Sup Res

Abstract

1. Introduction

Sup-Res is mobile-ready, offline-storage compatible and a great companion for finding liquidity pools, support and resistance levels with a scalable chart.

Those who are new to investing do so without any technical expertise. Additionally, individuals who have limited experience make their buys and sells in accordance with various charts and follow the price actions. The main focus of this work is technical analysis.

I worked on a piece of code that could be useful for users with little prior knowledge of price action. Don't invest if you really have no idea what you're doing. You should keep an eye on market trends before investing and try not to feel FOMO.

2. Objective

A great companion for finding key support and resistance levels on financial charts, cryptocurrencies.

This project demonstrates how Python, Pandas can be applied to a real-world Data Science problem.

3. Key Features / Modules

• Easily find support-resistance and liquidity levels on the chart
• Using it in almost any time series with sensitivity adjustment
• Export documents as HTML, PDF, .jpeg, .png
• Automatically share to your twitter followers with an image and text of support-resistance levels
• Supports Tradingview Pine Script
• Manageable via Telegram

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/arabacibahadir/sup-res.git
cd sup-res

pip install -r requirements.txt

python main.py <ticker> <timeframe>

python main.py BTCUSDT 1H

python main.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by arabacibahadir and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.