Automated Edge Detection & Execution for Prediction Markets
▶ View the interactive data-flow diagram
Scans thousands of Kalshi markets, cross-references 12 sportsbooks + free APIs (including MLB pitcher stats and ESPN rest data), identifies mispriced contracts with a normal CDF probability model, sizes bets with Kelly criterion (soft-capped above 15% edge per calibration), enforces 13 risk gates including per-sport edge floors, a $0.10 lottery-ticket price floor, NO-side favorite guard, a prediction-market safety gate, and per-sport series dedup (MLB/NHL 72h, others 48h), and executes limit orders — logging every decision with fill-accurate accounting for closing line value tracking.
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🏈 NFL · 🏀 NBA · ⚾ MLB · 🏒 NHL 🎓 NCAAB · NCAAF · 🥊 UFC · Boxing ⚽ Soccer · MLS · 🏎️ F1 · NASCAR ⛳ PGA · 🏏 IPL · 🎮 Esports 27 filters · 18 sports with Odds API edge detection |
🏈 Super Bowl 🏀 NBA Finals 🏒 Stanley Cup ⚾ World Series ⛳ PGA Tour N-way de-vig · cross-referenced against sportsbook outrights |
₿ Crypto (BTC, ETH, XRP, SOL, DOGE) 📊 S&P 500 + VIX 🌡️ Weather (13 cities) 🗳️ Politics 4 categories · CoinGecko, Yahoo Finance, NWS |
graph LR
A["12 Sportsbooks<br><sub>The Odds API</sub>"] --> D["Normal CDF<br>Fair Value"]
B["Team Stats<br><sub>ESPN / NHL / MLB</sub>"] --> D
C["Signals<br><sub>Weather, Pitchers, Rest, Sharp $</sub>"] --> D
D -->|"compare"| E["Kalshi Price"]
E -->|"Edge >= 3%"| F["Composite Score<br><sub>0-10 scale</sub>"]
F --> G["13 Risk Gates"]
G --> H["Kelly Sizing"]
H --> I["Limit Order + Log"]
style D fill:#8B5CF6,color:#fff,stroke:none
style G fill:#e74c3c,color:#fff,stroke:none
style I fill:#2ea44f,color:#fff,stroke:none
| Signal | Source |
|---|---|
| Normal CDF Model | Sport-specific stdev bell curve probabilities |
| Sharp Book Weighting | Pinnacle 3x, Circa 3x, DraftKings 0.7x |
| Team Stats | ESPN/NHL/MLB win% validates fair value |
| Sharp Money | Open-vs-close odds detect reverse line movement |
| Weather | NWS forecasts for 61 NFL/MLB outdoor venues |
| Pitcher Matchups | ERA, FIP, WHIP, K/9, rest days from MLB Stats API |
| Rest Days | NBA/NHL back-to-back fatigue detection |
| Book Disagreement | >4pt spread range flags injury news |
Important
Every scan defaults to preview mode. No money is risked until you pass --execute. Each scan row shows a Gate column (R18) that previews whether it will pass the static risk gates — ok if all clear, or a short label (score, conf, no-fav, pred-off, etc.) for the failing gate.
Every order must clear gates 1-7 (including 3.5, 4.5, 4.6, 4.7). Gates 8-9 cap sizing instead of rejecting.
| # | Gate | Action |
|---|---|---|
| 1 | Daily loss limit | Reject at -$250 |
| 2 | Position count | Reject at 50 open |
| 3 | Edge threshold | Reject below floor (3% global; 12% NBA; 10% NCAAB) |
| 3.5 | Market price floor | Reject bets priced below MIN_MARKET_PRICE (default $0.10 — lottery-ticket filter) |
| 4 | Composite score | Reject below 6.0/10 |
| 4.5 | Min confidence | Reject below MIN_CONFIDENCE (default medium) |
| 4.6 | NO-side favorite | Reject NO bets <25¢ unless edge ≥25% AND confidence=high |
| 4.7 | Prediction-market safety | Reject crypto/weather/spx/mentions/companies/politics unless ALLOW_PREDICTION_BETS=true (R25) |
| 5 | Duplicate check | Reject same market |
| 6 | Per-event cap | Reject at 2/game |
| 7 | Series dedup | Reject same matchup bet within window (MLB/NHL 72h, others 48h; per-sport via SERIES_DEDUP_HOURS_<SPORT>) |
| 8 | Bet size cap | Cap at $100 |
| 9 | Bet ratio cap | Cap at 3x batch median |
All limits configurable via .env. Gate 3.5 (MIN_MARKET_PRICE, R7) added 2026-04-22 — F10 from the 14-day review showed sub-10¢ bets at 1W-3L with the model claiming "+50% edge" on 8-10¢ longshots. Gate 4.5 (MIN_CONFIDENCE) and Gate 4.6 (NO_SIDE_*) added 2026-04-21 after low-confidence bets at -105% ROI and all 13 high-edge losers being NO-side on heavy favorites. NO bets below NO_SIDE_KELLY_PRICE_FLOOR (default 35¢) are additionally sized at half-Kelly. NBA floor bumped 0.08 → 0.12 in R14 (2026-04-24) after the 30-day calibration showed NBA Brier 0.3306 (worst of all sports). Confidence bumps now one-way (R13, 2026-04-24) — team stats, rest/B2B, and sharp-money signals can drop a tier but no longer bump up; upward bumps correlated with inflated claimed edge rather than better outcomes. Gate 4.7 (ALLOW_PREDICTION_BETS, R25) added 2026-04-24 after a prediction-market audit found all 6 modules (crypto/weather/spx/mentions/companies/politics) cache stale data with no TTL and produce nonsense fair values; the gate blocks those categories by default until the models are rebuilt. R8 (2026-04-29) adds an optional cross-category dedup that runs before the gates: when CROSS_CATEGORY_DEDUP_<SPORT>=true, ML+Total+Spread on the same game collapse to the highest-composite row instead of being treated as 3 independent bets — addresses F11 (12 matchups bet ≥2× in 14d, several same-day cross-category). Default off because cross-category correlation varies by sport. See Architecture
Bet size scales with edge, divided by batch count to control total exposure. Edge is soft-capped above 15% before sizing (trusted_edge()) to damp Kelly on likely-overstated signals — raw edge remains in gates and reports.
bet = max(unit, (kelly_frac / batch) * trusted_edge(edge) * bankroll)
| Edge | Trusted | 1 bet | 5 bets | 10 bets |
|---|---|---|---|---|
| 3% | 3% | $0.75 | $0.15 | $0.08 |
| 10% | 10% | $2.50 | $0.50 | $0.25 |
| 15% | 15% | $3.75 | $0.75 | $0.38 |
| 25% | 20% | $5.00 | $1.00 | $0.50 |
| 35% | 25% | $6.25 | $1.25 | $0.63 |
Example: $50 bankroll, KELLY_FRACTION=0.50. Capped by max bet ($100) and balance. Soft-cap: KELLY_EDGE_CAP=0.15, KELLY_EDGE_DECAY=0.5.
# 0. Clone repo and enter project
git clone https://github.com/michaelschecht/Edge-Radar.git
cd Edge-Radar
# 1. Create + activate virtual environment
python -m venv .venv
# macOS/Linux (bash/zsh):
source .venv/bin/activate
# Windows PowerShell:
# .venv\Scripts\Activate.ps1
# 2. Install dependencies and create env file
python -m pip install --upgrade pip
pip install -r requirements.txt
cp .env.example .env
# 3. Verify environment (API keys, dependencies)
python scripts/doctor.py
# 4. Preview opportunities (no money risked)
python scripts/scan.py sports --filter nba
# 5. Execute with risk controls
python scripts/scan.py sports --filter nba --execute --unit-size 1 --max-bets 5
# 6. Settle bets and view P&L
python scripts/kalshi/kalshi_settler.py report --detail --saveTip
All scanners share the same flags: --execute, --unit-size, --max-bets, --pick, --ticker, --save, --date, --exclude-open. Use --date tomorrow --exclude-open to avoid double-betting.
| Guide | What it covers |
|---|---|
| Setup Guide | First-time install, API keys + RSA private key generation, .env wiring, safe rollout plan (dry-run → low-stakes → normal), automation, ongoing monitoring, troubleshooting |
| Local Dashboard | Run the Streamlit dashboard on your own machine at http://localhost:8501 |
| Cloud Dashboard | Deploy your own instance to Streamlit Community Cloud (free tier) |
Expand any section for copy-paste CLI examples by workflow.
Sports Betting
python scripts/scan.py sports --filter nhl
python scripts/scan.py sports --filter mlb --execute --unit-size 1 --max-bets 10
python scripts/scan.py sports --filter mlb --date tomorrow --exclude-open
python scripts/scan.py sports --filter nba --saveChampionship Futures
python scripts/scan.py futures --filter nba-futures
python scripts/scan.py futures --filter mlb-futures --execute --unit-size 2 --max-bets 5
python scripts/scan.py futures --filter nba-futures --savePrediction Markets
python scripts/scan.py prediction --filter crypto
python scripts/scan.py prediction --filter weather
python scripts/scan.py prediction --filter crypto --execute --unit-size 1 --max-bets 5Portfolio & Settlement
python scripts/kalshi/kalshi_executor.py status --save
python scripts/kalshi/risk_check.py --report positions --save
python scripts/kalshi/kalshi_settler.py settle
python scripts/kalshi/kalshi_settler.py report --detail --saveBacktesting
python scripts/backtest/backtester.py
python scripts/backtest/backtester.py --simulate --save
python scripts/backtest/backtester.py --sport mlb --confidence high --min-edge 0.10Edge-Radar ships with two slash commands for Claude Code:
| Skill | Definition | Description |
|---|---|---|
/edge-radar |
.claude/skills/edge-radar/SKILL.md |
Unified command center — scan, bet, status, settle, risk, detail, backtest across Kalshi sports, futures, and prediction markets. |
/edge-radar-analysis |
.claude/skills/edge-radar-analysis/SKILL.md |
Post-hoc performance report — trade ledger + slices by sport, category, side, edge bucket, confidence, price, calibration, longshots, streaks, daily P&L. |
/edge-radar status # Balance, positions, P&L
/edge-radar scan nba # Preview NBA opportunities
/edge-radar bet mlb --unit-size 1 # Scan + execute on confirm
/edge-radar settle # Settle + P&L report
/edge-radar-analysis 30 --save # 30-day performance report to reports/Performance/
Routes natural language to the correct scanner, enforces all risk gates, always previews before executing. All CLI flags work inline.
Note
Requires Claude Code CLI, Desktop, or IDE extension.
Gemini CLI / OpenAI Codex — add the skill content to your GEMINI.md or AGENTS.md for equivalent functionality.
Pre-built scripts scan all sports, rank by composite score, and execute with Kelly sizing. See the Automation Guide.
# Install all scheduled tasks at once
python scripts/schedulers/automation/install_windows_task.py install all| Task | Schedule | Description |
|---|---|---|
daily-summary |
4:50 AM PT | Morning P&L digest — yesterday settled + open exposure + today pending + 7d context. Emailed at 5:00 AM PT (U2, 2026-04-30) |
scan |
8:00 AM ET | Preview scan — saves report, no bets |
execute |
8:00 AM ET | Scan + execute — places live orders |
settle |
11:00 PM ET | Settle bets, update P&L |
next-day |
9:00 PM ET | Scan + execute tomorrow's games |
calibration |
2:00 AM, 1st of month | 30-day calibration report — Brier, calibration curve, prescriptive recommendations |
Reports save to reports/Sports/schedulers/ with full execution details.
Edge-Radar/
├── .claude/ # Claude Code config (skills, commands, settings)
│ ├── commands/ # Slash-command definitions
│ ├── html/ # Rendered interactive data-flow diagram
│ ├── images/ # Logos and README assets
│ └── skills/ # /edge-radar, /edge-radar-analysis
├── .devcontainer/ # VS Code dev container spec
├── .github/
│ └── workflows/ # CI/CD + Streamlit Cloud deploy
├── app/
│ └── domain/ # Typed domain objects (Opportunity, RiskDecision, Execution*)
├── docs/ # All public documentation
│ ├── kalshi-futures-betting/ # Championship futures guide
│ ├── kalshi-prediction-betting/ # Crypto, weather, S&P guides
│ ├── kalshi-sports-betting/ # 27 sport filters, MLB filtering, sports guide
│ ├── mcp-config/ # MCP server reference
│ ├── scripts/ # Per-script detailed docs
│ ├── setup/ # SETUP_GUIDE.md, AUTOMATION_GUIDE.md
│ └── web-app/ # LOCAL.md, CLOUD.md
├── prompts/ # LLM prompts for analysis agents
│ ├── futures/
│ ├── portfolio/
│ ├── predictions/
│ └── sports-betting/
├── scripts/
│ ├── backtest/ # Equity curve, calibration, strategy simulation
│ ├── kalshi/ # Scan → Size → Execute → Settle pipeline
│ ├── prediction/ # Crypto, weather, S&P 500 scanners
│ ├── shared/ # Team stats, weather, tickers, logging, odds API
│ ├── scan.py # Unified entry point (routes to each scanner)
│ ├── doctor.py # Environment & credentials validator
│ └── bootstrap.py # Import-path setup for the venv .pth file
├── tests/ # 150+ pytest tests (domain, edge detection, fills, risk)
└── webapp/ # Streamlit dashboard
└── views/ # scan_page, portfolio_page, settle_page, backtest_page
Gitignored at the root (auto-created where needed): data/ (trade history), logs/, reports/ (scan + P&L reports), keys/ (RSA private keys), .venv/, repos/.
Backtesting Framework
Analyze settled trades for win rate, ROI, profit factor, Sharpe ratio, equity curves, max drawdown, and calibration data — broken down by sport, category, confidence level, and edge bucket.
| Metric | Description |
|---|---|
| Win Rate | Settled trades that won |
| ROI | Net P&L / total wagered |
| Profit Factor | Total wins / total losses |
| Sharpe Ratio | Risk-adjusted daily P&L return |
| Max Drawdown | Largest peak-to-trough decline |
| Calibration | Predicted vs. actual win rate by bucket |
The --simulate flag runs what-if scenarios across edge thresholds, confidence tiers, and categories. Use --save to export reports.
| Guide | Description |
|---|---|
| Setup Guide | Install, API keys, .env, safe rollout, automation, and monitoring — the single end-to-end operator guide |
| Automation Guide | Windows Task Scheduler for daily betting |
| Scripts Reference | Every script, flag, and example |
| Sports Guide | 27 filters, edge detection, daily workflow |
| Futures Guide | NFL, NBA, NHL, MLB, golf championships |
| Prediction Markets | Crypto, weather, S&P 500, politics |
| Architecture | Pipeline, risk gates, data flow |
| MLB Filtering | 10 filter categories for MLB picks |
| Local Dashboard | Run the Streamlit dashboard on your machine |
| Cloud Dashboard | Deploy your own instance to Streamlit Community Cloud |
| Roadmap | All enhancements — completed & pending |
| Changelog | Full project history |
All external data is free. Only Kalshi requires a funded account.
| API | Purpose |
|---|---|
| Kalshi | Market data + order execution (API key + RSA signing) |
| The Odds API | 12 US sportsbook odds (500 free req/mo) |
| ESPN | NBA, NFL, NCAAB, NCAAF standings + line movement |
| NHL Stats API | Standings, goal differential, last 10 record |
| MLB Stats API | Standings, run differential, pitcher stats |
| NWS | Hourly forecasts for 61 NFL/MLB outdoor venues |
| CoinGecko | Crypto prices + 24h volatility |
| Yahoo Finance | S&P 500 + VIX implied volatility |
Setup • Architecture • Scripts • Changelog
Built with Python, scipy, and too many API calls — Back to top