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https://github.com/luismr/megasena-2025-analysis

A comprehensive Python tool for analyzing Mega Sena lottery draws with multiple prediction strategies. Refactored following DRY (Don't Repeat Yourself) and SOLID principles for maintainability and extensibility.
https://github.com/luismr/megasena-2025-analysis

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A comprehensive Python tool for analyzing Mega Sena lottery draws with multiple prediction strategies. Refactored following DRY (Don't Repeat Yourself) and SOLID principles for maintainability and extensibility.

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README 

          
# Mega Sena Analysis Tool



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A comprehensive Python tool for analyzing Mega Sena lottery draws with multiple prediction strategies. Refactored following **DRY (Don't Repeat Yourself)** and **SOLID** principles for maintainability and extensibility.



## 📁 Project Structure



```

megasena/

├── input/                          # Input data files

│   └── mega_sena_resultados.csv    # Historical Mega Sena draw results

├── output/                         # Generated analysis reports

│   ├── mega_virada_analysis.txt

│   ├── number_frequency_analysis.txt

│   ├── weighted_analysis_all_draws.txt

│   └── weighted_analysis_results.txt

├── src/                            # Core modules (following SOLID principles)

│   ├── __init__.py                 # Package initialization

│   ├── data_loader.py              # CSV data loading (Single Responsibility)

│   ├── frequency_calculator.py     # Frequency calculation strategies (Open/Closed)

│   ├── output_formatter.py         # Result formatting and display

│   └── file_manager.py             # File I/O operations

├── scripts/                        # Executable analysis scripts

│   ├── predict_all_draws.py        # Analyze all historical draws

│   ├── predict_mega_virada.py      # Analyze only Mega da Virada draws

│   ├── predict_weighted_all_draws.py      # Weighted analysis (all draws)

│   └── predict_weighted_mega_virada.py    # Weighted analysis (Mega da Virada)

└── README.md                       # This file

```



## 🎯 Features



### Core Modules (SOLID Design)



1. **`data_loader.py`** - Single Responsibility Principle

   - Load all Mega Sena draws

   - Filter Mega da Virada draws (Dec 31st)

   - Filter by year range

   - Extract numbers from draws



2. **`frequency_calculator.py`** - Open/Closed Principle

   - **Strategy Pattern** for extensibility

   - Multiple calculation strategies:

     - Simple frequency (equal weight)

     - Weighted by recency (exponential)

     - Weighted by recency (linear)

     - Weighted favoring older draws

     - Recent N years/draws only



3. **`output_formatter.py`** - Single Responsibility

   - Format and display analysis results

   - Pattern analysis (even/odd, low/high, sum)

   - Consensus analysis across strategies

   - Complete 60-number rankings



4. **`file_manager.py`** - Single Responsibility

   - Save frequency analysis

   - Save strategy comparisons

   - Save detailed reports

   - Manage output directory



## 🚀 Usage



### Prerequisites



- Python 3.7 or higher

- No external dependencies required (uses only standard library)



### Running Analysis Scripts



All scripts should be run from the project root directory:



#### 1. Analyze All Historical Draws

```bash

python scripts/predict_all_draws.py

```

Analyzes all Mega Sena draws using simple frequency analysis.



**Output:** `output/number_frequency_analysis.txt`



#### 2. Analyze Mega da Virada Only

```bash

python scripts/predict_mega_virada.py

```

Analyzes only Mega da Virada draws (Dec 31st, 2008-2024) with pattern analysis.



**Output:** `output/mega_virada_analysis.txt`



#### 3. Weighted Analysis - All Draws

```bash

python scripts/predict_weighted_all_draws.py

```

Compares 5 different weighting strategies across all historical draws:

- Simple frequency

- Recent weighted more (exponential)

- Recent weighted more (linear)

- Older weighted more

- Last 5 years only



**Output:** `output/weighted_analysis_all_draws.txt`



#### 4. Weighted Analysis - Mega da Virada

```bash

python scripts/predict_weighted_mega_virada.py

```

Compares 5 different weighting strategies for Mega da Virada draws only.



**Output:** `output/weighted_analysis_results.txt`



## 🏗️ Architecture & Design Principles



### SOLID Principles Applied



#### **S - Single Responsibility Principle**

- Each module has one clear purpose:

  - `data_loader`: Load and filter data

  - `frequency_calculator`: Calculate frequencies

  - `output_formatter`: Format output

  - `file_manager`: Handle file I/O



#### **O - Open/Closed Principle**

- Easy to extend with new strategies without modifying existing code

- Add new `FrequencyStrategy` subclasses to introduce new calculation methods



#### **L - Liskov Substitution Principle**

- All `FrequencyStrategy` subclasses can be used interchangeably

- Consistent interface across all strategies



#### **I - Interface Segregation Principle**

- Small, focused interfaces

- Classes only depend on methods they use



#### **D - Dependency Inversion Principle**

- Scripts depend on abstract `FrequencyStrategy` interface

- Concrete implementations are injected at runtime



### DRY (Don't Repeat Yourself)



- **Before:** Each script duplicated CSV loading, frequency calculation, and output formatting

- **After:** Common functionality extracted into reusable modules

- **Result:** ~70% code reduction, easier maintenance



### Design Patterns Used



1. **Strategy Pattern** - `FrequencyStrategy` and subclasses

2. **Template Method** - Common analysis workflow in scripts

3. **Dependency Injection** - Strategies injected into `FrequencyCalculator`



## 📊 Analysis Strategies



### 1. Simple Frequency

Equal weight for all draws across history.



### 2. Weighted Frequency (Recent More - Exponential)

Recent draws have exponentially more weight (~7x for newest vs oldest).



### 3. Weighted Frequency (Recent More - Linear)

Recent draws have linearly more weight (~4x for newest vs oldest).



### 4. Weighted Frequency (Older More)

Older draws have more weight (favors historical stability).



### 5. Recent Only

Only considers the most recent N draws or years.



## 🔧 Extending the Tool



### Adding a New Frequency Strategy



1. Create a new class inheriting from `FrequencyStrategy`:



```python

from src.frequency_calculator import FrequencyStrategy



class MyCustomStrategy(FrequencyStrategy):

    def calculate(self, draws):

        # Your calculation logic

        return frequencies_dict

    

    def get_name(self):

        return "My Custom Strategy"

    

    def get_description(self):

        return "Description of what this does"

```



2. Add it to your script:



```python

calculator.add_strategy('my_custom', MyCustomStrategy())

```



### Adding a New Script



1. Create a new file in `scripts/`

2. Import the required modules from `src/`

3. Follow the existing script structure



## 🧪 Testing & CI/CD



### Quick Start

```bash

# Install dependencies

pip install -r requirements.txt



# Run tests

pytest



# Run with coverage

pytest --cov=src --cov-report=term-missing

```



### Test Coverage

- **55 unit tests** - 96% coverage

- **data_loader.py**: 98% | **file_manager.py**: 100% | **frequency_calculator.py**: 88% | **output_formatter.py**: 98%



### CI/CD Pipeline



**Automated on every push/PR:**

- ✅ **Tests** - Ubuntu, macOS, Windows × Python 3.9-3.12

- ✅ **Linting** - flake8, black, mypy

- ✅ **Coverage** - Automatic PR comments with detailed reports

- ✅ **Security** - Dependency & code scanning

- ✅ **Build** - Package validation



**GitHub Actions Workflows:**

- `ci.yml` - Main CI/CD pipeline (lint, test, coverage, build, security)

- `pr-comment.yml` - Coverage reports as PR comments

- `codeql.yml` - Security analysis

- `dependabot.yml` - Automatic dependency updates



**Coverage on PRs:**

Every PR gets an automatic comment with:

- Overall coverage % with color indicators (🟢 ≥90%, 🟡 ≥80%, 🟠 ≥70%, 🔴 <70%)

- Per-module breakdown

- Coverage trends



## 🤝 Contributing



### Quick Start



```bash

# 1. Clone the repository

git clone https://github.com/luismr/megasena-2025-analysis.git

cd megasena-2025-analysis



# 2. Install dependencies

pip install -r requirements.txt



# 3. Run tests

pytest



# 4. Run scripts

python scripts/predict_all_draws.py

```



### Submit a Pull Request



1. **Fork the repository** on GitHub

2. **Clone your fork:**

   ```bash

   git clone https://github.com/YOUR_USERNAME/megasena-2025-analysis.git

   cd megasena-2025-analysis

   ```



3. **Create a feature branch:**

   ```bash

   git checkout -b feature/your-feature-name

   ```



4. **Make your changes** and ensure quality:

   ```bash

   # Run tests

   pytest

   

   # Check coverage

   pytest --cov=src --cov-report=term-missing

   

   # Lint your code

   flake8 src/ scripts/

   

   # Format code

   black src/ scripts/

   ```



5. **Commit your changes:**

   ```bash

   git add .

   git commit -m "feat: add your feature description"

   ```



6. **Push to your fork:**

   ```bash

   git push origin feature/your-feature-name

   ```



7. **Create a Pull Request** on GitHub

   - Go to the [main repository](https://github.com/luismr/megasena-2025-analysis)

   - Click "Pull Requests" → "New Pull Request"

   - Select your fork and branch

   - Fill in the PR template

   - Submit!



### Contribution Guidelines



- ✅ Write tests for new features

- ✅ Maintain 90%+ code coverage

- ✅ Follow PEP 8 style guide

- ✅ Update documentation

- ✅ Use descriptive commit messages



### CI/CD Checks



Your PR will automatically run:

- Unit tests on multiple platforms

- Code coverage analysis

- Linting and formatting checks

- Security scans



A bot will comment with coverage report! 📊



## 📝 License



MIT License - see the [LICENSE](LICENSE) file for details.



Copyright (c) 2025 Luis Machado Reis



This is a personal analysis tool. Use at your own risk. Lottery is a game of chance.



## 🍀 Good Luck!



Remember: This tool is for educational and entertainment purposes. Past results do not guarantee future outcomes in random lottery draws.



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**Boa Sorte na Mega da Virada 2025! 🎉**