# Crypto multi-factor engine: seven trials, capacity decay, and no-deploy verdict

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**Status:** public research record; not peer reviewed; not an investment solicitation  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

## Abstract

AlphaForge ran a historical robustness matrix over crypto perpetual futures from 2021-01-01
through 2026-06-01. Seven charged identities belong to the broad multi-factor engine: a baseline
rank allocator; ML, regime, and combined overlays; daily rebalancing; a tighter no-trade band; and
mean-variance allocation. An eighth matrix identity explicitly selected carry and residual
reversal and is accounted for in the separate `crypto_carry` family.

None of the seven engine identities cleared its artifact-era DSR gate. Annualized Sharpe ranged
from -0.7901 to +0.0424 and maximum drawdown from 13.59% to 33.93%. The four baseline/overlay arms
all produced the same Sharpe 0.0424 and DSR 0.2112. Cross-sectional combinatorially symmetric
validation across all eight matrix trials reported PBO 0.8818 against a gate below 0.20 and no
deflated winner. The matrix verdict was **NO-DEPLOY**.

A capital sweep on the regime-gated engine showed Sharpe 0.4009 at $100,000, 0.0424 at $1 million,
and -0.3720 at $10 million under the committed impact model. This is useful capacity-failure
evidence, not a promise that the $100,000 result is repeatable. The family has no admitted sleeve
and no broker-reconciled forward record.

## Claim boundary

This paper reconciles seven immutable return identities to one persisted matrix and publishes the
positive, negative, capacity, and selection-adjusted evidence together. It does **not** establish
a forward Sharpe, expected maximum drawdown, independent economic mechanism, live result, or
future return. The artifact serializes `alpha_names: null`; therefore the exact expanded default
feature list is not recoverable from the matrix configuration alone. No feature-level attribution
is claimed.

## Research question and overlap decision

The engine asked whether a broad cross-sectional blend becomes robust through nonlinear sizing,
regime gating, cadence, turnover control, or covariance-aware allocation. Possible mechanisms
come from the component families—carry, momentum, reversal, liquidity, and defensive effects—not
from the word “multifactor.” Combining weak signals can reduce noise, but complexity can also
increase estimation error and hidden selection.

Liu, Tsyvinski, and Wu construct crypto market, size, and momentum factors
([Journal of Finance](https://doi.org/10.1111/jofi.13119)); Liu and Tsyvinski document crypto return
predictors and differences from conventional asset classes
([Review of Financial Studies](https://doi.org/10.1093/rfs/hhaa113)). Bailey and coauthors develop
the PBO framework used to test whether strategy selection is likely to overfit
([Journal of Computational Finance](https://doi.org/10.21314/JCF.2016.322)). These sources support
the research questions and validation tools, not this implementation's return.

The seven configurations are one engine family, not seven sleeves. Carry-tilted `C_carry` remains
in `crypto_carry`; named momentum trials remain in `crypto_momentum`. Shared data, instruments,
construction, and latent market exposures prevent those labels from becoming automatic economic
independence.

## Complete trial lineage

| Matrix identity | Change from baseline | Sharpe | Max DD | DSR | DSR gate |
|---|---|---:|---:|---:|---|
| `A_blend` | rank baseline | 0.0424 | 13.59% | 0.2112 | fail |
| `A_ml` | ML overlay | 0.0424 | 13.59% | 0.2112 | fail |
| `A_regime` | regime overlay | 0.0424 | 13.59% | 0.2112 | fail |
| `A_ml_regime` | ML plus regime | 0.0424 | 13.59% | 0.2112 | fail |
| `C_rebal24` | daily rebalance | -0.7901 | 33.93% | 0.0049 | fail |
| `C_band10` | 10-basis-point band | -0.1994 | 13.89% | 0.0935 | fail |
| `C_mvo` | MVO allocator | -0.0946 | 15.94% | 0.1373 | fail |

The public packet maps each row to its exact hypothesis key, config hash, immutable first ledger
record, source file, SHA-256 hash, 1,612 observations, skew, and kurtosis. All seven remain charged
to the current 228-identity union. The matrix's own DSR values use its complete eight-trial local
selection set and are labeled artifact-era evidence rather than current-union restatements.

## What the unchanged overlays mean

The four Block A arms returned byte-for-byte equivalent summary moments. That is not evidence that
ML and regimes work equally well. The persisted verdict reports that the regime gate was inactive
in early out-of-sample legs while its expanding fit window lacked 730 daily BTC observations and
fell back to the blend. The matrix also does not persist enough feature-level diagnostics to prove
where the ML path altered positions. The defensible conclusion is that these overlays did not
improve the measured return, not that they were validated.

Daily rebalancing was the strongest falsifier: turnover rose to 80.58x, Sharpe fell to -0.7901,
and maximum drawdown reached 33.93%. MVO lowered turnover to 15.19x but did not create positive
Sharpe. A tighter band also remained negative. Portfolio machinery did not rescue the edge.

## Selection and capacity evidence

The eight-column matrix—including the separately classified carry arm—used 5,000 CSCV
combinations over 1,612 daily observations. PBO was 0.8818; the required gate was below 0.20. No
configuration cleared DSR 0.95 and no deflated winner was selected.

| Initial capital | Sharpe | DSR | Max DD | Turnover | Final equity |
|---:|---:|---:|---:|---:|---:|
| $100,000 | 0.4009 | 0.4803 | 13.14% | 44.53x | $118,509.86 |
| $1,000,000 | 0.0424 | 0.2112 | 13.59% | 39.74x | $993,219.39 |
| $10,000,000 | -0.3720 | 0.0460 | 22.69% | 44.31x | $8,043,578.96 |

The sweep varies capital for the regime-gated engine without adding return identities. Under its
notional/ADV impact model, the already thin edge decays materially with size. This does not prove
true exchange capacity—the model still needs external calibration—but it decisively forbids a
claim of scalable backtested edge.

## Live and sleeve decision

The family is not an Alpaca paper sleeve, not an AlphaForge live sleeve, and not included in
published broker returns. There is no continuous broker-reconciled forward record for these seven
configurations. Historical capacity rows cannot substitute for live slippage, funding, fills,
venue outages, or counterparty risk.

**Decision: FAIL / NO DEPLOY.** The family contributes zero sleeves. Any future attempt must freeze
the expanded feature set rather than serialize `null`, preregister one configuration, establish
point-in-time universe lineage, use current-union deflation, calibrate impact from observed fills,
and accumulate forward evidence. It would be a new charged identity.

## Reproduction and authorship

- `scripts/grand_backtest.py` and `src/alphaforge/analytics/grand_matrix.py` — matrix driver and
  validation logic.
- `artifacts/grand_backtest/20260616T143620Z/matrix.json` — persisted measurements and capacity.
- `artifacts/grand_backtest/20260616T143620Z/verdict.md` — contemporaneous no-deploy decision.
- `artifacts/grand_backtest/20260616T143620Z/experiments.jsonl` — immutable dedicated ledger.
- `scripts/audit_crypto_multifactor_family.py` — deterministic seven-identity audit.
- [`/glassbox/crypto_multifactor_family.json`](/glassbox/crypto_multifactor_family.json) — public
  machine-readable family packet.
- [`/glassbox/trial_packet_manifest.json`](/glassbox/trial_packet_manifest.json) — global identity
  join and incomplete identity-level packet ledger.

Reproduction requires the pinned AlphaForge environment and underlying point-in-time market-data
lake. Public hashes make the persisted evidence tamper-evident; they do not make private market
data publicly reproducible or the work independently peer reviewed.

This record and the ALPHAC system were authored and directed by **Arhan Canli**, Founder of Canli
Capital. Credit is explicit and verifiable in the source and public corpus; scientific claims
remain bounded by the evidence.

## Conclusion

The multi-factor engine is valuable because it records a failed robustness test cleanly. Complex
overlays did not improve the baseline, faster trading made outcomes worse, PBO was high, and modeled
capacity decayed. The institutional decision is zero sleeves and continued evidence collection—not
a renamed backtest winner.