Financial Market Analyzer

A professional, modular Python data-analysis pipeline for financial time-series data. The project ingests CSV data, computes moving averages, and produces high-quality visualizations. It demonstrates separation of concerns, reproducible environments, automated test coverage, and synthetic data generation for testing.

Project Structure

• data/ - raw input CSV files (example: raw_data.csv)
• assets/ - tracked images / documentation assets (e.g., trend_plot.png)
• output/ - runtime artifacts (generated charts) — ignored by Git
• scripts/ - utility scripts (e.g., generate_data.py)
• src/ - core application code
  • analyzer.py - data-processing logic
  • visualizer.py - plotting code
• tests/ - pytest unit tests
• main.py - entry point
• requirements.txt - pinned dependencies
• .python-version (optional) - recommended Python version (e.g., 3.12)
• .gitignore - ignore rules

Prerequisites

• Python 3.12+ recommended
• Git
• pip

If you want to manage multiple Python versions locally, consider using pyenv. Place 3.12.x as the project version to ensure compatibility with the pinned dependencies.

Quickstart (recommended)

1. Clone the repository:

git clone https://github.com/yourusername/market_analyzer.git
cd market_analyzer

2. Create and activate a virtual environment:

# macOS / Linux
python -m venv venv
source venv/bin/activate

# Windows (PowerShell)
python -m venv venv
venv\Scripts\Activate.ps1

3. Install dependencies:

python -m pip install --upgrade pip setuptools wheel
python -m pip install -r requirements.txt

4. Generate synthetic test data:

python scripts/generate_data.py

5. Run the analysis and generate the visualization:

python main.py

6. View the result:

• A high-resolution plot trend_plot.png will be created in the output/ folder.
• To show the image in the README, copy the desired image to assets/trend_plot.png and commit it.

Usage Details

Data format

Place your CSV in data/raw_data.csv. Required columns:

• Date — in YYYY-MM-DD format
• Close — numeric closing price

Example:

Date,Close
2024-01-01,150.00
2024-01-02,152.50
...

Configuration

• Change moving-average window in main.py:

analyzer.calculate_moving_average(window=7)

• Visualizer auto-detects any SMA_<window> column and plots it.

Run as a module (alternative)

If you prefer running scripts as modules, ensure package folders have __init__.py. Example:

python -m scripts.generate_data

or run files directly:

python scripts/generate_data.py

Testing

Run the test suite with:

pytest

Tests verify moving-average calculations and basic data validation. Add tests to tests/ to cover new features.

Development Notes

• Use python -m pip to ensure installation into the active virtual environment:

python -m pip install -r requirements.txt

• If you see Pandas warnings about pyarrow, install it (it is included in requirements.txt).

Recommended Dependencies (see requirements.txt)

• pandas
• numpy
• pyarrow
• matplotlib
• pytest

All versions are pinned in requirements.txt for reproducibility.

License

Include a license file in the repo root (e.g., LICENSE with MIT License) to clarify usage terms.