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Leveraging Gate.io APIs to retrieve live/historical data for BTC, USDT, USDC, and ETH pairs, identifying arbitrage opportunities via Pandas analysis with tqdm integration

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LinUxTo5re/gateio_spot_arbitrage

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Gate.io Spot Arbitrage

Overview

This project aims to leverage Gate.io APIs to retrieve live and historical spot markets data for different cryptocurrency pairs, including BTC, USDT, USDC, and ETH. The primary objective is to identify potential arbitrage opportunities between these pairs by analyzing the gathered data.

Features

  • Retrieve live data from Gate.io API for multiple cryptocurrency pairs.
  • Obtain historical data for thorough analysis and comparison.
  • Utilize Pandas for data analysis to identify potential arbitrage opportunities.
  • Incorporate tqdm to display progress bars and enhance user experience throughout the project.

Technologies Used

  • Gate.io APIs
  • Python
  • Pandas
  • tqdm
  • requests

Project Structure

The project is organized into the following sections:

  1. Data Retrieval: Using Gate.io APIs to fetch live and historical market data for various cryptocurrency pairs.
  2. Data Processing: Employing Pandas for data analysis and manipulation to identify price differences and potential arbitrage opportunities.
  3. Visualization (if applicable): Visual representation of findings or statistical analysis (if included).
  4. Conclusion: Summary of identified arbitrage opportunities or insights obtained from the analysis.

Getting Started

To begin using this project, ensure you have Python installed. Clone this repository and install the required dependencies using the following commands:

git clone https://github.com/LinUxTo5re/gateio_spot_arbitrage.git
cd your-project-dir
pip install -r requirements.txt
python3 gateio_main.py

Open help.me for more information regarding script run

Screenshot

Screenshot_20240121-153133 Screenshot_20240121-153100

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Leveraging Gate.io APIs to retrieve live/historical data for BTC, USDT, USDC, and ETH pairs, identifying arbitrage opportunities via Pandas analysis with tqdm integration

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