Drawdown Analysis
Cocoa
CC · Cyclic market
A living read of where this market sits in its own drawdown history, using per-market quintile cuts because there is no industry convention for cyclic markets.
Where this market sits today
As of market close on 11/10/2026.
Native phase
Crisis low
Drawdown from 5-year high
-64.05%
Normalized phase
Normal
Own-history severity percentile
45.0
CC · Drawdown trajectory
Weekly closes of the primary drawdown metric (dd_roll5y) across full market history. Horizontal bands show the phase cuts; the gold marker is today’s reading (11/10/2026).
Scroll or drag the brush below the chart to zoom. All data from the Market Lab Analytics drawdown atlas.
Time in each phase
Share of this market’s full history spent in each own-history quintile. Because these buckets are defined by the market’s own quintile cuts, each phase holds roughly 20% of history by construction. The depth ranges shown are what matters: they reveal what a “Normal pullback” or a “Crisis low” actually means in percent terms for THIS market.
Based on 9,866 daily observations (39 years of history).
The numbers
Every drawdown lens we compute for this market, all at once. The primary drawdown metric (bolded by convention in the card above) is what drives the native phase label; the rest are context.
What the labels mean here
Cyclic markets like this one do not have an industry convention the way equities do. There is no equivalent of “correction equals -10%” for corn or coffee. Pretending otherwise is dishonest.
So the native phase uses this market’s OWN drawdown history, divided into five quintiles. Shallowest 20% of this market’s history gets “At cycle highs,” deepest 20% gets “Crisis low.” The labels mean the same thing across cyclic markets, but the depth thresholds are per-market because the markets themselves have different pain distributions.
- At cycle highs. Shallowest 20% of own history
- Normal pullback. 20 to 40th percentile
- Cyclical low. 40 to 60th percentile
- Stressed low. 60 to 80th percentile
- Crisis low. Deepest 20% of own history
The normalized phase uses the market’s full all-time drawdown as the severity denominator. The current severity of 45.0% means today’s drawdown is deeper than 45.0% of this market’s all-time drawdown history.
Today: Crisis low in this market’s own cycle terms, Normal on the full-history severity scale.
Cross-market drivers
When CC enters its deepest-decile drawdown state, which other markets systematically move with it? Below are the markets whose daily log-returns shift most reliably in the same deep-CC-drawdown window, filtered to those with a 95% confidence interval excluding zero.
Shifts are measured in basis points per day (1 bp = 0.01%). Positive = that market strengthens alongside CC’s deep drawdown. Negative = that market weakens alongside it.
Weakens alongside CC deep drawdowns
MW · Wheat (Minneapolis)
Daily log-return weakens by -5.5 bps/day when CC is in its deepest-decile drawdown state.
95% CI [-9.7, -0.4] bps/day
Strengthens alongside CC deep drawdowns
SOFR · SOFR
Daily log-return strengthens by +0.3 bps/day when CC is in its deepest-decile drawdown state.
95% CI [+0.0, +0.6] bps/day
How we think about drawdown
Drawdown math is not a one-size-fits-all formula. The same equation that honestly describes the S&P 500 is actively misleading for corn, and both of those break for the Dollar Index. So Market Lab Analytics computes drawdown three ways and chooses the one that fits each market’s nature.
For cyclic markets the primary drawdown is seasonal: price divided by the same-week-of-year historical maximum. This avoids dishonest reads where a corn price that is trading normally for its current supply-demand cycle still shows as a massive drawdown from an unrelated weather spike years earlier.
Every reading on this page is anchored in real market data. The phase labels are computed from the market’s own full history. The cross-market drivers are measured with block-bootstrap confidence intervals so only statistically distinguishable shifts are surfaced. If a number is on this page, you can trace it back to the data that produced it.