Scenario Constraints with Memory: A Finite-State Approach to Quantitative Financial Analysis

Battle Count: 62

The paper is primarily relevant to risk management, structured product analysis, and stress testing rather than direct quantitative trading strategy development. It provides exact worst-case/best-case bounds for path-dependent instruments (autocallables, barrier options) under scenario constraints, which is valuable for risk managers and structured product desks. The formal constraint language could inform scenario design for trading strategy backtesting. However, it does not address alpha generation, portfolio construction, or execution optimization directly. The approach complements rather than replaces simulation-based methods used in quantitative trading.

Implementation requires substantial knowledge of formal automata theory (deterministic finite automata, Moore machines, regular expressions), algebraic structures (semirings, max-plus algebra), and graph algorithms (Bellman-Ford variants for weighted automata). The synchronized product construction and extremal payoff analysis involve non-trivial algorithmic steps. The Java prototype demonstrates feasibility, but production deployment would require careful handling of state explosion, numerical precision for rational constants, and integration with existing financial systems. The declarative JSON input format lowers the barrier for end users, but the underlying formal machinery is complex.

The paper provides a publicly available Java research prototype on GitLab (https://gitlab.com/vitnberg/wffa) with JSON task descriptions, shell scripts for running experiments, and generated result files. The workflow is modular with four layers (automata, scenario, JSON, command-line). Experiments include payoff analysis tests, horizon-scalability tests, and constraint-scalability tests. All numerical constants are rational and encoded in binary. The paper includes full proofs of all theorems in appendices. However, it is a single-author independent researcher paper with a stylized case study rather than real market data.