Financial Market Data Visualization

Financial Market Data Visualization is a comprehensive collection of Jupyter Notebooks focused on transforming raw market and economic data into insightful visual stories. Leveraging libraries such as Matplotlib, Plotly, and Bokeh, this repository covers bond yields, business cycles, portfolio analytics, market microstructure, seasonality, and global economic indicators.

Repository Highlights

Below is a thematic grouping of the many notebooks in this repository. Browse the full list on GitHub for detailed titles.

Bond Yields & Interest Rates

• 91-Day and 10-Year Yield Analysis: South African and UK government securities notebooks
• Amalgamated Bond Yields: Alternative visualizations of long-term government debt

Business Cycle & Macro Indicators

• Business Cycle Indicators: US and global economic cycle visualizations
• World Bank Data: Population and development indicators plotted over time

Portfolio & Asset Class Analysis

• ETF Overview: Comparative performance dashboards across major asset classes
• Efficient Frontier & Mean-Variance: Portfolio optimization visualizers
• Annualized Returns & Calendar Returns: Seasonality heatmaps and return distributions

Market Microstructure & Trading Dynamics

• Bid-Ask Spread Visualization: Static and live data plots
• Dark Pool Activity & Order Flow: Experiments in order spoofing, payment for order flow, and market-maker simulations
• High-Frequency Trading (HFT): Prototype notebooks exploring tick-level behavior

Seasonality & Event Simulations

• January Returns Calendar: Return patterns by month and quarter
• Earnings Season Simulation: Interactive model of volatility around earnings

Correlations & Risk Heatmaps

• Stock Period Correlation: Heatmaps of correlations over rolling windows
• Sector & Asset-Class Corridors: Comparative risk-return matrices by group

Specialized Topics

• Green Finance Trends: ESG and sustainable investment visualizations
• Esports vs. Traditional Gaming: Comparative market-size and growth charts
• Natural Events: Visual attempts for environmental data such as LA fires

Getting Started

Prerequisites

• Python 3.7 or higher

• Jupyter Notebook or JupyterLab

• Install dependencies:

  pip install -r requirements.txt

If requirements.txt is missing, the core libraries include:

pip install pandas numpy matplotlib plotly bokeh seaborn

Usage

1. Clone the repo:

   git clone https://github.com/mano001-ctrl/Financial-Market-Data-Visualization.git
   cd Financial-Market-Data-Visualization

2. Launch Jupyter Lab or Notebook:

   jupyter lab

3. Open any .ipynb notebook and run the cells interactively.

Contributing

Contributions are welcome!

• File an issue to suggest new visualizations or data sources.
• Submit pull requests adding notebooks, improving existing plots, or updating dependencies.

License

Licensed under the MIT License. See LICENSE for details.

Contact

For questions, feedback, or collaboration ideas, please open an issue or connect via GitHub.