Candlestick Chart Plotter with Support and Resistance Identification

This repository contains a Python script to plot candlestick charts and identify key support and resistance levels using the Nifty 50 index data.

Overview

Technical analysis often involves identifying support and resistance levels to understand market trends. This project automates this process by:

• Loading Nifty 50 index data for a specified time range.
• Plotting candlestick charts using the mplfinance library.
• Identifying support and resistance levels with the help of the argrelextrema function from scipy.
• Overlaying the levels on the candlestick chart for visualization.

Features

• Dynamic Support and Resistance Levels: Adjust the sensitivity using the order parameter.
• Custom Chart Styling: Utilize custom themes for professional-looking charts.
• Visualization with Volume: Charts include volume bars for deeper market insights.

Prerequisites

Ensure you have the following Python packages installed:

• pandas
• mplfinance
• matplotlib
• scipy
• numpy

Install the required packages using pip:

pip install pandas mplfinance matplotlib scipy numpy

Data Format

The input data should be in CSV format with the following columns:

Date	Open	High	Low	Close	Shares Traded	Turnover (₹ Cr)
08-JAN-2024	21747.6	21763.95	21492.9	21513	231452935	21140.94
09-JAN-2024	21653.6	21724.45	21517.85	21544.85	228568589	24055.74

Rename the column Shares Traded to Volume during preprocessing.

How to Use

1. Clone this repository:

git clone https://github.com/asircar/candlestick-support-resistance.git

2. Navigate to the repository:

cd candlestick-support-resistance

3. Replace NIFTY 50-06-01-2024-to-06-01-2025.csv with your own dataset in the same format.

4. Run the script:

python script.py

Output

The script generates a candlestick chart with:

• Red dashed lines for resistance levels.
• Green dashed lines for support levels.
• Volume bars for additional context.

Example Chart

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Notes

This code is provided as-is, with no warranties or guarantees. It is free for anyone to view, use, or modify for their own purposes. Credit is appreciated but not required.

References

• Data Source: NSE India Historical Index Data
• Development Tools: This project was developed using GitHub Copilot and ChatGPT.

Contributions

Contributions, issues, and feature requests are welcome! Feel free to fork the repository and submit pull requests.

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Feel free to reach out with questions or suggestions. Happy coding!