🔬 Theory of Asymmetric Flow

Reference Implementation

La Pekillada

**Asymmetric Flow & Geometric Absorption Algorithm

📌 Overview

La Pekillada is the first reference implementation of the Asymmetric Flow & Geometric Absorption Algorithm, developed to study the behavior of the Theory of Asymmetric Flow through computational simulation.

Created and documented by:

@Jaypsmall

Project name:

La Pekillada

🧠 Theoretical Foundation

This repository is based on the Theory of Asymmetric Flow, a theoretical framework that studies the dynamic redistribution of exposure across multiple independent variables.

The theory proposes that the collective behavior of a multi-variable system should be analyzed as a whole rather than as isolated variables.

The theoretical framework is intentionally independent of any specific implementation, application domain, initial capital, iteration limit, or execution environment. Such constraints belong exclusively to particular algorithms and computational implementations derived from the theory.

The Asymmetric Flow & Geometric Absorption Algorithm provides a mathematical implementation of this theoretical framework.

La Pekillada is the first reference implementation created to experimentally evaluate the theory under controlled simulations.

🧩 Concept

The algorithm is based on a dynamic asymmetric redistribution model.

• Three independent variables operate simultaneously.
• The successful variable returns to minimum exposure.
• The unsuccessful variables increase their exposure progressively.
• The system studies whether accumulated exposure can be absorbed by future positive events.

The objective is to analyze the behavior of progressive allocation systems under different statistical conditions.

⚙️ Algorithm Logic

The model follows these rules.

Initial State

A = 1
B = 1
C = 1

Each variable starts with the minimum exposure unit.

Event Resolution

When an event occurs:

Winning Variable

The winning variable returns to the minimum value.

Winner = 1

Losing Variables

The losing variables increase.

Loser = Loser × 2

Example:

Initial

A = 1
B = 1
C = 1

Event

A wins

New state

A = 1
B = 2
C = 2

📊 Mathematical Model

The system studies:

• Exposure accumulation
• Capital consumption
• Geometric progression
• Variance impact
• Recovery behaviour
• Maximum risk thresholds

Main variables:

R = Available Resources

E = Exposure Level

V = Variance

T = Number of Iterations

🔬 Simulation Environment

The repository works as a mathematical laboratory.

Possible experiments:

• 100 rounds
• 1,000 rounds
• 100,000 rounds
• Monte Carlo simulations

Metrics evaluated:

✅ Final balance

✅ Maximum exposure reached

✅ Maximum drawdown

✅ Recovery periods

✅ System stability

🛡️ Risk Analysis

This project does not claim guaranteed profitability.

The model studies the point where:

• Geometric growth exceeds available capital.
• External limits affect execution.
• Variance creates extreme scenarios.

Important factors:

• Starting capital
• Maximum allowed exposure
• Number of iterations
• External constraints

🧪 Experimental Validation

Validation is performed through:

Monte Carlo Analysis

Thousands of independent simulations can be executed to measure:

• Probability distribution
• Average results
• Extreme events
• Risk of exhaustion

📂 Project Structure

theory-of-asymmetric-flow/

│
├── LICENSE
├── AUTHORS.md
├── README.md
├── main.py
├── simulator.py
│
├── results/
│   └── simulations.csv
│
└── docs/
    ├── THEORY.md
    ├── THESIS.md
    ├── MATHEMATICAL_MODEL.md
    └── SIMULATION_RESULTS.md

🚀 Usage

Clone repository:

git clone https://github.com/Jaypsmall/theory-of-asymmetric-flow

Run simulation:

python main.py

Configure parameters:

ROUNDS = 1000
INITIAL_CAPITAL = 10000
INITIAL_UNIT = 1

🔮 Future Development

Possible improvements:

• Real-time visualization
• Statistical dashboard
• Monte Carlo engine
• Risk heatmaps
• Probability analysis
• Machine learning pattern detection

📚 Documentation

• 📖 Theory of Asymmetric Flow
• 📘 Complete Thesis

🏛 Repository Architecture

This repository is organized into four conceptual layers.

1. Theory of Asymmetric Flow

The conceptual framework.

2. Asymmetric Flow & Geometric Absorption Algorithm

The mathematical algorithm derived from the theory.

3. La Pekillada

The first reference implementation.

4. Python Simulation

Computational experiments used to evaluate the implementation under controlled conditions.

📜 Project Information

Theory

Theory of Asymmetric Flow

Reference Algorithm

Asymmetric Flow & Geometric Absorption Algorithm

Reference Implementation

La Pekillada

Creator

@Jaypsmall

Type

Experimental mathematical theory, computational model, and simulation framework.

Status

Research and development.

📄 License

Copyright © 2026 Jaypsmall. All Rights Reserved.

This repository contains original theoretical work, mathematical models, algorithms, software implementations, documentation, simulations, and related materials created by the author.

No part of this repository may be copied, reproduced, modified, redistributed, incorporated into another project, or used for commercial purposes without the prior written permission of the author.

For licensing inquiries:

https://github.com/Jaypsmall

See the LICENSE file for the complete license terms.

⚠️ Disclaimer

This repository presents an experimental mathematical framework intended for research, simulation, and educational purposes.

The computational models and implementations are designed to explore theoretical behavior under controlled simulations and should not be interpreted as guarantees of real-world performance.

© 2026 @Jaypsmall

https://github.com/Jaypsmall/theory-of-asymmetric-flow