Strategy Guide
Weather Trader Complete Guide
From NOAA data setup to odds analysis, city selection to Simmer configuration. Master weather prediction markets with a data-driven approach.
📡 NOAA Data Access Tutorial
The National Oceanic and Atmospheric Administration (NOAA) provides free, authoritative weather data — the core data source for weather trading.
Step 1: Register for NOAA API Access
- Visit NOAA API Documentation
- No API key required (free public access), but rate limited: 1000 requests/hour
- Optional registration for update notifications
Step 2: Fetch City Weather Data
# Python Example: Fetch NYC Weather Data
import requests
# NOAA API endpoint
station_id = "KNYC" # NYC Central Park weather station
url = f"https://api.weather.gov/stations/{station_id}/observations/latest"
response = requests.get(url)
data = response.json()
# Extract key data
temperature = data['properties']['temperature']['value'] # Celsius
humidity = data['properties']['relativeHumidity']['value'] # Percentage
wind_speed = data['properties']['windSpeed']['value'] # km/h
precipitation = data['properties']['precipitationLastHour']['value'] # mm
print(f"Temperature: {temperature}°C")
print(f"Humidity: {humidity}%")
print(f"Wind Speed: {wind_speed} km/h")
print(f"Precipitation: {precipitation} mm")
Step 3: Fetch Historical Weather Data
# Fetch historical data (for backtesting)
from datetime import datetime, timedelta
def get_historical_weather(station_id, date):
"""Get weather data for a specific date"""
url = f"https://api.weather.gov/stations/{station_id}/observations/{date}"
response = requests.get(url)
return response.json()
# Example: Get data for 2025-12-25
historical_data = get_historical_weather("KNYC", "2025-12-25T12:00:00Z")
Step 4: Major City Weather Station Codes
| City | Station Code | Notes |
|---|---|---|
| New York (NYC) | KNYC | Central Park |
| Chicago | KORD | O'Hare Airport |
| Los Angeles | KLAX | LAX Airport |
| Seattle | KSEA | Seattle-Tacoma Airport |
| Miami | KMIA | Miami International Airport |
| Boston | KBOS | Logan Airport |
⚠️ Data Quality Notes:
- NOAA data may have 15-30 minute delay (not real-time)
- Some stations may pause reporting during extreme weather
- Recommend cross-validating with multiple sources (AccuWeather, Weather.com)
📊 Weather Market Odds Analysis Framework
1. Calculate Implied Probability
Convert Polymarket odds to implied probability:
# Odds to implied probability
def odds_to_probability(odds):
"""Decimal odds to probability"""
return 1 / odds
# Example: YES odds 2.5, NO odds 1.6
yes_odds = 2.5
no_odds = 1.6
yes_prob = odds_to_probability(yes_odds) # 0.40 = 40%
no_prob = odds_to_probability(no_odds) # 0.625 = 62.5%
# Normalize (remove platform vigorish)
total = yes_prob + no_prob
yes_prob_normalized = yes_prob / total # 39%
no_prob_normalized = no_prob / total # 61%
2. Compare with NOAA Forecast Probability
Get NOAA official forecast probability, identify value bet opportunities:
| Market | Polymarket Implied Prob | NOAA Forecast Prob | Value Assessment |
|---|---|---|---|
| NYC 12/25 Temp > 10°C | 40% | 65% | ✅ YES Value Bet |
| Chicago 12/25 Snow > 5cm | 70% | 45% | ✅ NO Value Bet |
| Seattle 12/25 Rain > 0mm | 55% | 52% | ❌ No Significant Value |
3. Value Bet Screening Criteria
- Minimum probability gap: NOAA prob - Polymarket prob > 15%
- Confidence: NOAA forecast published < 24 hours ago
- Liquidity: Market volume > 10k USDC
📊 Real Case Studies
Case 1: Successful Trade — NYC Warm Winter Prediction
Background: December 2025, Polymarket market "Will NYC temperature exceed 15°C on Dec 25?"
Market analysis:
- Polymarket implied probability: YES 35% (odds 2.85)
- NOAA 10-day forecast: 68% probability temp > 15°C
- Historical data: 10-year average temp 12°C on this date, but 2025 El Niño significant
Trade decision:
- Buy YES @ 2.85, position 500 USDC
- Expected value: (0.68 × 500 × 1.85) - (0.32 × 500) = 467 USDC
Result: Actual temp 17°C on 12/25, profit 425 USDC (+85%)
Key success factors: Significant NOAA vs market pricing gap + El Niño macro context
Case 2: Failed Trade — Chicago Blizzard Misjudgment
Background: January 2026, market "Will Chicago receive > 30cm snow in January?"
Market analysis:
- Polymarket implied probability: YES 55%
- NOAA monthly forecast: 40% probability (lower than market)
- But social media rumors: "Historic blizzard coming"
Poor decision:
- Followed market sentiment, bought YES @ 1.82
- Ignored NOAA long-term forecast lower probability
- Position too large (1000 USDC)
Result: January total snowfall only 18cm, lost 1000 USDC (-100%)
Lesson: Should not ignore authoritative data sources, avoid being swayed by market sentiment
Case 3: Seasonal Strategy — Seattle Rainy Season Arbitrage
Background: November 2025 - February 2026, Seattle winter rainfall markets
Strategy logic:
- Historical data: Seattle Nov-Feb average 45 rainy days
- Polymarket markets priced based on short-term forecasts (7 days)
- Exploit long-term climate data vs short-term market pricing gap
Execution:
- Weekly rolling buy "Week rain > 3 days" YES bets
- Average odds 2.1, historical win rate 58%
- Kelly formula optimal position: 8% per trade
Result: 14-week trading, total return 67%, max drawdown 15%
Key success factors: Long-term climate data advantage + disciplined position management
🏙️ City Selection Strategy
Not all city weather markets are worth trading. Here are the selection criteria:
Priority 1: High Liquidity Markets
| City | Avg Daily Volume | Recommendation | Rationale |
|---|---|---|---|
| New York (NYC) | 50k+ USDC | ⭐⭐⭐⭐⭐ | High population, high attention, excellent data quality |
| Chicago | 20k+ USDC | ⭐⭐⭐⭐ | Frequent extreme weather, active markets |
| Los Angeles | 15k+ USDC | ⭐⭐⭐⭐ | Stable climate, suitable for conservative strategies |
| Seattle | 10k+ USDC | ⭐⭐⭐ | Predictable rainy season, seasonal opportunities |
| Miami | 8k+ USDC | ⭐⭐⭐ | Hurricane season high volatility, high risk/reward |
Priority 2: Data Availability
- Prefer: Cities with complete NOAA historical data (30+ years)
- Avoid: Cities with sparse weather stations, poor data quality
Priority 3: Seasonal Opportunities
| Season | Recommended Cities | Trading Themes |
|---|---|---|
| Winter (Dec-Feb) | Chicago, Boston, NYC | Snowfall, temperature records |
| Spring (Mar-May) | Seattle, Portland | Rainy days, flood risk |
| Summer (Jun-Aug) | Miami, Houston | Hurricanes, heat records |
| Fall (Sep-Nov) | California cities | Wildfire risk, Santa Ana winds |
⚙️ Simmer Weather Trading Configuration
# Simmer Weather Trading Configuration Example
# File: simmer-config-weather.yaml
strategy:
name: "Weather Trader - NOAA Data Driven"
type: "weather"
data_sources:
primary: "NOAA API"
backup: "AccuWeather API"
refresh_interval: 3600 # Update hourly
target_markets:
cities: ["NYC", "Chicago", "Seattle", "Boston"]
market_types: ["temperature", "precipitation", "snow"]
min_liquidity: 10000 # Min volume 10k USDC
filters:
min_probability_edge: 0.15 # Min probability edge 15%
max_forecast_age: 86400 # Max forecast age 24 hours
min_odds: 1.8 # Min odds
max_odds: 5.0 # Max odds (avoid long tail)
risk_management:
max_position_size: 300 # Max 300 USDC per trade
max_daily_trades: 5 # Max 5 trades/day
max_weekly_loss: 500 # Weekly loss limit
max_drawdown: 0.20 # Max drawdown 20%
kelly_fraction: 0.5 # Kelly fraction (half-Kelly)
execution:
entry_timing: "T-24h" # Enter 24h before event
exit_timing: "T-1h" # Exit 1h before event (if tradable)
slippage_tolerance: 0.03 # 3% slippage tolerance
monitoring:
alert_on_entry: true
alert_on_exit: true
daily_report: true
weekly_performance_review: true
Configuration Notes
- data_sources: NOAA primary, AccuWeather backup for data reliability
- filters: Probability edge threshold is critical, avoid marginal trades
- risk_management: Use Kelly formula for dynamic position sizing
- execution: Timing affects odds, enter early for better prices
❓ Frequently Asked Questions
Q1: How much capital do I need to start weather trading?
A: Recommend at least 3000 USDC. Reasons:
- Single trades 200-500 USDC, need diversification across cities/events
- Weather has uncertainty, reserve 50% capital for consecutive losses
- Seasonal strategies require sustained positions
Q2: How to get real-time weather data?
A: Three options:
- NOAA API (free, 15-30 min delay)
- AccuWeather API (paid, real-time data)
- WeatherAPI (free tier + paid tier, balanced option)
Q3: Best time window for weather trading?
A: Depends on market type:
- Daily events (temp/precip): Enter 24-72 hours in advance
- Monthly events (total snowfall): Enter at month start, adjust mid-month
- Seasonal events (hurricane season): Pre-season positioning, rolling adjustments
Q4: How to handle forecast errors?
A: Three-layer protection:
- Cross-validate with multiple data sources (NOAA + AccuWeather)
- Only trade probability edge > 15%
- Strict position management, max 10% of capital per trade
Q5: Which seasons are best for weather trading?
A: Different opportunities each season:
- Winter: Snow, temperature markets (high volatility)
- Spring: Rain, flood markets (medium volatility)
- Summer: Hurricanes, heat markets (high volatility)
- Fall: Wildfire, storm markets (regional opportunities)