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Kalshi Odds Scanner Pro

Name: Kalshi Odds Scanner Pro
Author: themsquared

作者 Mike · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ⚠ suspicious

120

总下载

当前安装

版本数

在 OpenClaw 中安装

/install kalshi-odds-scanner-pro

功能描述

Real-time scanner comparing Kalshi odds to 6 sportsbooks, auto-buys 8%+ edge plays with Kelly sizing on NBA, NCAAB, NHL, and MLB markets.

使用说明 (SKILL.md)

Kalshi Odds Scanner Pro

Compare Kalshi prediction market prices vs 6 major sportsbooks in real-time. Fires automatically on 8%+ edge. Kelly-sized execution. The exact scanner used to deploy capital daily on Kalshi sports markets.

💰 Used to generate consistent returns on Kalshi sports markets. $79 value.

What It Does

Fetches live odds from The Odds API (6+ sportsbooks: DraftKings, FanDuel, BetMGM, Caesars, etc.)
Compares sportsbook-implied probabilities vs Kalshi ask prices
Fires on 8%+ edge (YES side) or 5%+ edge (NO side heavy favorites)
Kelly criterion position sizing (25% fractional Kelly, capped at $60)
NCAAB heavy-favorite NO-side insight: ~74% historical win rate when fav > 80%
Deduplicates — ONE side per game only

Setup

Copy odds_scanner.py to your polymarket/trading directory
Get a free API key at the-odds-api.com
Set your Kalshi API credentials:
- KALSHI_KEY_ID — your Kalshi API key ID
- ~/.config/kalshi/private_key.pem — your Kalshi private key

Edit constants at the top of the script:

ODDS_API_KEY = "your_key_here"
KALSHI_KEY_ID = "your_kalshi_key_id"

Usage

# Scan YES plays (default NBA)
python3 odds_scanner.py

# Scan NO plays (heavy favorites, 74% win rate)
python3 odds_scanner.py --side no

# Scan both YES and NO
python3 odds_scanner.py --side both

# Scan NCAAB (college basketball)
python3 odds_scanner.py --sport ncaab --side both

# Execute found plays on Kalshi
python3 odds_scanner.py --buy --sport nba --side both

# Set custom edge threshold
python3 odds_scanner.py --min-edge 0.10

Supported Sports

Key	League
`nba`	NBA Basketball
`ncaab`	NCAA Basketball
`nhl`	NHL Hockey
`mlb`	MLB Baseball

Edge Logic

YES side: sportsbook_prob - kalshi_yes_ask > 8%

Example: Sportsbooks say Lakers win 72%, Kalshi YES at 62% → +10% edge → BUY

NO side: (1 - sportsbook_prob) - kalshi_no_ask > 5%

Example: Sportsbooks say team wins 85%, Kalshi NO at 8% → true NO worth 15% → +7% edge → BUY NO

Kelly Sizing

f = (b*p - q) / b  × 0.25 (quarter Kelly)

MIN_BET = $10, MAX_BET = $60
RESERVE = $50 kept aside always

Integration

Works with ensemble.py and momentum.py in the same directory for multi-model consensus gating.

Requirements

Python 3.9+
cryptography library: pip install cryptography
The Odds API key (free tier: 500 requests/month)
Kalshi account with API access

安全使用建议

This script will read a Kalshi private key from ~/.config/kalshi/private_key.pem and call external APIs, and it can place live trades. Before using: 1) Do not run with --buy until you audit the code and test in dry-run mode; run only scans first. 2) Remove any hardcoded API keys or verify their provenance — the bundled ODDS_API_KEY and KALSHI_KEY_ID in the script are unexpected; treat them as suspicious. 3) Confirm the Kalshi endpoint (api.elections.kalshi.com used in code) is correct for sports trading — if it is wrong or unusual, stop and ask the author. 4) Be cautious integrating ensemble.py/momentum.py from untrusted sources; those local modules can execute arbitrary code. 5) Prefer using a limited/test Kalshi account or sandbox keys if available, and back up your private key before testing. 6) Ask the publisher for provenance (homepage, source repository, author identity, changelog) and for a dry-run / verbose-only mode to validate behavior without transacting. If the author provides a verified repo and explains the hardcoded keys are placeholders, confidence could be raised.

功能分析

Type: OpenClaw Skill Name: kalshi-odds-scanner-pro Version: 1.0.0 The skill bundle contains a functional Kalshi trading bot that performs automated financial transactions. It is classified as suspicious due to the presence of hardcoded API credentials (ODDS_API_KEY and KALSHI_KEY_ID) in odds_scanner.py, which is a significant security vulnerability. Additionally, the script accesses sensitive local files (~/.config/kalshi/private_key.pem) to sign requests and executes trades based on external data from The Odds API, posing a high financial risk. While the behavior aligns with the stated purpose, the combination of hardcoded secrets and automated financial execution warrants caution.

能力评估

ℹ Purpose & Capability

The name/description match the code: the script fetches sportsbook odds, compares to Kalshi prices, computes Kelly sizes, and can place buys. However the registry metadata declared no required credentials or env vars while both SKILL.md and the code clearly require Kalshi credentials and an Odds API key — this mismatch is unexpected and incoherent.

⚠ Instruction Scope

SKILL.md instructs editing constants in the script and placing a private key at ~/.config/kalshi/private_key.pem. The runtime instructions and code will read that private key and sign/submit trade requests to Kalshi. The SKILL.md also suggests integrating optional local modules (ensemble.py, momentum.py) which, if present, will be imported and influence decisions — this expands scope to arbitrary local code. The script contacts two external APIs (The Odds API and a Kalshi endpoint) and can execute real trades (when run with --buy). There are no instructions to run in dry-run mode by default, so a user could accidentally execute live trades.

✓ Install Mechanism

This is an instruction-only skill with a single Python file and no install spec; risk from install mechanism is low (nothing automatically downloaded or written). It does require the 'cryptography' Python package per SKILL.md which is a normal dependency.

⚠ Credentials

The registry metadata lists no required env vars, but the SKILL.md and code require (and expect) a Kalshi private key file and an Odds API key. The code includes hardcoded credentials (ODDS_API_KEY and KALSHI_KEY_ID) embedded in odds_scanner.py — hardcoded keys are suspicious and inconsistent with instructions that say 'get your own API key' and set constants. Requesting access to a private key file in the user's home directory is proportionate to signing trade requests, but it should have been declared explicitly in metadata; the undeclared/embedded credentials and mismatch are red flags.

✓ Persistence & Privilege

always is false and there's no install step that forces persistence or modifies other skill configurations. Autonomous execution (model invocation) is allowed by default but not exceptional here; combined with credential access and trading capability this increases potential impact if misused, so the user should be cautious about granting agent autonomy.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install kalshi-odds-scanner-pro
安装完成后，直接呼叫该 Skill 的名称或使用 /kalshi-odds-scanner-pro 触发
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

v1.0.0

Initial release

元数据

Slug kalshi-odds-scanner-pro

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题