SimulMarkets™ Methodology White Paper

Structured Stochastic Weighting and Monte Carlo–style Scenario Generation

SimulMarkets™ employs a sophisticated financial modeling approach, leveraging structured stochastic weighting and Monte Carlo–style scenario generation for dynamic market analyses and investment strategies.

Structured Stochastic Weighting

Structured stochastic weighting systematically introduces controlled randomness to financial indicators, improving predictive robustness and reflecting market uncertainties realistically.

Implementation Steps

• Indicator Selection: Indicators (RSI, MFI, EMA, BB, ATR, etc.) selected for historical predictive power.
• Baseline Weighting: Assign baseline weights based on historical accuracy.
• Structured Perturbation: Apply controlled random variations within defined limits.
• Weight Rebalancing: Regularly adjust weights to evolving market conditions.

Monte Carlo–style Scenario Generation

SimulMarkets™ utilizes Monte Carlo–style simulations, generating numerous plausible market trajectories informed by structured stochastic weighting.

Process Overview

• Scenario Initialization: Begin with historical market data as baseline.
• Stochastic Variability: Indicators' significance dynamically adjusted for each scenario.
• Scenario Propagation: Iteratively forecast market outcomes.
• Aggregation and Analysis: Compile thousands of scenarios into probabilistic market outcome distributions.

Signal-focused P&L Backtesting

SimulMarkets™ employs a distinctive backtesting methodology that prioritizes evaluating the Profit and Loss (P&L) generated from individual trading signals. Unlike traditional cumulative backtesting approaches, this method assesses each signal independently, directly quantifying its effectiveness and reinforcing the Monte Carlo–style scenario generation by emphasizing variability and uncertainty in financial outcomes.

Advantages of Structured Stochastic Methodology

• Enhanced realism combining deterministic and stochastic elements.
• Clearer risk quantification and improved risk management.
• Adaptability to changing market dynamics.

Practical Applications

SimulMarkets™ methods support:

• Dynamic portfolio optimization
• Real-time risk management
• Strategic asset allocation
• Performance evaluation and backtesting

Conclusion

SimulMarkets™ integrates structured stochastic weighting, Monte Carlo–style scenario generation, and a unique signal-focused P&L backtesting approach, delivering actionable insights and superior financial decision-making.