Triangular Arbitrage Detection in Forex Market

This project is a Python-based tool that identifies triangular arbitrage opportunities in the foreign exchange (Forex) market. It fetches historical exchange rates for currency pairs, calculates potential arbitrage opportunities, and saves the results to an in-memory DataFrame, with an option to export to CSV.

Features

• Fetches historical data for currency pairs from Yahoo Finance using the yfinance library.
• Identifies triangular arbitrage opportunities based on the exchange rates of three currency pairs.
• Provides real-time analysis by iterating over historical data to find and display arbitrage opportunities.
• Saves detected opportunities in a DataFrame, which can be accessed in memory, and optionally exported to a CSV file.

Triangular Arbitrage Overview

Triangular arbitrage involves exploiting price discrepancies between three related currency pairs, such as:

• EUR/USD
• USD/JPY
• EUR/JPY

The code calculates the cross rate for EUR/JPY using EUR/USD and USD/JPY, and compares it with the actual EUR/JPY market rate. If a discrepancy exists that exceeds a certain threshold, it is flagged as an arbitrage opportunity.

Arbitrage Formula

The cross rate is calculated using the following formula:

Calculated_EURJPY = EUR/USD * USD/JPY

If the difference between the calculated EUR/JPY and the actual EUR/JPY exceeds 0.01, an arbitrage opportunity is detected.

Prerequisites

To run this project, you'll need the following installed on your system:

• Python 3.x
• pandas for data manipulation.
• yfinance for fetching exchange rate data.

You can install the required libraries using the following command:

pip install pandas yfinance

How to Run the Code

1. Clone the repository or download the project files.

2. Install the required dependencies:

   pip install pandas yfinance

3. Run the script:

   python triangular_arbitrage.py

4. The program will:

   • Fetch historical exchange rate data for EUR/USD, USD/JPY, and EUR/JPY.
   • Calculate triangular arbitrage opportunities over the past year.
   • Display the detected opportunities in the terminal.

5. Optional: The detected arbitrage opportunities are saved in-memory as a DataFrame. You can also export them to a CSV file:

   Opportunities saved to 'arbitrage_opportunities.csv'.

Code Walkthrough

1. fetch_historical_data()

This function fetches historical exchange rate data from Yahoo Finance using the yfinance library. It takes in a currency pair (e.g., EURUSD) and returns a DataFrame containing the historical close prices for the specified period.

2. calculate_triangular_arbitrage()

This function takes in the exchange rates for EUR/USD, USD/JPY, and EUR/JPY on a specific date. It calculates the theoretical EUR/JPY rate using the EUR/USD and USD/JPY rates, and compares it with the actual EUR/JPY rate to determine whether an arbitrage opportunity exists.

3. list_past_arbitrage_opportunities()

This is the main function that:

• Fetches historical data for the three currency pairs.
• Joins the datasets to align exchange rates by date.
• Iterates through the historical data to detect and store arbitrage opportunities.
• Saves the detected opportunities in a DataFrame (stored_opportunities_df) and optionally exports them to a CSV file.

Sample Output

When the script runs, you will see output similar to this in the terminal:

Fetching historical data...
Data for EURUSD:
             Close
Date              
2023-01-02  1.0658
2023-01-03  1.0617
...
Joining historical data...
Joined Data (first few rows):
            EURUSD=X  USDJPY=X  EURJPY=X
Date                                   
2023-01-02    1.0658   130.760   139.30
2023-01-03    1.0617   131.120   139.10
...
Detected Arbitrage Opportunities:
          Date  Calculated_EURJPY  Actual_EURJPY Opportunity
0   2023-10-11         157.616904       157.598999         YES
1   2023-10-12         158.340050       158.328995         YES
...

Saving Results to CSV

If you want to save the detected opportunities to a CSV file, they will be saved as:

arbitrage_opportunities.csv

The CSV file will contain the following columns:

• Date: The date of the exchange rates.
• Calculated_EURJPY: The theoretical EUR/JPY cross rate based on EUR/USD and USD/JPY.
• Actual_EURJPY: The actual EUR/JPY rate from the market.
• Opportunity: Whether an arbitrage opportunity was detected (YES/NO).

Future Enhancements

• Live Data Integration: Extend the script to work with real-time exchange rates, allowing users to act on arbitrage opportunities as they emerge.
• Alert System: Integrate SMS, WhatsApp, or Telegram notifications to alert traders when an arbitrage opportunity is detected.
• Customization: Allow users to customize the currency pairs they want to monitor for arbitrage opportunities.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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