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Documentation Index

Fetch the complete documentation index at: https://docs.alterscope.org/llms.txt

Use this file to discover all available pages before exploring further.

Point estimates tell you where risk sits today; they don’t tell you how bad a bad day looks. Alterscope answers that with Monte-Carlo simulation — running many correlated shock scenarios to estimate loss in the tail — and with a liquidity exit simulation that estimates what it would actually cost to get out of a position.

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Monte-Carlo Value-at-Risk

Value-at-Risk (VaR) answers: over a given horizon, what loss is exceeded only X% of the time? Expected Shortfall (ES) answers the follow-up: when it is exceeded, how bad is it on average? Alterscope reports both at the 95% and 99% confidence levels.

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How it works

  1. Correlate the shocks. Factor shocks are drawn from a multivariate normal distribution, correlated using the EWMA factor covariance matrix via a Cholesky decomposition. This means a simulated “bad day” moves correlated factors together, the way real markets do.
  2. Simulate many paths. The engine draws a large number of scenarios (one thousand by default) and scales each to the requested horizon by the square root of time: 
    shock(horizon) = shock(1 day) · √(horizon_days / 252)
  3. Read the tail empirically. VaR and ES are taken as empirical quantiles of the sorted simulated outcomes — the 5th/1st percentile for 95%/99% VaR, and the mean of the losses beyond that point for ES. No closed-form distribution is assumed at the tail beyond the normal factor shocks that generate the paths.

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What we publish vs. withhold

PublishedWithheld (calibrated internally)
Gaussian factor-shock model, Cholesky correlationPer-factor volatility inputs
95% / 99% VaR and ES, empirical-quantile methodHealth-factor impact multipliers and stress thresholds
Default path count (1,000) and √-time horizon scaling—
The simulation draws normally-distributed factor shocks. This is a deliberate, transparent choice — it does not model fat tails, jumps, or a Student-t distribution, so true tail events can be more severe than the simulated VaR/ES suggest. Treat VaR/ES as a structured stress estimate, not a worst-case bound. See Limitations.

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Model validation: Deflated Sharpe Ratio

The Monte-Carlo engine also produces a distribution of Sharpe ratios and a simulated liquidation rate, which feed the Deflated Sharpe Ratio validation described under Risk factors. In short: the DSR compares the risk model’s predicted liquidation probability against the simulation, and flags when the model materially under- or over-estimates risk rather than quietly trusting one number over the other.

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Liquidity exit simulation

A position’s risk includes the cost of leaving it. Alterscope estimates exit cost — the slippage you’d absorb unwinding into available depth — at two levels of sophistication, gated by plan tier.

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Baseline: depth model (all tiers)

The always-available model estimates slippage as a function of how large your exit is relative to available liquidity. Slippage grows quadratically in the exit-size-to-depth ratio (a larger exit moves the price disproportionately), capped at a maximum: 
slippage_bps  ∝  (exit_size / available_liquidity)²        (capped)
The model also reports feasibility, post-exit utilization, and a risk classification. The slippage coefficient and risk thresholds are calibrated internally; the quadratic shape and the inputs (exit size, available depth) are published. This powers the liquidity-depth endpoint, which projects slippage at standard exit sizes.

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Advanced: Monte-Carlo exit (Enterprise / Custom)

For Enterprise and Custom tiers, Alterscope offers a Monte-Carlo exit simulation that models the realistic case where you are not the only one heading for the door. It accepts a caller-supplied competing_exit_usd that is added to your exit size, perturbs available depth by a phase-based deterministic shock across scenarios, and reports the resulting slippage distribution as percentiles (p50/p95/p99) plus a summary.
  • Tier-gated. The Monte-Carlo exit endpoint requires an Enterprise or Custom plan; non-enterprise callers are capped on run volume.
  • Sync vs. async. Small runs (≤100 simulations) return inline; larger runs are queued as a background job.
See Rate limits & tiers for what each plan includes, and Coverage & gaps for which markets have the depth data the advanced model needs.

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Where this shows up

VaR/ES and liquidity-exit results are returned by the simulation and liquidity endpoints in the API reference. As with every Alterscope response, the output carries freshness and quality metadata. The honest boundaries — especially the Gaussian tail assumption — are on the Limitations page.