Add README.md — project documentation
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# 🌤 WeatherBet — Polymarket Weather Trading Bot
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# 🌦️ WeatherBet — Polymarket Weather Trading Bot
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Automated weather market trading bot for Polymarket. Finds mispriced temperature outcomes using real forecast data from multiple sources across 20 cities worldwide.
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> Autonomous trading bot that exploits weather forecast errors to find mispriced Polymarket prediction markets — and self-improves over time.
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No SDK. No black box. Pure Python.
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[](https://www.python.org/downloads/)
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[](https://polygon.technology/)
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[](LICENSE)
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---
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## Versions
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## 🎯 What It Does
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### `bot_v1.py` — Base Bot
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The foundation. Scans 6 US cities, fetches forecasts from NWS using airport station coordinates, finds matching temperature buckets on Polymarket, and enters trades when the market price is below the entry threshold.
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No math, no complexity. Just the core logic — good for understanding how the system works.
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### `weatherbet.py` — Full Bot (current)
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Everything in v1, plus:
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- **20 cities** across 4 continents (US, Europe, Asia, South America, Oceania)
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- **3 forecast sources** — ECMWF (global), HRRR/GFS (US, hourly), METAR (real-time observations)
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- **Expected Value** — skips trades where the math doesn't work
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- **Kelly Criterion** — sizes positions based on edge strength
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- **Stop-loss + trailing stop** — 20% stop, moves to breakeven at +20%
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- **Slippage filter** — skips markets with spread > $0.03
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- **Self-calibration** — learns forecast accuracy per city over time
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- **Full data storage** — every forecast snapshot, trade, and resolution saved to JSON
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The bot monitors **6 US cities** (NYC, Chicago, Miami, Dallas, Seattle, Atlanta) and bets on Polymarket's temperature prediction markets using **real ECMWF weather forecasts** as its edge. When the forecast predicts a temperature bucket, but the market price implies a different probability, the bot calculates the Expected Value (EV) and places a trade if EV > threshold.
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---
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## How It Works
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## 💡 Why It Makes Money
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Polymarket runs markets like "Will the highest temperature in Chicago be between 46–47°F on March 7?" These markets are often mispriced — the forecast says 78% likely but the market is trading at 8 cents.
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**The edge is weather forecast accuracy.**
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The bot:
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1. Fetches forecasts from ECMWF and HRRR via Open-Meteo (free, no key required)
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2. Gets real-time observations from METAR airport stations
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3. Finds the matching temperature bucket on Polymarket
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4. Calculates Expected Value — only enters if the math is positive
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5. Sizes the position using fractional Kelly Criterion
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6. Monitors stops every 10 minutes, full scan every hour
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7. Auto-resolves markets by querying Polymarket API directly
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Polymarket traders rely on gut feel and consensus. This bot uses **ECMWF** — the world's most accurate weather model — to calculate the true probability of each temperature bucket, then compares it to the market price.
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---
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## Why Airport Coordinates Matter
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Most bots use city center coordinates. That's wrong.
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Every Polymarket weather market resolves on a specific airport station. NYC resolves on LaGuardia (KLGA), Dallas on Love Field (KDAL) — not DFW. The difference between city center and airport can be 3–8°F. On markets with 1–2°F buckets, that's the difference between the right trade and a guaranteed loss.
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| City | Station | Airport |
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|------|---------|---------|
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| NYC | KLGA | LaGuardia |
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| Chicago | KORD | O'Hare |
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| Miami | KMIA | Miami Intl |
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| Dallas | KDAL | Love Field |
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| Seattle | KSEA | Sea-Tac |
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| Atlanta | KATL | Hartsfield |
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| London | EGLC | London City |
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| Tokyo | RJTT | Haneda |
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| ... | ... | ... |
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---
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## Installation
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```bash
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git clone https://github.com/alteregoeth-ai/weatherbot
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cd weatherbot
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pip install requests
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```
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True Probability (from ECMWF) vs. Market Price (from Polymarket)
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```
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Create `config.json` in the project folder:
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When `Market Price < True Probability`, the market is **underpriced** → BUY.
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---
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## 🧮 The Math
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### Step 1 — True Probability (Gaussian Bucket Model)
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```python
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def bucket_prob(forecast_temp, t_low, t_high, sigma=2.0°F):
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"""
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The forecast says 72°F ± 2σ.
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What's the probability the actual high falls in the 70-75°F bucket?
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"""
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from scipy.stats import norm
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z_low = (t_low - forecast_temp) / sigma
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z_high = (t_high - forecast_temp) / sigma
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return norm.cdf(z_high) - norm.cdf(z_low)
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```
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### Step 2 — Expected Value
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```python
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def calc_ev(true_prob, market_price):
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"""
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EV = P(win) × payoff - P(lose) × cost
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If EV > 0, the market underprices this outcome.
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"""
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win = true_prob * (1 / market_price - 1) # profit if we win
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lose = (1 - true_prob) * 1 # we lose our stake
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return win - lose
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```
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**Example:**
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- Forecast: 72°F → 75% chance of 70-75°F bucket
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- Market price: $0.30 (implies 30% probability)
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- `EV = 0.75 × (1/0.30 - 1) - 0.25 = +1.25` → **Strong BUY**
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### Step 3 — Kelly Criterion (Optimal Bet Size)
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```python
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def calc_kelly(p, price):
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"""
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Kelly % = (bp - q) / b
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where b = 1/price - 1, p = true_prob, q = 1-p
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"""
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b = 1.0 / price - 1.0
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f = (p * b - (1.0 - p)) / b
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return round(min(max(f, 0.0) * KELLY_FRAC, 1.0), 4)
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def bet_size(kelly, balance):
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return round(min(kelly * balance, MAX_BET), 2)
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```
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- Uses **1/4 Kelly** (conservative fraction) to survive variance
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- Caps bet at `$2.00` per trade
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- **Only trades when EV ≥ 10%** (adaptive floor, self-improving)
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### Summary: Why This Strategy Wins
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| Component | Detail |
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|---|---|
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| **Edge** | ECMWF weather model is more accurate than consensus |
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| **Signal** | Mispriced markets when `Market Price < True Probability` |
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| **Sizing** | Kelly Criterion — mathematically optimal bet sizing |
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| **Filter** | EV ≥ 10% (adaptive), volume > $500, spread < 3% |
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| **Execution** | Real Polymarket CLOB on Polygon (not simulation) |
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| **Learning** | Self-tuning Kelly fraction + EV floor from trade history |
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---
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## 🧠 Self-Learning System
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After each trade, the bot records the outcome and adjusts its strategy:
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```
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data/learning/
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├── trade_log.json # All trades: city, bucket, cost, outcome, pnl
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└── model.json # Learned parameters per city/bucket
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```
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**Adaptation rules:**
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- Winrate < 45% → Kelly fraction ×0.8, EV floor +10%
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- Winrate > 55% + PnL > $2 → Kelly fraction ×1.1, EV floor −5%
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- Per-city winrate tracking adjusts confidence in each market
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- Starts conservative (25% Kelly) → converges to optimal as data accumulates
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---
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## ⚙️ Setup
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### Requirements
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- Python 3.13+
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- Polygon wallet with USDC.e (on chain 137)
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- Polymarket CLOB approval
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- Polymarket API credentials
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### Installation
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```bash
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git clone https://github.com/yourhandle/weatherbot.git
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cd weatherbot
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python3.13 -m venv venv
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source venv/bin/activate
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pip install -r requirements.txt
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```
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### Configuration
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Create `.env`:
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```env
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PK=your_polygon_private_key
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WALLET=your_polygon_address
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SIG_TYPE=0
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```
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Edit `config.json`:
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```json
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{
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"balance": 10000.0,
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"max_bet": 20.0,
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"min_ev": 0.05,
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"max_price": 0.45,
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"min_volume": 2000,
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"min_hours": 2.0,
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"max_hours": 72.0,
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"kelly_fraction": 0.25,
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"max_slippage": 0.03,
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"balance": 0,
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"max_bet": 2.0,
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"min_ev": 0.10,
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"min_volume": 500,
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"scan_interval": 3600,
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"calibration_min": 30,
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"vc_key": "YOUR_VISUAL_CROSSING_KEY"
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"telegram_bot_token": "your_token",
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"telegram_chat_id": "your_chat_id"
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}
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```
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Get a free Visual Crossing API key at visualcrossing.com — used to fetch actual temperatures after market resolution.
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### Run
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---
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## Usage
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```bash
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python weatherbet.py # start the bot — scans every hour
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python weatherbet.py status # balance and open positions
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python weatherbet.py report # full breakdown of all resolved markets
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# One-shot scan
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python bot_v3.py scan
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# Continuous trading loop
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python bot_v3.py run
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# Check status
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python bot_v3.py status
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```
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---
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## Data Storage
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## 📊 Architecture
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All data is saved to `data/markets/` — one JSON file per market. Each file contains:
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- Hourly forecast snapshots (ECMWF, HRRR, METAR)
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- Market price history
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- Position details (entry, stop, PnL)
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- Final resolution outcome
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This data is used for self-calibration — the bot learns forecast accuracy per city over time and adjusts position sizing accordingly.
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```
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bot_v3.py
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│
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├── Weather Data
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│ ├── ECMWF API — 10-day temperature forecast (primary signal)
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│ └── METAR — current obs for D+0 override
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│
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├── Signal Evaluation
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│ ├── bucket_prob() — Gaussian model → true probability
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│ ├── calc_ev() — expected value vs market price
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│ ├── calc_kelly() — optimal bet fraction
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│ └── Adaptive floor — self-learning EV threshold
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│
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├── Execution
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│ ├── py_clob_client — Polymarket CLOB on Polygon
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│ ├── place_buy_order — market order with 10s timeout
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│ └── on-chain settlement
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│
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├── Monitoring
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│ ├── Telegram — real-time trade alerts
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│ ├── Self-learning — trade_log + model.json
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│ └── 60-min loop — continuous scan
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│
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└── Market Resolution
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└── Outcome check — PnL update when market resolves
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```
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---
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## APIs Used
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## 🔐 Trading Flow
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| API | Auth | Purpose |
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|-----|------|---------|
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| Open-Meteo | None | ECMWF + HRRR forecasts |
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| Aviation Weather (METAR) | None | Real-time station observations |
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| Polymarket Gamma | None | Market data |
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| Visual Crossing | Free key | Historical temps for resolution |
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```
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1. Fetch ECMWF forecast for each city (D+0 to D+3)
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2. Query Polymarket for temperature bucket markets
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3. Calculate true probability (Gaussian model, σ=2°F)
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4. Compare to market price → calc EV
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5. If EV ≥ adaptive threshold → calculate Kelly bet size
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6. Execute market order on Polymarket CLOB (Polygon)
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7. Record trade → update self-learning model
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8. Send Telegram notification
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9. Repeat every 60 minutes
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```
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---
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## Disclaimer
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## ⚠️ Risk Management
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This is not financial advice. Prediction markets carry real risk. Run the simulation thoroughly before committing real capital.
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| Parameter | Value | Purpose |
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| Max bet | $2.00 | Cap per-trade exposure |
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| Kelly fraction | 25% | Survive variance (1/4 Kelly) |
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| Min EV | 10%+ | Only trade positive EV |
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| Min volume | $500 | Avoid illiquid markets |
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| Max spread | 3% | Avoid high-slippage markets |
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| Adaptive floor | 10-20% | Self-tuning from performance |
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---
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## 📦 Tech Stack
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- **Language:** Python 3.13
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- **Trading:** [py_clob_client](https://github.com/polymarket/py-clob-client) — Polymarket CLOB
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- **Weather:** ECMWF OpenMETAR / Open-Meteo API
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- **Chain:** Polygon (Chain ID 137) — USDC.e stablecoin
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- **Notifications:** Telegram Bot API
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- **Self-learning:** Pure Python JSON persistence (no DB needed)
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---
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## 📝 Disclaimer
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This bot trades real markets with real money. Past performance does not guarantee future results. Trade at your own risk. The bot is provided as-is for educational and research purposes.
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---
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*Built with 🐍 on Polygon — autonomous weather prediction trading.*
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