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Kalshi Fed Speech Signal Trader

作者 diagnostikon · GitHub ↗ · v1.0.2 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install kalshi-fed-speech-signal-trader
功能描述
Trades Fed rate markets on Kalshi based on hawkish/dovish sentiment signals from market question text. Scores net sentiment from keyword dictionaries and adj...
使用说明 (SKILL.md)

\r \r

Kalshi Fed Speech Signal Trader\r

\r

This is a template.\r The default signal uses static keyword dictionaries -- remix it with NLP sentiment models, live Fed speech transcripts via FRED API, or real-time news feeds.\r The skill handles all the plumbing (market discovery, trade execution, safeguards). Your agent provides the alpha.\r \r

Strategy Overview\r

\r Fed speeches contain hawkish and dovish signals that predict rate decisions. This skill scores net sentiment from keyword matching on market question text, then adjusts the fair probability of a rate cut. When the adjustment creates a gap vs. rate cut market prices, it trades.\r \r Key advantages:\r

  • No external data needed -- extracts signal from market question text itself\r
  • Extensible -- add new keywords, adjust weights, or plug in NLP models\r
  • Cross-signal aggregation -- pools sentiment across all Fed-related markets\r \r

Signal Logic\r

\r

Sentiment Scoring\r

\r

  1. Scan all Fed rate market questions for hawkish/dovish keywords\r
  2. Weight matches (some keywords stronger signals than others)\r
  3. Compute net sentiment: dovish_total - hawkish_total\r
  4. Adjust baseline cut probability by 5% per net unit\r
  5. Trade rate cut markets when |fair - market| >= entry_edge\r \r

Keyword Dictionaries\r

\r Hawkish (reduce cut probability): "inflation persistent", "tightening", "restrictive", "price stability", "higher for longer", etc.\r \r Dovish (increase cut probability): "data dependent", "labor softening", "gradual", "balanced", "appropriate to reduce", etc.\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

Risk Parameters\r

\r | Parameter | Default | Notes |\r |-----------|---------|-------|\r | Entry edge | 10% | Min fair-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 | 3 | 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-fed-speech-signal-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
## 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
| Variable | Default | Purpose |\r
|----------|---------|---------|\r
| `SIMMER_FED_SPEECH_ENTRY_EDGE` | `0.10` | Min divergence to trigger trade |\r
| `SIMMER_FED_SPEECH_EXIT_THRESHOLD` | `0.45` | Sell position when price reaches this level |\r
| `SIMMER_FED_SPEECH_MAX_POSITION_USD` | `5.00` | Max USDC per trade |\r
| `SIMMER_FED_SPEECH_MAX_TRADES_PER_RUN` | `3` | Max trades per execution cycle |\r
| `SIMMER_FED_SPEECH_SLIPPAGE_MAX` | `0.15` | Max slippage before skipping trade |\r
| `SIMMER_FED_SPEECH_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 package appears to do what it says: discover Kalshi Fed rate markets, score sentiment from market text, and trade via the Simmer SDK. Before enabling live trading: (1) keep the skill in dry-run mode and confirm behavior/output; (2) review the simmer-sdk PyPI/GitHub source to ensure the SDK doesn't exfiltrate secrets; (3) do not supply your SOLANA_PRIVATE_KEY until you trust the SDK and code—use a low-value/test key or limited-fund account first; (4) note the small documentation inconsistency (SKILL.md top metadata vs later sections) and confirm how the private key is read/used in the remainder of trader.py; (5) monitor network traffic when running with a test key to catch unexpected endpoints.
功能分析
Type: OpenClaw Skill Name: kalshi-fed-speech-signal-trader Version: 1.0.2 The skill is a legitimate financial trading bot designed to trade Fed rate markets on Kalshi based on sentiment analysis of market questions. It utilizes the 'simmer-sdk' to interact with the Simmer Markets API and requires sensitive credentials (SIMMER_API_KEY and SOLANA_PRIVATE_KEY) which are standard for its stated purpose. The code in trader.py is well-documented, follows the logic described in SKILL.md, and shows no signs of data exfiltration, malicious execution, or prompt injection.
能力标签
cryptorequires-wallet
能力评估
Purpose & Capability
Name/description (Kalshi Fed Speech Signal Trader) match the code and SKILL.md: the code discovers Kalshi markets (via the Simmer SDK), scores sentiment from market question text, and executes trades. The only declared external dependencies (simmer-sdk and trading keys) align with the trading purpose.
Instruction Scope
SKILL.md and trader.py stay within trading-related actions (market discovery, sentiment scoring, trade execution). Minor inconsistencies: the SKILL.md top metadata lists only SIMMER_API_KEY while later sections and clawhub.json also require SOLANA_PRIVATE_KEY; trader.py prints errors and exits if SIMMER_API_KEY is missing. The code uses a direct internal client call (client._request) in addition to higher-level methods, which is an implementation detail but worth auditing.
Install Mechanism
This is an instruction-only skill with a normal PyPI dependency (simmer-sdk). There are no downloads from arbitrary URLs, no archive extraction, and no unusual install steps in the files provided.
Credentials
The skill requests only SIMMER_API_KEY and SOLANA_PRIVATE_KEY (and some optional behavior controlled by env tunables). Those credentials are directly relevant: SIMMER_API_KEY for the Simmer API and a Solana private key for live Solana/DFlow trades. Because SOLANA_PRIVATE_KEY is high-value, the SKILL.md appropriately warns 'treat as a high-value credential'—you should audit the code and the simmer-sdk before providing it.
Persistence & Privilege
The skill is not forced always-on (always: false) and autostart/cron are not enabled by default. clawhub.json marks the automaton entrypoint as managed, but autostart is false. The skill does not request system-wide settings or other skills' credentials.
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-fed-speech-signal-trader
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /kalshi-fed-speech-signal-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
v1.0.2
Rescan
v1.0.1
Rescan
v1.0.0
Initial release
元数据
Slug kalshi-fed-speech-signal-trader
版本 1.0.2
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 3
常见问题

Kalshi Fed Speech Signal Trader 是什么?

Trades Fed rate markets on Kalshi based on hawkish/dovish sentiment signals from market question text. Scores net sentiment from keyword dictionaries and adj... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 109 次。

如何安装 Kalshi Fed Speech Signal Trader?

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

Kalshi Fed Speech Signal Trader 是免费的吗?

是的,Kalshi Fed Speech Signal Trader 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Kalshi Fed Speech Signal Trader 支持哪些平台?

Kalshi Fed Speech Signal 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 Fed Speech Signal Trader?

由 diagnostikon(@diagnostikon)开发并维护,当前版本 v1.0.2。

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