# AlphaTrend managed-futures trend: complete trial lineage and evidence boundary

**Short title:** AlphaTrend: managed-futures trend lineage  
**Author:** Arhan Canli, Founder, System Architect, and Quantitative Researcher, Canli Capital  
**Research system:** ALPHAC / AlphaForge  
**Family key:** `managed_futures_trend`  
**Status:** public research record; not peer reviewed; not an investment solicitation  
**Evidence date:** 2026-08-22

## Abstract

AlphaTrend is ALPHAC's managed-futures trend program. It takes volatility-normalized directional
positions from medium- and long-horizon price trends across liquid exchange-traded market proxies,
then combines them under a covariance-aware risk budget. This paper reconciles all 21 charged
hypothesis identities in the family rather than presenting the strongest backtest alone.

The result is promising as a diversifier but statistically unresolved. Nineteen identities have
finite Sharpe estimates ranging from -0.238 to +0.345. Six identities retain complete
walk-forward artifacts, seven retain source-bound summary reports, and eight retain immutable
ledger summaries without full curves. Thirteen have persisted artifact-era DSR values; zero clear
the 0.95 gate. The 17-market baseline had Sharpe 0.289 and 9.69% vol-matched maximum drawdown in a
replica screen. Correlation whitening reduced Sharpe to 0.186, a single 110-session signal reached
0.271, and expanding to 33 ETFs reduced Sharpe to 0.128 while worsening vol-matched drawdown to
14.42%. AlphaTrend remains a paper evidence-collection sleeve, not an established forward result.

## Claim boundary

This paper establishes the implemented hypothesis, complete family trial count, persisted results,
evidence gaps, and historical decisions. It does not establish future return, an achieved forward
Sharpe, expected maximum drawdown, capacity, live-money performance, or external attestation.
Paper execution through Alpaca is clearly separated from historical simulation.

## Economic hypothesis and literature

The falsifiable hypothesis is that the sign and magnitude of an asset's medium-horizon excess-price
move contains information about its next return, and that combining trends across genuinely
different markets improves risk-adjusted performance. Possible mechanisms include slow information
diffusion, investor underreaction, positioning constraints, and compensation for bearing reversal
risk. These mechanisms motivate a prior; they do not validate ALPHAC's implementation.

- Moskowitz, Ooi, and Pedersen document time-series momentum across futures and forward markets
  ([Journal of Financial Economics, 2012](https://doi.org/10.1016/j.jfineco.2011.11.003)).
- Hurst, Ooi, and Pedersen examine long historical evidence for trend following
  ([Journal of Portfolio Management, 2017](https://doi.org/10.3905/jpm.2017.44.1.015)).
- Lempérière and coauthors study trend following over roughly two centuries and emphasize crisis
  behavior and risk management
  ([Journal of Investment Strategies, 2014](https://doi.org/10.21314/JOIS.2014.055)).
- Baltas and Kosowski analyze momentum and trend construction in futures markets
  ([Journal of Financial and Quantitative Analysis, 2020](https://doi.org/10.1017/S0022109018001259)).

## ALPHAC implementation

The core signal blends three volatility-normalized price trends:

```text
trend(i,t,L) = [log P(i,t) - log P(i,t-L)] / [sigma(i,t) * sqrt(L)]
signal(i,t)  = mean(trend(i,t,63), trend(i,t,126), trend(i,t,252))
```

Weights are determined from information available through session `t` and become effective on the
next session. The principal ETF implementation uses 17 proxies spanning equities, rates,
commodities, and currencies, a 33-session volatility estimate, covariance-aware allocation,
monthly rebalancing, gross and name caps, 6 basis points per side, and 50 basis points annualized
short borrow. The full walk-forward path uses purged anchored-expanding windows. A separate
futures-proxy experiment is retained as a distinct identity because its instruments and data path
are different.

## Trial lineage

| Identity group | Charged identities | Evidence retained |
|---|---:|---|
| Core signal/cadence/universe studies | 14 | Six full walk-forwards; eight immutable ledger summaries |
| Allocator and signal-horizon screen | 3 | Source-bound summary report |
| Fixed and selected breadth screen | 4 | Source-bound summary report |
| **Total** | **21** | Every identity remains in the family and union burden |

The breadth count includes two post-hoc procedures: dropping the largest full-sample PnL
contributor and greedily adding markets only when estimated effective breadth rises. These are not
free diagnostics. They generated measured return configurations and are charged as selection
identities. Rounded equality between the ARP baseline and breadth baseline is not used to collapse
them because their exact return curves were not preserved together.

The machine-readable source of truth is
[`/glassbox/alphatrend_family.json`](/glassbox/alphatrend_family.json). It binds every hypothesis
key to the immutable ledger, strongest available artifact, source hash, exact configuration,
evidence grade, and missing measurements.

## Complete walk-forward evidence

| Configuration | Sharpe | Max DD | Turnover/yr | Artifact-era DSR | Decision |
|---|---:|---:|---:|---:|---|
| 63/126/252 blend, 17 ETFs, 21-session rebalance | 0.263 | 10.29% | 6.44× | ~0.000 | Fail DSR |
| 126-session signal | 0.175 | 13.32% | 5.47× | 0.704 | Fail DSR |
| 252-session signal | 0.182 | 11.52% | 4.93× | 0.700 | Fail DSR |
| Blend, 5-session rebalance | 0.278 | 10.91% | 12.31× | 0.798 | Fail DSR |
| Blend, 10-session rebalance | 0.311 | 10.19% | 9.39× | 0.816 | Fail DSR |
| Real-futures proxy path | -0.238 | 16.03% | 8.97× | ~0.000 | Fail DSR |

The highest complete-artifact Sharpe is not adopted as proof. It was one of several cadence and
signal choices, remained below the historical DSR gate, and increased turnover materially.

## Allocator and signal-horizon screen

All three rows share 5,161 sessions and are historical summaries without daily curves or kurtosis.
Maximum drawdown is shown after matching each stream to baseline realized volatility.

| Variant | Sharpe | Realized vol | Vol-matched max DD | Turnover/yr | Artifact-era DSR |
|---|---:|---:|---:|---:|---:|
| Baseline 63/126/252 blend | 0.289 | 4.49% | 9.69% | 4.65× | 0.000 |
| Correlation-whitened allocator | 0.186 | 2.75% | 12.76% | 7.79× | 0.000 |
| Single 110-session signal | 0.271 | 4.56% | 10.57% | 5.31× | 0.000 |

Correlation whitening did not improve the baseline. The 110-session signal was close but weaker.
Neither is promoted.

## Breadth experiment

| Basket/procedure | Sharpe | N_eff | Avg. pair correlation | Vol-matched max DD | Decision |
|---|---:|---:|---:|---:|---|
| Baseline 17 | 0.289 | 13.52 | 0.127 | 9.69% | Control |
| Expanded 33 | 0.128 | 11.01 | 0.250 | 14.42% | Reject |
| Expanded minus largest contributor (SHY) | 0.217 | 10.11 | 0.264 | 10.04% | Reject |
| Greedy N_eff-pruned 22 | 0.204 | 14.76 | 0.153 | 9.58% | Reject |

More tickers did not mean more independent bets. The 33-market expansion raised average
correlation, reduced effective breadth, reduced Sharpe, and worsened drawdown. The selected
22-market basket raised estimated N_eff relative to baseline but still reduced Sharpe. This is a
useful null: breadth acquisition must be exposure-aware and independently validated, not counted by
ticker quantity.

## Uncertainty, missing evidence, and capacity

Eight identities have only immutable ledger summaries; two of those have non-finite Sharpe because
their reduced instrument sets did not support a valid estimate. They remain charged and visible.
Summary-only ARP and breadth reports preserve no exact daily curves or kurtosis, so current-union
DSR cannot be honestly reconstructed for those rows. The family packet therefore reports original
DSR only where it exists and never substitutes a rounded estimate.

No persisted AlphaTrend capacity sweep measures market impact, ETF creation/redemption effects,
borrow availability, or stressed liquidity across AUM. Capacity is **unmeasured**. A gross cap and
liquid ticker list are implementation controls, not capacity evidence.

## Paper execution and forward burden

AlphaTrend is one of the broker-executed Alpaca paper sleeves. Its public paper record is useful
only if it remains continuous, broker-reconciled, and configuration-controlled. The record is too
short to establish a forward Sharpe or the program's expected maximum-drawdown objective. Historical
simulations selected from the 228-identity union cannot be relabelled as forward evidence.

The family earns at most one sleeve slot. It remains in evidence collection because the mechanism
is distinct and the paper program needs diversifiers, not because a backtest passed the governing
standard. Any production change requires a versioned declaration; any future research variant must
be registered before returns are inspected.

## Reproduction and provenance

Run from the repository environment:

```text
uv run python scripts/audit_alphatrend_family.py
uv run python scripts/build_trial_packet_manifest.py
uv run pytest -q tests/unit/test_alphatrend_family.py tests/unit/test_trial_packet_manifest.py
```

The audit reads only immutable experiment ledgers and persisted artifacts. It opens no holdout,
records no trial, and fails if identity count, artifact Sharpe, source hash, or evidence mapping
drifts. Authorship and research leadership are credited to Arhan Canli; that credit does not alter
the statistical claim boundary.