Seasonality Price Drift

Overview

This pillar analyzes recurring, predictable price patterns in assets, particularly commodities, based on specific times of the year. It identifies historical tendencies for prices to rise or fall during certain calendar windows, providing a statistical edge.

What It Does

The pillar aggregates historical price data for an asset over multiple years, typically 5, 10, and 15 year periods. It then calculates the average price performance and 'win rate' for specific calendar windows, such as a month or quarter. This process reveals statistically significant seasonal trends, like increased heating oil demand in winter or harvest pressure on corn prices in the fall.

Why It Matters

It provides a quantifiable edge by identifying high-probability trading windows that are driven by fundamental, recurring real-world events. By understanding these seasonal tailwinds or headwinds, traders can better time their predictions and anticipate market movements before they are fully priced in.

How It Works

First, the pillar selects a commodity and gathers its historical daily or weekly price data for the last 15+ years. It then normalizes this data to calculate the percentage change for each specific period (e.g., the month of July) across every year. Finally, it averages these results to create a seasonality plot and calculates the percentage of years the period saw a positive return, known as the win rate.

Methodology

The core calculation is the average percentage price change for a commodity within a specific calendar window (e.g., day, week, month) over N years. The formula is: AvgDrift = Σ((Price_end - Price_start) / Price_start) / N. It also computes a 'Win Rate', defined as the percentage of years the price moved in the historically dominant direction during that window. Analysis is typically performed on 5, 10, and 15-year lookback periods to assess trend consistency.

Edge & Advantage

This pillar offers a statistical edge by pinpointing recurring market inefficiencies driven by predictable events like crop cycles or energy demand, often before the mainstream market reacts.

Key Indicators

• 5/10/15 Year Seasonality Plots

  high

  A visual chart showing the average price performance of an asset throughout a calendar year.

• Month-over-Month Win Rate

  high

  The percentage of past years where an asset's price increased during a specific month.

• Harvest Pressure Timing

  medium

  Identifies typical price dips in agricultural commodities corresponding with harvest seasons when supply peaks.

Data Sources

• CME Group Historical Data

  Provides futures and options price data for a wide range of commodities.

• USDA Reports (e.g., WASDE)

  Official supply and demand estimates for major agricultural commodities.

• Energy Information Administration (EIA)

  Weekly reports on petroleum and natural gas inventories and demand.

Example Questions This Pillar Answers

• → Will the price of Natural Gas futures close higher in December than in November?
• → Will Corn (ZC) futures trade above $5.00 per bushel before September 1st?
• → Will the price of Gold be higher in Q1 2025 than it was in Q4 2024?

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