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diagnostikon

Kalshi Fed Speech Signal Trader

by diagnostikon · GitHub ↗ · v1.0.2 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install kalshi-fed-speech-signal-trader
Description
Trades Fed rate markets on Kalshi based on hawkish/dovish sentiment signals from market question text. Scores net sentiment from keyword dictionaries and adj...
README (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
Usage Guidance
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.
Capability Analysis
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.
Capability Tags
cryptorequires-wallet
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install kalshi-fed-speech-signal-trader
  3. After installation, invoke the skill by name or use /kalshi-fed-speech-signal-trader
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.2
Rescan
v1.0.1
Rescan
v1.0.0
Initial release
Metadata
Slug kalshi-fed-speech-signal-trader
Version 1.0.2
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 3
Frequently Asked Questions

What is Kalshi Fed Speech Signal Trader?

Trades Fed rate markets on Kalshi based on hawkish/dovish sentiment signals from market question text. Scores net sentiment from keyword dictionaries and adj... It is an AI Agent Skill for Claude Code / OpenClaw, with 109 downloads so far.

How do I install Kalshi Fed Speech Signal Trader?

Run "/install kalshi-fed-speech-signal-trader" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Kalshi Fed Speech Signal Trader free?

Yes, Kalshi Fed Speech Signal Trader is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Kalshi Fed Speech Signal Trader support?

Kalshi Fed Speech Signal Trader is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Kalshi Fed Speech Signal Trader?

It is built and maintained by diagnostikon (@diagnostikon); the current version is v1.0.2.

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