Documented systematic models

AlphaShelf.

A research catalogue of systematic digital asset models for institutional portfolios. Built for allocation. Ready for deployment.

Request strategy overview Explore model catalogue

For asset managers and funds evaluating model access for live portfolios.

Research documented 4 model families Risk specified

Model catalogue Systematic models mapped to portfolio objectives

Model families 4 strategy modules Delivery Research, license, implementation Audience Asset managers and funds

Objective Strategy Delivery

ProtectLeftTailOverlay

UncorrelatedCrossFinderStat-arb

ParticipateInertiaTrend

GrowOverCompoundYield

Strategy thesis Risk profile Live / backtest evidence Deployment requirements Licensing terms

The portfolio problem

Digital asset investors need more than passive exposure.

Long-only exposure can leave portfolios concentrated in market beta. Systematic models provide a rules-based way to define downside controls, diversification, trend exposure, and return enhancement.

Portfolio diagnosis Beta is not a strategy.

Drawdown sensitivity

BTC / ETH dependence

Internal build burden

Large drawdowns

Investors de-risk at the worst time.

High beta dependence

Returns rely too much on BTC and ETH direction.

Expensive buildout

Research, validation, and execution take time.

Backtest uncertainty

Strategies need robustness, not just attractive charts.

Operational burden

Selection, monitoring, and implementation take resources.

Model families

Four model families. Distinct portfolio roles.

Start with the mandate and assess each model by objective, risk, evidence, and implementation requirements.

01 Protect

Reduce downside participation.

Model: LeftTail

For investors who want to stay long but reduce exposure during adverse regimes.

02 Uncorrelated

Target lower-beta return drivers.

Model: CrossFinder

For portfolios that need uncorrelated or lower-beta return drivers.

03 Participate

Participate in persistent trends.

Model: Inertia

For investors who want systematic upside capture without emotional timing decisions.

04 Grow

Add a systematic return sleeve.

Model: OverCompound

For investors seeking yield-style enhancement around existing digital asset holdings.

Model documentation

Research, risk, and implementation in one review.

LeftTail

Stay invested while managing drawdowns.

Use case: Systematic hedge overlay for long-only digital asset portfolios.
Benefit: Seeks to reduce drawdowns and downside participation.
Implementation: Overlay on existing exposure with dynamic hedge adjustment.

CrossFinder

Target uncorrelated alpha with lower market exposure.

Use case: Cross-sectional statistical arbitrage across digital assets.
Benefit: Targets uncorrelated alpha with lower market beta.
Implementation: Ranking, portfolio construction, and systematic risk controls.

Inertia

Capture crypto trends with rules-based discipline.

Use case: Trend participation across liquid digital assets.
Benefit: Participates when trends are strong and steps back when signals weaken.
Implementation: Signal strength, risk scaling, and regime-aware exposure.

OverCompound

Grow existing exposure with systematic yield generation.

Use case: Yield-style enhancement around existing digital asset holdings.
Benefit: Designed to add an income-oriented layer to portfolio exposure.
Implementation: High-level module pending detailed strategy material.

Mandate fit

Systematic models for professional mandates.

01Asset managersAdd systematic overlays or alpha sleeves.

02Long-only crypto fundsUse hedge overlays to manage downside exposure.

03Multi-strategy fundsAdd stat-arb, momentum, or overlay modules.

04Family officesReview institutional-style strategies without internal buildout.

Why external model access

Deploy institutional-grade strategies without building the full stack.

Building systematic digital asset strategies internally requires research talent, data infrastructure, validation, execution logic, monitoring, and ongoing model review. AlphaShelf helps clients access existing strategy research and license only the modules that fit their mandate.

Avoid building a full quant research team. Reduce development and validation time. Review strategies before committing. License only what fits the mandate. Preserve internal resources for allocation, risk, and execution.

Due diligence package

Packaged for serious strategy review.

Each model is presented with the information required to evaluate fit, robustness, implementation requirements, and risk profile.

Institutional review fileFit, risk, implementation

01Strategy logic

Objective, thesis, signal design, and instruments traded.

02Risk profile

Drawdown behavior, volatility, beta, correlation, and capacity.

03Deployment terms

Execution assumptions, infrastructure needs, and licensing options.

Strategy objectiveInvestment thesisInstruments tradedSignal description Historical performanceRisk metricsDrawdown analysisCorrelation and beta profile Capacity estimateExecution assumptionsImplementation requirementsLicensing terms

Implementation paths

Flexible access based on your infrastructure.

Access AlphaShelf through APIs, execution software, hosted infrastructure, or dedicated deployment support.

Signal layer

Signal API

Systematic quantitative forecasts delivered through a secure API.

Execution layer

Execution Software

Automated execution and exchange connectivity software.

Deployment layer

Strategy Deployment

Deployment of licensed quantitative models into dedicated client environments.

Infrastructure layer

Infrastructure Hosting

Dedicated cloud infrastructure with monitoring and maintenance.

Integration layer

Technical Integration

Integration with exchanges, OMS, EMS and proprietary systems.

Support layer

Enterprise Support

Software updates, monitoring and technical assistance.

Find the right model

Find the model that fits your portfolio.

For qualified investors, asset managers, funds, proprietary traders, and systematic traders seeking model-driven digital asset strategies.

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