A trading strategy isn't just about picking winners — it's about having a repeatable process that generates positive expected value over time. Weather markets are uniquely suited to systematic strategies because the inputs (forecast models) and outputs (observed weather) are measurable.
Why Systematic Beats Intuitive
Most weather market participants trade on intuition: "it feels like it'll rain" or "the weather app says 80°F." This creates persistent mispricings because:
- Weather apps show deterministic forecasts, not probabilities. "High of 75°F" doesn't tell you the probability of exceeding 75°F.
- Recency bias causes traders to overweight yesterday's weather
- Most traders don't check multiple models or consider ensemble uncertainty
A systematic approach using model consensus exploits all three of these edges.
Step 1: Define Your Universe
Don't try to trade every contract. Focus on: - Cities you understand: Start with 2-3 cities. Weather patterns vary dramatically — Phoenix thermals work differently than Boston nor'easters. - Categories with strong model skill: Temperature forecasts are more reliable than precipitation. Start there. - Time horizons: Day 1-2 contracts have the most accurate model inputs. Day 3+ introduces more uncertainty.
Step 2: Set Entry Criteria
Your edge score threshold determines your selectivity: - Score ≥ 70: High-confidence signals. Fewer trades but higher expected hit rate. - Score 50-69: Moderate signals. More volume but lower individual confidence. - Score < 50: Generally noise. Avoid unless you have additional context.
Also require: - Both GFS and ECMWF agreeing (ensemble concordance) - Bias-adjusted edge still positive - Sufficient liquidity (bid-ask spread < $0.08)
Step 3: Size Your Positions
The Kelly Criterion provides a mathematical framework for position sizing:
Kelly fraction = (edge × odds - 1) / (odds - 1)
In practice, use fractional Kelly (25-50% of the Kelly-optimal size) to reduce variance. Never risk more than 5% of your bankroll on a single contract.
Step 4: Paper Trade First
Celsi's paper trading portfolio lets you execute your strategy without financial risk. Track for at least 50 trades to get a statistically meaningful sample.
Key metrics to monitor: - Hit rate: What percentage of your trades resolve correctly? - Average edge at entry: Are you finding real edge or noise? - P&L curve: Is it trending upward or flat?
Step 5: Track and Adapt
Weather model accuracy isn't static. Seasonal changes, model updates, and market efficiency shifts mean your strategy needs periodic review.
Use Celsi's verification dashboard to monitor: - Is your preferred model's bias changing? - Are edge scores compressing (market getting more efficient)? - Which cities/categories generate the most consistent edge?
Common Mistakes
- Overtrading: Taking every signal dilutes your edge. Be selective.
- Ignoring bias: Raw model output ≠ reality. Always check city-specific bias.
- Chasing losses: A losing streak doesn't mean your strategy is broken — it means variance is real. Review your process, not your results.
- Ignoring liquidity: A 20-point edge means nothing if you can't get filled at a reasonable price.
Expected Results
A well-calibrated weather trading strategy targeting 60+ edge scores should aim for: - 55-65% hit rate (after bias correction) - Positive expected value per trade of 8-15 cents - Sharpe ratio > 1.0 over a 100+ trade sample
Start with paper trading, validate your edge, then scale gradually.