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Kalshi Odds Scanner Pro

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

Install

openclaw skills install @themsquared/kalshi-odds-scanner-pro

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

  1. Copy odds_scanner.py to your polymarket/trading directory
  2. Get a free API key at the-odds-api.com
  3. 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

KeyLeague
nbaNBA Basketball
ncaabNCAA Basketball
nhlNHL Hockey
mlbMLB 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
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