🔬 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
🏛 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:
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
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