Skip to main content
Alterscope summarizes the risk of a protocol or position as a 0–100 score built from seven factor categories. The score is not a black box: every score carries a per-factor attribution that shows exactly which factors moved it and by how much, and the weights that combine the factors are kept stable over time using established portfolio-risk methods rather than ad-hoc tuning.

​
The seven factor categories

Each subject is scored across seven categories. Higher is safer (0 = critical, 100 = strong). Each category decomposes into named sub-factors — for example, the smart-contract category is built from audit score, code age, formal verification, and code complexity; the oracle category from source redundancy, latency risk, manipulation resistance, and coverage. The full sub-factor set is the input layer the model scores against.

​
How factors become a score

The composite score is a weighted sum of the seven category scores:
The category weights sum to one and are validated to do so. The specific weight values are calibrated internally and are dynamically re-balanced over time by hierarchical risk parity (described below) rather than fixed by hand. We publish the structure — seven categories, weighted sum, weights summing to one — and withhold the calibrated weight values, which are part of the model’s moat. Scores map to plain-language bands so a non-quant reader can act on them:

​
Explainability: factor attribution

Every score is accompanied by an attribution that decomposes the composite into per-factor contributions, so you can answer “why did this score move?” without guessing. The attribution uses Shapley values — the game-theoretic method for fairly dividing a result among the inputs that produced it. Because the model has exactly seven factors, Alterscope computes the exact Shapley value over all 2⁷ = 128 factor coalitions rather than an approximation:
where φᵢ is factor i’s contribution, S ranges over subsets of the other factors, and v(·) is the scoring function evaluated with the absent factors held at a neutral baseline. The attributions satisfy the efficiency property: they sum exactly to the score’s deviation from the neutral baseline, so nothing is unexplained.
These are Shapley values over Alterscope’s deterministic scoring function — they explain how each factor moved the composite away from a neutral baseline. They are not feature-importances from a trained black-box model; the scoring function itself is transparent.

​
Stability: covariance and hierarchical risk parity

Factor scores co-move — a liquidity shock and a market shock are not independent. To weight factors sensibly, Alterscope estimates how they move together and re-balances accordingly, using two methods drawn from quantitative portfolio risk.

​
EWMA covariance

The factor covariance matrix is estimated with an exponentially-weighted moving average, which gives recent observations more weight than old ones:
where rₜ is the vector of factor changes at time t and λ is the decay parameter. λ is calibrated internally (it differs by asset class and is tuned to the volatility regime); we publish the recursion, not the tuned value. The covariance is refreshed on a regular schedule and converted to a correlation matrix and a distance matrix (d = √(½(1 − ρ)), after López de Prado) for the clustering step below.

​
Hierarchical risk parity (HRP)

Rather than inverting a noisy covariance matrix directly (which is unstable), Alterscope applies Hierarchical Risk Parity (López de Prado) to re-balance the factor weights. HRP runs in three stages:
  1. Tree clustering — group factors by how closely they co-move, using the correlation-distance matrix.
  2. Quasi-diagonalization — reorder the matrix so similar factors sit together.
  3. Recursive bisection — split the tree top-down and allocate weight by inverse variance, so volatile clusters get less weight.
The resulting weights are validated to be non-negative and to sum to one, and the re-balance runs on a periodic cadence. This is what keeps the factor weights stable and noise-resistant over time instead of lurching with every data point.

​
Model validation: the Deflated Sharpe Ratio

When Alterscope evaluates a risk model or strategy on historical data, it guards against backtest overfitting — the trap of a result that looks good only because many variants were tried. The check is the Deflated Sharpe Ratio (DSR) of Bailey & López de Prado:
The observed Sharpe ratio SR is discounted by the expected maximum Sharpe you would see by chance given the number of trials and the estimate’s own standard error:
A low or negative DSR flags a result that probably won’t hold out of sample; the model emits an overfitting flag and a suggested haircut. The DSR also cross-checks the factor model against the Monte-Carlo simulation (see VaR & Monte Carlo): if the model’s predicted liquidation probability disagrees materially with the simulation, that gap is surfaced rather than smoothed over.
“DSR” here is the Deflated Sharpe Ratio, an overfitting / multiple-testing correction. It is a model-validation tool, not a live risk score on a position.

​
What we publish vs. withhold

​
Where this shows up

Factor scores, bands, and attributions are returned by the factor and risk-analysis endpoints in the API reference. Every response also carries freshness and quality metadata — see Freshness & quality — so you know how much to trust a given score before acting on it. The honest boundaries of this model are on the Limitations page.