Fuku Predictions — Conversational Kalshi Trading Skill

Trade prediction markets through conversation. The agent learns what you care about, builds a personalized profile, then scans Kalshi markets for opportunities that match your style.

Three Modes

1. Profile Building (Interactive)

User describes preferences → agent builds a trading profile → saves for reuse.

2. Conversational Scanning

Agent scans markets using the profile → presents matching opportunities → user approves trades.

3. Autonomous Trading

Agent scans and trades automatically within risk limits.

Setup

Dependencies

pip install httpx cryptography python-dotenv

Kalshi API Key

Create .env in the skill directory:

KALSHI_API_KEY_ID=your_key_id
KALSHI_PRIVATE_KEY="-----BEGIN RSA PRIVATE KEY-----
...
-----END RSA PRIVATE KEY-----"

Get credentials: https://kalshi.com/profile/api

Defining Preferences

Users express what they care about in natural language:

Situational: "I want home dogs getting 7+ points in CBB" · "Show me letdown spots after big wins" · "Find revenge games where the underdog lost by 15+ last time"

Player Mismatches: "Games where the best player has a 50+ FPR gap" · "Matchups when a star player is injured"

Statistical: "Only games with top 30 defenses" · "Pace mismatches (fast vs slow)" · "Spreads under 3 points"

Risk & Sizing: "$5 bets on highest confidence plays" · "Max 8 trades per day" · "Quarter-Kelly sizing"

Agent Tools

Profile Management

# Process user preference input
python3 scripts/agent_interface.py --input "I want home dogs getting 7+ points in CBB"

# Scan using a profile
python3 scripts/agent_interface.py --scan --profile default

# List profiles
python3 scripts/agent_interface.py --input "list my profiles"

Market Browser

# Tonight's markets with predictions and edges
python3 scripts/browse.py

# Filter by sport or game
python3 scripts/browse.py --sport cbb
python3 scripts/browse.py --game "Duke" --date 2026-03-03

# Change bet display amount (default $5)
python3 scripts/browse.py --bet 10

Direct Kalshi Access

python3 scripts/kalshi_client.py balance
python3 scripts/kalshi_client.py positions
python3 scripts/kalshi_client.py markets --series KXNBASPREAD

Presenting Markets to Users

Always include: the market, price (dollars), model prediction, edge, payout, and recommendation.

Talk in dollars, not contracts. Users say "$5 on Boston" — convert to contracts internally.

Three-tier display per market type:

• Main line — contract closest to 50¢ (market consensus)
• 🔒 Safer — highest edge (high confidence, modest payout)
• 🎰 Riskier — near model's predicted line (~50% model probability, bigger payout, ≥3% edge required)

Edge icons: 🔥 ≥20% · ✅ ≥10% · 📊 ≥5% · ➖ <5%

Example:

🏀 Boston @ Milwaukee — 7:30 PM
📊 Our model: BOS -8.4 | Total 224.1

• BOS -2.5 at 50¢ → 70% model (+20% edge 🔥) — $5 pays $10
  ↳ 🔒 Safer: BOS -1.5 at 57¢ → 82% model (+25% edge) — $5 pays $8
  ↳ 🎰 Riskier: BOS -8.5 at 31¢ → 50% model (+19% edge) — $5 pays $16
• Over 215.5 at 52¢ → 79% model (+27% edge 🔥) — $5 pays $9

💰 Balance: $49.95
Want me to put money on any of these?

Dollar-to-Contract Math

"$5 on BOS -8.5" at 31¢ → floor($5 / $0.31) = 16 contracts × $0.31 = $4.96 cost → $16.00 payout if YES → $11.04 profit.

Trading

from kalshi_client import KalshiClient
c = KalshiClient()

# Buy
c.place_order(ticker="KXNBA...", side="yes", action="buy",
              count=16, order_type="limit", yes_price=31)

# Sell to exit
c.place_order(ticker="KXNBA...", side="yes", action="sell",
              count=16, order_type="limit", yes_price=current_bid)

Edge Math

Normal distribution probability conversion (no scipy):

• Uses math.erfc for CDF
• Sport-specific σ: CBB spread 12.0 / total 11.0, NBA 11.0 / 10.5, NHL 1.5 / 1.3, Soccer 1.2 / 1.1
• Player props: σ = 30% of predicted value (min 2.0)

Kalshi Market Structure

• Series (sport): KXNBASPREAD, KXNBATOTAL, KXNBAGAME
• Event (game): KXNBASPREAD-26MAR02BOSMIL
• Market (contract): KXNBASPREAD-26MAR02BOSMIL-BOS7 → "Boston wins by over 7.5?"

Pricing: YES/NO in cents (1-99). YES 31¢ = 31% implied. 1 contract = $1 max payout.

Supported Sports

Autopilot Config

config/config.json:

{
  "strategy": "model_follower",
  "sports": ["nba", "cbb"],
  "min_edge_pct": 3.0,
  "max_daily_loss_pct": 10,
  "max_daily_bets": 15,
  "sizing": "quarter_kelly",
  "mode": "approve"
}

Modes: dry_run (log only) · approve (ask user) · auto (hands-free)

Safety

• Max daily loss limit (default 10%)
• Position size caps (default 5% per trade)
• Kill switch: touch KILL_SWITCH in skill directory
• All trades logged locally to trades.json
• API keys never leave the machine

Kalshi API Auth

RSA-PSS signatures. The client handles this automatically.

Signing quirk: Portfolio endpoints sign path WITHOUT query strings. Market endpoints sign WITH. See _SIGN_PATH_ONLY in kalshi_client.py.

Fuku Prediction API (Public)

Base: https://cbb-predictions-api-nzpk.onrender.com

Files