Candlestick charts are a popular way to visualize price movements and trading patterns in the financial industry. In this notebook, we will create candlestick charts for a stock or group of stocks and then calculate the correlation between these stocks using a heatmap.
In this template, we will be making use of the following packages:
- pandas: Used for data manipulation and analysis.
- numpy: Used for numerical operations.
- plotly.graph_objects: Used to create interactive candlestick charts.
- yfinance: Used to download market data from the Yahoo! Finance API.
- datetime: Used for date and time operations.
- pytz: Used for timezone handling.
- seaborn: Used for creating the correlation heatmap.
- matplotlib.pyplot: Used for plotting the heatmap.
To get started, you need to input the ticker symbols of the stocks you want to visualize. The symbols should be separated by commas (e.g., 'SPOT, AAPL, GOOGL'). The code will prompt you to input the ticker symbols.
After entering the ticker symbols, you will be prompted to enter the historical data's start and end dates for the date range. The code will convert the input dates into datetime objects with the appropriate timezone.
The code will loop through each ticker symbol and retrieve the historical market prices for the specified date range. It will then create a candlestick chart for each stock using the Plotly library. The candlestick chart visualizes the opening, closing, highest, and lowest prices for each trading day.
The candlestick charts are interactive, allowing you to zoom in on specific periods and hover over the candlesticks to view detailed information.
After plotting the candlestick charts, the code will calculate the correlation matrix between the closing prices of the selected stocks. The correlation matrix represents the relationships between pairs of stocks, indicating how they move in relation to each other.
To visualize the correlation matrix, a heatmap is created using the seaborn and matplotlib libraries. The heatmap assigns different colors to represent the strength of correlations, with darker colors indicating stronger positive or negative correlations.
The correlation heatmap provides insights into the interdependencies and relationships between the stocks, helping you understand their behavior as a group.
Conclusion By following these steps, you can visualize historical stock data using candlestick charts and gain insights into the correlations between different stocks. This information can be valuable for portfolio analysis, risk assessment, diversification strategies, and making informed investment decisions.
Feel free to customize the code and explore additional functionalities to further enhance your analysis and visualization of historical stock data.