Correlation matrix | Heat Map | python | matplotlib | seaborn смотреть онлайн

In this micro video you will get the code to create a Correlation matrix | Heat Map | python | matplotlib | seaborn
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# Correlation Matrix Heatmap

This Python script fetches historical price data for a list of tickers from Yahoo Finance using `yfinance`, calculates the correlation matrix, and visualizes it as a heatmap using `seaborn` and `matplotlib`.

## Requirements

To run the code, you need to have Python and the following Python libraries installed:

- yfinance
- pandas
- seaborn
- matplotlib

You can install these libraries using pip:

```bash
pip install yfinance pandas seaborn matplotlib
```

## Usage

1. Clone this repository to your local machine or copy the Python script to your project directory.

2. Make sure you have the required libraries installed as mentioned in the "Requirements" section.

3. Open the Python script and modify the `tickers` list to include the financial asset symbols you want to analyze.

4. Run the Python script. It will fetch historical data, calculate the correlation matrix, and display it as a heatmap.

## Example

Suppose we want to analyze the correlation between the following stock market indices: S&P 500, Hang Seng Index, DAX, Nikkei 225, Dow Jones Industrial Average, Swiss Market Index, Euro Stoxx 50, Russell 2000, S&P/ASX 200, and NZX 50.

The `tickers` list in the Python script would be as follows:

```python
tickers = ["^GSPC", "^HSI", "^GDAXI", "^N225", "^DJI", "^SSMI", "^STOXX50E", "^RUT", "^AXJO", "^NZ50"]
```

After running the script, you will see a heatmap titled "Correlation Matrix Heatmap" representing the correlation coefficients between each pair of tickers.

## License

This code is provided under the MIT License. Feel free to use and modify it for your purposes.

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#python #matplotlib #seaborn