How the Commodity
Seasonal Calendar Works
The Commodity Seasonal Calendar aggregates multi-decade historical futures data, peer-reviewed agricultural economics research, and government supply-demand reports to identify recurring seasonal buy and sell windows across 13 commodities. This page explains exactly where the data comes from, how signals are classified, and what the limitations are.
Important Disclaimer
This calendar is for educational and informational purposes only. Seasonal patterns represent historical tendencies, not guarantees of future performance. Commodity markets are subject to geopolitical events, weather, supply shocks, and macroeconomic forces that can override seasonal tendencies in any given year. Nothing on this site constitutes financial, investment, or trading advice. Always conduct your own due diligence and consult a qualified financial professional before making trading or purchasing decisions.
Our Methodology
Seasonal analysis is only as reliable as the methodology behind it. We follow a five-step process to ensure every signal on the calendar is grounded in data, cross-validated, and anchored to a real-world fundamental driver.
Multi-Decade Historical Data
Seasonal signals are derived from a minimum of 15 years of continuous front-month futures price data, with primary sources using 20–38 year datasets. Short-term anomalies (single-year supply shocks, geopolitical events) are smoothed by the length of the historical window.
Cross-Source Validation
Every seasonal signal is validated against at least two independent sources before being included. A signal that appears in Hirsch's Almanac but is not corroborated by MRCI or Equity Clock data is either downgraded to ACCUM or excluded entirely.
Signal Classification
Months are classified as BUY (primary seasonal buy window, historically strong with >60% win rate), ACCUM (secondary buy / accumulate, moderate strength), SELL (historically weak, reduce or avoid), or NEUT (transitional, no strong directional bias). Average monthly returns are shown where reliable multi-decade data is available.
Fundamental Anchoring
Each seasonal pattern is anchored to a fundamental demand driver — not just price history. Crude oil seasonality reflects the U.S. driving season and refinery maintenance cycles. Gold seasonality reflects Indian wedding and festival demand. Corn seasonality reflects USDA planting and harvest calendars. This prevents the calendar from being purely data-mined.
Annual Review
Seasonal data is reviewed annually as new crop years, energy cycles, and market structure changes accumulate. Patterns that have weakened significantly over the most recent 5-year period are flagged or reclassified. The calendar reflects the most recent available data from each source.
Signal Classification Key
Primary seasonal buy window. Historically strong with >60% win rate across the study period. Average monthly return is positive.
Secondary buy / accumulate. Moderate seasonal strength. Consider building a position but with tighter risk management.
Historically weak period. Seasonal headwinds dominate. Reduce existing longs or avoid new entries.
Neutral / transitional month. No strong directional seasonal bias. Trade based on other technical or fundamental factors.
Primary Data Sources
The following sources are used to construct and validate the seasonal signals displayed in the calendar. Each source is evaluated for data depth (years of history), methodology transparency, and independence from the others.
The foundational text for commodity seasonality research. Jeffrey Hirsch, editor of the annual Stock Trader's Almanac (first published by his father Yale Hirsch in 1966), published the Commodity Trader's Almanac identifying recurring seasonal patterns across energy, metals, and agricultural futures markets. His five-pillar framework integrates seasonality with fundamentals, technicals, monetary policy, and sentiment.
MRCI is the industry standard for commodity seasonal pattern research, providing computer-generated seasonal studies based on 15- and 30-year historical futures price data. Their methodology calculates the percentage of years a given trade was profitable over the study period, providing a statistically grounded basis for seasonal entry and exit windows.
Equity Clock provides seasonal charts for major commodity futures contracts based on 20 years of continuous front-month price data. Their charts display the average monthly return and the percentage of years each month was positive, offering a clean visual confirmation of seasonal tendencies across energy, metals, and agricultural markets.
The University of Illinois farmdoc project and Purdue University's agricultural economics department have published peer-reviewed studies on fertilizer price seasonality spanning 38+ years (1967–2009 and beyond). Their research on nitrogen (anhydrous ammonia, urea), phosphate (DAP), and potash (MOP) seasonal pricing forms the backbone of the fertilizer section of this calendar.
The EIA publishes authoritative data on crude oil and natural gas supply, demand, storage, and price seasonality. Their weekly petroleum status reports and natural gas storage reports provide the fundamental demand drivers (driving season, heating season, injection/withdrawal cycles) that underpin the energy seasonal patterns shown in this calendar.
The USDA's monthly WASDE report is the primary driver of agricultural commodity price seasonality. Planting intentions (March), crop progress (May–August), and harvest estimates (September–November) create predictable seasonal price patterns in corn, wheat, and soybeans that have been documented across multiple decades of futures market data.
Seasonax and SeasonalCharts.de provide independent quantitative seasonal analysis for commodity futures, cross-validating patterns identified in MRCI and Equity Clock data. Their tools calculate average returns, win rates, and statistical significance for seasonal windows, providing an additional layer of verification for the signals displayed in this calendar.
Uranium seasonal patterns are derived from World Nuclear Association market reports and UxC (Ux Consulting Company) uranium market data. Unlike exchange-traded commodities, uranium is traded via long-term utility contracts, creating a distinct seasonal procurement cycle tied to utility budget cycles (Q4–Q1) and annual refueling outage schedules.
CRU Group and IMARC Group publish annual and quarterly fertilizer market reports covering sulfur, phosphate, potash, and nitrogen pricing cycles. Their analysis of global supply chains, Chinese export windows, and agricultural application calendars informs the seasonal patterns for the fertilizer category in this calendar.
Sources by Commodity
The table below shows the primary and secondary data sources used for each commodity, along with the fundamental demand driver that anchors the seasonal pattern.
| Commodity | Primary Source | Secondary Source | Fundamental Driver |
|---|---|---|---|
| Crude Oil (WTI/CL) | EIA, Equity Clock (20yr) | MRCI, Hirsch Almanac | U.S. driving season, refinery maintenance |
| Natural Gas (NG) | EIA, Equity Clock (20yr) | MRCI, Seasonax | Heating season (Nov–Feb), cooling season (Jul–Aug) |
| Gold | Equity Clock (20yr), MRCI | Hirsch Almanac, SeasonalCharts | Indian festival/wedding demand, USD cycle |
| Silver | Equity Clock (20yr), MRCI | Seasonax | Industrial demand cycle, gold correlation |
| Copper | Equity Clock (20yr), MRCI | SeasonalCharts | Chinese construction season, LME inventory cycle |
| Corn | USDA WASDE, MRCI | Equity Clock, Hirsch Almanac | Planting (Mar–May), harvest (Sep–Oct) |
| Wheat | USDA WASDE, MRCI | Equity Clock, Hirsch Almanac | Winter/spring wheat harvest pressure (Jun–Aug) |
| Soybeans | USDA WASDE, MRCI | Equity Clock, Hirsch Almanac | South American harvest (Feb–Apr), U.S. harvest (Oct) |
| Uranium (U3O8) | World Nuclear Assoc., UxC | Cameco reports | Utility contract procurement cycle (Q4–Q1) |
| Nitrogen (Urea/NH₃) | Purdue Univ. (38yr study), farmdoc | CRU Group | Spring planting demand (Mar–May), fall pre-buy (Aug–Nov) |
| Phosphate (DAP/MAP) | Purdue Univ. (38yr study), farmdoc | IMARC Group | Spring application season, Chinese export windows |
| Potash (MOP) | Purdue Univ. (38yr study), farmdoc | CRU Group | Spring/fall application seasons, Canpotex contract cycles |
| Sulfur (S) | CRU Group, IMARC Group | SunSirs, 888Chem | Oil refinery by-product supply, spring sulfuric acid demand |
Known Limitations
Patterns Can Weaken Over Time
As seasonal patterns become widely known and traded, their predictive power can diminish. This is well-documented in academic literature on the 'January Effect' in equities, which has weakened significantly since the 1980s. We monitor for this in annual reviews.
Supply Shocks Override Seasonality
Geopolitical events (wars, sanctions), extreme weather (droughts, hurricanes), and unexpected policy changes can override seasonal tendencies in any given year. The 2022 natural gas crisis is a prime example of seasonality being overwhelmed by a structural supply shock.
Futures vs. Spot Price Differences
Seasonal patterns are derived from futures contract data, which includes roll costs, contango/backwardation, and basis risk. Physical commodity buyers (e.g., farmers purchasing fertilizer) may experience different price dynamics than futures traders.
Data Mining Risk
Any analysis of historical price data carries the risk of overfitting — finding patterns that existed in the past but have no predictive power going forward. We mitigate this by requiring fundamental anchoring for every signal and cross-validating across multiple independent sources.
Uranium Market Opacity
Unlike exchange-traded commodities, uranium is traded via private long-term contracts. Seasonal patterns are derived from spot price indices (UxC, TradeTech) which represent a small fraction of total market volume. Uranium seasonality is less statistically robust than exchange-traded commodities.
Fertilizer Regional Variation
Fertilizer price seasonality varies significantly by region (U.S. Corn Belt vs. Europe vs. Brazil). The patterns shown reflect primarily U.S. and North American market dynamics based on Purdue University and farmdoc research.
About This Project
The Commodity Seasonal Calendar was built to make professional-grade seasonal analysis accessible to individual traders, farmers, and commodity market participants who previously had to purchase expensive subscriptions to MRCI or Seasonax to access this type of data. The calendar synthesizes publicly available research from government agencies, universities, and industry publications into a single, easy-to-use visual interface.
The project is inspired by the work of Jeffrey Hirsch, editor of the Stock Trader's Almanac and author of the Commodity Trader's Almanac, whose decades of research demonstrated that seasonal patterns in financial and commodity markets are real, persistent, and exploitable — when used as one input among many, not as a standalone trading system.
The site is updated annually as new data becomes available. Community members can log in to leave notes on specific commodity/month combinations, sharing real-world observations that complement the historical data.