algorithmic-trading-anthology-july-2026-cross-sectional-momentum-agricultural-futures

Conclusion: Failed

Failed on the reward for the risk, the confidence range, profit concentration, consistency across the history, the comparison against buying and holding and standing out from the search. Indicative performance of (6.1%) a year is 12.0 points below the 6.0% returned by holding the same universe continuously, return for risk taken sits at (0.53) against the benchmark's 0.48, and the worst fall of 196.2% is 134.3 points above the benchmark's 61.9%.

Figure 1 · Growth of $1

2002–2026 · net of 15bp round-trip costs

What one dollar became, after costs: $0.23 by 2026. Shown against Control, S&P 500, rebased to the same starting dollar.

Return for risk

(0.53)

range (0.97) to (0.10)

Ann. return

(6.1%)

volatility 11.5%

Max drawdown

196.2%

over 292 months

Beats the search

0.0%

chance, after correction

Method

Hypothesis

Futures-convention cross-sectional momentum: long the top tercile, short the bottom tercile of trailing 12m returns, inverse-vol weighted within each side, dollar-neutral.

Rule

UNIVERSE = 15 agricultural futures

LOOKBACK = 12 months

MIN_NAMES = 8

function positions(bar):

for symbol in UNIVERSE:

score[symbol] = sum(returns(symbol, LOOKBACK, ending bar))

if count(score) < MIN_NAMES:

return {} # too thin a field to rank

return long_short(score, count(score) / 3, bar)

# ── execution ────────────────────────────────────────────────────────

COST_PER_TRADE = 15 bps of the notional that changes hands

function on_bar(bar, book):

if not bar.is_month_end:

return # decisions are made monthly only

target = positions(bar) # the rule above

target = scale_to_gross(target, 1.0)

rebalance(book, target, bar)

function scale_to_gross(w, limit):

gross = sum(abs(w)) # total capital at work

if gross == 0:

return w # flat is a valid target

return w * limit / gross # leverage fixed, never implicit

function rebalance(book, target, bar):

for symbol in union(book.symbols, target.symbols):

delta = target[symbol] - book.weight(symbol)

if delta > 0:

buy(symbol, delta, fill = next_bar.close)

else if delta < 0:

sell(symbol, -delta, fill = next_bar.close)

book.charge(COST_PER_TRADE * abs(delta))

# Orders are filled at the next month's close, never the one the decision

# was made on. There is no stop_loss(), take_profit(), limit order or

# position cap: a holding changes only when positions() returns a

# different target at the next month end.

Analysis

Diagnostic value

Cross-sectional momentum is among the best-documented premia in finance. Finding it significantly negative in agricultural futures is not a refutation of momentum; it is a symptom.

Momentum applied to spliced continuous series buys instruments that jumped at the roll, then holds the new contract through its decay. The measured return is a property of the data-assembly convention with the sign reversed. This is the same artifact that inflated the calendar-seasonality result, observed from the opposite direction.

Evaluation

Table 1 · Performance

2002-03 – 2026-06 · 292 months, 292 invested

Measure	Value
Annualised return	(6.1%)
Annualised volatility	11.5%
Return for risk taken	(0.53)
95% range	(0.97) to (0.10)
t-statistic	(2.60)
Maximum drawdown	196.2%
Hit rate	41.8%
Annual turnover	6.45×
Return skew	(0.04)
Return kurtosis	4.35

+ 6 more rows− fewer rows

The range is measured by resampling the history in blocks, so runs of good and bad months stay intact.

Table 2 · Robustness

Diagnostic	Value	Reads as	Bad	Okay	Yay
Design half (pre-2015)	(0.47)	return for risk	≤ 0	0 – 0.40	≥ 0.40
Holdout half (2015+)	(0.60)	return for risk	≤ 0	0 – 0.40	≥ 0.40
Top-5-month share of profit	(29.8%)	how much rode on a few months — lower is better	≥ 40%	25 – 40%	< 25%
Top-2-year share of profit	(20.1%)	concentration	—	—	—
Distinct trading episodes	1	how many independent runs this really is	< 50	50 – 100	≥ 100
Chance the edge is real	0.5%	non-normality corrected	—	—	—
Chance it beats the whole search	0.0%	chance of beating the whole search by luck	—	—	—

bad/ok boundary is the pre-registered criterion · ok/good is a margin above it, not a criterion · blank where none was registered

+ 3 more rows− fewer rows

The last row asks whether the result would stand out from the hundred-odd ideas tried here, rather than being judged alone.

Table 3 · Pre-registered criteria

measured against C3_RP_LONG

Check	What it asks	Result
1	Reward large enough for the risk	FAIL
2	Confidence range clear of zero	FAIL
3	Profit not concentrated in a few months	FAIL
4	Worked in both halves of the history	FAIL
5	Beat buying and holding	FAIL
6	Stood out from the whole search	FAIL

+ 2 more rows− fewer rows

These six checks were written down before the strategy was run.

Table 4 · Net return by calendar year

after costs

Year	Net return
2002	(3.5%)
2003	(13.5%)
2004	(20.5%)
2005	(9.0%)
2006	5.1%
2007	7.5%
2008	(6.1%)
2009	1.1%
2010	(15.2%)
2011	(27.1%)
2012	(2.5%)
2013	0.7%
2014	7.6%
2015	(21.2%)
2016	1.1%
2017	(23.1%)
2018	(10.2%)
2019	(20.3%)
2020	(14.6%)
2021	(12.1%)
2022	(6.7%)
2023	12.1%
2024	17.6%
2025	3.0%
2026	1.9%

+ 21 more rows− fewer rows

Table 5 · Concentration detail

Measure	Value
Best years	2024:+18%, 2023:+12%, 2014:+8%
Worst year	2011:-27%

Reference

Instruments

last price and one-month change, live from Yahoo Finance

Symbol	Notes
ZW=F	Chicago SRW Wheat — continuous futures on CBOT, quoted in US cents.
KE=F	KC HRW Wheat — continuous futures on CBOT, quoted in US cents.
ZC=F	Corn — continuous futures on CBOT, quoted in US cents.
ZS=F	Soybeans — continuous futures on CBOT, quoted in US cents.
ZM=F	Soybean Meal — continuous futures on CBOT, in USD.
ZL=F	Soybean Oil — continuous futures on CBOT, quoted in US cents.
ZR=F	Rough Rice — continuous futures on CBOT, in USD.
SB=F	Sugar No. 11 — continuous futures on ICE Futures, quoted in US cents.
KC=F	Coffee C — continuous futures on ICE Futures, quoted in US cents.
CC=F	Cocoa — continuous futures on ICE Futures, in USD.
CT=F	Cotton No. 2 — continuous futures on ICE Futures, quoted in US cents.
OJ=F	Orange Juice — continuous futures on ICE Futures, quoted in US cents.
LE=F	Live Cattle — continuous futures on CME, quoted in US cents.
GF=F	Feeder Cattle — continuous futures on CME, quoted in US cents.

list truncated in the source record

Sample

2002-03 – 2026-06 · 292 months

Sources

NOAA Physical Sciences Laboratory. Climate indices. ONI, Niño 3.4, MEI v2, DMI, PDO, AMO, TNA, QBO, solar flux. psl.noaa.gov/data/climateindices/list

Queensland Department of Agriculture and Fisheries. Southern Oscillation Index. The Long Paddock. Monthly, 1876–. www.longpaddock.qld.gov.au/soi/soi-data-files

Yahoo Finance. Historical market prices. Adjusted close, month-end. Retrieved with yfinance. finance.yahoo.com

Revision history

Rev	Stage	Status	Change
01	Draft	—	Direction and criteria fixed before the experiment ran
02	Tested	Failed	Fell short on the reward for the risk, the confidence range, profit concentration, consistency across the history, the comparison against buying and holding, standing out from the search; control C3_RP_LONG