Spread Velocity Metrics for Weather Derivatives Traders
Weather derivatives traders access Resolved Markets to monitor continuous orderbook snapshots for 30 cities' daily weather prediction contracts on Polymarket. The platform captures full bid/ask depth across temperature, precipitation, and condition markets with millisecond-precision timestamps, enabling traders to identify probability shifts before major weather systems move. With 11.4M+ historical snapshots and 20Hz capture rates, traders can model weather sentiment evolution, detect hedging flows from agricultural and energy participants, and correlate prediction market repricing with commodity futures. The unified API provides seamless integration with commodities, energy, and agriculture portfolios, making Resolved Markets essential infrastructure for weather derivative strategy implementation.
Weather derivatives traders use Spread Velocity Metrics to price climate risk against Polymarket consensus. Resolved Markets captures orderbook microstructure signals from Polymarket with tick-level features extracted from full bid/ask depth, including spread compression, depth imbalance, queue position, quote flicker that map directly to physical weather exposure.
Data challenges Weather Derivatives Traders run into
Spread Velocity Metrics from Resolved Markets is built around the data gaps Weather Derivatives Traders hit when they try to work with raw Polymarket feeds.
Weather forecasts from meteorological services lack market pricing insights
Traditional weather data comes from meteorological services (NWS, NOAA) providing probabilistic forecasts: '70% chance of rain.' But these forecasts ignore how market participants actually value weather outcomes. A 70% rain forecast might translate to 40% odds in derivative markets if precipitation would help crops (bullish for farmers) but only 20% if it would damage already-wet fields. Resolved Markets shows the market's true pricing via Polymarket orderbooks, revealing sophisticated participants' risk assessments you can trade against.
Delayed hedging flow detection from agricultural and energy markets
Agricultural hedgers and energy traders react to weather prediction changes hours or days before those changes appear in meteorological models. Polymarket weather prediction markets represent early sentiment about weather development. When major hedge funds start buying 'warm tomorrow' contracts, they're detecting climate pattern shifts before forecasters. Resolved Markets' 20Hz capture in crypto markets shows microsecond-scale sentiment shifts; weather markets reveal similar timing in macro sentiment. Traders who detect these hedging flows first gain edge.
Difficulty quantifying uncertainty in weather outcomes across multiple timeframes
Single-point probability estimates (70% rain) oversimplify reality. Weather outcomes have distributions: 'Most likely 2-3 inches, but 10% tail risk of 6+ inches.' Orderbook structure reveals this: dense clustering at certain temperature/precipitation levels indicates consensus, while wide spreads at extreme outcomes indicate uncertainty. Resolved Markets provides full depth arrays showing exactly where the market clusters conviction and where tail risk is priced. This distribution view is impossible from point forecasts.
Limited history of prediction market prices for weather-driven derivatives
Most weather derivative platforms don't maintain historical price data. Your models can't backtest strategies using prediction market repricing patterns, because those patterns aren't archived. Resolved Markets maintains 11.4M+ snapshots across all tracked weather markets. Train models on how orderbook depth evolved before major storms, how spreads expanded during uncertainty windows, and how quickly consensus formed as outcomes crystallized.
Built for quantitative work on Spread Velocity Metrics
Orderbook-level prediction-market data that doesn't exist anywhere else.
Millisecond-precision tracking of weather outcome probabilities across 30 cities
Meteorological forecasts update every 6-12 hours. Resolved Markets updates every snapshot (variable frequency for weather, but continuous monitoring). When a major weather system intensifies unexpectedly, prediction markets often detect it within hours via orderbook shifts. Temperature and precipitation contracts reprices as sophisticated participants (agricultural hedgers, energy traders) update expectations. Your algorithms detect these repricing patterns minutes or hours before official forecast updates, enabling edge on derivative positioning.
Full bid/ask depth reveals market consensus and tail risk distribution shapes
The orderbook depth distribution reveals risk perception. When rain probability contracts show tight clustering at 60-65% odds with sparse offers below 40%, the market has consensus that rain is likely, but tail risks (severe drought) are severely underpriced by consensus and overpriced by hedgers. This shape is invisible in point estimates. Resolved Markets' depth arrays let you engineer features on spread shape, concentration, and tail density—precise tools for weather risk pricing.
11.4M+ historical snapshots enable weather pattern prediction model development
Train machine learning models on 11.4M+ snapshots to predict weather market repricing patterns. Sequences like 'temperatures rising from 72F to 75F likelihood, precipitation falling from 35% to 25%, and volatility expanding' predict specific outcomes in the next 24 hours. Backtest across diverse weather seasons (winter storms, summer droughts, hurricane season) to build models robust to regime changes. Deploy live predictions via WebSocket to detect similar patterns in real-time.
Early hedging flow detection from agricultural and energy market participants
Commodity futures (corn, wheat, crude oil, natural gas) are driven substantially by weather. When Polymarket weather derivatives for agricultural regions suddenly reprice, sophisticated traders know commodity volatility is coming. Resolved Markets enables you to monitor all 30 weather cities simultaneously, detect probability shifts before commodity markets overreact, and position ahead of agricultural seasonal demand shifts. Cross-market awareness makes you smarter about weather-driven macro hedging.
How Weather Derivatives Traders use Spread Velocity Metrics
Seven categories, hundreds of markets
Prediction markets across crypto, sports, economics, weather, and more — live and historical orderbook data, all queryable through one API.
Crypto
BTC, ETH, SOL, XRP — up/down markets every 5m to 1d.
Equities
S&P 500 (SPX) daily open — up or down predictions.
Social
Elon Musk tweet counts — weekly prediction ranges.
Sports
NBA, NFL, EPL — game outcomes and season predictions.
Economics
Fed decisions, jobs reports — FOMC meetings and macro data.
Weather
44 cities daily — temperature, hurricanes, Arctic ice.
Hyperliquid
BTC, ETH, SOL, XRP perp orderbooks — 1/sec sampling.
Tick-level orderbook snapshots
Every snapshot includes full bid/ask depth, mid prices, spreads, and crypto spot price.
| Side | Bid | Size | Ask | Size | Spread |
|---|---|---|---|---|---|
| UP | 0.5400 | 1,240 | 0.5500 | 1,100 | 1.00% |
| UP | 0.5300 | 980 | 0.5600 | 1,450 | 3.00% |
| UP | 0.5200 | 1,560 | 0.5700 | 890 | 5.00% |
| UP | 0.5100 | 2,100 | 0.5800 | 2,300 | 7.00% |
| UP | 0.5000 | 1,800 | 0.5900 | 1,700 | 9.00% |
| UP | 0.4900 | 3,200 | 0.6000 | 3,100 | 11.00% |
cryptoLowCardinality(String)BTCtimeframeLowCardinality(String)5mtoken_sideEnum8('UP','DOWN')UPtimestampDateTime64(3)2026-05-09 03:14:12.061crypto_priceFloat64$80,471.01best_bidFloat640.5400best_askFloat640.5500mid_priceFloat640.5450spreadFloat640.0100bidsArray(Tuple(F64,F64))[(0.54,1240),...]asksArray(Tuple(F64,F64))[(0.55,1100),...]Comprehensive market coverage
Prediction markets across multiple categories, captured continuously with high-frequency precision.
Spread Velocity Metrics ships with
What Weather Derivatives Traders build with Spread Velocity Metrics
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Three steps from signup to live Spread Velocity Metrics in your application.
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Sign Up FreeExplore the API
Browse 11 endpoints with live examples. Test requests directly from the docs.
API ReferenceStart Building
Integrate live Spread Velocity Metrics into your research pipeline, trading bot, or analytics platform.
fetch('/v1/markets/live', { headers: { 'X-API-Key': key } })
curl -H 'X-API-Key: rm_xxx' 'https://api.resolvedmarkets.com/v1/markets/live' | jq '.[] | select(.category=="weather")'rm-api download --category weather --days 90Wiring Spread Velocity Metrics into your workflow
Weather desks integrate Spread Velocity Metrics via REST for daily snapshot pulls, WebSocket for live storm-event tracking, and the CLI for historical seasonal studies.
- PyTorch Geometric example for orderbook GNNs
- Reference implementation of VPIN in the Python SDK
Why Weather Derivatives Traders pick Spread Velocity Metrics
- 30-city weather prediction market coverage with full orderbook depth reveals market consensus and tail risk distributions impossible to see in point forecasts
- 20Hz-equivalent capture rates and millisecond timestamps enable detection of hedging flows and sentiment shifts hours before meteorological forecast updates
- 11.4M+ historical snapshots with temperature, precipitation, and condition outcomes enable training of weather market repricing prediction models
- Unified API enables correlation analysis between weather prediction repricing and commodity futures, unlocking cross-asset hedging strategies
Why Spread Velocity Metrics matters
Spread Velocity Metrics matters for weather trading because physical exposure needs a parallel pricing source. tick-level features extracted from full bid/ask depth on spread compression, depth imbalance, queue position, quote flicker delivers exactly that, in a structured daily feed.
Spread Velocity Metrics in context
Weather derivatives have always been data-constrained. Spread Velocity Metrics from Resolved Markets adds prediction-market consensus to the modeling toolkit, with tick-level features extracted from full bid/ask depth on spread compression, depth imbalance, queue position, quote flicker providing daily structured snapshots.
Frequently asked: Spread Velocity Metrics for Weather Derivatives Traders
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How does Polymarket weather data compare to meteorological forecasts like NOAA?
NOAA provides scientific forecasts ('65% chance of rain'), but doesn't price risk. Polymarket reveals how markets value that risk: maybe rain probability is priced at 40% despite NOAA's 65%, indicating sophisticated traders think NOAA is wrong. Or rain might be 80% in Polymarket while NOAA says 65%, showing hedgers are over-pricing tail risk. Resolved Markets gives you the market's probability estimate, which differs materially from scientific forecasts and creates trading opportunities.
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Can we detect agricultural hedging flows through weather prediction markets?
Yes, this is a primary use case. When farmers face drought risk, they buy rain probability contracts (they profit if rain falls). Large buys on rain outcomes signal hedging demand. Resolved Markets' orderbook snapshots show exactly when large buy orders appear, their sizes, and implied volume. You can model: 'Large buy orders appeared on rain outcomes for Midwest cities 2 days before corn futures rallied.' Detect these patterns in real-time to frontrun commodity moves.
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What's the geographic coverage of the 30-city weather tracking?
Our 30 cities span major agricultural regions (Midwest corn belt), energy production centers (Texas, Gulf Coast), population centers (NYC, LA, Chicago), and commodity exchange hubs. Coverage includes US dominance with strategic global cities. Each city has continuous prediction contracts for temperature ranges, precipitation probability, and weather conditions. The API allows you to query by city, region, or all 30 simultaneously. Custom coverage can be discussed for specific hedging portfolios.
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How far in advance do weather prediction markets reprice ahead of storms?
Historical data shows repricing typically accelerates 48-72 hours before major weather events, as meteorological models converge on potential outcomes. Polymarket participants start positioning as confidence increases. Resolved Markets' historical archive enables you to backtest: train models on the sequence of repricing patterns (spread widening, then narrowing as consensus forms) to detect emerging storms. In real-time, WebSocket streaming lets you track probability evolution, set alerts when repricing accelerates.
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Can we correlate weather prediction repricing with commodity futures timing?
Yes, our API supports batch queries across multiple markets. Query weather prediction orderbooks and export to join with commodity futures data (from your existing sources). The unified timestamp ensures precise alignment. Analyze: when did Polymarket weather predictions reprice? When did commodity futures subsequently move? Build lagged correlation models. Deploy live: stream Polymarket weather updates via WebSocket, monitor for repricing patterns, and execute hedge trades in commodity futures based on the lead-lag relationships.
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Does Spread Velocity Metrics include historical weather seasons?
Yes. ClickHouse history covers months of Spread Velocity Metrics, allowing weather desks to study seasonal patterns and replay prior storm seasons.
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Which weather markets does Spread Velocity Metrics cover?
Spread Velocity Metrics spans spread compression, depth imbalance, queue position, quote flicker — 30-city temperature ranges, hurricane probabilities, Arctic ice limits, and other climate-related Polymarket contracts.
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How do weather traders use Spread Velocity Metrics for hedging?
Traders compare implied probabilities from Spread Velocity Metrics against NOAA forecasts and physical weather exposure to size hedges and price climate risk.
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How do I compute VPIN from Spread Velocity Metrics?
Bucket trades by volume from the Spread Velocity Metrics time series, then compute the absolute difference between buy-side and sell-side volume per bucket. VPIN is the moving average of those differences. Most quant teams ship a 50-line Python implementation.
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Does Spread Velocity Metrics include derived features or just raw orderbook?
Both. Spread Velocity Metrics ships raw bid/ask arrays plus derived best_bid, best_ask, mid_price, and spread columns. You can compute additional features (depth imbalance, queue position, VPIN) from the raw arrays.