System Requirements Document

1. Introduction

This document outlines the system requirements for the WinGo AI BRAIN PRO, an advanced AI-driven prediction engine designed to decode game patterns and provide accurate predictions based on the last 10 results. The system aims to enhance prediction accuracy by utilizing a new AI model that employs a unique decoding method, ensuring no consecutive losses.

2. System Overview

The WinGo AI BRAIN PRO is a sophisticated AI engine that leverages historical game data to predict future outcomes. It employs a variety of mathematical formulas and pattern detection techniques to ensure high accuracy and prevent consecutive losses. The system is designed to be robust, with a focus on decoding complex game patterns and providing reliable predictions.

3. Functional Requirements

• As a user, I want the AI to decode the game using the last 10 results, ensuring predictions are based on comprehensive historical data with a unique decoding method.
• As a user, I want the AI to generate predictions using a new, hard-to-decipher method that is not based on previous calculation logic or patterns, ensuring no consecutive losses.
• As a user, I want the AI to prevent two consecutive losses by employing an override mode that adjusts predictions accordingly.
• As a user, I want the AI to dynamically generate new mathematical formulas and backtest them to find the most accurate prediction.
• As a user, I want the AI to detect and follow patterns to enhance prediction accuracy.
• As a user, I want the AI to provide a fully accurate prediction by combining hidden calculations with pattern detection, ensuring no consecutive losses.
• As a user, I want the system to update predictions every 60 seconds using live data fetched via an API.
• As a user, I want the system to display prediction confidence levels and indicate when override mode is active.
• As a user, I want the system to track and display win/loss statistics, including consecutive losses.
• As a user, I want the system to provide a detailed breakdown of the prediction process, including active formulas and patterns used.

4. User Personas

• Gamer: A user who frequently engages with prediction games and seeks to improve their chances of winning through accurate predictions.
• Data Analyst: A user interested in analyzing prediction patterns and outcomes to refine strategies.
• Developer: A user responsible for maintaining and enhancing the AI engine's capabilities.

5. Core User Flows

1. Prediction Generation:

   • User initiates a scan for predictions.
   • System fetches the last 10 results via API.
   • AI engine decodes results using mathematical formulas and pattern detection.
   • System displays the prediction, confidence level, and any active override mode.

2. Win/Loss Tracking:

   • System updates win/loss statistics after each prediction.
   • User views updated statistics and historical prediction outcomes.

3. Pattern Detection:

   • System detects active patterns from historical data.
   • User views detected patterns and their impact on predictions.

6. Visuals Colors and Theme

• Background: Dark theme with shades of black and deep blue.
• Primary Colors: Neon purple, cyan, and gold for highlights and interactive elements.
• Text: Light gray for readability against the dark background.

7. Signature Design Concept

• Modern and Futuristic: The design should convey a sense of advanced technology and precision.
• Interactive Elements: Use of animations and transitions to enhance user engagement.

8. Interaction Model & Motion Direction

• Smooth Transitions: Implement smooth scrolling and transitions between different sections and states.
• Responsive Design: Ensure the interface adapts seamlessly to different screen sizes and devices.

9. Non-Functional Requirements

• Performance: The system must process and generate predictions within 2 seconds of receiving data.
• Reliability: Ensure 99.9% uptime for the prediction engine and API connectivity.
• Scalability: The system should handle increased data loads and user interactions without performance degradation.

10. Tech Stack

• Frontend: React.js
• Backend: Python + FastAPI
• Database: MySQL with Alembic migrations

11. Assumptions and Constraints

• The system relies on the availability and accuracy of the API data.
• The AI engine must operate within the constraints of the provided tech stack and design guidelines.
• The system should not introduce any new UI elements that deviate from the existing design unless specified.

12. Glossary

• AI Engine: The core component responsible for generating predictions based on historical data.
• Override Mode: A feature that adjusts predictions to prevent consecutive losses.
• Pattern Detection: The process of identifying recurring sequences in historical data to inform predictions.