Cali Advisory uses a rule-based, model-driven methodology to read client profiles, assign them to a risk band, set a crypto exposure limit, and construct diversified portfolios across cash, bonds, equities, real assets, and digital assets. The process is designed to be transparent, repeatable, and supervisable.
This page describes the planned methodology for Cali Advisory's Internet Investment Adviser model. It is part of the firm's research and design work and does not itself constitute active investment-advisory services.
Cali Advisory evaluates ten core inputs that together describe a client's risk tolerance, financial situation, experience, and time horizon. Each answer is mapped to a numeric value and contributes to an overall suitability score:
Each of these inputs has a predefined scoring scale in the engine code (see compute_profile_score in the advisory engine module). The sum of those scores becomes the client's total suitability score.
The total suitability score is mapped into one of five risk bands. Each band describes a different balance between capital preservation and growth:
The function determine_risk_band in the engine module defines exactly how score ranges map into these bands. The mapping can be tuned over time as real usage and supervision feedback accumulate.
Digital assets (such as BTC, ETH, and other supported tokens) are treated as a distinct risk sleeve. Total crypto exposure is capped based on both:
The engine uses band-specific maximums (for example, 0–2% in Capital Preservation, up to 20% in Aggressive) and then applies stricter limits if a client indicates low familiarity or low comfort. If crypto familiarity and comfort are both minimal, the cap is forced to zero regardless of band.
Crypto exposure is further split into core crypto (larger, more established assets) and satellite crypto (higher-volatility assets), typically using a 70/30 split inside the crypto sleeve.
Each risk band is associated with a base allocation across high-level sleeves:
The function base_band_allocation defines these starting weights. Once a client's crypto cap is known, the engine introduces crypto exposure by proportionally reducing equity sleeves rather than cash and bond exposures, then assigns crypto between core and satellite sleeves and normalizes the result so the total equals 100%.
This methodology is intentionally simple and interpretable in its initial version. It is not an optimizer in the sense of mean–variance or factor models, and it does not attempt to forecast returns. Instead, it focuses on:
Over time, Cali Advisory may refine scoring weights, band thresholds, or sleeve definitions based on observed behavior, stress testing, and supervisory review. Any material changes would be documented and incorporated into the firm's disclosures.
Cali Advisory (DBA) expects to operate as an Internet Investment Adviser, where individualized advice is delivered exclusively through an online platform, using a documented methodology like the one described here.
The full details of services, risks, and fee arrangements will be described in the firm's Form ADV and, if applicable, Form CRS. This methodology description is intended to help risk, compliance, and product teams understand how the advisory engine behaves at a high level.
For more context, see: