HFND Positioning Analysis

This project aims to analyze the current positioning of hedge funds using the HFND ETF, which attempts to replicate major hedge fund styles such as long/short equity, global macro, event-driven, fixed income arbitrage, emerging markets, managed futures, and multi-strategy.

Features

• Data cleaning and preprocessing.
• Filtering securities based on weightings.
• Visualizing the data using Plotly.

Getting Started

Prerequisites

To run this project, you need to have the following Python libraries installed:

• pandas
• plotly

You can install these libraries using pip:

pip install pandas plotly

How to Run

1. Clone the repository or download the HFND_positioning.py file.
2. Ensure you have the required libraries installed.
3. Run the HFND_positioning.py script:

   python HFND_positioning.py

Script Details

The script performs the following steps:

1. Loading the Data:

   • The script loads data from a specified URL (CSV file) containing the ETF's holdings.

2. Data Cleaning:

   • It strips any leading or trailing spaces from the column names.
   • Converts the 'Weightings' column from a string percentage to a numeric value.

3. Data Filtering:

   • The script sorts the dataframe by 'Weightings' in descending order.
   • Filters out securities with weightings between -2% and 2%.

4. Visualization:

   • A vertical bar chart is created using Plotly.
   • The chart uses a gradient color scale (Viridis) to represent the weightings.
   • The x-axis is sorted by the total descending order of the weightings.
   • The chart is titled based on the date extracted from the data.

Example Output

The script will produce an interactive bar chart displaying the security weightings. The chart will look similar to this:

License

This project is licensed under the MIT License.

Acknowledgments

• Data source: Unlimited ETFs
• Libraries: pandas, plotly