Technical Core

Platform Documentation

Detailed mathematical explanations, model configurations, and risk algorithms powering the RAUTREX quant terminal.

Documentation

Core Modules

Our Monte Carlo simulation engine models prospective portfolio asset distribution using **Geometric Brownian Motion (GBM)**. It projects 10,000 distinct price scenarios across selected intervals to capture the absolute volatility boundary.

Stochastic Differential Equation

dS_t = μ S_t dt + σ S_t dW_t

Where μ represents the drift coefficient (expected return), σ is the volatility parameter, and dW_t is the stochastic Wiener process increment modeling randomized market impulses.

Metrics Derived

  • Expected Tail Loss (ETL): Computes prospective loss at 95% and 99% significance levels.
  • Probability of Ruin: Evaluates target portfolio capital depletion thresholds based on randomized cash flow variances.
  • Dynamic Variance Expansion: Projects asset pricing ranges out to 252 business days (1 calendar trading year).