade |\r
| `SIMMER_CRYPTO_CYCLE_MAX_TRADES_PER_RUN` | `5` | Max trades per execution cycle |\r
| `SIMMER_CRYPTO_CYCLE_SLIPPAGE_MAX` | `0.15` | Max slippage before skipping (0.15 = 15%) |\r
| `SIMMER_CRYPTO_CYCLE_MIN_LIQUIDITY` | `0` | Min market liquidity USD (0 = disabled) |\r
\r
## Dependency\r
\r
`simmer-sdk` is published on PyPI by Simmer Markets.\r
- PyPI: https://pypi.org/project/simmer-sdk/\r
- GitHub: https://github.com/SpartanLabsXyz/simmer-sdk\r
- Publisher: [email protected]\r
\r
Review the source before providing live credentials if you require full auditability.\r
安全使用建议
This skill appears to do what it says (price BTC bins using a halving-cycle model and place trades via Simmer/Kalshi). Before enabling live trading: 1) Verify the simmer-sdk package source and review its code (SKILL.md provides links). 2) Treat SIMMER_API_KEY and especially SOLANA_PRIVATE_KEY as high‑value credentials—use a separate/truncated account or small fund allocation for testing. 3) Run the skill in dry‑run mode and inspect logs/output to confirm behavior. 4) Ask the publisher to fix the registry metadata misreporting required env vars and to document what TRADING_VENUE/AUTOMATON_MAX_BET do. 5) If you are uncomfortable providing a raw private key, request an alternative signing mechanism (hardware or delegated signing) or limit the bot to paper trading only.
能力标签
cryptorequires-wallet
能力评估
Purpose & Capability
The skill claims to price and trade Bitcoin year‑end bins via the Simmer SDK/Kalshi and the files (trader.py, SKILL.md, clawhub.json) request SIMMER_API_KEY and a SOLANA_PRIVATE_KEY for live trading. Requiring a Simmer API key and a private key is proportionate for a live trading skill. Minor inconsistency: registry metadata shown at the top of the evaluation listed ‘Required env vars: none’, whereas clawhub.json and SKILL.md both declare SIMMER_API_KEY and SOLANA_PRIVATE_KEY. This appears to be a documentation/registry mismatch rather than malicious behavior.
Instruction Scope
SKILL.md and trader.py focus on market discovery, probability computation, and trading execution. The instructions and code reference only trading-related actions (Simmer client calls, market parsing, optional journal logging). The skill defaults to dry‑run and only executes live trades when explicitly run with --live. No instructions attempt to read unrelated system files.
Install Mechanism
No binary install spec is embedded (instruction-only + included Python code). The declared dependency is the simmer-sdk PyPI package, which is reasonable for this integration but is a third‑party package—review its source (links are provided in SKILL.md) before supplying live credentials.
Credentials
The skill requires two high‑sensitivity items: SIMMER_API_KEY (API authority) and SOLANA_PRIVATE_KEY (base58 private key for signing live trades). These are expected for a live trading agent, but they are high-value credentials. The code also reads optional env vars (TRADING_VENUE, AUTOMATON_MAX_BET) that are not listed in the top-level registry metadata; that mismatch should be clarified. Only provide full private keys to code you trust; consider using an account with limited funds or an ephemeral signing key where possible.
Persistence & Privilege
The skill is not forced-installed (always: false) and autostart is false. The automaton metadata shows an entrypoint (trader.py) and managed:true, meaning the skill can be run/managed by the platform but will not start automatically after install. The agent retains the normal ability to invoke the skill autonomously unless you disable model invocation (default behavior for skills).
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install kalshi-crypto-cycle-model-trader
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /kalshi-crypto-cycle-model-trader 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.1
Trigger rescan — all metadata consistent
v1.0.0
Kalshi Crypto Cycle Model Trader v1.0.0 – Initial Release - Trades BTC year-end price markets on Kalshi using the 4-year halving cycle pattern to compute fair value probabilities. - Implements a lognormal return model with diminishing cycle ROI for model-driven trading signals. - Features conviction-based position sizing, configurable risk/tuning parameters, and automatic trade execution (dry-run by default). - Includes safeguards: entry/exit thresholds, position/size limits, slippage and liquidity checks. - Easily adjustable via Simmer UI tunables; requires ‘SIMMER_API_KEY’ and ‘simmer-sdk’.
元数据
Slug kalshi-crypto-cycle-model-trader
版本 1.0.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 2
常见问题

Kalshi Crypto Cycle Model Trader 是什么?

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 simme... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 119 次。

如何安装 Kalshi Crypto Cycle Model Trader?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install kalshi-crypto-cycle-model-trader」即可一键安装,无需额外配置。

Kalshi Crypto Cycle Model Trader 是免费的吗?

是的,Kalshi Crypto Cycle Model Trader 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Kalshi Crypto Cycle Model Trader 支持哪些平台?

Kalshi Crypto Cycle Model Trader 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Kalshi Crypto Cycle Model Trader?

由 diagnostikon(@diagnostikon)开发并维护,当前版本 v1.0.1。

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