Skip to content

Analytics layer

Understand the portfolio without outsourcing the decision.

Expreci's intended analytics layer treats event contracts as binary contingent claims and provides research views for exposure, joint outcomes, probability paths, and liquidity.

Unlike wallet-level PnL trackers, these views are built for multi-contract books where outcomes are dependent, mutually exclusive, or staggered in time.

01

Portfolio exposure views

Aggregate long and short outcome exposure across markets, event families, maturities, and user-defined themes. Preserve both cash-cost and payoff-state views.

02

Joint scenario evaluation

Apply coherent combinations of binary outcomes across related contracts, including states that cannot be represented by moving each market independently.

03

Probability path analysis

Study how portfolio value and marginal exposure evolve under inferred or user-supplied probability trajectories without interpreting those trajectories as guaranteed forecasts.

04

Liquidity-aware valuation

Separate midpoint marks from depth-constrained liquidation views and expose assumptions about book depth, order priority, fees, and partial fills.

Portfolio representation

Binary payoff does not imply one-dimensional risk

One contract resolves to a bounded payoff, but a portfolio can contain overlapping conditions, mutually exclusive outcomes, correlated event families, staggered resolution dates, and different liquidity profiles. Summing independent market PnL obscures those relationships.

Expreci's intended representation separates position inventory, settlement logic, market marks, and scenario assumptions. A scenario can be inferred, supplied, or selected by the user. It is an analytical input, not a guarantee, recommendation, or instruction to trade.

Joint outcome engine

Evaluate states, constraints, and resulting payoffs

  1. Define or select a portfolio snapshot.
  2. Specify joint event outcomes or probability assumptions.
  3. Validate logical constraints between event states.
  4. Revalue claims and attribute the change by position.
  5. Keep the input assumptions attached to every result.

Scenario response

Scenario result
{
  "scenario_id": "scn_01K2F8CX8R4M",
  "assumptions": [
    { "event": "fed_cut_by_september", "outcome": true },
    { "event": "inflation_below_target", "outcome": false }
  ],
  "portfolio": {
    "marked_value": "184250.00",
    "scenario_value": "121600.00",
    "change": "-62650.00"
  },
  "dependence_model": "user_supplied_joint_states"
}

Probability paths

Explore a path, not just an endpoint

A terminal outcome scenario answers “what if these claims resolve this way?” A probability path answers a different question: “how would marked exposure evolve if market-implied probabilities followed this inferred or user-supplied sequence?” The path can reveal concentration, convexity-like behavior, and sensitivity near key thresholds.

Liquidity context

Marks and realizable values are separate views

A midpoint-based mark is useful for consistency but does not imply executable value. Intended liquidity views consume book depth under explicit fill assumptions, show slippage by size, and flag where incomplete books make a result unsuitable.

Boundary vs. PnL trackers

The object of analysis is the joint payoff structure

Simple tracker
Reports entry cost, current mark, and realized or unrealized change for each position.
Expreci intent
Preserves those accounting views while making settlement states, cross-contract dependence, path assumptions, and liquidity constraints queryable.
Not included
Trade recommendations, prescriptive portfolio optimization, suitability determinations, or managed execution.
Register analytics interest